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Decision-making in orthopaedic surgery
Hageman, M.G.J.S.
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Citation for published version (APA):Hageman, M. G. J. S. (2018). Decision-making in orthopaedic surgery
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Download date: 09 Jul 2018
Curriculum Vitae
Michiel Hageman was born in Al Jubail, Saudi Arabia on April 7th, 1985. After a
short interlude in The Netherlands, Michiel lived with his family in Malaysia until
1992. Back in the Netherlands, after graduating from high school (VWO, Den Haag)
in 2004, he studied at the medical school of the University of Amsterdam. During
his study Michiel worked for the Bio-Implant Service (BIS) the Netherlands as
orthopaedic tissue-donation surgeon. In his final year of his bachelor he conducted
a research internship at the department of orthopaedic surgery of the Academic
Medical Center Amsterdam (prof. dr. C.N. van Dijk). The experiences at BIS, his
research internship and clinical internship at the AMC made him enthusiastic to
continue working in the medical field of orthopaedic surgery. After obtaining the
medical doctor’s degree in 2011, he worked as PhD student at the department of
Orthopaedic Hand and Upper Extremity of the Massachusetts General Hospital,
Boston – United States as well as the Slotervaart Ziekenhuis in Amsterdam, which
finally resulted in this thesis. During his time in Boston Michiel developed a special
interest in “Shared Decision Making” and “Decision Aids” to facilitate the decision-
making. Together with his friend and colleague Teun Teunis, Michiel launched
PATIENT+, dedicated to support shared decision-making with digital decision
aids. Subsequently Michiel and Teun wrote the book SAMEN Beslissen: waarom
moeilijk doen als het SAMEN kan? and were awarded the best value best health care
initatieve of 2017 (Doelmatigheidsprijs 2017).
In 2014, Michiel started his training for orthopaedic surgery at the department
of general surgery at the Onze Lieve Vrouwe Gasthuis (dr. M.Gerhards).
He continued his residency at the department of orthopaedic surgery at the AMC
(prof. dr. C.N. van Dijk) and Slotervaart Ziekenhuis (dr. H. van der Vis). During his
clinical work, Michiels’ interests to innovate and develop products to improve
health care further increased. At the end of 2017 he decided to focus solely on
PATIENT+. Michiel will lead and support the team of PATIENT+ to develop, integrate
and evaluate decision aids into innovative health care delivery systems.
ISBN 978 94 91549 88 5
omsl.proefschrift.Hageman.indd 1 26-02-18 14:06
Decision-making in Orthopaedic Surgery
M.G.J.S. Hageman
© 2018 M.G.J.S. Hageman, Amsterdam, the Netherlands
Design: Joen design, Wormer
This thesis was prepared at the Orthopaedic Hand and Upper Extremity Service, Massachusetts
General Hospital, Harvard Medical School, Boston, MA, United States of America and the
Department of Orthopaedic Surgery, Academic Medical Center, University of Amsterdam,
Amsterdam, the Netherlands.
All rights reserved. The copyright of the published and accepted articles has been transferred to
the respective publishers. No part of this publication may be reproduced, stored in a retrieval
system of any nature, or transmitted in any form or by any means, mechanically, by photocopying,
recording, or otherwise, without prior written permission from the author.
Hereby I want to gratefully acknowledge the research support I received from Anna Fonds|NOREF,
Marti-Keuning Eckhardt Stichting en het SGS-Achmea- fonds.
Decision-making in Orthopaedic Surgery
ACADEMISCH PROEFSCHRIFT
ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam
op gezag van de Rector Magnificus prof. dr. i.r. K.I.J. Maex
ten overstaan van een door het College voor Promoties ingestelde commissie,
in het openbaar te verdedigen in de Agnietenkapel
op dinsdag 17 april 2018, te 12.00 uur
door
Michiel Gerardus Johannes Staro Hageman
geboren te Al Jubail, Saoedi- Arabië
PROMOTIECOMMISSIE
Promotores: Prof. dr. C.N. van Dijk AMC - UVA
Prof. dr. D.C. Ring The University of Texas
at Austin
Overige leden: Dr. E.R.A. van Arkel MC Haaglanden
Dr. J.A.M. Bramer AMC - UVA
Prof. dr. S.E. Geerlings AMC - UVA
Prof. dr. I.C. Heyligers Universiteit Maastricht
Prof. dr. J.A.M. Kremer Radboud universiteit
Prof. dr. M. Maas AMC - UVA
Prof. dr. M.P. Schijven AMC - UVA
Faculteit der Geneeskunde
Voor Pilou, Nicoline, mijn zusjes en ouders
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6
TABLE OF CONTENTS
7
PART 1 GENERAL INTRODUCTION
CHAPTER 1 Introduction and thesis outline
PART 2 DECISION-MAKING IN ORTHOPAEDIC SURGERY
CHAPTER 2 Variation in recommendation for surgical
treatment for compressive neuropathy
J Hand Surg Am. 2013 May;38(5):856-62.
CHAPTER 3 The factors influencing the decision-making of
operative treatment for proximal humeral
fractures
J Shoulder Elbow Surg. 2015 Jan;24(1):21-6.
CHAPTER 4 How surgeons make decisions when the evidence is
inconclusive
J Hand Surg Am. 2013 Jun;38(6):1202-8.
CHAPTER 5 Do previsit expectations correlate with satisfaction
of new patients presenting for evaluation with an
orthopaedic surgical practice?
Clin Orthop Relat Res. 2014 Apr;39(9):11999-014.
CHAPTER 6 Carpal tunnel syndrome: assessment of surgeon
and patient preferences and priorities for decision-
making
J Hand Surg Am. 2014 Sep;39(9):1799-1804.e1.
CHAPTER 7 RCT: The influence of decision aids on decisional
conflict and satisfaction of patient with hip or knee
osteoarthritis
Submitted to KSSTA. 2017 Nov.
10
11
14
15
29
43
57
69
83
8
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CHAPTER 8 Do upper extremity trauma patients have different
preferences for shared decision-making than
patients with non-traumatic conditions?
Clin Orthop Relat Res. 2015 Nov;473(11):3542-8.
PART 3 GENERAL DISCUSSION
CHAPTER 9 Summary and discussion
CHAPTER 10 Dutch summary and discussion
PhD portfolio 130
List of publications 133
Acknowledgements 135
99
110
111
121
9
10
PART 1
GENERAL INTRODUCTION
11
CHAPTER 1
Introduction and thesis outline
There is substantial variation in the rates and type of operative and non-
operative treatment that cannot be explained by demographics, pathophysiology,
or comorbidities.1-3 There should be some variation in medical treatments. But it’s
difficult to justify variation from surgeon-to-surgeon. The observed variation should
derive entirely from variation in patient preferences based on their values. The
surgeon-to-surgeon variation demonstrates some important opportunities: we can
do better to assist patients in becoming aware of their values, ensuring that their
initial preferences are not based on misconceptions so that they can consider all the
available options, and then helping them choose the option that suits them.
Some of the variation is explained by differences in opinion among
surgeons. For example, some surgeons offer patients with symptoms of carpal
tunnel and normal electro-diagnostic testing surgery based on symptoms alone,
and others do not. It is unclear whether factors such as workers’ compensation,
litigation and less specific symptoms are associated with recommendation for
surgery.3-5 In the second chapter of this thesis we measured factors associated
with variation in recommendation for operative and non-operative treatment
for compressive neuropathy.
Another area of debate is the role of operative treatment for fractures of
the proximal humerus. Surgery is considered for approximately 1 in 5 patients, but
there is no consensus on which fractures benefit from surgery or which procedure
to perform.6 The data to date are limited and inconclusive.6,7 In chapter 3, we
measured the factors that influence agreement between surgeons on treatment
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recommendations and the factors that lead a surgeon to recommend operative
treatment and type of surgery (ie, fixation vs arthroplasty).
Variation in recommendations for operative and non-operative
treatment seems greater for the least objectively verifiable issues. The “Evidence-
Based Guidelines” from the American Academy of Orthopaedic Surgeons are
largely inconclusive for lack of evidence.8 Well-designed, prospective, randomized
controlled trials frequently show no difference or a small and possibly not
clinically relevant difference between 2 treatments.9,10 The fourth chapter
measured how health care providers decide which option to recommend to their
patients when the evidence is inconclusive.
In 2010 among 2,500 common treatments 51% were classified as having
insufficient evidence, 23% likely to be beneficial, 7% requiring trade-offs between
benefits and harms, 5% unlikely to be beneficial, 3% likely to be ineffective or
harmful, and 11% as clearly beneficial.11 A decision aid could inform patients
about the best available evidence and ongoing areas of debate in order to limit
the effect of both patient and surgeon bias and improve the patient’s comfort and
participation in the decision.12 Chapter 5 measured the priorities and preferences
of patients and hand surgeons facing decisions about management of CTS.
Patient satisfaction measures are increasingly used to evaluate the
quality of medical service.13 Many factors play into satisfaction, including
patient’s understanding of their own health and patient’s rating of the quality
of their care and perhaps expectations.14,15 We therefore measured in chapter 6
how previsit expectations affected satisfaction in the orthopaedic practice.
It is thought that it is important to make a shared decision when the evidence
is inconclusive, when there is more than one reasonable option, when there is no clear
advantage in outcomes or when each benefit of harm may be valued differently.16
In shared decision-making the caregiver provides expertise and evidence, and the
patient and caregiver choose diagnostic and treatment options consistent with their
values and preferences.17,18 In chapter 7 we measured the effect of decision aids on the
magnitude of decisional conflict, anxiety, knowledge, satisfaction, physical function
and quality of life to patients with knee and hip osteoarthritis.
Patients with traumatic problems are thought to be less capable of and
less interested in participating in decisions because they feel vulnerable and
time-pressured. In addition, patients with greater symptoms of depression or
less self-efficacy might have less desire or confidence about participation in the
decision-making process and might prefer to fall back to a paternalistic style of
medical care and take a more passive role. In chapter 8, we measured patient
13
preferences for shared decision-making in relation to the acuity of the diagnosis
and to psychological factors.
The final chapter provides a summary and discussion followed by an overall
conclusion and future perspective based on the study results presented in this thesis.
REFERENCES
1. Frymoyer JW. Degenerative Spondylolisthesis: Diagnosis and Treatment. J Am Acad Orthop
Surg 1994;2:9-15.2. Duszak R, Jr., Behrman SW. National trends in percutaneous cholecystostomy between 1994
and 2009: perspectives from Medicare provider claims. J Am Coll Radiol 2012;9:474-9.3. Fanuele J, Koval KJ, Lurie J, Zhou W, Tosteson A, Ring D. Distal radial fracture treatment: what
you get may depend on your age and address. The Journal of bone and joint surgery American
volume 2009;91:1313-9.4. de Beer J, Petruccelli D, Gandhi R, Winemaker M. Primary total knee arthroplasty in patients
receiving workers’ compensation benefits. Can J Surg 2005;48:100-5.5. Harris I, Mulford J, Solomon M, van Gelder JM, Young J. Association between compensation
status and outcome after surgery: a meta-analysis. Jama 2005;293:1644-52.6. Handoll HH, Ollivere BJ, Rollins KE. Interventions for treating proximal humeral fractures in
adults. Cochrane Database Syst Rev 2012;12:CD000434.7. Misra A, Kapur R, Maffulli N. Complex proximal humeral fractures in adults--a systematic
review of management. Injury 2001;32:363-72.8. American Academy of Orthopaedic Surgeons Clinical Practice Guideline on the Diagnosis and
Treatment of Osteochondritis Dissecans Rosemont (IL). American Academy of Orthopaedic
Surgeons (AAOS); 2010.9. Gibbs L, Gambrill E. Evidence-based practice: Counterarguments to objections. . Resarch on
Social Work Practice 2002;12:452-76.10. Pawson R. Evidence Based Policy: In search of a method. Evaluation 2002;8:157-81.11. How much do we know. British Medical Journal 2010;Clinical Evidence 2010.12. Legare F, O’Connor AM, Graham ID, Wells GA, Tremblay S. Impact of the Ottawa Decision
Support Framework on the agreement and the difference between patients’ and physicians’
decisional conflict. Med Decis Making 2006;26:373-90.13. Hudak PL, Wright JG. The characteristics of patient satisfaction measures. Spine (Phila Pa
1976) 2000;25:3167-77.14. Hickson GB, Clayton EW, Entman SS, et al. Obstetricians’ prior malpractice experience and
patients’ satisfaction with care. Jama 1994;272:1583-7.15. Soroceanu A, Ching A, Abdu W, McGuire K. Relationship between preoperative expectations,
satisfaction, and functional outcomes in patients undergoing lumbar and cervical spine
surgery: a multicenter study. Spine (Phila Pa 1976) 2012;37:E103-8.16. Stiggelbout AM, Van der Weijden T, De Wit MP, et al. Shared decision making: really putting
patients at the centre of healthcare. BMJ 2012;344:e256.17. Slover J, Shue J, Koenig K. Shared decision-making in orthopaedic surgery. Clin Orthop Relat
Res 2012;470:1046-53.18. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.
14
PART 2
DECISION-MAKING IN ORTHOPAEDIC
SURGERY
15
Michiel G.J.S Hageman, MD, Stephanie J.E. Becker, MD, Arjan G.J. Bot, MD, Thierry Guitton, MD, PhD,
David Ring, MD, PhD, the Science of Variation Group.*
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
J Hand Surg Am. 2013 May;38(5):856-62.
CHAPTER 2
Variation in recommendation for
surgical treatment for compressive
neuropathy
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ABSTRACT
Background It is our impression that there is substantial, unexplained variation in hand surgeon recommendations for treatment of peripheral mono-neuropathy. We tested the null hypothesis that specific patient and provider factors do not influence recommendations for surgery.Methods Using a web-based survey, hand surgeons recommended surgical or nonsurgical treatment for patients in two different scenarios. Six elements of the first scenario (symptoms, circumstances, mindset, diagnosis, objective testing, and expectations) had two possibilities that were each independently and randomly assigned to each rater. For the second scenario, two different scenarios were randomly assigned to each rater. Multivariable logistic regression sought factors associated with a recommendation for surgery.Results A total of 186 surgeons of the Science of Variation Group completed a survey regarding recommendation of surgery for two different patients based on clinical scenarios. Recommendations for surgery did not vary significantly according to provider characteristics.For the various elements in scenario 1, recommendation for surgery was more likely for patients who were self-employed and continued to work and who had objective electro-diagnostic abnormalities. For the two vignettes used in scenario 2, a recommendation for surgery was associated with abnormal electrophysiology.Conclusions The findings of this study suggest that – at least in a survey setting – surgeons prefer to offer peripheral nerve decompression to patients with abnormal electrophysiology, particularly those with effective coping strategies.
INTRODUCTION
Pathophysiology and demographics cannot explain the substantial geographic
variation in rates of surgery. Cholecystectomy for silent gallstones and lumbar
spine surgery are known examples of small area variation in surgical rates.1,2
The rates and types of surgical treatment of distal radius fractures in the
United States Medicare population also demonstrate small area variation based
primarily on sex and age.3
17
Another area of variation is differences in opinion. Some of the most debatable
issues in hand and upper extremity surgery are the least scientific, meaning
the least objectively verifiable. For instance, diagnosis and treatment of radial
tunnel and pronator syndromes (diagnoses defined in part by normal electro-
diagnostic testing) varies substantially: some surgeons make these diagnoses
and offer surgical treatment routinely, whereas others consider these diagnoses
illness constructs (an illness that exists only because we agree to behave as if
it exists) and do not find them useful for patients. There is debate regarding
whether idiopathic median neuropathy at the carpal tunnel should be considered
a syndrome (a constellation of symptoms and signs) or an objectively verifiable
median neuropathy at the carpal tunnel (pathophysiology/ disease). For example,
some surgeons offer patients with normal electro-diagnostic testing surgery
based on symptoms alone, and others do not. Workers’ compensation, litigation,
and less specific symptoms are associated with worse outcomes from surgery,4-10
but it is unclear whether these factors affect recommendations for surgery.10,11
In this study, we surveyed a large group of hand surgeons regarding
recommendations for surgery for peripheral nerve disorders. We tested the
null hypothesis that specific patient and provider factors do not influence
recommendations for surgery.
MATERIAL AND METHODS
A total of 235 surgeons of the Science of Variation Group were asked to complete
a survey regarding recommendation of surgery for two different patients based
on clinical scenarios.
The Science of Variation Group is an international collaboration
of practicing surgeon observers that studies variation in the definition,
interpretation, classification, and treatment of human illness. Collaborative
authorship and scientific curiosity and camaraderie are the only incentives for
participation.
The study protocol was approved by our institutional review board.
Incentives, other than acknowledgment as part of the Science of Variation
Group, were not provided. Of the total 235 surgeons, 186 completed the survey
(73%). (Table 1).
Evaluation
After logging in to the website, each observer entered his demographic and
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professional information: sex, country or region of practice, years in independent
practice, supervision of trainees, and surgical subspecialty.
The observers were then presented with two scenarios and asked
whether they would recommend surgical treatment (yes or no). In the
first scenario (scenario 1), six elements of the scenario were randomized
independently. The following was a constant part of scenario 1: “A 55-year-
old woman, a journalist, presents with symptoms unresponsive to splinting,
medication, modification of activities, and hand therapy.” Afterward, information
about (1) symptoms, (2) circumstances, (3) mindset, (4) diagnosis, (5) objective
Table 1 Demographics n=186
n %
Sex Men 167 90 Women 19 10
Practice Asia 3 2 Canada 1 1 Europe 9 4 United Kingdom 4 2 United States 161 87 Other 8 4
Years In practice 0-5 55 30 6-10 38 20 11-20 56 30 21-30 37 20
Supervise Yes 122 66 No 64 34
Specialization Hand and wrist 180 97 Other 6 3
19
testing, and (6) expectations was presented. Each of these elements had two
alternatives, A and B, which were randomly assigned. The alternatives for
symptoms were (1A) symptoms consist of numbness of the thumb, index,
middle, and ring fingers that occasionally wake her from sleep, are present
most mornings, and also occur with hair drying, driving, and other bent-wrist
activities; and (1B) symptoms consist of forearm and wrist pain with typing and
occasional numbness of the entire hand. The alternatives for circumstances
were (2A) she is not currently working; she has an open workers’ compensation
claim that is in dispute; and she has hired a lawyer to represent her; and (2B) she
is self-employed and continues to work. The alternatives for mindset were (3A)
she can type for only 10 to 15 minutes at a time, and the pain is excruciating;
and (3B) nothing. The alternatives for diagnosis were (4A) a diagnosis of
carpal tunnel syndrome is made, and (4B) a diagnosis of pronator syndrome
is made. The alternatives for objective testing were (5A) electro-diagnostic
testing demonstrates motor and sensory nerve dysfunction consistent with
the diagnosis, and (5B) electro-diagnostic testing is normal. The alternatives for
expectations were (6A) her primary care doctor sent her to you for surgery; and
(6B) none.
The observers were then presented with a second scenario and asked
whether they would recommend surgical treatment (yes or no). In this case, one
of two complete scenarios was randomly assigned:
Scenario A: A 55-year-old woman, a journalist, presents with symptoms
unresponsive to splinting, medication, modification of activities, and hand
therapy. Symptoms consist of forearm and wrist pain with typing. She is
tender over the lateral side of the proximal forearm. She can type for only 10
to 15 minutes at a time. The pain is excruciating. She is not currently working.
She has an open workers’ compensation claim that is in dispute. She has hired
a lawyer to represent her. Electro-diagnostic testing demonstrates dysfunction
of the radial nerve in the proximal forearm. Her primary care doctor diagnosed
radial tunnel syndrome and sent her to you for surgery.
Scenario B: A 55-year-old woman, a journalist, presents with symptoms
unresponsive to splinting, medication, modification of activities, and hand
therapy. Symptoms consist of forearm and wrist pain with typing. She is
tender over the lateral side of the proximal forearm. She is self-employed and
continues to work. Electro-diagnostic testing is normal.
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Table 2 Bivariate analysis for demographics combined with scenario 1 and 2 n=186
Independent parameters
Scenario 1 Sex
Location
Years in practise
Supervision
Specialization
Scenario 2 A Sex
Location
Years in practise
Supervision
Specialization
MenWomen
United StatesOther
0-56-1011-2021-30
YesNo
Hand and wristOther
MenWomen
United StatesOther
0-56-1011-2021-30
YesNo
Hand and wristOther
8714
9110
33202721
6734
974
38
5
637
129
139
2716
421
805
7015
22182916
5530
832
57
5
755
19101914
4220
611
0.073
0.12
0.64
0.054
0.54
0.54
0.68
0.94
0.60
0.79
Recommended TreatmentNon-operative Operative P-Value n n
continue >
21
Independent parameters
Scenario 2 B Sex
Location
Years in practise
Supervision
Specialization
Scenario 2A + B Sex
Location
Years in practise
Supervision
Specialization
MenWomen
United StatesOther
0-56-1011-2021-30 YesNo
Hand and wristOther
MenWomen
United StatesOther
0-56-1011-2021-30
YesNo
Hand and wristOther
576
954
21112011
4122
459
9511
1591
33203320
6838
1015
153
315
3843
126
018
728
1070
22182317
5426
791
0.40
0.80
0.11
0.90
0.27
0.93
0.74
0.87
0.63
0.19
Recommended TreatmentNon-operative Operative P-Value n n
continued table 2
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Statistical analysis
Our primary outcome measure for both scenarios was the decision to operate
or not. The association between the outcome measures, demographics, and the
elements of the scenarios were investigated using the chi-square test. All factors
that had p < 0.10 in bivariate analysis were inserted in a backward, stepwise
(likelihood) binary logistic regression to find the factors associated with the
decision to operate.
RESULTS
Observer demographics are recorded in Table 1. Region of practice, years in
practice, and type of specialization did not influence recommendation for
surgery for either scenario (Table 2).
Scenario 1 and treatment
In scenario 1, objective testing (electro-diagnostic tests demonstrate nerve
dysfunction) and circumstances (self-employed and continues to work) were
significantly associated with the decision to operate. Sex and supervision
satisfied the criteria for entry in the backward logistic regression. In the final
model with predictors for recommending surgery, only patients who were
self-employed and continued to work (OR 2.9, 95% CI 1.4 to 5.9) and who had
objective electro-diagnostic abnormalities (OR 12, 95% CI 5.8 to 25) were
retained in the model (Nagelkerke R2 0.38, p < 0.001) (Table 3).
Scenario 2 and treatment
The demographics of the surgeons were not significantly associated with
the decision to operate in either scenario A (patient is not working and has
abnormal electro-diagnostic testing), scenario B (self-employed and a normal
electro-diagnostic test), or both scenarios combined (Table 2). Surgeons who
were randomized to scenario A were significantly more likely to operate
(p < 0.001) (Table 4).
DISCUSSION
Our impression is that there is substantial variation in recommendations for
peripheral nerve surgery, particularly for more debatable diagnoses, such as
pronator syndrome. The findings of this study suggest that surgeons tend to
23
Table 3 Bivariate analysis - scenario 1 and 2 n=186
Independent parameters
49
52
57
44
52
49
4952
25
76
55
46
49
36
32
53
45
40
4144
66
19
36
49
0.21
0.011
0.84
0.97
<0.001
0.10
Recommended TreatmentNon-operative Operative P-Value n n
Scenario 1 Symptoms
Symptoms consist of numbness of the thumb, index, long and ring fingers which occasionally waker her from sleeping.Symptoms consist of forearm and wrist pain with typing and occasional numbness of the entire hand.
CircumstancesShe is not working open WC claim in dispute, has hired a lawyer.Self-employed and continues to work.
MindsetShe can only type for 10-15 minutes at a time. The pain is excruciating.None.
DiagnosisA diagnosis of CTS is made.A diagnosis of pronator syndrome is made.
Objective testingEMG demonstrates motor and sensory nerve dysfunction consistent with the diagnosis.EMG is normal.
ExpectationsHer primary care doctor sent her to you for surgery.None.
continue >
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continued table 3
Independent parameters
43
63
62
18
<0.001
Recommended TreatmentNon-operative Operative P-Value n n
Scenario 2 Type A
A 55-year-old woman journalist presents with symptoms unresponsive to splinting, medication, modification of activities and hand therapy. Symptoms consist of forearm and wrist pain with typing. She is tender over the lateral side of the proximal forearm. She can only type for 10-15 minutes at a time. The pain is excruciating. She is not currently working. She has an open worker’s compensation claim that is in dispute. She has hired a lawyer to represent her. Electrodiagnotic testing is normal. Her primary care doctor diagnosed her with radial tunnel syndrome and sent her to you for surgery.
Type BA 55-year-old woman journalist presents with symptoms unresponsive to splinting, medication, modification of activities and hand therapy. Symptoms consist of forearm and wrist pain with typing. She is tender over the lateral side of the proximal forearm. She is self-employed and continues to work. Electrodiagnotic testing demonstrates dysfunction of the radial nerve in the proximal forearm.
25
Table 4 Logistic regression predicting likelihood of suggesting operative treatment n=186
Category / Variable*
Scenario 01 - Would you suggest operative treatment?
2.9
12
1.4
5.8
5.9
25
0.38
95.0% CI for OR Odds Ratio Lower Upper Nagelkerke R2
SociologicalSelf-employed and continues to workEMG demonstrates motor and sensory
Objective testing
nerve dysfunction consistent with the diagnosis
offer peripheral nerve decompression in patients who continue to work and
have abnormal electrophysiology, and that abnormal electrophysiology takes
priority. This was an unexpected finding, because if surgeons offer surgery
based primarily on reliable and valid objective testing, there should be limited
variation in treatment recommendations. On the other hand, our best statistical
models could account for only 38% of the variation in recommendations for
surgery, suggesting that other, unmeasured factors (such as reimbursement,
rapport, etcetera) account for substantial variation.
This study should be interpreted in light of the fact that the observers are
predominantly in academic practice and are largely from the United States. This
study was also limited to the designed scenarios, which may have influenced
the responses. One shortcoming is that physical examination findings were not
one of the 6 components of the scenario presented. This was intentional, based
on the need to limit the number of variables for statistical reasons and the fact
that the subjective aspect of physical examination findings is more difficult to
represent in a Web-based scenario; however, we plan to develop methods for
looking into this. Short patient scenario’s fail to capture all the elements of the
patient-physician interaction, but even studies that simplify this
complex interaction (which all such studies must inevitably do, to some extent)
can provide useful feedback. In our opinion, the elements that we studied likely
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represent the critical aspects of the clinical encounter and form a basis from
which all surgeons and all patients can evaluate their particular circumstance.
Another shortcoming is that what surgeons say in surveys may or may not
reflect what they say to actual patients. Finally, although a large number of
surgeons participated, the group may be different in many ways from the
average hand surgeon and may not represent the full variation in opinions
among hand surgeons worldwide. We also want to mention that our reference
to compressive neuropathy is speculative, particularly for the more debatable
diagnoses such as pronator syndrome and radial tunnel syndrome.
In the typical patient-provider interaction, symptom intensity and
magnitude of disability (which can be grouped together as illness behavior)
seem to have a more substantial influence on treatment choices than
pathophysiology (e.g. electro-diagnostic abnormalities). For instance, if a patient
with carpal tunnel syndrome and electro-diagnostic abnormalities experiences
a decrease in symptoms with nonsurgical treatment (less illness), then neither
the patient nor the surgeon find surgical treatment appealing, even though
there is some evidence that carpal tunnel syndrome is a progressive disorder
that can cause permanent nerve damage (in other words, there has been no
change in the disease). Conversely, many surgeons will offer surgical treatment
on the basis of symptoms alone, even if electrophysiological testing is normal
(more illness than expected based on objective pathophysiology). In contrast
to a disease like type 1 diabetes mellitus – in which patients must take their
insulin even if they are feeling well or risk diabetic ketoacidosis – the idea of
surgery to stop pathophysiology and prevent nerve damage in carpal tunnel
syndrome seems counterintuitive to most patients and surgeons, and illness
behavior seems at times to have a relatively greater influence on treatment than
pathophysiology does. The results of our survey suggest that – at least among
a group of surgeons that is largely academic and primarily based in the United
States – these impressions may be incorrect because, on average, surgeons
indicated that they rely more on electro-diagnostic testing than we expected.
Another unexpected finding was the absence of an influence based
on the referring provider’s expectations. Anecdotally, depending on the
reimbursement setting, some of us have the impression that some surgeons
are concerned about satisfying the primary care doctor, without whose referrals
their practice would be less busy and, therefore, less profitable.
Finally, we expected to find a difference between debatable (e.g.
pronator and radial tunnel syndromes) and widely accepted (e.g. carpal tunnel
27
syndrome) diagnoses, but this did not have a measurable influence on surgeon
recommendations. Perhaps these diagnoses are less debated than we thought,
or perhaps these diagnoses are accepted if there are measurable electro-
diagnostic abnormalities.
According to this survey, patient circumstances (e.g. workers’
compensation in dispute) and objective testing have more influence than
symptoms, mindset, diagnosis, and expectations on surgeon recommendations
for peripheral nerve decompression. Additional studies are needed to identify
factors that influence actual day-today decision-making, the sources of
variation, and how informed, shared decision-making, using techniques such
as decision aids, might reduce this variation, increase patient satisfaction, and
provide optimal health care as resourcefully as possible.
*From the Orthopaedic Hand and Upper Extremity Service, Massachusetts General Hospital,
Boston, MA. The Science of Variation Group: Abhijeet L. Wahegaonkar, Aida E. Garcia G, Alan
Schefer, Alberto Pérez Castillo, Andrew L. Terrono, Andrew W. Gurman, MD, T. Apard, Barry Watkins,
Asif Ilyas, Bernard F. Hearon, MD, Brian P.D. Wills, MD, Bruce I. Wintman, Carrie Swigart, Catherine
Spath, Cesar Dario Oliveira Miranda, Charles A. Goldfarb, Charles Cassidy, Charles Metzger, Charles
Eaton, Chris Wilson, Christopher J. Walsh, Christopher J. Wilson, Christopher M. Jones, Colby Young,
Craig A. Bottke, MD, Daniel A Osei, D. Kay Kirkpatrick, Daniel Polatsch, David E. Tate, Jr, David L.
Nelson, MD, David M. Kalainov, David M. Lamey, MD, Doug Hanel, David M. Ostrowski, MD, David
R. Miller, Desirae M. McKee, David Ruchelsman, Ekkehard Bonatz, Eon K. Shin, Eric P. Hofmeister,
Evan S. Fischer, MD, F. ThomasD. Kaplan, C.H. Fernandes, Jamie E. Forigua, Fidel Ernesto Cayón
Cayón, Frank J. Raia, Frank L. Walter, Gary K. Frykman, MD, Gary M. Pess, MD, Gary R. Kuzma, Georg
M. Huemer, Gregory Dee Byrd, George W. Balfour, Gladys Cecilia Zambrano Caro, German Ricardo
Hernandez, Gregory DeSilva, H. Brent Bamberger, DO, H.W. Grunwald, Hal MccUtchan, Harrison
Solomon, MD, Hervey L. Kimball, J.E.B. Stuart, InesC.Lin, Jack Choueka, James G. Reid, James M.
Boler, Jay Pomerance, Jeff W. Johnson, Jeffrey Yao, Jim Calandruccio, Jennifer B.Green, Jennifer
Moriatis Wolf, Jessica A. Frankenhoff, Jerome W. Oakey, Jochen Fischer, John Howlett, John Jiuliano,
John M. Erickson, John McAuliffe, John P. Evans, John Taras, Jorge G. Boretto, Jonathan Isaacs, Jose
A. Ortiz, Jr, José Fernando Di Giovanni, Jose Nolla, Joshua M. Abzug, Julie Adams, L.C. Bainbridge,
Karel Chivers, Karl-Josef Prommersberger, Kevin J. Malone, Kendrick Lee, Lawrence S. Halperin, MD,
Lawrence Weiss, Leon Benson, Lewis B. Lane, Lior Paz, Lisa Lattanza, M. Jason Palmer, Louis Catalano
III, Marc J. Richard, Marco Rizzo, Martin Boyer, Maurizio Calcagni, Megan M. Wood, Michael
Baskies, Michael W. Grafe, Michael Behrman, Michael Jones, Michael Quinn, Michael Nancollas,
Michael W. Kessler, Miguel A. Pirela-Cruz, Milan M. Patel, NaquiraEscobarLuisFelipe, NeilG.Harness,
MD,NgoziM.Akabudike, NicholasJ.Horangic, Oleg M. Semenkin, Nicky L. Leung, Patrick T. McCulloch,
Patrick W. Owens, Paul A. Martineau, Paul Bettinger, Paul Guidera, Peter E. Hoepfner, Prasad
Sitaram, Peter H. DeNoble, Peter Jebson, Philip Coogan, Phani Dantuluri, R. Glenn Gaston, MD,
Ralf Nyszkiewicz, Ralph M. Costanzo, Ramon de Bedout, Randy Hauck, Renato M. Fricker, Richard
28
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S. Gilbert, Richard L. Hutchison, Richard W. Barth, Rick Papandrea, Robert M. Szabo, Robert R. L.
Gray, Ross Nathan, Rozental, Sander Spruijt, Russell Shatford, Ryan Klinefelter, Samir Sodha, Ryan
P. Calfee, Sanjeev Kakar, Saul Kaplan, Scott F. Duncan, Scott Mitchell, Seth Dodds, Sidney M. Jacoby,
Stephen A. Kennedy, Stanley Casimir Marczyk, Stephen W. Dailey, MD, Steve Kronlage, Steven Alter,
Steven Beldner, Steven J. McCabe, Stuart M. Hilliard, Thomas J. Fischer, Taizoon Baxamusa, C. Taleb,
Thomas F. Varecka, Theresa Wyrick, Timothy G. Havenhill, Todd Siff, Victoria D. Knoll, Vipul P. Patel,
W. Arnnold Batson, Warren C. Hammert, and William J. Van Wyk, MD.
REFERENCES
1. Frymoyer JW. Degenerative Spondylolisthesis: Diagnosis and Treatment. J Am Acad Orthop
Surg 1994;2:9-15.2. Duszak R, Jr., Behrman SW. National trends in percutaneous cholecystostomy between 1994
and 2009: perspectives from Medicare provider claims. J Am Coll Radiol 2012;9:474-9.3. Fanuele J, Koval KJ, Lurie J, Zhou W, Tosteson A, Ring D. Distal radial fracture treatment:
what you get may depend on your age and address. The Journal of bone and joint surgery
American volume 2009;91:1313-9.4. Cassidy JD, Carroll LJ, Cote P, Lemstra M, Berglund A, Nygren A. Effect of eliminating
compensation for pain and suffering on the outcome of insurance claims for whiplash injury.
N Engl J Med 2000;342:1179-86.5. de Beer J, Petruccelli D, Gandhi R, Winemaker M. Primary total knee arthroplasty in patients
receiving workers’ compensation benefits. Can J Surg 2005;48:100-5.6. Harris I, Mulford J, Solomon M, van Gelder JM, Young J. Association between compensation
status and outcome after surgery: a meta-analysis. Jama 2005;293:1644-52.7. MacKenzie EJ, Bosse MJ, Kellam JF, et al. Early predictors of long-term work disability after
major limb trauma. J Trauma 2006;61:688-94.8. Scuderi C, Khedroo F. Herniation of the intervertebral disc; diagnosis, treatment and resume
of follow-up study. J Int Coll Surg 1955;23:194-204.9. Wong JY. Time off work in hand injury patients. J Hand Surg Am 2008;33:718-25.10. Day CS, Alexander M, Lal S, et al. Effects of workers’ compensation on the diagnosis and
surgical treatment of patients with hand and wrist disorders. J Bone Joint Surg Am
2010;92:2294-9.11. McGlaston TJ, Kim DW, Schrodel P, Deangelis JP, Ramappa AJ. Few insurance-based differences
in upper extremity elective surgery rates after healthcare reform. Clin Orthop Relat Res
2012;470:1917-24.
29
CHAPTER 3
The factors influencing the
decision-making of operative
treatment for proximal humeral
fractures
Hageman MG, Jayakumar P, King JD, Guitton TG, Doornberg JN, Ring D; Science of Variation Group.
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
J Shoulder Elbow Surg. 2015 Jan;24(1):e21-6.
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ABSTRACT
Background The factors influencing the decision-making of operative treatment for fractures of the proximal humerus are debated. We hypothesized that there is no difference in treatment recommendations between surgeons shown radiographs alone and those shown radiographs and patient information. Secondarily, we addressed (1) factors associated with a recommendation for operative treatment, (2) factors associated with recommendation for arthroplasty, (3) concordance with the recommendations of the treating surgeons, and (4) factors affecting the inter-rater reliability of treatment recommendations.
Methods A total of 238 surgeons of the Science of Variation Group rated 40 radiographs of patients with proximal humerus fractures. Participants were randomized to receive information about the patient and mechanism of injury. The response variables included the choice of treatment (operative vs non-operative) and the percentage of matches with the actual treatment.
Results Participants who received patient information recommended operative treatment less than those who received no information. The patient information that had the greatest influence on treatment recommendations included age (55%) and fracture mechanism (32%). The only other factor associated with a recommendation for operative treatment was region of practice. There was no significant difference between participants who were and were not provided with information regarding agreement with the actual treatment (operative vs non-operative) provided by the treating surgeon.
Conclusions Patient information – older age in particular – is associated with a higher likelihood of recommending non-operative treatment than radiographs alone. Clinical information did not improve agreement of the Science of Variation Group with the actual treatment or the generally poor inter-observer agreement on treatment recommendations.
31
INTRODUCTION
The role of operative treatment for fractures of the proximal humerus is
debated. Surgery is considered for approximately one in five patients, but there
is no consensus on which fractures benefit from surgery or which procedure
to perform.1 The data to date are limited and inconclusive.1,2 A recent Cochrane
review found no statistically significant difference between operative and
non-operative treatment regarding patient-reported functional scores and
EuroQoL results at 1 year from 3 randomized control trials with a total of
153 participants.1 However, compared to non-operative treatment, operative
treatment had superior EuroQoL scores at two years of follow-up in two
randomized control trials with a total of 101 participants.1
Among a small group of surgeons at two level-1-trauma centers, Okike et
al3 identified younger age, operative treatment of other musculoskeletal injuries,
Arbeitsgemeinschaft f ür Ostesynthesefragen (AO) classification, translation-type
displacement, associated glenohumeral dislocation, and surgeon subspecialty
(upper extremity specialists were more likely to operate than traumatologists) as
factors associated with operative intervention. The use of arthroplasty rather than
internal fixation was associated with a higher Charlson score and more severe
Neer and AO classifications.3-5 Many of these factors relate to the radiographic
appearance of the fracture, whereas some relate to patient or surgeon factors.
We were curious about the factors that influence agreement between
surgeons on treatment recommendations and the factors that lead a surgeon to
recommend operative treatment and type of surgery (i.e. fixation vs arthroplasty). We
used the Science of Variation Group (SVOG), an international Web-based collaborative
of practicing surgeons, to test the primary null hypothesis that there is no difference
in treatment recommendations regarding operative vs non-operative treatment
between surgeons shown radiographs alone and those shown radiographs and
patient information such as age, sex, hand dominance, and fracture mechanism.
Secondarily, we addressed (1) factors associated with a recommendation for
operative treatment, (2) factors associated with recommendation for arthroplasty,
(3) concordance with the recommendations of the treating surgeons, and (4) factors
affecting inter-rater reliability of treatment recommendations.
MATERIALS AND METHODS
We asked the surgeons of the SOVG to complete a survey regarding the
recommendation of operative or non-operative treatment for a series of
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proximal humeral fractures. The SOVG is an international collaboration of fully
trained surgeon observers that studies variation in the definition, interpretation,
classification, and treatment of human illness. Collaborative authorship,
scientific curiosity, and camaraderie are the only incentives for participation.
Participating members viewed the radiographs of 20 fractures of the
proximal humerus treated operatively and 20 treated non-operatively. These were the
radiographs used by the surgeon caring for the patient and were not standardized.
Participants were randomized to receive information about the patient, including sex,
age, American Society of Anesthesiologists (ASA) classification, and hand dominance,
and mechanism of injury or not, in a 1-to-1 allocation.
Evaluation
The 40 proximal humeral fractures were selected from a separate case-control
study in which 66 patients, 33 treated operatively and 33 treated non-
operatively, were matched for fracture type, age, sex, and ASA classification. The
treating surgeons classified those fractures as 2-part surgical neck fractures in
7 pairs of patients, 3-part fractures in 9 pairs, and 4-part fractures in 4 pairs.
Participants viewed anterior-posterior and lateral radiographs.
Each participant provided demographic and professional information:
sex, world region of practice, years in independent practice, supervision of
trainees, and surgical subspecialty. Each observer was asked two questions for
each set of images: (1) Would you recommend surgery? And, if so, (2) What is
your preference: open reduction and internal fixation, percutaneous pinning,
or arthroplasty? In addition, the observers who received patient information
were asked a third question: What information was most influential? They could
choose from the following answers: (1) patient characteristics (sex, age, ASA,
hand dominance), (2) fracture mechanism, (3) other (Table 1).
Of the 238 surgeons who completed the survey, 130 were randomized to
receive information and 108 were randomized to receive radiographs alone. The
cohorts were comparable (Table 2).
Statistical analysis
The response variables included the choice of treatment (operative vs
non-operative) and the percentage of matches with the actual treatment.
Associations between response variables and categorical explanatory variables
were assessed using X2 tests. Factors with p < 0.10 in bivariate analysis were
entered into a multiple logistic regression analysis.
Table I Patient characteristics
Parameters
Mean
60
65
56 n 9
24
1216
5
2310
1716
1018
41 7
26 9
24
Range
13 - 86
19 - 90
15 - 83
Range
33 - 92
40 - 94
0 - 29
7 - 89
P value
0.74
0.42
<0.01
0.59
0.22
0.40
0.62
0.40
0.52
0.59
SD
15
15
17 %
1436
1824
7.6
3515
2624
1628
52
1139
1436
SD
13
12
8.1
25 %
3317
2917
4.5
3020
2921
2220
7.8
7.642
3317
Nonoperative groupn=33
Operative fixation group n=33
Time
Age at injury (Y) Age at follow-up (Y)
Duration injury until operative treatment
Duration of follow-up (Months)
Sex
MenWomen
Neer classification
2 Part3 Part4 Part
Fracture mechanism
Type 1 (Slipped)Type 2 (High energy trauma)
Dominant arm effected
YesNo
Comorbidity scale
ASA 1ASA 2ASA 3ASA 4
Diabetes
YesNo
Smocking
YesNo
Sex
Neer Classification
Fracture mechanism
Dominant arm effected
Comorbidity scale
Diabetes
Smocking
Mean
59
62
14
35 n
1122
1911
3
2013
1914
1413
5
528
2211
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The percentage of agreement (1) with other observers and (2) with the
original treating surgeon was calculated for surgeons who received information
or not, and the κ-multi-rater measure was also measured. The κ values were
interpreted with use of the guidelines proposed by Landis and Koch.6,7
An a priori power analysis indicated that a cohort of 200 surgeons
randomized equally to review radiographs with or without patient information
would provide 80% power to detect a mean difference of 0.10 in inter-observer
reliability based on κ-multi-rater, assuming a pooled standard deviation of 0.25
(moderate effect size: 0.10/0.25 = 0.40) using a parametric Z-test with a two-
tailed α-level of 0.05 and assuming an underlying normal distribution in the
patient population.
RESULTS
Participants who received patient information recommended operative
treatment less than those who received no information (61% vs 66%; p < 0.01;
Table 3). The only other factor associated with a recommendation for operative
treatment was region of practice: participants from Asia were more likely than
participants from Canada to recommend surgery (72% vs 51%; p < 0.01; Table 2).
In multivariable analysis, the only factor associated with a recommendation
of non-operative treatment was receiving clinical information in addition to
radiographs, which explained 10% of the variation in recommendation for non-
operative treatment (odds ratio, 4.3;r2 = 0.10; p < 0.01).
Participants provided with patient information were more likely to
recommend arthroplasty (24% vs 17%; p < 0.01) and less likely to recommend
open reduction and internal fixation (69% vs 76%; p < 0.01; Table 4). The patient
information that had the greatest influence on treatment recommendations
included age (55%) and fracture mechanism (32%; Table 5).
There was no significant difference between participants provided with
information and participants not provided information regarding agreement
with the actual treatment (operative vs non-operative) provided by the treating
surgeon (65% vs 67% on average; p = 0.11). Agreement with the actual treatment
provided by the treating surgeon varied significantly by location of practice
(p < .01; Table 6).
Inter-observer agreement regarding recommendations for operative
treatment was poor (average, 0.008; range, 0.007 to –0.008; Supplementary (Table 7).
κ
35
Table 2 Demographics of the participants n=228
Parameters
SexMenWomen
Location of practiceAsiaAustraliaCanadaEuropeUnited KingdomUnited States of AmericaOther
Years In practice0-56-1011-2021-30
Supervise TraineesYesNo
Fractures per year0-56-1011-20>20
SpecializationGeneral orthopaedicsOrthopaedic traumatologyShoulder and elbowHand and wristOther
Radiographs plus additional informationn (%)
122 (52) 8 (3.2)
9 (3.6) 4 (1.6) 7 (2.8) 40 (17) 5 (2.0) 59 (24) 12 (4.8)
41 (18) 29 (12) 34 (15) 26 (11)
119 (50) 11 (5.6)
11 (4.8) 32 (13) 41 (17) 50 (21)
7 (2.8) 58 (24) 27 (11) 32 (15) 6 (2.4)
Radiographs without additional informationn (%)
101 (42) 7 (2.8)
2 (0.8) 2 (0.8) 4 (1.6) 27 (12) 0 62 (25) 11 (4.4)
40 (17) 18 (7.3) 33 (13) 17 (6.9)
95 (39) 13 (5.6)
14 (5.6) 23 (9.7) 34 (14) 37 (15)
6 (2.4) 35 (14) 27 (11) 39 (16) 1 (0.4)
Table 3 Bivariable analysis about the participants’ recommendation
Operative treatment
%
64
Operative treatment
6166
6365
72695162546173
62626664
6365
63616565
6263626660
Operative treatment
%
46
Operative treatment
4350
4647
58533045374560
44444949
4647
47414947
4246444950
Non-operative
%
38
Non-operative
3934
3735
28314938463927
38383436
3735
37393535
3837383440
Non-operative
%
54
Non-operative
5750
5453
42477055635540
56565151
5453
53595153
5854565150
Min - Max
0.23-1
P-Value
<0.01
0.8
<0.01
0.24
0.48
0.57
0.41
Min - Max
0-1
P-Value
<0.01
0.93
<0.01
0.25
0.54
0.18
0.62
Operative treatment Nonoperative treatment
OverallObservers
Information group
Radiographs + informationRadiographs - additional information
SexMenWomen
Location of practiceAsiaAustraliaCanadaEuropeUnited KingdomUnited States of AmericaOther
Years In practice0-56-1011-2021-30
Supervise TraineesYesNo
Fractures per year0-56-1011-20>20
SpecializationGeneral orthopaedicsOrthopaedic traumatologyShoulder and elbowHand and wristOther
Table 4 Comparison of percentage of arthroplasty as prefered osteosynthesis for the information and non information group
Parameters Arthroplasty
ORIF
Pin
95% CI
Information groupRadiographs plus additional informationRadiographs without additional information
Parameter
Information groupRadiographs plus additional informationRadiographs without additional information
Parameter
Information groupRadiographs plus additional informationRadiographs without additional information
mean24%17%
mean69%76%
mean5.9%6.8%
P-Value
<0.01
P-Value
<0.01
P-Value
0.54
Upper
0.1
Upper
0.75
Upper
0.02
Lower
0.04
Lower
0.71
Lower
-0.04
Table 5 Additional information used by the information group for decision making regarding the preferred treatment
SexAgeASAHand dominanceFracture mechanismOther
n9
7220124268
%7.255158.93252
37
Table 6 Bivariable analysis comparing the recommendation of the participant and the actual treatment
Operative treatment
%
80
Operative treatment
7783
8082
85867179717986
79778179
8078
78788081
8279808069
Operative treatment
%
46
Operative treatment
4350
4647
58533045374560
44444849
4647
47414947
4246444950
Non-operative
%
20
Non-operative
2317
2020
15142921292114
21231921
2022
22222019
1821202031
Non-operative
%
54
Non-operative
5750
5453
42477055635540
66665151
5453
52595153
5854575150
Min - Max
0.2-1.0
P-Value
<0.01
0.45
<0.01
0.39
0.52
0.62
0.23
Min - Max
0.0-1.0
P-Value
<0.01
0.93
<0.01
0.26
0.93
0.14
0.62
Operative treatment Nonoperative treatment
OverallObservers
Information group
Radiographs + informationRadiographs - information
SexMenWomen
Location of practiceAsiaAustraliaCanadaEuropeUnited KingdomUnited States of AmericaOther
Years In practice0-56-1011-2021-30
Supervise TraineesYesNo
Fractures per year0-56-1011-20>20
SpecializationGeneral orthopaedicsOrthopaedic traumatologyShoulder and elbowHand and wristOther
39
Table 7 Agreement measurement
123456789
10111213141516171819202122232425262728293031323334353637383940
ASE
0.2390.0220.9860.4720.0310.1370.986
*0.0670.2390.0590.5540.1310.3410.0760.9860.6880.4720.2140.2390.131
*0.1030.0930.2910.0450.0840.1440.2550.6880.0340.0380.0840.3140.4720.2020.2720.2720.6880.108
Z-Value
-0.038-0.423-0.009-0.019-0.297-0.067-0.009
*-0.136-0.038-0.154-0.017-0.070-0.027-0.121-0.009-0.013-0.019-0.043-0.038-0.070
*-0.089-0.098-0.032-0.206-0.109-0.064-0.036-0.013-0.269-0.244-0.109-0.029-0.019-0.045-0.034-0.034-0.013-0.085
Kappa
-0.009-0.009-0.009-0.009-0.009-0.009-0.009
*-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009
*-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009-0.009
P-Value
0.9690.6720.9930.9850.7660.9470.993
*0.8920.9690.8770.9870.9440.9790.9030.9930.9890.9850.9660.9690.944
*0.9290.9220.9750.8370.9130.9490.9710.9890.7880.8070.9130.9770.9850.9640.9730.9730.9890.932
Radiographs plus additional information Radiographs without additional information
Z-Value
-0.048-0.308-0.013-0.030-0.137-0.638-0.071-0.056-0.098-0.050-0.114-0.018-0.040-0.022-0.059-0.053
*-0.008-0.689-0.087-0.074
*-0.091-0.104-0.023-0.056-0.567-0.046-0.037-0.021-0.121-0.211-0.058-0.035
*-0.058-0.118-0.155-0.017-0.056
P-Value
0.9620.7580.9890.9760.8910.5230.9440.9550.9220.9600.9090.9860.9680.9820.9530.958
*0.9940.4910.9310.941
*0.9280.9170.9820.9550.5710.9630.9710.9830.9030.8330.9540.972
*0.9540.9060.8770.9860.956
Kappa
-0.007-0.007-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008
*-0.008-0.008-0.008-0.008
*-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008-0.008
*-0.008-0.008-0.008-0.008-0.008
ASE
0.1530.2420.5580.2490.5510.1180.1060.1340.7680.1510.6640.4220.1930.3470.1310.148
*0.9880.1140.9050.107
*0.8670.7580.3460.1410.0140.1740.2200.3780.6650.0380.1400.232
*0.1400.6910.5250.4760.146
*=Based on negative interobserver variability no information was given regarding agreement.
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DISCUSSION
The recommendations for managing proximal humeral fractures vary
substantially. Recent studies demonstrate the poor levels of reliability in the
treatment of these injuries.8,9 We were interested in the relative influence of
patient information and surgeon characteristics on the decision-making process
in treating proximal humeral fractures.
This study should be considered in light of its shortcomings. Most
regions were represented by small numbers of observers, and the findings may
not be representative of the average surgeon in those regions. The low κ-values
may be due to the κ-paradox: when the prevalence of an outcome is low, it could
cause an imbalance that generates a lower κ than one might expect based
on the agreement. Also, our study did not include fracture classification as an
explanatory variable, although inter-observer reliability of fracture classification
is limited amongst orthopedic surgeons.8,10 In addition, a consecutive selected
case series would have limited spectrum bias. However, a relatively even
distribution between operatively and non-operatively treated patients is needed
to avoid the κ-paradox. Readers should also keep in mind that observers were
shown the unstandardized anterior-posterior and lateral radiographs used by
the treating surgeons to direct management, thereby reflecting daily practice.
Some surgeons use other radiographic views and computed tomography scans,
including 3-dimensional reconstructions, more routinely in the decision process.
We found that patient information – older age in particular – is
associated with a higher likelihood of recommending non-operative treatment
than radiographs alone. This is consistent with the observations of Okike et al.3
We also identified regional variations in treatment recommendations. This was
in concordance with earlier published reports that demonstrated wide regional
variations for surgical treatment adjusted for age, sex, and race in populations
of elderly patients with proximal humeral fractures ranging from 0% in many
regions to almost 70% in Duluth, Minnesota, USA..11,12 Of interest, the sex,
level of experience in years in practice, number of proximal humeral fractures
treated per year, and specialization did not have a significant influence on the
decision to operate. This is in contrast to other studies, which report shoulder
and upper extremity specialists are more likely to choose operative intervention
than general orthopedic trauma specialists.11 The effect of specialty may vary
in specific centers with small samples of each type of surgeon, but our larger,
broader cohort suggests that specialty has relatively little influence.
The provision of clinical information also influenced the recommended
41
type of operative treatment, with arthroplasty (as with non-operative treatment)
favored by surgeons who received information about the patients. This
suggests that – with current biases – older, more infirm, and inactive patients
are less likely to be treated operatively and are more likely to be treated with
arthroplasty if they do have operative treatment.
Clinical information did not improve agreement of the SOVG
participants with the actual treatment or the generally poor inter-observer
agreement on treatment recommendations. The poor agreement may be
unreliable due to the κ paradox. A study by Petit et al12 documented moderate
inter-observer agreement on the surgical management (non-operative,
closed manipulation and reduction, open reduction and internal fixation, and
hemiarthrosplasty) of 38 proximal humeral fractures among 8 fellowship-
trained orthopedic surgeons.
Treatment recommendations for proximal humeral fractures are
influenced by patient information – older age in particular – but most of the
variation in recommendations remains unaccounted for. The highly variable
and inconsistent influence of patient factors on surgeon recommendations
belies variations in surgeon preferences and values that are likely at the root of
the substantial treatment variations documented in this and other studies. We
speculate that greater involvement of the patient in decision-making is likely to
decrease variation across caregivers.
CONCLUSION
Patient information – older age in particular – is associated with a higher
likelihood of recommending nonoperative treatment than radiographs alone.
Clinical information did not improve agreement of the SOVG with the actual
treatment or the generally poor inter-observer agreement on treatment
recommendations.
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REFERENCES
1. Handoll HH, Ollivere BJ, Rollins KE. Interventions for treating proximal humeral fractures in
adults. Cochrane Database Syst Rev 2012;12:CD000434.2. Misra A, Kapur R, Maffulli N. Complex proximal humeral fractures in adults--a systematic
review of management. Injury 2001;32:363-72.3. Okike K, Lee OC, Makanji H, Harris MB, Vrahas MS. Factors associated with the decision for
operative versus non-operative treatment of displaced proximal humerus fractures in the
elderly. Injury 2013;44:448-55.4. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic
comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373-
83.5. Neer CS, 2nd. Displaced proximal humeral fractures. I. Classification and evaluation. The
Journal of bone and joint surgery American volume 1970;52:1077-89.6. Cohen J. A Coefficient of agreement for nominal scales. Educ Psychol Meas 1960;20:37-46.7. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics
1977;33:159-74.8. Bruinsma WE, Guitton TG, Warner JJ, Ring D, Science of Variation G. Interobserver reliability of
classification and characterization of proximal humeral fractures: a comparison of two and
three-dimensional CT. J Bone Joint Surg Am 2013;95:1600-4.9. Foroohar A, Tosti R, Richmond JM, Gaughan JP, Ilyas AM. Classification and treatment of
proximal humerus fractures: inter-observer reliability and agreement across imaging
modalities and experience. Journal of orthopaedic surgery and research 2011;6:38.10. Brorson S, Rasmussen JV, Frich LH, Olsen BS, Hrobjartsson A. Benefits and harms of locking
plate osteosynthesis in intraarticular (OTA Type C) fractures of the proximal humerus: a
systematic review. Injury 2012;43:999-1005.11. Bell JE, Leung BC, Spratt KF, et al. Trends and variation in incidence, surgical treatment,
and repeat surgery of proximal humeral fractures in the elderly. J Bone Joint Surg Am
2011;93:121-31.12. Sporer SM, Weinstein JN, Koval KJ. The geographic incidence and treatment variation of
common fractures of elderly patients. J Am Acad Orthop Surg 2006;14:246-55.
43
CHAPTER 4
How surgeons make decisions when
the evidence is inconclusive
Hageman MG, Guitton TG, Ring D; Science of Variation Group.
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
J Hand Surg Am. 2013 Jun;38(6):1202-8.
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ABSTRACT
Background To address the factors that surgeons use to decide between two options for treatment when the evidence is inconclusive.
Methods We tested the null hypothesis that the factors surgeons use do not vary by training, demographics, and practice. A total of 337 surgeons rated the importance of seven factors when deciding between treatment and following the natural history of the disease and twelve factors when deciding between two operative treatments using a 5-point Likert scale between “very important” and “very unimportant.”
Results According to the percentages of statements rated very important or somewhat important, the most popular factors influencing recommendations when evidence is inconclusive between treatment and following the natural course of the illness were “works in my hands,” “familiarity with the treatment,” and “what my mentor taught me.” The most important factors when evidence shows no difference between 2 surgeries were “fewer complications,” “quicker recovery,” “burns fewer bridges,” “works in my hands” and “familiarity with the procedure.” Europeans rated “works in my hands” and “cheapest/most resourceful” of significantly greater importance and “what others are doing,” “highest reimbursement,” and “shorter procedure” of significantly lower importance than surgeons in the United States. Observers with fewer than ten years in independent practice rated “what my mentor taught me,” “what others are doing” and “highest reimbursement” of significantly lower importance compared to observers with ten or more years in independent practice.
Conclusions Surgeons deciding between two treatment options, when the evidence is inconclusive, fall back to factors that relate to their perspective and reflect their culture and circumstances, more so than factors related to the patient’s perspective, although this may be different for younger surgeons.
45
INTRODUCTION
Evidence-based medicine has been defined as “the conscientious, explicit, and
judicious use of current best evidence in making decisions about the care
of individuals and populations.” In practice, this involves an integration of
individual clinical expertise with the best available external clinical evidence
from systematic research.1,2 Patients and health care providers look to scientific
evidence to help guide their medical decisions.
The “Evidence-Based Guidelines” from the American Academy of
Orthopaedic Surgeons have been largely inconclusive for lack of evidence.3
Well-designed, prospective, randomized controlled trials frequently show
no difference or a small and possibly unimportant difference between two
treatments.4,5 Clinical evidence in 2010 classified 2,500 common treatments
as 51% having insufficient evidence, 23% likely to be beneficial, 7% requiring
trade-offs between benefits and harms, 5% unlikely to be beneficial, 3% likely
to be ineffective or harmful, and 11% being clearly beneficial.6 How do health
care providers decide which option to recommend to their patients when the
evidence is inconclusive?
This study addresses the factors that surgeons deciding between two
options fall back to when the data are inconclusive. Specifically, we tested
the null hypothesis that the top fallback principles do not vary by training,
demographics, and practice.
MATERIALS AND METHODS
Using an institutional review board–approved protocol, we asked the
400 surgeons of the Science of Variation Group to complete a survey about
decision-making in the face of inconclusive evidence, and 337 participated.
The Science of Variation Group is an international collaboration of fully trained
surgeon observers that studies variation in the definition, interpretation,
classification, and treatment of human illness. Collaborative authorship,
scientific curiosity, and camaraderie are the only incentives for participation.
Evaluation
The observers were first asked to enter their demographic and professional
information: sex, country or world region of practice, years in independent
practice, supervision of trainees, and surgical subspecialty. Next, the observers
were given the following context:
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“The American Academy of Orthopaedic Surgeons Evidence-Based Guidelines have been largely inconclusive for lack of evidence. It is difficult to show a difference in a well-designed prospective randomized, controlled trial – most will show little or no difference between treatments. Therefore it is important to decide – before starting the study – what our fallback will be. How do we decide between treatment options when the data are either insufficient or otherwise inconclusive?”
In this context, participants were asked to rate the importance of seven
factors when deciding between operative treatment and palliative treatment
(e.g. the natural history of the disease) and twelve factors when deciding
between two operative treatments (Table 1), with a comment section for listing
additional factors. The ratings were based on a 5-point Likert-scale between
very important and very unimportant. The statements were developed by
brainstorming. One author created a list, and the other authors edited until all
authors felt that the list covered all potential fallback options.
Statistical analysis
Categorical data were presented as frequencies and percentages. The
statements were ranked from highest to lowest by adding the percentages of
the very important and somewhat important (Figs. 1,2). The write-in answers
were grouped by subject. In addition, the Likert-scale was translated to an
ordinal scale from 2 (very important) to -2 (very unimportant), and the mean on
each scale across the entire sample was calculated. We analyzed the influence
of nationality, years in practice, fractures treated per year, and specialization
on preferred fallbacks. The subcategory “years in practice” was dichotomized
to less than or equal to ten years and more than ten years of experience to
facilitate analysis. For continuous variables, we used a Mann Whitney U test to
compare two groups and a Kruskal-Wallis test for multiple groups. We evaluated
differences between subgroups with the Mann Whitney U-test.
Observer demographics
The demographics for the 338 respondents are listed in (Table 2).
47
Table 1 Geographic difference and factors
Variable
The importance, that a given treatment is better than the course of the illness without treatment
The importance when comparing two surgeries for a given problem:
Mean
Works in my handsFamiliarity with treatmentWhat my mentor taught meDo something vs. Do nothingWhat others are doingPatients requiring the procedureHighest reimbursement
Fewer complicationsQuick recoveryBurns fewer bridgesWorks in my handsFamiliarity with procedureCheapest/ most resourcefulShorter procedureAesthics: Smaller or fewer scarsWhat my mentor taught meWhat others are doingPatients requiring the procedureHighest reimbursement
US
-1.3 -0.84 -0.58 -0.29 -0.11 0.28 1.2
-1.8 -1.4 -1.4 -1.3 -1.2 -0.69 -0.51 -0.46 -0.39 -0.11 0.26 1.08
EU
-1.04 -1.04 -0.68 -0.53 -0.40 0.03 0.78
-1.86 -1.52 -1.15 -1.15 -1.21 -0.47 -0.76 -0.50 -0.53 -0.23 0.12 0.63
P-Value
0.02 0.07 0.38 0.14 0.02 0.06 <0.01
0.21 0.15 0.02 0.13 1.00 0.05 0.02 0.74 0.23 0.32 0.24 <0.01
Mean. Dif.
-0.26 0.20 0.10 0.24 0.29 0.25 0.40
0.07 0.12 -0.21 -0.15 0.00 -0.22 0.25 0.04 0.15 0.12 0.14 0.45
RESULTS
Statement rating
According to the percentages of statements rated very important or somewhat
important, the most popular fallbacks when evidence cannot demonstrate that
a given treatment is better than following the natural course of the illness are
noted in Figure 1. The top fallbacks when evidence shows no difference between
two surgeries are noted in Figure 2.
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Very important
Somewhat important
Neutral
Somewhat unimportant
Very unimportant
Works in my hands
Familiarity with treatment
What my mentor taught me
Do something vs. Do nothing
What other are doing
Patients are requesting the procedure
Hight reimbursement
0 10 20 30 40 50 60 70 80 90 100
Figure 1
0 10 20 30 40 50 60 70 80 90 100
Fewer complications
Quick recovery
Burns fewer bridges
Works in my hands
Familiarity with procedure
Cheapest/most resourceful
Shorter procedure
Aesthics: Smaller or fewer scars
What my mentor taught me
What other are doing
Patients requesting
Highest reimbursement
Figure 2
49
Table 2 Demographics n=338
n %
Sex Men 306 91 Women 32 9 Location of practice Asia 19 6 Australia 6 2 Canada 18 5 Europe 92 27 United Kingdom 11 3 United States of America 174 52 Other 17 5 Years In practice 0-5 106 31 6-10 72 21 11-20 102 30 21-30 57 17 Supervise Yes 279 83 No 58 17 Fractures per year 0-5 61 18 6-10 71 21 11-20 109 32 >20 99 29 Specialization General orthopaedics 21 6 Orthopaedic traumatology 120 36 Shoulder and elbow 54 16 Hand and wrist 129 38 Other 13 4
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United States versus Europe
Using the average values on the numeric conversion of the Likert-scale,
Europeans rated “works in my hands,” “burns fewer bridges,” and “cheapest/
most resourceful” of significantly greater importance and “what others are
doing,” “highest reimbursement,” and “shorter procedure” of significantly lower
importance than surgeons in the United States (Table 1).
Years in practice
Observers with ten or fewer years in independent practice rated “what my
mentor taught me,” “what others are doing,” and “highest reimbursement” of
significantly lower importance compared to observers with more than ten years
in independent practice (Table 3).
Orthopedic specialty
General orthopedists rated “what my mentor taught me” of greater importance
than orthopedic traumatologists and hand and wrist surgeons. In addition,
general orthopedists rated “what others are doing” of greater importance than
shoulder and elbow surgeons and hand and wrist surgeons (Table 4).
Write-in answers
The most common write-in answers were “best available outcome/evidence-
based” (14 surgeons), “common sense and risk for patients” (5 surgeons), and
“shared decision-making or patient’s opinion” (4 surgeons) (Table 5).
DISCUSSION
Because evidence-based medicine is an amalgamation of individual clinical
expertise and best available evidence, the question arises, what is the basis for
provider recommendations when the best evidence is inconclusive? We found
that the most popular factors that surgeons use to make recommendations
when evidence is inconclusive relate primarily to the surgeon’s perspective
(e.g. “works in my hands,” “familiarity with the treatment,” “what my mentor
taught me”) rather than the patient’s perspective (e.g. “doing something vs
doing nothing,” “patients are requesting the procedure”). Exceptions include
“fewer complications” and “quicker recovery,” which benefit both the surgeon
and the patient. Highest reimbursement was also rated relatively unimportant,
particularly in Europe but across all countries and regions.
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Table 3 Difference in experience
Variable
The importance, that a given treatment is better than the course of the illness without treatment
The importance when comparing two surgeries for a given problem:
Mean
Works in my handsFamiliarity with treatmentWhat my mentor taught meDo something vs. Do nothingWhat others are doingPatients requiring the procedureHighest reimbursement
Fewer complicationsQuick recoveryBurns fewer bridgesWorks in my handsFamiliarity with procedureCheapest/ most resourcefulShorter procedureAesthics: Smaller or fewer scarsWhat my mentor taught meWhat others are doingPatients requiring the procedureHighest reimbursement
<10
-1.2 -0.99 -0.84 -0.52 -0.35 0.14 0.92
-1.8 -1.4 -1.3 -1.3 -1.3 -0.61 -0.69 -0.47 -0.65 -0.25 0.13 0.71
>10
-1.3 -0.92 -0.47 -0.39 -0.01 0.27 1.1
-1.9 -1.5 -1.3 -1.3 -1.2 -0.65 -0.54 -0.47 -0.31 -0.05 0.30 1.1
P-Value
0.56 0.50 <0.01 0.34 <0.01 0.27 0.08
0.072 0.47 0.83 0.58 0.25 0.71 0.13 0.95 <0.01 0.058 0.14 <0.01
Mean. Dif.
0.05 -0.06 -0.37 -0.13 -0.34 -0.13 -0.21
0.09 0.05 -0.02 0.04 -0.90 0.04 -0.14 -0.010 -0.33 -0.20 -0.16 -0.36
This study should be interpreted in light of the fact that the 337
participating surgeons may not be representative of the average surgeon,
because many surgeons in the surveyed group are in academic practice. Also,
important options such as “I share the decision with the patient” were not
offered because it was our intention to study the recommendation of the
surgeon before accounting for the patient’s preferences. Finally, there is evidence
that incentives such as reimbursement can have a subconscious influence that
may not be accounted for by this survey.7
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Table 4 Difference in specialty - Post hoc tukey test
Variable
The importance, that a given treatment is better than the course of the illness without treatment
The importance of the following fractors when comparing two surgeries for a given problem
What my mentor taught me
What others are doing
What my mentor taught me
General Orthopaedics
General Orthopaedics
General Orthopaedics
Shoulder and elbow
vs
Orthopaedic Traumatology
Hand and Wrist
Shoulder and elbow
Hand and wrist
Other
Hand and wrist
Other
Other
P-Value
0.033
0.033
0.033
0.033
0.048 0.011
0.003
0.037
Mean. Dif.
-0.62
-0.65
-0.62
-0.65
-0.92 -0.71
-1.20
-0.82
That health care providers fall back to their personal preferences based
on experience is no surprise.8 On the other hand, it is notable that factors
related to quality, safety, and efficiency such as “cheapest/most resourceful,”
“shorter procedure,” and “what others are doing” (in the sense of diminished
unwarranted variation) were rated relatively unimportant. The fact that
Europeans rated “cheapest/more resourceful” significantly more important
than Americans may reflect the prevalence of national health care in Europe,
leading to a greater awareness of the management of limited resources. In
53
Table 5 Others
Category
The importance, that a given treatment is better than the course of the illness without treatment
The importance when comparing two surgeries for a given problem
Best available outcome/ evidence basedCommen senseCombination of common sense and evidenceBasic principlesCost of the treatmentCost of the treatment vs. Doing nothingDo whats best for patiëntDoing something means to me, having the knowledgeExplore Complementary and Alternative Medicine inGuidelines/ ProtocolI would choose the procedure for myselfI have always taughtInnovationLeast bias from industry fundingRisk for patiëntPatients comfortpatients’ perception of the condition-/ shared decision making
Best available outcome/ evidence basedAvailable equimentConsesus local colleaguesCommen senseEquimentLong lasting effectLess painfulPatiënt functional demandsPhilosophical or legal medical aspects p.e.(americShared decision makingTrack recordWhat is “popular”
Total
14 5 1 1 1 1 1 1 1 3 2 1 1 1 5 1 4
10 1 1 2 1 2 1 1 1 1 1 1
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contrast, surgeons from the United States rated “what others are doing,”
“highest reimbursement,” and “shorter procedure” more important than
European surgeons. It is not clear whether these factors relate most to quality
and efficiency or marketing and profitability of health care in a for-profit system,
or both.
Less experienced surgeons placed significantly less importance on “what
my mentor taught me,” “what others are doing,” and “highest reimbursement.”
This might reflect a change in mindset as the emphasis is placed on evidence
and as we continue to address the rising costs of health care.
The write-in answers revealed that surgeons prefer to fall back to the
“best available outcome/evidence-based” even when the scenario is that the
evidence is inconclusive. Patient-centered care/shared decision-making was also
mentioned, which is entirely applicable. The involvement of patients in decision-
making is particularly important when the evidence is inconclusive. Decision
aids (independent structured guides, either written, video, or web-based)
have been shown to decrease decision conflict and, for some illnesses, use of
resources.9-12 These merit additional study.
In other words, rather than studying the surgeon’s recommendation
before accounting for the patient’s preferences, it might have been preferable
for our survey to include the option of following the patient’s preference when
evidence is inconclusive. On the other hand, we have an obligation to consider
resources, safety, simplicity, consistency, efficiency, practicality, optimism,
and patient self-management as important goals in and of themselves, and
this is part of the expertise that we share with our patients. Patients look
to their surgeons for expertise regarding the optimal fallback options when
evidence is inconclusive. Perhaps – on the basis of the results of this survey
study – surgeons will be motivated to develop consensus regarding the fallback
principles that best support optimal health.
*The Science of Variation Group: A. Lee Osterman, A.B. Spoor, A.L. van der Zwan, Abhay Shrivastava,
Abhijeet L. Wahegaonkar, Aida E. Garcia G., M.A. Aita, Alberto Pérez Castillo, Alexander Marcus,
Amy Ladd, Andrew L. Terrono, Andrew P. Gutow, Andrew Schmidt, AngelaA. Wang, Anica Eschler,
Anna N. Miller, Annette K.B. Wikerøy, Antonio Barquet, April D. Armstrong, Arie B. van Vugt,
Asheesh Bedi, Ashok K. Shyam, Augustus D. Mazzocca, Axel Jubel, Babst Reto H., Betsy M. Nolan,
Bob Arciero, Van den Bremer, Brent Bamberger, Bret C. Peterson, Brett D. Crist, Brian J. Cross, Brian
L. Badman, C. Noel Henley, Carl Ekholm, Carrie Swigart, Chad Manke, Charalampos Zalavras,
55
Charles A. Goldfarb, Charles Cassidy, Charles Cornell, Charles L. Getz, Charles Metzger, Chris
Wilson, Christian Heiss, Christian J. Perrotto, Christopher J. Wall, Christopher J. Walsh, Christos
Garnavos, Chunyan Jiang, Craig Lomita, Craig M. Torosian, Daniel A. Rikli, Daniel B. Whelan, Daniel
C. Wascher, Daniel Hernandez, Daniel Polatsch, Daphne Beingessner, Darren Drosdowech, David
E. Tate, Jr, David Hak, David J. Rowland, David M. Kalainov, David Nelson, David Weiss, Desirae
M. McKee, D. F. P. van Deurzen, Donald Endrizzi, Konul Erol, Joachim P. Overbeck, Wolfgang Baer,
Eckart Schwab, Edgardo Ramos Maza, Edward Harvey, Edward K. Rodriguez, Elisabeth Prelog-
Igler, Emil H. Schemitsch, Eon K. Shin, Eric P. Hofmeister, F. Thomas D. Kaplan, F.J.P. Beeres, Fabio
Suarez, C.H. Fernandes, Fidel Ernesto Cayón Cayón, Filip Celestyn Dolatowski, Fischmeister Martin,
Francisco Javier Aguilar Sierra, Francisco Lopez-Gonzalez, Frank Walter, Franz Josef Seibert, Fred
Baumgaertel, Frede Frihagen, P.C. Fuchs, Georg M. Huemer, George Kontakis, George S. Athwal,
George S.M. Dyer, George Thomas, Georges Kohut, Gerald Williams, German Ricardo Hernandez,
Gladys Cecilia Zambrano Caro, Grant Garrigues, Greg Merrell, Gregory DeSilva, Gregory J. Della
Rocca, Gustavo Regazzi, Gustavo Borges Laurindo de Azevedo, Gustavo Mantovani Ruggiero, H. J.
Helling, Hal Mc Utchan, Hans Goost, Hans J. Kreder, PaulaM. Hasenboehler, Howard D. Routman,
Huub van der Heide, I. Kleinlugtenbelt, Iain McGraw, Ian Harris, Ibrahim Mohammad Ibrahim, Ines
C. Lin, A. Iossifidis, J. Andrew I. Trenholm, J. Carel Goslings, J. Michael Wiater, Jack Choueka, Jaimo
Ahn, James Kellam, Jan Biert, Jay Pomerance, Jeff W. Johnson, Jeffrey A. Greenberg, JeffreyYao,
JeffryT.Watson, JenniferL. Giuffre, JeremyHall, Jin-YoungPark, Jochen Fischer, Joel Murachovsky,
JohnHowlett, JohnMcAuliffe, John P. Evans, JohnTaras, Jonathan Braman, JonathanL. Hobby,
Jonathan Rosenfeld, Jorge Boretto, Jorge Orbay, Jorge Rubio, JoseA. Ortiz, Jr, Joseph Abboud,
Joseph M. Conflitti, Joseph P.A.M. Vroemen, Julie Adams, J.V.Clarke, K.Kabir, Karel Chivers, Karl-Josef
Prommersberger, Keith Segalman, Kendrick Lee, Kevin Eng, Kimberlly S. Chhor, K.J. Ponsen, Kyle
Jeray, l. Marsh, L.M.S.J. Poelhekke, Ladislav Mica, Lars C. Borris, Lawrence Halperin, Lawrence Weiss,
Leon Benson, Leon Elmans, Leonardo Alves de Mendonca, Jr, Leonardo Rocha, Leonid Katolik, Lisa
Lattanza, Lisa Taitsman, Lob Guenter, Louis Catalano III, Luis Antonio Buendia, Luke S. Austin, M.
Jason Palmer, M.R. de Vries, M.R. Krijnen, Maarten W.G.A. Bronkhorst, Mahmoud I. Abdel-Ghany,
M.A.J. Van de Sande, Marc Swiontkowski, Marco Rizzo, Marcus Lehnhardt, Marinis Pirpiris, Mark
Baratz, MarkD. Lazarus, MartinBoyer, Martin Richardson, Matej Kastelec, Matt Mormino, Matthew
D. Budge, Matthias Turina, Megan M. Wood, Michael Baskies, Michael Baumgaertner, Michael
Behrman, Michael Hausman, Michael Jones, Michael LeCroy, Michael Moskal, Michael Nancollas,
Michael Prayson, Michael W. Grafe, Michael W. Kessler, Michel P.J. Van den Bekerom, Mike
Mckee, Milind Merchant, Minos Tyllianakis, Naquira Escobar Luis Felipe, NealC. Chen, NeilSaran,
NeilWilson, Nicholas L. Shortt, Niels Schep, Nigel Rossiter, N.G. Lasanianos, Nikolaos Kanakaris,
Noah D. Weiss, Norah M. Harvey, P.V. van Eerten, Parag Melvanki, Patrick T. McCulloch, Paul A.
Martineau, Paul Appleton, Paul Guidera, Paul Levin, Peter Giannoudis, Peter J. Evans, Peter Jebson,
Peter Kloen, Peter Krause, Peter R.G. Brink, J.H. Peters, Philip Blazar, Philipp N. Streubel, Porcellini,
Prashanth Inna, S. Prashanth, PunitaV. Solanki, QiugenWang, M. Quell, R. Bryan Benafield, Jr, R.
Haverlag, R. W. Peters, Rajat Varma, Ralf Nyszkiewicz, Ralph M. Costanzo, Ramon de Bedout, Ashish
S. Ranade, Raymond Malcolm Smith, Reid Abrams, Renato M. Fricker, Reza Omid, Richard Barth,
Richard Buckley, Richard Jenkinson, Richard S. GIlbert, Richard S. Page, Richard Wallensten, Robert
D. Zura, Robert J. Feibel, Robert R.L. Gray, Robert Tashijan, Robert Wagenmakers, Rodrigo Pesantez,
Roger van Riet, Rolf Norlin, Roman Pfeifer, Ronald Liem, Roy G. Kulick, Rozental, Rudolf W. Poolman,
Russell Shatford, Ryan Klinefelter, Ryan P. Calfee, Sam Moghtaderi, Samir Sodha, Sander Sprujt,
Sanjeev Kakar, Saul Kaplan, Schandelmaier, Scott Duncan, Sebastian Kluge, Sebastian Rodriguez-
Elizalde, SergioL. Checchia, Sergio Rowinski, Seth Dodds, Shep Hurwit, K. Sprengel, W.A.H. van der
Stappen, Steve Kronlage, Steven Beldner, StevenJ. McCabe, StevenJ. Morgan, StevenJ. Rhemrev,
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StuartHilliard, Taco Gosens,Takashi Sasaki, C. Taleb, Tamir Pritsch, Theodoros Tosounidis, Theresa
Wyrick, Thomas DeCoster, Thomas Dienstknecht, Thomas G. Stackhouse, Thomas Hughes,
Thomas Wright, Thuan V. Ly, Timothy G. Havenhill, Timothy Omara, Todd Siff, Toni M. McLaurin,
Tony Wanich, Johannes M. Rueger, Frederico C.M. Vallim, Vani J. Sabesan, Vasileios S. Nikolaou,
Verhofstad, Victoria D. Knoll, Vidyadhar Telang, Vishwanath M. Iyer, Vispi Jokhi, W. Arnnold Batson,
W. Jaap Willems, Wade R. Smith, William Dias Belangero, J. Wolkenfelt, Yoram Weil.
REFERENCES
1. Guyatt G. Evidence-based medicine. ACP J Club 1991;114.2. Sackett DL, Straus S, Richard SR, Rosenberg W, Haynes RB. Evidence-based medicine: How to
practice and Teach EBM. London, Churchill Livingstone 2000.3. American Academy of Orthopaedic Surgeons Clinical Practice Guideline on the Diagnosis and
Treatment of Osteochondritis Dissecans Rosemont (IL). American Academy of Orthopaedic
Surgeons (AAOS); 2010.4. Gibbs L, Gambrill E. Evidence-based practice: Counterarguments to objections. . Resarch on
Social Work Practice 2002;12:452-76.5. Pawson R. Evidence Based Policy: In search of a method. Evaluation 2002;8:157-81.6. How much do we know. British Medical Journal 2010;Clinical Evidence 2010.7. Esposito TJ, Maier RV, Rivara FP, Carrico J. Why Surgeons Prefer Not to Care for Trauma
Patients. Arch Surg 1991;126:292-7.8. Thomas G, Pring R. Evidence-Based Practise in Education. Youblishercom 2004.9. Kennedy AD, Sculpher MJ, Coulter A, et al. Effects of decision aids for menorrhagia on
treatment choices, health outcomes, and costs: a randomized controlled trial. Jama
2002;288:2701-8.10. Ozanne EM, Annis C, Adduci K, Showstack J, Esserman L. Pilot trial of a computerized decision
aid for breast cancer prevention. Breast J 2007;13:147-54.11. Slover J, Shue J, Koenig K. Shared decision-making in orthopaedic surgery. Clin Orthop Relat
Res 2012;470:1046-53.12. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.
57
CHAPTER 5
Do pre-visit expectations correlate
with satisfaction of new patients
presenting for evaluation with an
orthopaedic surgical practice?
Hageman MG, Briet JP, Bossen JK, Blok RD, Ring D, Vranceanu M.
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
Clin Orthop Relat Res. 2014 Apr;39(9):11999-014.
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ABSTRACT
Background Patient satisfaction is associated with increased compliance, improved treatment outcomes, and decreased risk of litigation. Factors such as patient understanding and psychological well-being are recognized influences on satisfaction. Less is known about the relationship between pre-visit expectations and satisfaction. Questions/purposes (1) Are there correlations among pre-visit expectations, met expectations, and patient satisfaction? (2) What are the categories of expectations, and which one(s) correlate with satisfaction?
Methods 86 new patients presenting to a hand surgery practice of a tertiary referral hospital with 70% direct primary care referrals, mostly with elective concerns, indicated their pre-visit expectations (Patient Intention Questionnaire [PIQ]). Immediately after the visit, the same patients rated the degree to which their pre-visit expectations were met (Expectation Met Questionnaire [EMQ]) and their satisfaction level (Medical Interview Satisfaction Scale). These tools have been used in primary care office settings and claim good psychometric properties, and although they have not been strictly validated for responsiveness and other test parameters, they have good face validity. We then conducted a multivariable backward linear regression to determine whether (1) scores on the PIQ; and (2) scores on the EMQ are associated with satisfaction.
Results Satisfaction correlated with met expectations (r = 0.36; p =0.001) but not with pre-visit expectations (r = 0.01, p=0.94). We identified five primary categories of pre-visit expectations that accounted for 50% of the variance in PIQ: (1) ‘‘Information and Explanation’’; (2) ‘‘Emotional and Understanding’’; (3) ‘‘Emotional Problems’’; (4) ‘‘Diagnostics’’; and (5) ‘‘Comforting’’. The only category of met expectations that correlated with satisfaction was Information and Explanation (r = 0.43; p=0.001).
Conclusions Among patients seeing a hand surgeon, met expectations correlate with satisfaction. In particular, patients with met expectations regarding information and explanation were more satisfied with their visit. Efforts to determine the most effective methods for conveying unexpected information warrant investigation.
59
INTRODUCTION
Patient satisfaction measures are increasingly used to evaluate the quality of
medical service.1 Patient satisfaction is associated with increased compliance,
improved treatment outcomes across a variety of medical settings2, decreased
risk of litigation3, and patient ratings of the quality of their care. Patient
satisfaction is affected by patient understanding of their own health and
psychological well-being.4 Socio-demographic factors can also affect patient
satisfaction.5-10
It is likely, however, that other factors – as yet unexplored – may
influence patient satisfaction with a medical encounter. Met expectations are
associated with better patient satisfaction in population surveys and primary
care settings.10-12 However, the relationship between pre-visit or pre-operative
expectations and satisfaction is inconsistent.11,13 A study among primary care
patients found that pre-visit expectations (whether they were realistic or
unrealistic) were not associated with satisfaction.11 Research among orthopaedic
patients undergoing surgery for lower back pain found that higher pre-visit
expectations of pain relief were associated with lower satisfaction, whereas
higher pre-visit expectations of improved function were associated with
higher satisfaction.13 Because of the inconsistencies across studies in terms of
the association between pre-visit expectations and satisfaction11,13 as well as
the paucity of research on met expectations outside of primary care settings,
we sought to evaluate the relationships among pre-visit expectations, met
expectations, and satisfaction with a hand surgery outpatient visit. Specifically,
we aimed to identify (1) patient pre-visit expectations (level and type) for a hand
surgery office visit; and (2) the association of pre-visit expectations and met
expectations with satisfaction.
This study attempts to answer the following questions: (1) Are there
correlations among pre-visit expectations, met expectations, and patient
satisfaction? (2) What are the categories of expectations, and which one(s)
correlate with satisfaction?
MATERIAL AND METHODS
This was an observational cross-sectional study. Between September 2012 and
December 9, 2012, adult, English speaking patients presenting to the practice
of one of three orthopaedic hand surgeons (JJ, CM, DCR) for an initial evaluation
were invited to enroll under a protocol approved by our Human Research
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Committee. The study was described in detail and the treating physician/study
staff obtained informed consent.
Participants/Study Subjects
One hundred two patients were enrolled in the study. Of these, six were
excluded as a result of lack of English proficiency, three declined participation
after enrollment, and seven patients did not complete the second part of the
questionnaire after their medical appointment, most claiming lack of time.
Analyses were done on a final sample of 86 patients (Table 1).
Before the medical encounter with the hand specialist, patients
completed the Patient Intentions Questionnaire (PIQ)14 and a demographics
and medical profile questionnaire. After the encounter patients, completed the
Expectations Met Questionnaire (EMQ)12 and the Medical Interview Satisfaction
Scale (MISS).12,15,16
Measurement tools
The PIQ 14 consists of 34 equally weighted statements measuring a patient’s pre-
visit expectations and specific goals for a primary care medical visit.12 Examples
include: ‘‘I want my GP [general practitioner] to understand the problem’’;
‘‘I want the GP to explain my emotional problems.’’ All items in the PIQ were
scored on a 3-point Likert-scale (agree, uncertain, or disagree). We modified the
term ‘‘GP’’ to ‘‘doctor’’ in the PIQ questions to match the study setting (Appendix
1 [Supplemental materials are available with the online version]). The PIQ score
represents the percentage of expectations endorsed before the visit divided by
the total potential pre-visit expectations.
The EMQ12 consists of the same 34 statements on the PIQ aimed to
determine if a patient’s expectations were met after the visit. For example,
‘‘The doctor understood the problem’’; ‘‘The doctor explained my emotional
problems.’’ Comparable with the PIQ, all items in the EMQ were scored on a
3-point Likert-scale (agree, uncertain, or disagree). The EMQ was scored as
percentage of met expectation per item in the PIQ as initially endorsed by the
patient.
The results of the PIQ and EMQ were divided into three groups according
to low pre-visit and met expectation (0%–35%), moderately and uncertain pre-
visit and met expectation (36%–80%), and highly pre-visit and met expectation
(81%–100%), consistent with previously developed methodology.10
The MISS15-17 includes 21 items measuring satisfaction with a medical
61
Table 1 Patients demographics n=86
Mean
4416
7.9
n4343
72
44012
487154421
14
568095511
333
4361 3
776
sd
162.9
2
%5050
87
55012
568165521
16
6590
106611
393
5071 4
907
Range
19-7710-222--10
Age, yEducation Overall health (sd)
SexMenWomen
RaceWhiteBlack or African AmericanAsianAmerican Indian or Alaskan NativeMore than one raceOther or unknown
Diagnosis Acute injuriesNon-specific arm painCarpal Tunnel SyndromeGanglionDequervain Trigger fingerDupuytrenOsteoarthritisOther
Work statusWorking full timeWorking part timeHomemakerRetiredUnemployed, able to workUnemployed, unable to workWorkers compensationCurrently on sick leave
Marital StatusSingleLiving with partnerMarriedSeparated/ DivorcedWidowed
PhysicianSurgeon 1Surgeon 2Surgeon 3
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encounter (e.g. ‘‘The doctor seemed to take my problems seriously’’). All items
were scored on a 7-point Likert-scale from very unsatisfied to completely
satisfied. In the digital version of the MISS, question 6 (‘‘The doctor seemed
to be interested in me as a person’’) was constantly skipped for all patients as
a result of a mistake in how the questionnaire was adapted from the paper-
based questionnaire. This question was part of the Rapport subscale, which
is comprised of seven other similar questions, which represent this aspect
of the visit well (e.g. ‘‘The doctor seemed warm and friendly to me’’ and ‘‘The
doctor seemed to take my problems seriously’’). A mean satisfaction index score
was calculated by dividing the total satisfaction score by the total number of
answered questions.
The primary measures used in this study have good psychometric
properties as evidenced by internal consistency reliability a between 0.84 and
0.9710 as well as validation in patients with back pain18 and in primary care.10,12,15
Statistical Analysis
An a priori power analyses indicated that a sample of 84 patients total would
provide 80% statistical power with a= 0.05 for a moderate effect size of 0.5
based on an analysis of variance (ANOVA). Continuous data were presented as
means when normally distributed. When data were not normally distributed,
we reported the median with interquartile range. Mean imputation was used
to account for missing values. Four patients skipped one question in the PIQ and
four patients skipped one question in the EMQ. One patient missed one question
in the MISS questionnaire. To determine the categories of desired expectations
on the PIQ, we performed a factor analysis with the help of the statistical
orthogonal principal component analysis through the Varimax rotation. A
question was related to a specific factor if there was a loading of minimal 0.40
or more. This method was used and validated in prior research.10 We used the
Spearman correlations to test for correlation between continuous variables. The
strength of the correlation was interpreted by the following guidelines: small
strength (r = 0.10–0.29), medium strength (r = 0.30– 0.49), and large strength
(r = 0.50–1.0).19 We used ANOVA to test for differences in satisfaction by
categories of expectations met and by type of expectation on the PIQ. We
conducted a multivariable backward linear regression to determine whether (1)
score on the PIQ; and (2) score on the EMQ were associated with satisfaction.
We included all variables with p- 0.10 in bivariate analysis.
63
RESULTS
Correlations Among Pre-visit Expectations, Met Expectations, and Patient
Satisfaction
Satisfaction correlated with met expectations (r = 0.36, p= 0.001) but not with
pre-visit expectations (r = 0.01, p= 0.94). The best linear regression model for
greater satisfaction included met expectations alone and explained 27% of the
variance. Four (5%) patients had low pre-visit expectations, 74 (86%) moderate
pre-visit expectations, and eight (9%) had high pre-visit expectations.
The degree of met expectations was low in 4 patients (5%), moderate
in 33 (38%), and high in 49 (57%). Preliminary bivariate analysis identified
differences in satisfaction in patients with low, moderate, and high met
expectations.
Categories of Expectations and Correlations With Satisfaction
Factor analysis identified five primary categories of previsit expectation that
accounted for 50% of the variance in PIQ: (1) ‘‘Information and Explanation’’;
(2) ‘‘Emotional and Understanding’’; (3) ‘‘Emotional Problems’’;(4) ‘‘Diagnostics’’;
and (5) ‘‘Comforting’’. Cronbach’s a ranged from 0.76 to 0.90 indicating overall
good to excellent reliability for all factors. Patients’ goals for the visit with the
hand surgeon focused more on ‘‘Information and Explanation’’, ‘‘Comforting’’,
and ‘‘Diagnostics’’ than on ‘‘Emotional Understanding’’ and ‘‘Emotional
Problems’’ (Table 2). The only category of met expectations that correlated with
satisfaction was ‘‘Information and Explanation’’ (r = 0.43; p= 0.001)
(Table 3). Interestingly, among the pre-visit expectation categories, the category
‘‘Information and Explanation’’ was highly met, whereas the other four factors
were met to a moderate or low extent.
DISCUSSION
Patient satisfaction is an important measure, because it is associated with
increased compliance, improved treatment outcomes, and decreased risk of
litigation. Many factors play into satisfaction, including patient’s understanding
of their own health and patient’s rating of the quality of their care and perhaps
expectations. We therefore investigated how pre-visit and met expectations
affect satisfaction and looked for categories of expectations that influence
satisfaction. We found that high pre-visit expectations did not correlate with
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satisfaction with a hand surgery outpatient visit but met expectations did.
Patients had the highest expectations about information and explanation
followed by diagnostics and comforting, both of which were endorsed more that
emotional support.
This study should be considered in light of its shortcomings. One
limitation of this study is that the PIQ was developed for primary care practice.
Little is known about the repeatability, responsiveness, and the floor/ceiling
effects. The clinical situation in a primary care practice may be different when
a patient is rating their primary doctor with whom they are quite familiar as
opposed to a specialist they have never met. Nevertheless, the high Cronbach’s
a values give us confidence in the methodology described by Williams et al12
using the factor analysis, which is also a reliable method in other settings,
including orthopaedic practices. Additional validation of these questionnaires in
an orthopaedic practice is merited. Another limitation is the absence of question
6 from the MISS, but we think there is sufficient overlap with other questions
evaluating rapport that this probably has little or no effect on the results.
In our study, there was no association between level of pre-visit
expectations and patient satisfaction. The association between pre-visit
Table 2 Desired and met expectations divided by categories
Factor desired
%
96
38
5.5
61
74
Factor not
desired and met
% 3
26
9
20
14
Factor desired and met
%
83
26
2
33
51
Factor not
desired and not
met% 1
37
86
19
12
Factor desired and not
met %
13
11
3
27
23
Total
%
100
100
100
100
100
Factor 1 (Information and explanation)
Factor 2 (Emotional and understanding)
Factor 3 (Emotional problems)
Factor 4 (Diagnostics)
Factor 5 (Comforting)
65
expectations and satisfaction appears to depend on setting, patient population,
and type of pre-visit expectations.2,11,13 The fact that the majority of patients
in this sample had moderate pre-visit expectations (few had low or high
expectations) may have limited our ability to test the association of pre-visit
expectations and satisfaction. The finding that met expectations correlate with
satisfaction in patients with upper extremity illness is consistent with prior
studies in other populations.10-12 For instance, satisfaction and expectations
were strongly correlated in studies of patients undergoing THA.20,21 This may be
a foregone conclusion because measures of met expectations and measures
of patient satisfaction may be assessing the same construct. Future research
should replicate these findings with a larger sample of patients, perhaps with
one or more diagnoses associated with a greater rate of high expectations.
As one might expect, the pre-visit expectations reported by patients
undergoing hand surgery focused more on ‘‘Information and Explanation’’,
‘‘Comforting’’, and ‘‘Diagnostics’’ than on ‘‘Emotional Understanding’’ and
‘‘Emotional Problems’’. The only category of pre-visit expectation that correlated
with satisfaction was ‘‘Information and Explanation’’. As a result, attempts to
improve patient satisfaction might focus on establishing appropriate pre-
Table 3 Correlation of percentage of met expectations with satisfaction
P-Value
<0.001
0.15
0.72
0.71
0.07
Pearson rho
0.43
0.15
0.04
0.04
0.19
Patient Satisfaction (MISS)
Percentage of met expectations
Factor 1 (Information and explanation)
Factor 2 (Emotional and understanding)
Factor 3 (Emotional problems)
Factor 4 (Diagnostics)
Factor 5 (Comforting)
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visit expectations perhaps by corresponding directly with the primary care
doctor (‘‘curbside consult’’), providing evidenced-based information in an
understandable and meaningful form (e.g. decision aids) before the visit, and
even pre-visit triage and education.
It has been more difficult to determine factors associated with patient
satisfaction than factors associated with other aspects of the illness experience
such as symptoms and disability. Collective research suggests that satisfaction
relates to factors like patient understanding, depression, pain intensity22-24
as well as effective communication,25 but there is not a strong relationship
with pre-visit expectations. Given the sense of many physicians that pre-visit
expectations do seem to lead to disappointment, future research regarding
pre-visit expectations might benefit from a focus on a specific paradigm where
unrealistically high expectations are common while accounting for psychological
factors, effective communication skills, time spent waiting for the doctor, and
time spent with the doctor.
Acknowledgments: We thank Drs Chaitanya Mudgal and Jesse Jupiter for
allowing us to enroll their patients.
67
REFERENCES
1. Hudak PL, Wright JG. The characteristics of patient satisfaction measures. Spine (Phila Pa
1976) 2000;25:3167-77.2. Soroceanu A, Ching A, Abdu W, McGuire K. Relationship between preoperative expectations,
satisfaction, and functional outcomes in patients undergoing lumbar and cervical spine
surgery: a multicenter study. Spine (Phila Pa 1976) 2012;37:E103-8.3. Hickson GB, Clayton EW, Entman SS, et al. Obstetricians’ prior malpractice experience and
patients’ satisfaction with care. Jama 1994;272:1583-7.4. GE H, MA W, F H. Components and predictors of patient satisfaction. Br J Health Psychol
1996;1:65-85.5. Fox JG, Storms DM. A different approach to sociodemographic predictors of satisfaction with
health care. Soc Sci Med A 1981;15:557-64.6. Greene JY, Weinberger M, Mamlin JJ. Patient attitudes toward health care: expectations of
primary care in a clinic setting. Soc Sci Med Med Psychol Med Sociol 1980;14A:133-8.7. Hall JA, Feldstein M, Fretwell MD, Rowe JW, Epstein AM. Older patients’ health status and
satisfaction with medical care in an HMO population. Med Care 1990;28:261-70.8. Like R, Zyzanski SJ. Patient satisfaction with the clinical encounter: social psychological
determinants. Soc Sci Med 1987;24:351-7.9. Williams SJ, Calnan M. Key determinants of consumer satisfaction with general practice. Fam
Pract 1991;8:237-42.10. Zebiene E, Razgauskas E, Basys V, et al. Meeting patient’s expectations in primary care
consultations in Lithuania. Int J Qual Health Care 2004;16:83-9.11. Bowling A, Rowe G, McKee M. Patients’ experiences of their healthcare in relation to their
expectations and satisfaction: a population survey. Journal of the Royal Society of Medicine
2013;106:143-9.12. Williams S, Weinman J, Dale J, Newman S. Patient expectations: what do primary care
patients want from the GP and how far does meeting expectations affect patient
satisfaction? Fam Pract 1995;12:193-201.13. Iversen MD, Daltroy LH, Fossel AH, Katz JN. The prognostic importance of patient pre-
operative expectations of surgery for lumbar spinal stenosis. Patient Educ Couns
1998;34:169-78.14. Salmon P, J. Q. Patient’s intentions in primary care: measurement and preliminary
investigation. Psychol Health 1989;3:103-10.15. Kinnersley P, Stott N, Peters T, Harvey I, Hackett P. A comparison of methods for measuring
patient satisfaction with consultations in primary care. Fam Pract 1996;13:41-51.16. Meakin R, Weinman J. The ‘Medical Interview Satisfaction Scale’ (MISS-21) adapted for British
general practice. Fam Pract 2002;19:257-63.17. Wolf MHPSMJSA, Stiles W.B., . The medical interview Satisfaction Scale: Development of a
scale to measure patients’ perceptions on physician behaviour. J Behav Med 1978;1:391.18. Georgy EE, Carr EC, Breen AC. Back pain management in primary care: development and
validity of the Patients’ and Doctors’ Expectations Questionnaire. Quality in primary care
2013;21:113-22.19. Cohen JW. In: Hillsdale, ed. Statistical power anlays for behavioral sciences (2nd edn). NJ::
Lawrence Erlbaum Associates; 1988:79-81.20. Mancuso CA, Jout J, Salvati EA, Sculco TP. Fulfillment of patients’ expectations for total hip
arthroplasty. J Bone Joint Surg Am 2009;91:2073-8.
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21. Mancuso CA, Salvati EA, Johanson NA, Peterson MG, Charlson ME. Patients’ expectations and
satisfaction with total hip arthroplasty. J Arthroplasty 1997;12:387-96.22. Archer KR, Castillo RC, Wegener ST, Abraham CM, Obremskey WT. Pain and satisfaction in
hospitalized trauma patients: the importance of self-efficacy and psychological distress. The
journal of trauma and acute care surgery 2012;72:1068-77.23. Lozano Calderon SA, Paiva A, Ring D. Patient satisfaction after open carpal tunnel release
correlates with depression. J Hand Surg Am 2008;33:303-7.24. O’Toole RV, Castillo RC, Pollak AN, MacKenzie EJ, Bosse MJ, Group LS. Determinants of patient
satisfaction after severe lower-extremity injuries. J Bone Joint Surg Am 2008;90:1206-11.25. Bartlett EE, Grayson M, Barker R, Levine DM, Golden A, Libber S. The effects of physician
communications skills on patient satisfaction; recall, and adherence. J Chronic Dis
1984;37:755-64.
69
CHAPTER 6
Carpal tunnel syndrome:
assessment of surgeon and patient
preferences and priorities for
decision-making
Hageman MG, Kinaci A, Ju K, Guitton TG, Mudgal CS, Ring D; Science of Variation Group.
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
J Hand Surg Am. 2014 Sep;39(9):1799-1804.e1.
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ABSTRACT
Background This study tested the null hypothesis that there are no differences between the preferences of hand surgeons and those patients with carpal tunnel syndrome (CTS) facing decisions about management of CTS (i.e. the preferred content of a decision aid).
Methods 103 hand surgeons of the Science of Variation Group and 79 patients with CTS completed a survey about their priorities and preferences in decision-making regarding the management of CTS. The questionnaire was structured according the Ottawa Decision Support Framework for the development of a decision aid.
Results Important areas on which patient and hand surgeon interests differed included a preference for non-painful, non-operative treatment and confirmation of the diagnosis with electro-diagnostic testing. For patients, the main disadvantage of non-operative treatment was that it was likely to be only palliative and temporary. Patients preferred, on average, to take the lead in decision-making, whereas physicians preferred shared decision-making. Patients and physicians agreed on the value of support from family and other physicians in the decision-making process.
Conclusions There were some differences between patient and surgeon priorities and preferences regarding decision-making for CTS, particularly the risks and benefits of diagnostic and therapeutic procedures. Clinical relevance Information that helps inform patients of their options based on current best evidence might help patients understand their own preferences and values, reduce decisional conflict, limit surgeon-to-surgeon variations, and improve health.
INTRODUCTION
Decision aids (videos, web sites, or handouts that contained balanced
information about diagnostic and treatment options) can help patients
understand their values and preferences and more fully participate in decision-
71
making.1 The Ottawa Decision Support Framework (ODSF) is an evidence-based,
practical theory used to guide the development of decision aids. It uses a 3-step
process: measure the needs of patients and their providers, provide decision
support tailored to patients’ needs, and evaluate the decision-making process
and outcomes.1 The ODSF asserts that unresolved needs will affect decision
quality,2 which in turn can affect illness behavior, health outcomes, emotions,
and resource utilization.1,3
There are many misconceptions about carpal tunnel syndrome (CTS) and
its treatment. There are also many areas of debate including the role of electro-
diagnostic testing, the best operative technique, and the indications for surgery
for mild (normal electro-diagnostic testing) or severe (atrophy, static numbness)
disease. A decision aid could inform patients of the best available evidence and
ongoing areas of debate in order to limit the effect of both patient and surgeon
bias and improve the patient’s comfort and participation in the decision.
This study assessed the priorities and preferences of patients and
hand surgeons facing decisions about management of CTS. We tested the null
hypothesis that there are no differences in priorities and preferences of patients
with CTS and hand surgeons.
MATERIAL AND METHODS
Using an institutional review board-approved protocol, we surveyed hand
surgeon members of the Science of Variation Group (SOVG) and 79 new patients
diagnosed with CTS after the first consultation with one treating physician
regarding factors that influence decision-making and their preferences about
decision aids. The patients were English speaking, 18 years or older, able to fill
out the questionnaire, and not pregnant with CTS eventually verified by electro-
diagnostic testing presenting between May 2012 and April 2013.
The study was described in detail to the patients, and the research
assistant obtained informed consent. One hundred three hand surgeon-
members of the SOVG completed the survey (Appendix A, available on the
Journal’s Web site at www.jhandsurg.org). The SOVG is an international
collaboration of hand surgeons. Incentives, other than acknowledgment as part
of the SOVG, were not provided. None of the surgeons were involved in the care
of the patients surveyed. After logging into the web site, each surgeon entered
identifying demographic and professional information: sex, country or region of
practice, years in practice, supervision of trainees, and surgical subspecialty. The
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surgeons were then presented with an online survey based on the ODSF.4,5
91 patients were enrolled, but 1 patient was excluded for not being
able to navigate the online questionnaire and 11 patients declined participation.
The mean age of the 79 patients who completed the study was 55 years (SD = 16;
range, 20-90 y), and 29 patients (35%) were men (Appendix B, available on the
Journal’s Web site at www.jhandsurg.org).
Measurement tools
The survey was based on the ODSF. There is a general framework that measures
the following aspects of various treatment options: desirability; advantages
and disadvantages; probability of choosing; preferred way to arrive at a final
decision; who, if anyone, is usually involved in the decision-making process;
what would help to arrive at a final decision; ways to facilitate the decision-
making process; the type of information desired; and who should prepare the
information.
When surveying patients and caregivers with respect to a specific
disease, one simply inserts common diagnosis and treatment options into
the framework. For instance, for CTS we provided the widely used treatment
options of orthosis fabrication, corticosteroid injection, and surgery (Appendix C,
available on the Journal’s Web site at www.jhandsurg.org).
Statistical analysis
A post hoc power analysis showed that 103 subjects of the SOVG and 79 patients
with CTS with the observed effect size of 0.54 provided 93% power to detect a
significant difference using a 2-tailed Student t-test, setting alpha level at 0.05.
Continuous data were presented as the mean when normally distributed. The
Student t-test and chi-square test were used to assess the association between
continuous or categorical preferences and independent variables, such as
patient and surgeons. The Fisher exact test was used instead of the chi-square if
the sample sizes were smaller than 5.
RESULTS
Patients found all treatment options – corticosteroid injection in particular –
less desirable than surgeons did (Table 1). When citing advantages of treatment,
patients ranked “Does not involve surgery” highly, whereas surgeons considered
“No major risk or side effects” as most important. Surgeons also added many
73
Table 1 Comparison of the desirability and likability of the different treatment opportunities between patients and physicians
Comparison of the desirability of the different treatment options
How likely to choose the following treatment options
Table 2 Advantage of the treatment options
Patients
Patients
Physician
Physician
P-Value
P-Value
SplintingCorticosteroid injectionSurgery
SplintingCorticosteroid injectionSurgery
SplintingDoes not involve surgeryNo major risk or side effectsAbility to stop the treatment at any timeOther
Corticosteroid injectionDoes not involve surgeryNo major risk or side effectsOther
EMGIt can help confirm the diagnosisDocuments baseline nerve functionOther
Carpal tunnel release surgeryHighest succes rateNo major risk or side effectsOther
mean
2.8 1.8 2.5
3.4 2.4 4.2
n
4026
5
6
461214
7600
6760
sd
0.13 0.093 1.3
1.6 1.3 1.1
%
5234
6.5
7.8
641719
1000.00.0
928
0.0
sd
0.078 0.066 0.88
0.97 1.4 0.094
%
2765
5.9
2.0
464015
3659
5.0
573211
<0.01 <0.01 <0.01
<0.01 <0.01 0.28
<0.01
<0.01
<0.01
<0.01
mean
3.3 2.7 3.1
4.4 3.2 4.3
n
2766
6
2
464015
3660
5
583211
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write-in explanations for their answers including “diagnostic and therapeutic
benefits” for corticosteroid injections and “most effective and reliable treatment”
for operative treatment. From the patients’ perspective, the disadvantage of
non-operative treatment was that it was likely palliative and temporary and the
disadvantage of operative treatment was pain. From the surgeons’ perspective,
the disadvantage of an orthosis was that it was palliative and some patients do
not like to wear them. For surgeons, the disadvantage of a corticosteroid shot
was pain and potential risk of nerve injury, and the disadvantage of surgery was
the risk and recovery time (Table 2). Surgeons were more likely to choose orthosis
Table 3 Disadvantage of the treatment options
Patients Physician P-Value
SplintingWearing the splint can be uncomfortableDoesn’t solve the problem, only manages it Will not prevent progression of the diseaseOther
Corticosteroid injectionIt’s only temporaryThe shot hurtsSmall risk of skin discolorationOther
EMGPainfulTime consumingExpensiveOther
Carpal tunnel release surgeryPainSmall risk of nerve damageSmall risk of infectionThe scar
n
13
45
15
0
48870
3123
40
2735
81
18
62
21
0.0
761311
0.0
5340
6.9 0.0
384911
1.4
36
47
13
5.0
599
28 4.0
48122912
3241
9.418
<0.01
<0.01
<0.01
<0.01
n
36
47
13
5
609
284
48122912
2735
815
75
Table 4 What is the best way to arrive at a final decision regarding the treatment plan
Table 5 Who, if anyone, is usually involved in the decision-making process
Patients
Patients
Physician
Physician
P-Value
P-Value
The health provider decides for the patient
The health provider advises and the patient and provider make a shared decision
The health provider advises and the patient decides
SpouseFamilyFriendPrimary care physician
n 5
27
47
n
2723
214
% 6
34
59
%
4135
3.021
% 0
74
26
%
3531
8.426
<0.01
0.46
n 0
76
27
n
3329
825
fabrication and corticosteroid injection than patients, but they were comparably
likely to choose surgery (Table 1).
Patients disliked the pain of electro-diagnostic testing but valued
confirming the diagnosis (Table 2). Some surgeons disliked the time involved,
saw it as a waste of resources, felt it added confusion and was not helpful for
determining treatment, and lacked confidence in the test results owing to the
technical variations and subjective aspects of the tests. Providers used it mostly
as a baseline but also to help confirm or rule out CTS (Table 3).
Patients preferred to be advised by the surgeon and decide for
themselves, whereas surgeons preferred a shared decision (Table 4). Patients and
surgeons had similar thoughts about who should be involved in the decision
(Table 5). Patients placed more value on their preferences and support from
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others in decision-making, and surgeons placed more emphasis on specific risks
and benefits (Table 6). Patients valued second opinions more than surgeons
(Table 6). Surgeons valued a video format for information more than patients,
who largely favored web-based information (Table 6). Patients and surgeons
considered guidance in deliberation (e.g. decision aids) as the most important
content for patients. In addition, patients found information about treatment
options more important than surgeons (Table 7).
Surgeons and patients agreed that experts should prepare the decision
aids (Table 7).
Table 6 Help with final decision
Which of the following would help arriving at a final decision on one treatment option?
Ways to facilitate the decision-making process
Format to facilitate the decision-making process
Patients Physician P-Value
The health provider’s recommendation
Information on the various treatment options
Information on the incidence of specific benefits and risks
Personal preferencesInformation on how others go about
decidingSupport from others
Second opinionDiscussion groupsInformation materials
BookletWebVideo
n
23
11
17
142
11
246
43
1322
8
%
29
14
22
18 2.6
14
33 8.2
59
305119
%
24
19
41
10 4.0
1.0
8.91477
232553
<0.01
<0.01
<0.01
n
24
19
41
104
1
91478
222451
77
DISCUSSION
In this study of patient- and surgeon-preferred content of a decision aid for CTS,
areas in which patients with CTS and surgeons agreed included the frequency of
choosing operative carpal tunnel release and the support from family, spouse,
and primary care physician in the decision-making process. We did, however,
find differences and rejected our null hypothesis. Important areas in which
patients and hand surgeons had different perspectives included the likelihood
of choosing an orthosis or corticosteroid injection as the preferred therapeutic
option; the opportunity to confirm the diagnosis with electro-diagnostic
testing; and the choice of non-operative, non-painful treatments. Patients
also considered the disadvantage of non-operative treatment that it was likely
palliative and temporary, whereas for surgeons, the disadvantage of an orthosis
was that some patients found it cumbersome. In addition, for patients, the
disadvantage of operative treatment was pain, whereas for surgeons, it was the
Table 7 What should the information material contain
What should the information material contain?
Who should prepare the material?
Patients Physician P-Value
BasicTreatment opportunitiesBenefits of treatmentRisks of treatmentProbabilitiesPersonal implicationsGuidens in delibaration
ExpertSocietyGovermentInsurance companyConsumer associationFor profit organisationNon-profit organization
n
282687
10
25510182
%
4.7
19 4.7
14191623
6012
2.4 0.0 2.4
19 4.8
%
1.0 1.0 1.0
10222046
65
0.0 2.2 2.2 6.6
24 0.0
<0.01
<0.01
n
111
10222046
590226
220
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actual scar and other surgical risks. Finally, patients preferred a more individual
role in decision-making, whereas physicians preferred a shared role in the
decision-making process.
This study had several shortcomings. Although we had relatively large
samples, these results may not be generalizable to the average surgeon or
patient and may differ across cultures. Patients at different stages of disease or
decision-making might have different preferences for decision-making – a factor
that we did not study. It is also possible that patients treated in more intimate
health delivery settings such as those in community-based private practice
offices (as opposed to the large tertiary care hospital-based practices in this
study) may exhibit different opinions than those seen in our study population.
Surgeon expertise and patient impressions are often in conflict.
Examples include the patient’s sense that CTS is a consequence of her or his
actions when best evidence suggests it is largely genetic6; that CTS with static
numbness and weakness has only been present for months when such advanced
disease develops over years to decades; and that orthoses and injections can
cure. Prior studies showed that decision aids can normalize and depersonalize
these conflicts, provide patients information they can reflect on at their own
pace and in their own way, and ensure that patients are empowered to make
decisions based on an better understanding of their disease.7 In particular, our
study identified several areas in which patients would like more information
about CTS – the specific risks and benefits of various diagnostic and therapeutic
options, health provider recommendations, and help clarifying their own
preferences.
There are many areas of debate among hand surgeons regarding the
management of CTS. Where current best evidence allows room for debate,
patients can benefit from an understanding of the range of options and the
source of the debate. When the best option is a matter of debate among
hand surgeons, patient preferences and values should take priority in the
decision-making process. A decision aid can help patients understand their
own preferences and values so that they can make a decision that they will
not second-guess. There is evidence that this lowers decisional conflict, which
might lead to increased satisfaction with care and decreased symptoms and
disability.8,9
Our study revealed the apparent contradiction that patients preferred
non-operative treatment but not injections. Perhaps patients want the problem
fixed with as little discomfort and inconvenience as possible. One hypothesis
79
worth testing is that patients may be more willing to invest in discomfort and
hope when a treatment is disease modifying and when the potential benefits
outweigh the risks.
Our study may help with the development of decision aids that will help
patients understand their choices, feel supported, and become more involved
in their recovery. We found that patients and surgeons have different priorities
and preferences regarding decision-making, particularly the risks and benefits of
diagnostic and therapeutic procedures. A decision aid that helps inform patients
of their options based on current best evidence and helps patients understand
their own preferences and values could improve health by reducing decisional
conflict and encouraging patients to take a more active role in their recovery.
Future studies will address the effectiveness of the decision aid developed
based on this study on decisional conflict, anxiety, symptoms, disability, and
satisfaction for patients with CTS.
*The Science of Variation Group: Joshua M. Abzug, Julie Adams, Gallo Fabio Arbelaez, T. Aspard,
George W. Balfour, H. Brent Bamberger, Romero Jose Camilo Barreto, Michael Baskies, W. Arnold
Batson, Taizoon Baxamusa, Ramon de Bedout, Steven Beldner, Prosper Benhaim, Leon Benson, G.
Jorge Boretto, Martin Boyer, Gregory Dee Byrd, Ryan P. Calfee, Gladys Cecilia Zambrano, Charles
Cassidy, Louis Catalano III, Karel Chivers, Ralph M. Costanzo, Phani Dantuluri, Gregory DeSilva, Seth
Dodds, John P. Evans, Naquira Escobar Luis Felipe, C.H. Fernandes, Thomas J. Fischer, Jochen Fischer,
M. Renato Fricker, Gary K. Frykman, Aida E. Garcia, R. Glenn Gaston, José Fernando Di Giovanni,
Charles A. Goldfarb, Michael W. Grafe, H.W. Grunwald, Warren C. Hammert, Randy Hauck, Ricardo
German Hernandez, Eric Hofmeister, Richard L. Hutchison, Asif Ilyas, Jonathan Isaacs, Sidney
M. Jacoby, Peter Jebson, Christopher M. Jones, Michael Jones, Sanjeev Kakar, David M. Kalainov,
Thomas D. Kaplan, Saul Kaplan, Leonid Katolik, Stephen A. Kennedy, Michael W. Kessler, Hervey L.
Kimball, G. A. Kraan, Paul A. Martineau, John McAuliffe, Steven J. McCabe, Desirae M. McKee, Greg
Merrell, Charles Metzger, Michael Nancollas, David L. Nelson, Ralf Nyszkiewicz, Jose A. Ortiz, Patrick
W. Owens, Jason M. Palmer, Lior Paz, Gary Pess, Daniel Polatsch, Frank J. Raia, Marc J. Richard,
Marco Rizzo, Rozental, David Ruchelsman, Oleg M. Semenkin, Aguilar Javier Francisco Sierra, Todd
Siff, Samir Sodha, Catherine Spath, Sander Spruijt, Thomas F. Stackhouse, Carrie Swigart, Robert
Szabo, John Taras, Jason Tavakolian, Andrew Terrono, Thomas F. Varecka, Abhijeet L. Wahegaonkar,
Christopher J. Walsh, Frank L. Walter, Lawrence Weiss, Brian P. D. Wills, Chris Wilson, Christopher
Wilson, Jennifer Moriatis Wolf, Megan Wood, and Colby Young.
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Appendix A Demographic informationt of the observers
Parameters n %Sex Men 92 89 Women 11 11 Location of practice Asia 2 1.9 Canada 1 1.0 Europe 6 5.8 United Kingdom 2 1.9 United States of America 84 82 Other 8 7.8 Years In practice 0-5 34 33 6-10 23 22 11-20 26 25 21-30 20 19 Supervise Yes 77 75 No 26 25 Specialization Hand surgeons 102 99 Other 1 1
81
Appendix B Demographic information of the patients n= 84
Parameter Mean SD Range Age (y) 55 16 20-90Education (y of School, n=84) 15 2.9 1-22 Number % Sex
Man 29 35 Woman 55 65
Marital status
Single 14 17 Living with partner 3 3.6 Married 50 60 Separated/Divorced 11 13 Widowed 6 7.2
Work status (n=81) Working full time 40 49 Working part time 8 9.9 Homemaker 3 3.7 Retired 16 20 Unemployed, able to work 4 4.9 Unemployed, unable to work 10 12
Physician
Surgeon 01 10 12 Surgeon 02 21 25 Surgeon 03 53 63
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REFERENCES
1. Legare F, O’Connor AM, Graham ID, Wells GA, Tremblay S. Impact of the Ottawa Decision
Support Framework on the agreement and the difference between patients’ and physicians’
decisional conflict. Med Decis Making 2006;26:373-90.
2. Fischloff B, Slovic P, S. L. Knowing what you want: measuring labile values. . Hillsdale (NJ):
Lawrence Erlbaum Associates Inc.; 1980.
3. O’Connor AM, Tugwell P, Wells GA, et al. A decision aid for women considering hormone
therapy after menopause: decision support framework and evaluation. Patient Educ Couns
1998;33:267-79.
4. Jacobsen MJ, O’Connor AM. Population Needs Assessment. Available from (last entered on
June 3rd 2013) wwwohrica/decisionaid 2007.
5. O’Connor AM S, D, & Jacobsen MJ. Ottawa Decision Support Tutorial (ODST): Improving
Practitioners’ Decision Support Skills Ottawa Hospital Research Institute: Patient Decision
Aids, 2011. Web. 2011 Nov 30.
6. Lozano-Calderon S, Anthony S, Ring D. The quality and strength of evidence for etiology:
example of carpal tunnel syndrome. J Hand Surg [Am] 2008;33:525-38.
7. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.
8. Barry M, Cherkin DC, Chang Y, Fowler FJ, Skates SA. A randomzed trial of a multimedia
shared decision-making program for men facing a treatment decision for benign prostatic
hyperplasia. Disease Management and Clinical Outcomes 1997;1:5-14.
9. Kennedy AD, Sculpher MJ, Coulter A, et al. Effects of decision aids for menorrhagia on
treatment choices, health outcomes, and costs: a randomized controlled trial. Jama
2002;288:2701-8.
83
CHAPTER 7
Randomized controlled trial:
the influence of decision aids on
decisional conflict and satisfaction
of patient with hip or knee
osteoarthritis
Hageman MG, MD (1), Poolman RW, MD, PhD (2), Du Long J, MD (1), Vuijk D, MD (1), Vervest T,
MD, PhD (3), Kerkhoffs GMMJ, MD, PhD, Prof. (1), Haverkamp D, MD, PhD (4).
(1) Department of Orthopaedic Surgery at Academic Medical Center, Amsterdam, NL.
(2) Department of Orthopaedic Surgery at OLVG-Oost, JointResearch, Amsterdam, NL.
(3) Department of Orthopaedic Surgery at Tergooi ziekenhuis, Hilversum, NL.
(4) Department of Orthopaedic Surgery at Slotervaart ziekenhuis, Slotervaart Center of
Orthopedic Research and Education (SCORE), Amsterdam, NL.
Submitted to KSSTA Nov 2017.
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ABSTRACT
Background There is an increasing interest in shared decision making. Decision aids are thought to be able to support patients and healthcare providers to make a shared decision, especially when there is more than one reasonable option, when there is no clear advantage in outcomes or when each benefit of harm may be valued differently. We hypothesized that there is no difference in decisional conflict comparing patients managed with a decision aid compared to patients without. Our secondary hypothesis was that there is no difference between patients managed with a decision aid compared to patients without, with respect to anxiety, knowledge, satisfaction, preferred treatment at enrollment and physical function and quality of life at 26 weeks follow-up. Methods This multi center randomized controlled trial included patients with knee or hip osteoarthritis who had not consulted an orthopedic surgeon for the same complain in the past. At the first encounter, patients enrolled in the control group were treated according standard care, while patients enrolled in the intervention group were managed with a decision aid. After the encounter patients were asked to complete a survey about decisional conflict (DCS), preferred treatment, gained knowledge, physical function (KOOS/HOOS), pain (NRS), anxiety (PASS-20) quality of life (EQ-5D) and satisfaction. The long-term follow-up was carried out after 26 weeks and evaluated the HOOS/KOOS, PASS, EQ-5D, satisfaction and the preferred treatment. Results At the first encounter, there was a significant difference in the total-DCS, knowledge and satisfaction score comparing the intervention group managed with the decision aid to the control group managed without. At follow-up there was no significant difference between both groups on the physical function, pain and quality of life scores.
Conclusions Our RCT showed that immediately after the consult about their treatment patients managed with a decision aid had less decisional conflict, more knowledge and were more satisfied with the delivered care compared to patients who were managed according to standard care. After 26 weeks there was no difference in decisional conflict between the patients managed with a decision aid compared to patients managed without.
85
INTRODUCTION
Patients with osteoarthritis of the hip and knee have various treatment options
ranging from watchful waiting to surgery. At certain stages of disease patients
and doctors have a choice. It is thought that for those diagnoses patients and
physicians could make a decision together, known as shared decision-making
(SDM).1,2
To support patients to make a shared-decision, healthcare provides are
thought to provide accurate, comprehensive and neutral information about
the treatment options.3 In addition patients have to share their values when
reflecting on the possible outcomes. However, clear communication between
the patient and physician leading to a shared decision is challenging. Especially,
since time is limited during the clinical encounter and the complex information
need to be understood before patients are able to clarify their values.4 This may
compromise the outcome of the surgery and may result in disappointment and
even regret.
Decision aids are made to support patients and physicians in the
decision-making about the optimal treatment for their diagnoses.5,6 A decision
aid provides the patient evidence based information and helps them to
consider what benefits and harms are important to them. The decision aids
are additionally to the information explained by the physician, rather than to
replace the consultation process.7
Previous studies about the effect decision aids in general medicine and
orthopaedic surgery showed promising results regarding decisional conflict,
anxiety, knowledge and satisfaction.3,7,8 For example, Achaval et al. examined the
effect of an education booklet, video booklet and decision tool on the decisional
conflict among patient with knee osteoarthritis in a tertiary referral center
in the United States of America.8 It showed a significant overall reduction in
decisional conflict. Furthermore, Arterburn et al showed that the introduction
of a decision aid changed patients preference for joint replacement, resulting in
26 percent fewer hip replacement surgeries, 38% fewer knee replacements.9
The objective of this study was determining the effect of decision
aids on the magnitude of decisional conflict, anxiety, knowledge, satisfaction,
physical function and quality of life to patients with knee and hip osteoarthritis.
We hypothesized that there is no difference in decisional conflict comparing
patients managed with a decision aid compared to patients without. Our
secondary hypothesis was that there is no difference between patients managed
with a decision aid compared to patients without, with respect to anxiety,
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knowledge, satisfaction, preferred treatment at enrollment and physical
function and quality of life, 26 weeks after the treatment decision was made.
MATERIAL AND METHODS
Study design and setting
This multicenter hypothesis blinded randomized controlled trial was carried out
at secondary and tertiary referral hospitals after approval from the Institutional
Research Board. Adult patients (18 years or older) with osteoarthritis of knee or
hip, Dutch fluency and literacy, who had not consulted an orthopedic surgeon
for the same complaints in the past, were invited to participate. The waiting
time for the operation was similar in the participating hospitals therefore the
first effects of the operative treatment and the non-operative treatment could
be expected after 26 weeks. For that reason the long term follow-up was carried
out after 26 weeks.
Participants/study subjects
New patients diagnosed with osteoarthritis of the knee or hip were asked to
consider participation in this randomized controlled trial.
Description of experiment, treatment or surgery
The online decision aids were developed by patients and physicians according
to the International Patient Decision Aids Standards and based on a previous
study, carried out by this research group, assessing patients and physicians
needs when deciding about the optimal treatment.4,11 The decision aids were
comprised of 5 steps comparing operative (total joint prosthesis) versus non-
operative treatment (lifestyle advice, painkillers and corticosteroid injections).
Step 1: Informed the patient about the osteoarthritis and the treatment options.
Step 2: Informed the patient about the benefits, harms, scientific uncertainties
and probabilities of outcome. The treatment options were elaborated in a table,
which showed the clinical outcomes after operation or non-operative treatment
regarding satisfaction, physical function, pain and complications in text and
were clarified by icon-based risk graphs Step 3: The most important points
were tested through a short quiz. Step 4: Values clarification, asking patients to
consider which benefits and risks matter most to them and Step 5: Asked the
participant to reflect on responses and make a decision regarding the treatment.
Prior to the implementation of this study an implementation workshop was
87
given to support the treating physicians in the implementation process of the
decision aid. The control group was managed without decision aids and received
standard care, reflecting routine practice by attending physicians.
Description of follow-up routine
After 26 weeks we called the patients for follow-up by telephone. If the patients
did not answer their phones at three different time points, we sent them the
questionnaires by mail or email.
Variables, outcome measures, data sources, and bias
After the consult about their treatment patients were asked to complete a
survey about decisional conflict (DCS), preferred treatment, gained knowledge,
physical function (KOOS/HOOS), pain (NRS), anxiety (PASS-20) quality of life
(EQ-5D) and satisfaction.1,10,12-14 The questionnaires took by email or phone
call approximately twenty minutes to complete. Follow-up evaluated the
final decision, DCS, HOOS/KOOS, PASS, EQ-5D and satisfaction. The Decisional
Conflict Scale (DCS) is a reliable and valid measure of personal perceptions
of: a) uncertainty in the face of options, b) modifiable factors contributing to
uncertainty such as feeling uninformed, unclear about personal values, or
unsupported in decision-making; and c) effective decision-making such as
feeling the choice is informed, values-based, likely to be implemented, and
expressing satisfaction with the choice.15 It consists of sixteen questions, with a
total score ranging from 0 (no decision conflict) to 100 (highest level of decision
conflict). The knowledge questionnaire was comprised of four questions and
was used to measure the patients’ knowledge of treatment options and risks.
The following questions were survey: Question 1: “Could painkillers sufficiently
reduce complaints due to osteoarthritis?”; Question 2: “Is the primary goal
of operative treatment to reduce pain?”; Question 3: “Do the majority of the
prosthesis last longer than 10 years? Question” 4: “Should joint replacement
be the first choice, when conservative treatments did not work? The score
ranged from 0 (no correct answers) to 4 (all correct answers)”.15 The decision
questionnaires contained two separate questionnaires. One questionnaire
inquired what phase of decision-making patients were in and which treatment
they preferred. The second questionnaire was to inquire whether patients have
made their definitive decision. The Hip disability and Osteoarthritis Outcome
Score (HOOS) measured patients’ symptoms, pain, activity limitations in daily
living, function in sport and recreation and quality of life. This score consisted
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of forty questions, divided in the subscales mentioned above. For every subscale
there is a score ranging from 0 (extreme symptoms) to 100 (no symptoms).12
The Knee Injury and Osteoarthritis Outcome Score (KOOS) measured patients’
symptoms, pain, activity limitations daily living, function in sport and recreation
and quality of life. This score consisted of 42 questions, divided in the subscales
mentioned above. For every subscale there was a score ranging from 0 (extreme
symptoms) to 100 (no symptoms).1 To measure pain intensity we used question
8 of the KOOS or HOOS, which asked whether the patient has had knee or hip
pain during the last week.1,12 The short Pain Anxiety Symptoms Scale (PASS-
20) was used to measure patients’ pain-related anxiety and fear. It consisted
of twenty questions with a score ranging from 0 (no anxiety and fear) to 100
(extreme anxiety and fear).16 The EuroQol 5 Dimensions (EQ-5D) questionnaire
was used to measure health-related quality of life. It consisted of 5 questions
concerning mobility, self-care, usual activities, pain/discomfort and anxiety/
depression.13 It was also comprised of a Visual Analogue Scale (VAS) on which the
patients could score their health condition ranging from 0 (worst imaginable
health condition) to 100 (best imaginable health condition). The satisfaction
questionnaire consisted of three questions to measure patients’ satisfaction
with given information, the clinic and the physician. Patients could score each
question from 0 (no satisfaction) to 10 (complete satisfaction).
Demographics, description of study population
The baseline characteristics were similar in the two study groups. The
intervention group comprised 33 men and 33 women, who were on average
68-years-old (SD: 11). The control group comprised 30 men and 35 women, who
were on average 66-years-old (SD: 10). No significant differences were noted
with respect to physical function, pain and quality of life (Table 1).
Accounting for all patients / study subjects
In approximately eighteen months 145 participants were identified as being
eligible to participate in this study. All participants consented to participate
and were randomized. Among the 145 participants, 10 patients in the
intervention group and 4 patients in the control group did not complete the
first questionnaire due to time constraints leaving a total of 131 participants,
in which 66 patients were assigned to the intervention group and 65 patients
to the control group. 54 patients did not respond at follow up or could not be
contacted after three written requests and three requests by phone. (Table 1)
89
Table 1 Demographics
Intervention Control
SexMen
AgeLevel of educationDuration of pain (in weeks?)
Marital statusSingleUnmarriedMarriedDivorcedWidowed
Working statusWorking, full timeWorking, part timeSick leaveRetiredUnemployed, able to workUnemployed, unable to work
LocationLeft hipright hipboth hipsleft kneeright kneeboth knees
Tried non-operative treatment beforeYes
HospitalHospital 1Hospital 2Hospital 3Hospital 4
No.33
Mean681555
No.16
830
19
No.16
71
3413
No.9
153
1318
6
No.25
No.9
212610
%50
Sd11
2.276
%251347
214
%2611
255
25
%1423
52028
9
%39
%14323915
%46
Sd10
2.075
%201358
36
%1319
356
55
%1537
92014
5
%28
%3
354220
No.30
Mean661547
No.13
837
24
No.8
122
3633
No.1024
613
93
No.18
No.2
232713
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STATISTICAL ANALYSIS
Based on previous studies, scores of 25 or lower on the Decisional Conflict Scale
(DCS) (0-100) are associated with low decisional conflict and following-through
with decisions. On the other hand, scores of 39 or higher are associated with
heightened mental conflict resulting in a delay in decision-making.15 Thus, our
study examined whether the use of decision aids results in an increased rate
of patients following-through with decisions. A sample size of 128 patients
was chosen for patients with osteoarthritis of the knee or hip to detect an
effect size of 50% on the decisional conflict scale with a type I error of 0.05 and
a type II error of 0.20 based on a two-tailed prediction. Continuous data was
presented as the mean and standard deviation when normally distributed.
When comparing the intervention and the control group regarding continuous
dependent variables and dichotomous independent variables Student t-test was
used for normally distributed data and the Mann-Whitney U-test for skewed
data. The Kruskal Wallis test was used for ordinal data. In bivariate analysis,
the association between continuous dependent and continuous independent
variables was investigated using Spearman correlation. Associations with a
P-value less than 0.05 were considered statistically significant.
RESULTS
There was a significant difference (p < 0.001) in the total-DCS, comparing the
intervention group (mean=25) and the control group (mean=39) immediately
after the first encounter. The intervention group also scored significantly lower
on all DCS-subscales about (information, values clarity, support, uncertainty
and effective decision making) than the control group (Table 2). At the 26 weeks
follow-up there was no significant difference (p=0.17) in the DCS-total score
comparing the intervention group (mean=31) and the control group (mean=35).
Only on the subscale values clarity there was a significant difference (p=0.027)
between the intervention group and control group, favoring the intervention
group (mean=33 vs. mean=47, respectively) (Table 3).
Patients managed with a decision scored significantly higher on the
knowledge scale than the patients managed without (p < 0.01), after the first
encounter. At 26 weeks follow-up there was no significant difference in the
phase of decision-making, treatment preference and final choice (Table 2).
Patient allocated to the intervention group were significantly more
satisfied about the given information (mean=8.6 vs. mean=7.6; p < 0.001) and
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Table 2 Outcomes after the first encounter
Intervention Control P-Value
Decisional conflict scaleInformed subscoreValues clarity subscoreSupport subscoreUncertainty subscoreEffective decision subscoreTotal score
SatisfactionInformationVisit outpatient clinicPhysician
Anxiety (Pass)
Knowledge
Stage of decision makingHave not begun to think about the treatment optionsHave not begun to think about the treatment options, but I am interested to do soI am considering the treatment options nowI am close to select an optionI have already made a decision, but am still willing to reconsiderI have already made a decision and I am unlikely to change my mind
What treatment option do you prefer?Watchful waitingLifestyle changesPhysiotherapyPainkillersCorticosteroid injectionProsthesisOther
Mean
322527232025
8.68.38.9
20
3.7
No.
1
0
103
15
37
51
2237
262
Sd
201613161512
1.11.50.9
17
0.6
%
1.5
0
154.523
56
7.61.533
4.51139
3
Sd
202216151311
1.81.71.7
19
0.9
%
3.1
9.2
141.515
57
4.64.620
4.61942
6.2
0.033 <0.001 <0.001 <0.001 0.001 0.000 0.000 0.30 0.01 0.29 0.0073
0.11
0.46
Mean
395045352839
7.68.08.3
23
3.3
No.
2
6
91
10
37
33
133
1227
4
continue >
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their physician (mean=8.9 vs mean=8.3; p= 0.01) compared to the patients who
were allocated to the control group at enrolment, after the first encounter. On
the other hand, there was no significant difference in satisfaction about the visit
to the outpatient clinic (p= 0.30). At 26 weeks follow-up the results showed that
the patient managed with a decision aid were still significantly more satisfied
about the information given (p= 0.0038) but not about the visit of outpatient
clinic (p = 0.35) or their physician (p = 0.34) (Table 2). The results showed no
difference in preference among the different treatment options, after the first
encounter and 26 weeks follow-up (Table 2).
There was no difference in the magnitude of experienced anxiety
(p=0.29), after the first encounter and at follow-up. At 26 weeks follow-up there
was also no significant difference between both groups on the physical function,
pain and quality of life scores (Table 4).
Intervention Control P-Value
Did you make a final choice
Yes
If yes, what did you chooseWatchful waitingLifestyle changesPhysiotherapyPainkillersCorticosteroid injectionProsthesisOther
Mean
50
41
1616
211
Sd
76
82
322
1242
2
Sd
79
3.95.916
3.92249
0
0.84
0.26
Mean
51
2382
1125
0
continued table 2
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Table 3 Outcomes at follow up
Intervention Control P-Value
Decisional conflict scaleInformed subscoreValues clarity subscoreSupport subscoreUncertainty subscoreEffective decision subscoreTotal score
SatisfactionInformationVisit outpatient clinicPhysician
Anxiety (Pass)
If yes, what did you chooseWatchful waitingLifestyle changesPhysiotherapyCorticosteroid injectionProsthesisOther
Mean
363337292331
8.47.98.2
17
4012
200
Sd
262625171515
0.91.71.7
14
150
3.77.474
0
Sd
232717151411
2.11.51.6
14
117.17.13.664
7.1
0.38 0.027 0.17 0.22 0.55 0.17 0.0038 0.35 0.34 0.92
0.59
Mean
314744342535
7.17.88.0
16
3221
182
Table 4 Clinical outcomes at follow up
Intervention Control
Hoos pain totalHoos symptoms totalKoos pain totalKoos symptoms total EQ5D MobileEQ5D CareEQ5D ActivityEQ5D PainEQ5D Anxiety
Mean
72767256
1.61.11.41.71.1
Sd
23171613
0.490.330.500.610.31
Sd
23272011
0.420.500.500.680.32
Mean
72697057
1.81.31.41.81.1
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DISCUSSION
We found that patients, making a shared decision, with a decision aid had less
decisional conflict, more knowledge and increased clarity of values. The results
also showed that this did not influence the stage of decision-making, treatment
preference, physical outcome or quality of life on the long term.
The results of this study should be evaluated in the light of its
strengths and shortcomings. A strenght is that this randomized controlled trial
was carried out at tertiary and secondary referral centers, with patients from
multicultural background. The first shortcoming is that patients with knee or
hip osteoarthritis may experience different levels of decisional conflict with
respect to their diagnosis. On the other hand subsequent analysis showed no
significant difference between both groups at baseline. The second shortcoming
is the high percentage of loss to follow-up. As a result no strong conclusion
can be drawn from the follow-up results. The third shortcoming was that the
implementation workshop was given only once, while not every physician
participated. The physicians who participated in the workshop also managed the
patients in the control group. As a result the standard care may have improved
after the workshop and the patients in de control group could have been better
informed compared to the patients before the implementation workshop.
Another shortcoming was that there were 14 patients who were excluded due to
time constraints at the outpatient clinic. Patients in the intervention group may
have stopped not only due to the length of the questionnaires, but also due to
the time the decision aid took to work through (approximately 15-20 min). This
may have influenced the outcomes as well. Also 54 patients (27 patients in the
intervention group and 27 in the control group) did not respond to the follow
up or could not be contacted after three written requests and three requests by
phone.
The finding that patients managed with a decision aid experienced
less decisional conflict at enrollment compared to patients managed with usual
care is in concordance with previous studies.7,8,16,17 The systematic review of
Stacey et al included 115 RCT’s comparing decision aids to usual care, showing a
significant average decrease in the level of decisional conflict. The average score
of 25 in the decision aid group is associated with low decisional conflict and
following-through with decisions, while the score of 39 in the control groups
is associated with increased mental conflict resulting in delayed decision-
making.15 The reduced sub-scores also showed that patients felt more informed
about the options, clearer about their values and comfortable with their choices.
95
Besides for values clarity, this difference was not maintained at follow-up.
This may be explained by the fact that effect of decision aids decreases over
time when the final decision was made. This was also found in a randomized
controlled trial about total knee arthroplasty by Stacey et al with one year
follow-up.7
The decision aid appeared to have a positive effect on the level
of satisfaction about the given information and the treating physician at
enrollment, although the satisfaction about the physician decreased over time.
Among the 14 studies comparing decision aids to usual care, 10 measured
satisfaction with the decision-making process, 3 measured satisfaction
received and 1 measured satisfaction counseling. Of those 14 studies, 5 showed
statistically significant improvement in satisfaction with decision making
process and the information provided.17 Montori et al. found no difference in
satisfaction with the given information between patient, clinicians had higher
level of satisfaction. The finding that decision aids did not influence the level
of anxiety is also consistent with previous studies.17 None of the previous
studies demonstrated differences in effect on patients stated anxiety at one
month, three months or one year. One might expect that patients who are
better informed about potential drawbacks might be more anxious about the
outcome than patients, who are less informed, however this is not reflected
by the results. We found no significant change in the preference of surgery
between both groups. Previous studies reported mixed results. For example
Deyo et al. found no difference in preference of surgery for herniated disc in
the detailed versus simple decision aid.2 On the other hand Arterburn et al.
described a decrease in elective surgery after implementation of a decision aid
after one year.9 As in other studies, we found no difference in physical outcome
or quality of life. Stacey et al. reports 12 studies measuring various condition
specific health outcomes. 10 studies compared decision aids versus usual care
and one a detailed decision aid versus a simple decision aid. 9 of the 12 studies
found no significant effect on physical outcome. It is not surprising that no
effect on health outcome is found in our study, since there is a comparable
preference in treatment options and previous results showed that patients
physical outcomes improve in the operatively and non-operatively treated
groups. The positive effect of decision aids on reducing decisional conflict,
improving patient’s knowledge about risks and benefits, being informed and
feeling clear about their values provides supports for using them in clinical
practice. Both knowledge and understanding of probable risk are important
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to make an informed decision making possible. Further research is needed to
develop decision aids based on individual characteristics, resulting in more
advanced complication and success rates based. Additional research is required
to determine the not only the effect of personalized decision aids, but also the
cost-effectiveness and adherence of decision aids.
In conclusion we found that our patients managed with a decision aid
had less decisional conflict, more knowledge and increased clarity of values at
the initial encounter with their attending surgeon.
97
REFERENCES
1. de Groot IB, Favejee MM, Reijman M, Verhaar JA, Terwee CB. The Dutch version of the Knee
Injury and Osteoarthritis Outcome Score: a validation study. Health and quality of life
outcomes 2008;6:16.2. Deyo RA, Cherkin DC, Weinstein J, Howe J, Ciol M, Mulley AG, Jr. Involving patients in clinical
decisions: impact of an interactive video program on use of back surgery. Medical care
2000;38:959-69.3. Skou ST, Roos EM, Laursen MB, et al. A Randomized, Controlled Trial of Total Knee
Replacement. N Engl J Med 2015;373:1597-606.4. du Long J, Hageman M, Vuijk D, Rakic A, Haverkamp D. Facing the decision about the
treatment of hip or knee osteoarthritis: What are patients’ needs? Knee Surg Sports
Traumatol Arthrosc 2016.5. Slover J, Shue J, Koenig K. Shared decision-making in orthopaedic surgery. Clin Orthop Relat
Res 2012;470:1046-53.6. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.7. Stacey D, Briere N, Robitaille H, Fraser K, Desroches S, Legare F. A systematic process for
creating and appraising clinical vignettes to illustrate interprofessional shared decision
making. J Interprof Care 2014;28:453-9.8. de Achaval S, Fraenkel L, Volk RJ, Cox V, Suarez-Almazor ME. Impact of educational and patient
decision aids on decisional conflict associated with total knee arthroplasty. Arthritis Care Res
(Hoboken) 2012;64:229-37.9. Arterburn D, Wellman R, Westbrook E, et al. Introducing decision aids at Group Health
was linked to sharply lower hip and knee surgery rates and costs. Health Aff (Millwood)
2012;31:2094-104.10. O’Connor AM. Validation of a decisional conflict scale. Medical decision making : an
international journal of the Society for Medical Decision Making 1995;15:25-30.11. EuroQol G. EuroQol--a new facility for the measurement of health-related quality of life.
Health policy 1990;16:199-208.12. de Groot IB, Reijman M, Terwee CB, et al. Validation of the Dutch version of the Hip disability
and Osteoarthritis Outcome Score. Osteoarthritis Cartilage 2009;17:132.13. Elwyn G, O’Connor A, Stacey D, et al. Developing a quality criteria framework for patient
decision aids: online international Delphi consensus process. BMJ 2006;333:417.14. International Patient Decision Aid Standards (IPDAS) Collaboration. 2013. at ipdas.ohri.ca/.)15. O’Connor AM, Tugwell P, Wells GA, et al. A decision aid for women considering hormone
therapy after menopause: decision support framework and evaluation. Patient Educ Couns
1998;33:267-79.16. McCracken LM, Dhingra L. A short version of the Pain Anxiety Symptoms Scale (PASS-20):
preliminary development and validity. Pain research & management : the journal of the Canadian
Pain Society = journal de la societe canadienne pour le traitement de la douleur 2002;7:45-50.17. Stacey D, Hawker G, Dervin G, et al. Decision aid for patients considering total knee
arthroplasty with preference report for surgeons: a pilot randomized controlled trial. BMC
Musculoskelet Disord 2014;15:54.
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99
CHAPTER 8
Do upper extremity trauma patients
have different preferences for shared
decision-making than patients with
non-traumatic conditions?
Hageman MG, Makarawung DJ, Briet JP, van Dijk CN, Ring D.
Orthopaedic Hand and Upper Extremity Service, Harvard Medical School, Massachusetts General
Hospital, Boston, MA, USA.
Clin Orthop Relat Res. 2015 Nov; 473(11):3542-8.
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ABSTRACT
Background Shared decision-making is a combination of expertise, available scientific evidence, and the preferences of the patient and surgeon. Some surgeons contend that patients are less capable of participating in decisions about traumatic conditions than non-traumatic conditions.Questions/purposes (1) Do patients with non-traumatic conditions have different preferences for shared decision-making when compared with those who sustained acute trauma? (2) Do disability, symptoms of depression, and self-efficacy correlate with preference for shared decision-making?
Methods In this prospective, comparative trial, we evaluated a total of 133 patients presenting to the outpatient practices of two university-based hand surgeons with traumatic or non-traumatic hand and upper extremity illnesses or conditions. Each patient completed questionnaires measuring their preferred role in healthcare decision-making (Control Preferences Scale [CPS]), symptoms of depression (Patients’ Health Questionnaire), and pain self-efficacy (confidence that one can achieve one’s goals despite pain; measured using the Pain Self-efficacy Questionnaire). Patients also completed a short version of the Disabilities of the Arm, Shoulder, and Hand questionnaire and an ordinal rating of pain intensity.
Results There was no difference in decision-making preferences between patients with traumatic (CPS: 3 ± 2) and non-traumatic conditions (CPS: 3 ± 1 mean difference = 0.2 [95% confidence interval, 0.4 to 0.7], p = 0.78) with most patients (95 versus 38) preferring shared decision-making. More educated patients preferred a more active role in decision-making (beta = 0.1, r = 0.08, p = 0.001); however, differences in levels of disability, pain and function, depression, and pain-related self-efficacy were not associated with differences in patients’ preferences in terms of shared decision-making.
Conclusions Patients who sustained trauma have on average the same preference for shared decision-making compared with patients who sustained no trauma. Now that we know the findings of this study, clinicians
101
might be motivated to share their expertise about the treatment options, potential outcomes, benefits, and harms with the patient and to discuss their preference as well in a semi-acute setting, resulting in a shared decision.
INTRODUCTION
In shared decision-making the caregiver provides expertise and evidence, and
the patient and caregiver choose diagnostic and treatment options consistent
with their values and preferences.1 There is evidence that empowering patients
to participate in decision-making with the help of decision aids (videos, web
sites, or pamphlets that help patients understand their options and become
aware of their preferences) results in increased satisfaction and physical
function and reduced decisional conflict, anxiety, and resource utilization.2
Patient preferences for involvement in decision-making may vary by age, sex,
socioeconomic status, type of illness, and illness behavior, and perhaps the
gravity or acuity of the decision.3,4
Many surgeons hold the opinion that patients with traumatic problems
are less capable of and less interested in participating in decisions because they
feel vulnerable and time-pressured. Although to our knowledge this has not been
studied, many of our colleagues insist that patients with a painful acute fracture
cannot fully participate in the decision-making process and need the doctor to
recommend treatment. In addition, patients with greater symptoms of depression
or less self-efficacy might have less desire or confidence about participation in the
decision-making process and might prefer to fall back to a paternalistic style of
medical care and take a more passive role. Depressed mood and ineffective coping
strategies can make people feel more resigned, passive, and helpless. We therefore
wished to assess hand surgery patient preferences for shared decision-making in
relation to the acuity of the diagnosis and to psychological factors.
This study tested the following hypotheses: (1) Do patients with non-
traumatic conditions have different preferences for shared decision-making
when compared with those who sustained acute trauma? (2) Do disability,
symptoms of depression, and self-efficacy correlate with preference for shared
decision-making?
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MATERIAL AND METHODS
After approval from our institutional research board, all new, non-pregnant,
English-speaking patients 18 years or older presenting to one of two hand
surgeons (DR, CM) were asked to participate in this prospective study. The
researcher informed the patient about the study details and informed consent
was obtained. Patients were enrolled between November 2012 and April 2013.
We asked 135 patients to participate in the study: 1 (0.7%) declined
and 134 were enrolled before seeing the treating physician. One patient
was excluded from the study as a result of invalid answers on one of the
questionnaires. The analyses were conducted on 133 patients (68 men
and 65 women) with a mean age of 47 ± 17 years (range, 18–86 years). The
demographics of trauma and non-trauma cohorts were comparable (Table1).
There was also no difference in levels of education comparing the trauma (mean,
16 years; range, 9–16 years) and non-trauma cohorts (mean, 15 years; range,
0–20 years; p = 0.10). Conditions categorized as traumatic included: fracture,
laceration, sprain, tendon injury, and amputation. All other diagnoses were
considered non-traumatic; examples included arthrosis, carpal tunnel, trigger
finger, and another discrete diagnosis.
Mearsurement Tools
At the time of enrollment, patients completed a demographic survey, including
level of education, and the following questionnaires: the Control Preferences
Scale (CPS), the short version of the Disabilities of the Arm, Shoulder and Hand
questionnaire (QuickDASH), the Pain Self-efficacy Questionnaire (PSEQ), the
short version of the Patients Health Questionnaire (PHQ-2), and an 11-point
ordinal pain intensity score.
After the encounter with the physician, the research assistant registered
whether the patient was a trauma or non-trauma patient. Education, as the
number of years of school, was measured on a continuous scale with graduation
from high school scored as 12.
The CPS is a validated measure of a patient’s preferred role in healthcare
decision-making.5 Patients rank-order five possible approaches to decision-
making, resulting in a score that is scaled from 1 (most active role) to 6 (most
passive role). A score of 3 or lower indicates a preference for shared decision-
making.5
The QuickDASH is a short version of the DASH and is used to determine
arm-specific disability.6,7 It consists of 11 questions, which are answered on a
Table 1 Demographics
Trauma cohort Non-trauma cohort
ParameterAgeEducation
SexWomenMen
Marital statusSingleLiving with PartnerMarriedSeparated/DivorcedWidowed
Work statusWorking, full timeWorking, part timeHome makerRetiredUnemployed and able to workUnemployed and unable to workOn worker compensationCurrently on sick leave
DiagnosisAcute injuryNon specific arm painTrigger fingerCarpal tunnel syndromeGanglion cystArthroseDequervain’sDupuytrenEpicondylitisBursitisGiant cell tumorCubital Tunnel syndromeOther
PhysicianPhysician IPhysician II
Health outcomesQuick DASHPainPSEQPHQ
Mean4516
3037
32
124
54
425073424
67
1750
434.542
1.4
Range 18-86
9-26
4555
48
1.536
9.06.0
63
7.50
104.55.63.06.0
50
2575
2.3-863.9-5.1
2-600-6
Range20-86
0-20
5347
30
4.648
9.17.6
61
9.13.017
6.13.01.50.0
6.7121315
6.718
5.05.06.71.71.73.015
3664
0-804.7-6.0
0-600-6
Mean 4915
3531
20
332
65
4062
114210 47894
113411129
2442
315.447
1103
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5-point Likert-scale. The total score is scaled to range from 0 (no disability) to 100
(most severe disability).
The PSEQ is a questionnaire designed to assess a patient’s confidence
that they can achieve their goals despite pain.8,9 It involves ten items, which
can be scored by the patient on a 7-point Likert-scale, ranging from 0 (not at all
confident) to 6 (completely confident). The outcome score is calculated by adding
up the items on a scale ranging from 0 to 70. A higher score indicates greater
confidence. Mean imputation was used for two missing values.
The PHQ-2 was used to evaluate symptoms of depression. The PHQ-2
is a validated two-question measure of symptoms of depression.10,11 The two
questions are answered on a 4-point Likert-scale ranging from 0 (not at all) to
3 (nearly everyday) and the overall score ranges from 0 to 6.
The Numeric Rating Scale is an 11-point ordinal measure of pain
intensity.
Statistical Analysis
An a priori power analysis for our primary study question determined that
64 patients in the trauma cohort and 64 patients in the non-trauma cohort
would provide 80% power to detect a 0.50 SD (medium) difference in average
CPS score with α = 0.05 using a two-tailed Student’s t-test. We enrolled 135
patients to have at least 64 patients for each cohort accounting for dropouts and
incomplete questionnaires.
In bivariate analysis, Pearson’s correlation was used for continuous
variables. The strength of a correlation between 0.10 to 0.29, 0.30 to 0.49, and
0.50 to 1.0 is interpreted as small, medium, and large correlation, respectively.12
The Student’s t-test was used for the CPS (ordinal variable) when comparing
between two groups; and analysis of variance was used to compare differences
in CPS (again, ordinal variable) when more than two groups were present such
as based on marital status. Variables with p < 0.10 were inserted in a backward,
stepwise, multivariable linear regression analysis of factors associated with
CPS. When categorical variables were inserted in multivariable analysis, dummy
codes were generated when there were more than two categories.
RESULTS
There was no difference between trauma (mean CPS: 3; SD: 2) and non-trauma
patients’ (mean CPS: 3; SD: 1) preferred level of shared decision-making (mean
105
Table 2 Bivariable analyses
Control preference scale
ParameterNonelective versus Elective patients
Trauma cohortNon-trauma cohort
SexWomenMen
Marital statusSingleLiving with PartnerMarriedSeparated/DivorcedWidowed
Work statusWorking, full timeWorking, part timeHome makerRetiredUnemployed and able to workUnemployed and unable to workOn worker compensationCurrently on sick leave
DiagnosisAcute injuryNon specific arm painTrigger fingerCarpal tunnel syndromGanglion cystArthroseDequervain’sDupuytrenEpicondylitisBursitisGiant cell tumorCubital Tunnel syndromOther
PhysicianPhysician IPhysician II
Mean
3.12.9
3.03.0
3.22.02.92.93.3
2.92.54.53.42.73.25.02.8
3.13.13.62.43.52.62.32.32.2
36
2.52.5
33
SD
1.71.4
1.51.6
1.71.41.51.70.87
1.51.62.11.51.71.81.71.7
1.71.60.71.71.71.30.961.21.2
--
0.710.58
1.61.5
P-Value
0.78
0.96
0.39
0.27
0.62
0.78
continue >
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difference = 0.2 [95% confidence interval, 0.4 to 0.7], p = 0.78; Table 2). Again,
scores of 3 or lower on the CPS suggest a desire on the part of the patient to
engage in shared decision-making.
More educated patients had a greater desire to participate in decision-
making (coefficient = 0.27, p < 0.01); but age, duration of complaint, disability
level, pain or pain self-efficacy, and symptoms of depression were not associated
with preferences for shared decision-making (Table 2).
DISCUSSION
Assuming that patients with acute injury are less interested or capable of
participating in decision-making risks devaluing their preferences. We found
that patients with acute hand and upper extremity trauma prefer to be as
engaged in decision-making as patients with non-traumatic conditions. As
education levels increased, patients’ desires to participate in shared decision-
making also increased, which is consistent with prior research.13-15 Coping
strategies and symptoms of depression did not affect decision-making
preferences.
This study should be considered in light of its shortcomings. First, the
setting was limited to hand and upper extremity conditions. These findings may
only generalize to other conditions or other practice settings, but that seems
unlikely. It is possible that for some specific conditions, however, such as very
severe trauma, the findings would be different. On the other hand, the lack of
Control preference scale
Health outcomesAge
EducationDuration of injuryQuick DASHPainPSEQPHQ
Coefficient
-0.03-0.27-0.0040.150.082
-0.10.00
P-Value
0.69<0.01
0.960.080.350.240.96
continued table 2
107
correlation between the duration since injury and the CPS suggests that time
pressure does not have a strong influence.
Patients have similar levels of desire for shared decision-making,
regardless of whether the condition was traumatic or non-traumatic. Decision-
making preferences were addressed in a study of Korean patients with carpal
tunnel syndrome.16 33% of patients felt less involved in the decision-making
regarding carpal tunnel release than they desired. 76% of patients who preferred
shared decision-making had lower scores on the DASH questionnaire compared
with those who preferred a fully active or fully passive role.16 There is some
evidence that decision aids can help patients achieve their preferred role in
decision-making.17,18 In general, patients who actively contribute to their health
care have better functional outcome, choose less invasive treatments, and are
more satisfied with their options.13,19-21 Patients’ outcomes and their satisfaction
seem to be enhanced by higher levels of patient engagement. Providing patients
with their desired level of involvement in decision-making is an important part
of improving patient engagement and clinical results.
It may be surprising that the magnitude of education is the only factor
associated with the desire to participate in shared decision-making and that
age, duration of complaint, magnitude of disability level, pain intensity, and
psychological factors did not have a measurable influence. There is a bias that
shared decision-making is more acceptable to younger patients22, but the
finding that age is not associated with preferences for participation in the
decision-making process agrees with prior studies.3 Furthermore, one might
guess that depressed mood and ineffective coping strategies might make
people feel more resigned, passive, and helpless; our findings suggest that these
factors do not influence preferences for participation in the decision-making
process. Many surgeons are of the opinion that injured patients must rely and
prefer to rely on the surgeon’s advice and feel less capable of participating
in decision-making (as a result of pain, limited time to decide, etcetera) than
patients with non-traumatic problems. One might also assume that older
patients prefer a more paternalistic style and that patients with greater stress,
distress, and less effective coping strategies will be more passive. This study in
combination with prior studies demonstrate that shared decision-making is
preferred by both trauma and non-trauma patients without obvious differences
between those two groups of patients.23-25 Patients, regardless of their level of
education, deserve to participate in shared decision-making, but to give less
well-educated patients the confidence to do so, appropriate tools need to be
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developed. A decision aid appropriate for low levels of health literacy might
increase a less educated patient’s confidence that they can participate in
decision-making. In our opinion, it is safe to assume that all patients prefer to
participate in decision-making unless they suggest otherwise. Surgeons should
provide accurate, balanced, dispassionate information to patients so that they
can understand their preferences. We believe that most surgeons would agree
that, given the uncertainty about the best management of many problems,
the preferences of the patient should feature prominently in decision-making.
Future research should help determine the best way to inform patients so that
they feel adequately involved in the decision-making process and surgeon-to-
surgeon variation in management is minimized. We think decision aids hold
promise for achieving these goals and plan to develop aids and test their impact
on decisional conflict, surgeon-to-surgeon variation, satisfaction with patient
care, symptoms, and disability. Future studies on greater scale are warranted to
assess if decision aids improve health outcome by encouraging patients to take a
more active role in their recovery and reduce variation.
REFERENCES
1. Stiggelbout AM, Van der Weijden T, De Wit MP, et al. Shared decision making: really putting
patients at the centre of healthcare. BMJ 2012;344:e256.2. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.3. Charles C, Gafni A, Whelan T. Decision-making in the physician-patient encounter: revisiting
the shared treatment decision-making model. Soc Sci Med 1999;49:651-61.4. Légaré F. Establishing patient decision aids in primary care: Update on the knowledge base.
Z Evid Fortbild Qual Gesundhwes 2008;102:427-30.5. Degner LF, Sloan JA, Venkatesh P. The Control Preferences Scale. The Canadian journal of
nursing research = Revue canadienne de recherche en sciences infirmieres 1997;29:21-43.6. Beaton DE, Katz JN, Fossel AH, Wright JG, Tarasuk V, Bombardier C. Measuring the whole or
the parts? Validity, reliability, and responsiveness of the Disabilities of the Arm, Shoulder
and Hand outcome measure in different regions of the upper extremity. J Hand Ther
2001;14:128-46.7. Gummesson C, Ward MM, Atroshi I. The shortened disabilities of the arm, shoulder and hand
questionnaire (QuickDASH): validity and reliability based on responses within the full-length
DASH. BMC musculoskeletal disorders 2006;7:44.8. Asghari A, Nicholas MK. Pain self-efficacy beliefs and pain behaviour. A prospective study.
Pain 2001;94:85-100.9. Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur J Pain
2007;11:153-63.
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10. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item
depression screener. Medical care 2003;41:1284-92.11. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.
J Gen Intern Med 2001;16:606-13.12. Cohen JW. In: Hillsdale, ed. Statistical power anlays for behavioral sciences (2nd edn). NJ::
Lawrence Erlbaum Associates; 1988:79-81.13. Hack TF, Degner LF, Dyck DG. Relationship between preferences for decisional control and
illness information among women with breast cancer: a quantitative and qualitative
analysis. Soc Sci Med 1994;39:279-89.14. Hibbard JH, Cunningham PJ. How engaged are consumers in their health and health care,
and why does it matter? Research brief 2008:1-9.15. Uldry E, Schafer M, Saadi A, Rousson V, Demartines N. Patients’ Preferences on Information
and Involvement in Decision Making for Gastrointestinal Surgery. World J Surg 2013.16. Gong HS, Huh JK, Lee JH, Kim MB, Chung MS, Baek GH. Patients’ preferred and retrospectively
perceived levels of involvement during decision-making regarding carpal tunnel release. J
Bone Joint Surg Am 2011;93:1527-33.17. Kennedy AD, Sculpher MJ, Coulter A, et al. Effects of decision aids for menorrhagia on
treatment choices, health outcomes, and costs: a randomized controlled trial. Jama
2002;288:2701-8.18. Murray E, Davis H, Tai SS, Coulter A, Gray A, Haines A. Randomised controlled trial of an
interactive multimedia decision aid on hormone replacement therapy in primary care. BMJ
2001;323:490-3.19. Degner LF, Kristjanson LJ, Bowman D, et al. Information needs and decisional preferences in
women with breast cancer. Jama 1997;277:1485-92.20. Golin C, DiMatteo MR, Duan N, Leake B, Gelberg L. Impoverished diabetic patients whose
doctors facilitate their participation in medical decision making are more satisfied with their
care. J Gen Intern Med 2002;17:857-66.21. Legare F, Stacey D, Briere N, et al. Healthcare providers’ intentions to engage in an
interprofessional approach to shared decision-making in home care programs: a mixed
methods study. J Interprof Care 2013;27:214-22.22. Frosch DL, Kaplan RM. Shared decision making in clinical medicine: past research and future
directions. Am J Prev Med 1999;17:285-94.23. Hutchinson RH, Barrie JL. The effects of shared decision making in the conservative
management of stable ankle fractures. Injury 2015.24. Slover J, Shue J, Koenig K. Shared decision-making in orthopaedic surgery. Clin Orthop Relat
Res 2012;470:1046-53.25. Youm J, Chenok KE, Belkora J, Chiu V, Bozic KJ. The emerging case for shared decision making
in orthopaedics. Instr Course Lect 2013;62:587-94.
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PART 3
GENERAL DISCUSSION
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CHAPTER 9
Summary and Discussion
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SUMMARY AND DISCUSSION
There is substantial variation in rates and type of operative and non-operative
treatment that cannot be explained by demographics, pathophysiology, or
comorbidities alone.1-3 Although there should be some variation in medical
treatments, variation from surgeon-to-surgeon is more difficult to justify
than variation from patient-to-patient. The objectives of this thesis were to
address several aspects of orthopaedic decision-making from the surgeon and
patient perspectives: factors associated with variation in recommendation for
operative and non-operative treatment, how health care providers decide which
option to recommend to their patients when the evidence is inconclusive, the
priorities and preferences facing decisions, pre-visit expectation associated with
satisfaction of delivered care, the effect of decision aids, and patient preferences
for shared decision-making in relation to the acuity of the diagnosis and to
psychological factors.
To measure the factors leading to substantial, unexplained variation in
hand surgeon recommendations for treatment of peripheral mono-neuropathy,
chapter 2 tested the null hypothesis that specific patient and provider factors
do not influence recommendations for surgery. Using a web-based survey,
hand surgeons recommended surgical or nonsurgical treatment for patients
in two different scenarios. Six elements of the first scenario (symptoms,
circumstances, mindset, diagnosis, objective testing, and expectations) had
two possibilities that were each independently and randomly assigned to each
rater. For the second scenario, two different scenarios were randomly assigned
to each rater. Multivariable logistic regression sought factors associated with a
recommendation for surgery.
A total of 186 surgeons of the Science of Variation Group completed a
survey regarding recommendation of surgery for two different patients based
on clinical scenarios. Recommendations for surgery did not vary significantly
according to provider characteristics. For the various elements in scenario
1, recommendation for surgery was more likely for patients who were self-
employed and continued to work and who had objective electro-diagnostic
abnormalities. For the two vignettes used in scenario 2, a recommendation for
surgery was associated with abnormal electrophysiology.
The findings of this study suggest that – at least in a survey setting
– surgeons prefer to offer peripheral nerve decompression to patients with
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abnormal electrophysiology, particularly those with effective coping strategies.
This was an unexpected finding, because if surgeons offer surgery based
primarily on reliable and valid objective testing, there should be limited
variation in treatment recommendations. It may be that the surgeon-observers
in the SOVG are not representative of the average surgeon.
Recommendations for managing proximal humeral fractures vary
substantially.4,5 Recent studies demonstrate substantial surgeon-to-surgeon
inconsistencies in the treatment of these injuries.4,5 We were interested in
the relative influence of patient information and surgeon characteristics
on the decision-making process in treating proximal humeral fractures.
We hypothesized in chapter 3 that there is no difference in treatment
recommendations between surgeons shown radiographs alone and those
shown radiographs and patient information.
We surveyed a total of 238 surgeons who rated forty radiographs of
patients with proximal humerus fractures. Participants were randomized to
receive information about the patient and mechanism of injury. The response
variables included the choice of treatment (operative vs non-operative) and the
percentage of matches with the actual treatment.
Participants who received patient information recommended operative
treatment less than those who received no information. The patient information
that had the greatest influence on treatment recommendations included age
(55%) and fracture mechanism (32%). The only other factor associated with a
recommendation for operative treatment was region of practice. There was no
significant difference between participants who were and were not provided
with information regarding agreement with the actual treatment (operative vs
non-operative) provided by the treating surgeon.
Treatment recommendations for proximal humeral fractures are
influenced by patient information – older age in particular – but most of the
variation in recommendations remains unaccounted for. The highly variable and
inconsistent influence of patient factors on surgeon recommendations belies
variations in surgeon preferences and values that are likely at the root of the
substantial treatment variations documented in this and other studies.
Because evidence-based medicine is an amalgam of individual clinical
expertise and best available evidence, the question arises what is the basis for
provider recommendations when the best evidence is inconclusive? Chapter 4
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tested the null hypothesis that the factors surgeons use do not vary by training,
demographics, and practice. A total of 337 surgeons rated the importance of
seven factors when deciding between treatment and following the natural
history of the disease and twelve factors when deciding between two operative
treatments using a 5-point Likert-scale between “very important” and “very
unimportant.”
We found that the factors that surgeons use most to make
recommendations when evidence is inconclusive relate primarily to the
surgeon’s perspective (e.g. “works in my hands,” “familiarity with the treatment,”
“what my mentor taught me”) rather than the patient’s perspective (e.g.
“doing something vs doing nothing,” “patients are requesting the procedure”).
Exceptions include “fewer complications” and “quicker recovery”. Highest
reimbursement was also rated relatively unimportant, particularly in Europe but
across all countries and regions. The write-in answers revealed that surgeons
prefer to fall back to the “best available outcome/evidence-based” even when
the scenario is that the evidence is inconclusive. Patient-centered care/shared
decision-making was also mentioned.
That health care providers fall back to their personal preferences based
on experience is no surprise.6 Especially, since patients look to their surgeon’s
expertise regarding the optimal fallback options when evidence is inconclusive.
However, where current best evidence allows room for debate, patients can
benefit from an understanding of the range of options and the source of the
debate. The involvement of patients in decision-making is particularly important
when the evidence is inconclusive because patient may value the potential
outcomes of the treatment differently.
The study described in chapter 5 assessed the priorities and preferences
of patients and hand surgeons facing decisions about management of CTS.
We tested the null hypothesis that there are no differences in priorities and
preferences of patients with CTS and hand surgeons.
One hundred three hand surgeons of the Science of Variation Group and
79 patients with CTS completed a survey about their priorities and preferences
in decision-making regarding the management of CTS. The questionnaire
was structured according the Ottawa Decision Support Framework for the
development of a decision aid.7
Important areas on which patient and hand surgeon interests
differed included a preference for non-painful, non-operative treatment and
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confirmation of the diagnosis with electro-diagnostic testing. For patients, the
main disadvantage of non-operative treatment was that it was likely to be only
palliative and temporary. Patients preferred, on average, to take the lead in
decision-making, whereas physicians preferred shared decision-making. Patients
and physicians agreed on the value of support from family and other physicians
in the decision-making process.
There were some differences between patient and surgeon priorities
and preferences regarding decision-making for CTS, particularly the risks and
benefits of diagnostic and therapeutic procedures. Clinical relevant information
that helps inform patients of their options based on current best evidence might
help patients understand their own preferences and values, reduce decisional
conflict, limit surgeon-to-surgeon variations, and improve health. In particular,
our study identified several areas in which patients would like more information
about CTS: the specific risks and benefits of various diagnostic and therapeutic
options, health provider recommendations, and help clarifying their own
preferences.
The study described in chapter 6 assessed the correlations among
pre-visit expectations, met expectations, and patient satisfaction and what
categories of expectations correlated with satisfaction.
86 new patients presenting to a hand surgery practice of a tertiary
referral hospital with 70% direct primary care referrals, mostly with elective
concerns, indicated their pre-visit expectations (Patient Intention Questionnaire
[PIQ]). Immediately after the visit, the same patients rated the degree to which
their pre-visit expectations were met (Expectation Met Questionnaire [EMQ])
and their satisfaction level (Medical Interview Satisfaction Scale). These tools
have been used in primary care office settings and claim good psychometric
properties, and although they have not been strictly validated for responsiveness
and other test parameters, they have good face validity. We then conducted a
multivariable backward linear regression to determine whether (1) scores on the
PIQ; and (2) scores on the EMQ are associated with satisfaction.
Satisfaction correlated with met expectations but not with pre-visit
expectations. We identified five primary categories of pre-visit expectations that
accounted for 50% of the variance in PIQ: (1) ‘‘Information and Explanation’’; (2)
‘‘Emotional and Understanding’’; (3) ‘‘Emotional Problems’’; (4) ‘‘Diagnostics’’;
and (5) ‘‘Comforting’’. The only category of met expectations that correlated with
satisfaction was Information and Explanation.
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Among patients seeing a hand surgeon, met expectations correlate with
satisfaction. In particular, patients with met expectations regarding information
and explanation were more satisfied with their visit. Efforts to determine
the most effective methods for conveying unexpected information warrant
investigation.
Prior studies showed that decision aids can normalize and depersonalize
these conflicts, provide patients information they can reflect on at their own
pace and in their own way, and ensure that patients are empowered to make
decisions based on an better understanding of their disease.8 We hypothesized
in chapter 7 that there is no difference in decisional conflict comparing patients
with hip- or knee osteoarthroses managed with a decision aid compared to
patients without. Our secondary hypothesis was that there is no difference
between patients managed with a decision aid compared to patients without,
with respect to anxiety, knowledge, satisfaction, preferred treatment at
enrollment and physical function and quality of life, 26 weeks after the
treatment decision was made.
This multi center randomized controlled trial included patients with
knee or hip osteoarthritis who had not consulted an orthopedic surgeon for
the same complaint in the past. At the first encounter, patients enrolled in the
control group were treated according standard care, while patients enrolled in
the intervention group were managed with a decision aid. After the encounter
patients were asked to complete a survey about decisional conflict (DCS),
preferred treatment, gained knowledge, physical function (KOOS/HOOS), pain
(NRS), anxiety (PASS-20) quality of life (EQ-5D) and satisfaction.9-14 The long-term
follow-up was carried out after 26 weeks and evaluated the HOOS/KOOS, PASS,
EQ-5D, satisfaction and the preferred treatment.
The results showed that decision aids help to inform patients about their
options based on current best evidence and helps patients understand their
own preferences and values by reducing decisional conflict. Future studies on
greater scale are warranted to assess if decision aids improve health outcome
by encouraging patients to take a more active role in their recovery and reduce
variation.
Some surgeons contend that patients are less capable of participating
in decisions about traumatic conditions than non-traumatic conditions. We
tested in chapter 8 whether patients with non-traumatic conditions have
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different preferences for shared decision-making when compared with those
who sustained acute trauma. We also tested whether disability, symptoms
of depression, and self-efficacy correlate with preference for shared decision-
making?
In this prospective, comparative trial, we evaluated a total of 133
patients presenting to the outpatient practices of two university-based hand
surgeons with traumatic or non-traumatic hand and upper extremity illnesses
or conditions. Each patient completed questionnaires measuring their preferred
role in healthcare decision-making (Control Preferences Scale [CPS])15, symptoms
of depression (Patients’ Health Questionnaire)12,16, and pain self-efficacy
(confidence that one can achieve one’s goals despite pain; measured using the
Pain Self-efficacy Questionnaire)17. Patients also completed a short version of the
Disabilities of the Arm, Shoulder, and Hand questionnaire and an ordinal rating
of pain intensity18.
The results showed that there was no difference in decision-making
preferences between patients with traumatic and non-traumatic conditions
with most patients preferring shared decision-making. More educated patients
preferred a more active role in decision-making; however, differences in levels of
disability, pain and function, depression, and pain-related self-efficacy were not
associated with differences in patients’ preferences in terms of shared decision-
making.
Patients who sustained trauma have on average the same preference for
shared decision-making compared with patients who sustained no trauma.
Conclusions and future directions
This thesis identified several opportunities for increasing patient involvement
in decision-making and satisfaction, reducing decision conflict, and limiting
surgeon-to-surgeon variations in care, all of which merit additional study.
First, objective testing should have more influence than symptoms, mindset,
diagnosis, circumstances, and expectations on surgeon recommendations.
Second, surgery is best used to address verifiable objective pathophysiology
where current best evidence confirms that the benefits of surgery outweigh
the harms. The measured influence of patient circumstances on surgeon
recommendations demonstrates how surgeon biases can have inordinate
influence. Third, the evidence that surgeons fall back to their comfort zone,
independent of factors related to the patient’s perspective suggests that if
surgeons were more comfortable with discomfort, they might be more likely
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to acquiesce to patient preferences in areas with no clear best choice. Fourth,
focus on establishing appropriate pre-visit expectations by providing evidenced-
based information in an understandable and meaningful form (e.g. decision
aids) before the visit. Finally, where current best evidence allows room for
debate, patients decision aids might also help limit decision conflict, support
the conversation about the available options and improve health outcomes by
encouraging patients to take a more active role in their care. It might even limit
surgeon-to-surgeon variations, increase safety, efficiency, and resourcefulness.
While the findings of this thesis suggest that decision aids might be
effective in supporting orthopaedic decision-making from the surgeon and
patients perspectives, further implementation studies are needed to determine
how to incorporate decision aids in care pathways on greater scale and to test
the influence on practice variance and health outcomes.
REFERENCES:
1. Frymoyer JW. Degenerative Spondylolisthesis: Diagnosis and Treatment. J Am Acad Orthop
Surg 1994;2:9-15.2. Duszak R, Jr., Behrman SW. National trends in percutaneous cholecystostomy between 1994
and 2009: perspectives from Medicare provider claims. J Am Coll Radiol 2012;9:474-9.3. Fanuele J, Koval KJ, Lurie J, Zhou W, Tosteson A, Ring D. Distal radial fracture treatment:
what you get may depend on your age and address. The Journal of bone and joint surgery
American volume 2009;91:1313-9.4. Bruinsma WE, Guitton TG, Warner JJ, Ring D, Science of Variation G. Interobserver reliability of
classification and characterization of proximal humeral fractures: a comparison of two and
three-dimensional CT. J Bone Joint Surg Am 2013;95:1600-4.5. Foroohar A, Tosti R, Richmond JM, Gaughan JP, Ilyas AM. Classification and treatment of
proximal humerus fractures: inter-observer reliability and agreement across imaging
modalities and experience. Journal of orthopaedic surgery and research 2011;6:38.6. Thomas G, Pring R. Evidence-Based Practise in Education. Youblishercom 2004.7. O’Connor AM S, D, & Jacobsen MJ. Ottawa Decision Support Tutorial (ODST): Improving
Practitioners’ Decision Support Skills Ottawa Hospital Research Institute: Patient Decision
Aids, 2011. Web. 2011 Nov 30.8. Stacey D, Bennett CL, Barry MJ, et al. Decision aids for people facing health treatment or
screening decisions. Cochrane Database Syst Rev 2011:CD001431.9. Aaronson NK, Muller M, Cohen PD, et al. Translation, validation, and norming of the Dutch
language version of the SF-36 Health Survey in community and chronic disease populations.
J Clin Epidemiol 1998;51:1055-68.
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10. de Groot IB, Favejee MM, Reijman M, Verhaar JA, Terwee CB. The Dutch version of the Knee
Injury and Osteoarthritis Outcome Score: a validation study. Health and quality of life
outcomes 2008;6:16.11. EuroQol G. EuroQol--a new facility for the measurement of health-related quality of life.
Health policy 1990;16:199-208.12. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.
J Gen Intern Med 2001;16:606-13.13. McCracken LM, Dhingra L. A short version of the Pain Anxiety Symptoms Scale (PASS-20):
preliminary development and validity. Pain research & management : the journal of the
Canadian Pain Society = journal de la societe canadienne pour le traitement de la douleur
2002;7:45-50.14. van Oldenrijk J, Sierevelt IN, Haverkamp D, Harmse IW, Poolman RW. Re: Validation of the
Dutch version of the Hip disability and Osteoarthritis Outcome Score (HOOS). Osteoarthritis
Cartilage 2009;17:133-4.15. Degner LF, Sloan JA, Venkatesh P. The Control Preferences Scale. The Canadian journal of
nursing research = Revue canadienne de recherche en sciences infirmieres 1997;29:21-43.16. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item
depression screener. Medical care 2003;41:1284-92.17. Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur J Pain
2007;11:153-63.18. Gummesson C, Ward MM, Atroshi I. The shortened disabilities of the arm, shoulder and hand
questionnaire (QuickDASH): validity and reliability based on responses within the full-length
DASH. BMC musculoskeletal disorders 2006;7:44.
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CHAPTER 10
Dutch summary and discussion
Nederlandse samenvatting en
discussie
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SAMENVATTING EN DISCUSSIE
Er bestaat grote praktijkvariatie in de frequentie en het soort operatieve en niet-
operatieve behandelingen die niet kan worden verklaard door demografische
gegevens, pathofysiologie of co-morbiditeit.1-3 Hoewel er altijd enige variatie
in medische behandelingen bestaat, is die van chirurg tot chirurg moeilijker
te rechtvaardigen dan die van patiënt tot patiënt. De doelstelling van dit
proefschrift was om diverse aspecten van besluitvorming te adresseren vanuit
zowel het perspectief van de patiënt als dat van de chirurg. Het gaat om
de factoren die verband houden met de variatie in aanbevelingen voor een
operatieve of niet-operatieve behandeling, met de vraag hoe zorgverleners voor
een behandeloptie beslissen als de bewijslast vanuit de literatuur ontoereikend
is, met de prioriteiten en voorkeuren die patiënten en chirurgen hebben
wanneer zij een besluit moeten nemen, met de relatie tussen de verwachtingen
omtrent de zorg en de mate van tevredenheid over de zorg die is ontvangen, met
het effect van keuzehulpen en met de voorkeuren van patiënten ten aanzien van
samen beslissen, in relatie tot psychologische factoren en de diagnose.
Om zicht te krijgen op de factoren die leiden tot een grote, onverklaarde
variatie tussen handchirurgen in hun aanbevelingen over de behandeling van
perifere neuropathie, hebben we in hoofdstuk 2 de nulhypothese getest dat
bepaalde patiënt- en chirurgafhankelijke variabelen geen invloed hebben op die
aanbevelingen.
Door gebruik te maken van een online-vragenlijst gaven handchirurgen
een operatief of niet-operatief behandeladvies in twee verschillende scenario’s.
In het eerste werden zes elementen (demografische variabelen, symptomen,
coping, diagnose, aanvullende diagnostiek en verwachtingen) willekeurig
gecombineerd en toegewezen aan de ondervraagden. In het tweede scenario
werden twee vooraf gedefinieerde scenario’s willekeurig toegewezen aan
een beoordelaar. Door middel van een voorspellend rekenmodel (multivariate
regressie) werd getracht factoren te identificeren die een relatie hadden met
het advies om te opereren. In totaal vulden 186 handchirurgen, betrokken bij de
Science of Variation Group, de vragenlijsten over de verschillende scenario’s in.
De resultaten lieten zien dat adviezen voor een operatieve behandeling
niet significant verschilden tussen de ondervraagde chirurgen. Operatieve
behandeling werd in scenario 1 eerder geadviseerd bij patiënten die zelfstandig
werkten en doorwerkten en bij wie sprake was van objectieve elektro-
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diagnostische afwijkingen. In de twee opties van scenario 2 hing een advies om
te opereren samen elektro-diagnostische afwijkingen.
De bevindingen van deze studies suggereren dat – althans in deze
testsetting – chirurgen liever een operatief behandeladvies geven aan patiënten
met afwijkende elektro-diagnostische resultaten, vooral die patiënten met
een sterke coping-strategie. Dit was een onverwachte bevinding. Wanneer
chirurgen bij voorkeur op basis van betrouwbare en gevalideerde testuitkomsten
aanbevelingen zouden doen, zou er een beperkter verschil in behandeladviezen
moeten zijn.
Ook de aanbevelingen voor de behandeling van proximale humerus
fracturen verschillen sterk.4,5 Recente studies tonen behoorlijke inconsistenties
tussen chirurgen daarin aan. We waren benieuwd naar de invloed van
informatie over de patiënt en de kenmerken van chirurgen op de besluitvorming
rond deze aandoening. In hoofdstuk 3 gingen we uit van de hypothese dat er
geen significant verschil zou worden gevonden tussen de behandeladviezen van
chirurgen die uitsluitend een röntgenfoto van de proximale humerus zouden
krijgen, versus chirurgen die naast de röntgenfoto ook informatie over de
patiënt zouden ontvangen.
Door middel van een online-vragenlijst beoordeelden 238 chirurgen
40 patiënten met een proximale humerus fractuur op basis van een röntgenfoto.
Door willekeurige selectie kregen zij hier wel of geen aanvullende informatie
bij over de patiënt en de toedracht van het ongeval. De uitkomstvariabelen
betroffen het behandeladvies (operatief versus niet-operatief) en het percentage
waarin dat behandeladvies overeenkwam met de uiteindelijke behandeling.
Chirurgen die aanvullende informatie over de patiënt ontvingen,
adviseerden minder vaak een operatie dan de chirurgen die alleen de
röntgenfoto kregen. De patiëntgegevens die de meeste invloed op het
behandeladvies hadden, waren leeftijd (55%) en fractuurmechanisme (32%).
De enige andere factor van invloed was de regio waar de chirurg werkzaam
was. Wat betreft de overeenkomst tussen behandeladvies en uiteindelijke
behandeling was er geen significant verschil tussen chirurgen die wel en
chirurgen die geen aanvullende informatie hadden ontvangen.
Het behandeladvies voor een proximale humerus fractuur wordt
beïnvloed door patiëntinformatie, leeftijd in het bijzonder, maar het grootste
deel van de praktijkvariatie blijft onverklaard. Patiëntinformatie leidt niet tot
een grotere mate van overeenstemming over het behandeladvies tussen de
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ondervraagden onderling, noch tussen de ondervraagden en de uiteindelijke
behandeling.
Omdat evidence based medicine de optelsom is van de individuele
expertise van de arts en het best beschikbare bewijs rijst de vraag hoe
zorgverleners beslissen wanneer dat best beschikbare bewijs ontoereikend is.
Hoofdstuk 4 toetst de nulhypothese dat de factoren die van invloed zijn op de
besluitvorming, niet variëren naar gelang training, demografische gegevens of
specialisme.
In deze studie beoordeelden in totaal 337 chirurgen het belang van zeven
factoren wanneer ze moesten kiezen tussen een behandeling en de ziekte op
zijn beloop laten, en twaalf factoren wanneer ze moesten kiezen tussen twee
operatieve behandelingen. Ze gebruikten daarvoor een 5-punts Likert-scale,
variërend van “zeer onbelangrijk” tot “zeer belangrijk”.
De resultaten laten zien dat de factoren die het meest van invloed
waren op de beslissing van de chirurg, te maken hadden met zijn individuele
perspectief (bijvoorbeeld: “wat werkt in mijn handen”, “bekendheid met de
behandeling”, “wat mijn mentor mij heeft geleerd”) in plaats van met de
perspectieven van de patiënt (bijvoorbeeld: “iets doen versus niets doen”,
“de patiënt vraagt om de behandeling”). Uitzonderingen waren “minder
complicaties” en “sneller herstel”. Financiële compensatie werd ook als
relatief onbelangrijk beoordeeld, met name in Europa, maar ook daarbuiten.
De antwoorden op de openvragen toonden aan dat chirurgen bij voorkeur
terugvallen op de “best beschikbare bewijslast uit de literatuur”, zelfs als die
bewijslast in het scenario als ontoereikend was beschreven. “Patient centered
care” en “gezamenlijke besluitvorming” werden ook genoemd.
Dat zorgverleners terugvallen op hun persoonlijke voorkeur, gebaseerd
op hun ervaring, is geen verrassing.6 Vooral omdat ook patiënten naar die
ervaring kijken wanneer de bewijslast ontoereikend is. Toch kunnen patiënten,
juist als die bewijslast ruimte biedt voor discussie, baat hebben bij een beter
begrip van alle opties en van de bron voor de discussie. Patiënten bij de
besluitvorming betrekken is met name in die situaties belangrijk. Dan kan
het immers zijn dat zij de potentiële behandeluitkomsten verschillend zullen
waarderen.
De studie beschreven in hoofdstuk 5 beoordeelt de waarden
en voorkeuren van patiënten en handchirurgen met betrekking tot de
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besluitvorming over de behandeling van carpaal tunnel syndroom (CTS). We
testten de nulhypothese dat er geen verschil is tussen de waarden en voorkeuren
van de patiënten en die van de handchirurgen.
103 handchirurgen, betrokken bij de Science of Variation Group, en 79
patiënten met CTS vulden een vragenlijst in over hun waarden en voorkeuren
bij de besluitvorming over de behandeling. De vragenlijst was gebaseerd op het
Ottawa Decision Support Framework voor de ontwikkeling van keuzehulpen.7
Belangrijke gebieden waarop patiënten en chirurgen van mening
verschilden, betroffen de voorkeur voor pijnloze, niet-operatieve behandeling en
de bevestiging van de diagnose door middel van elektro-diagnostische testen.
Patiënten vonden het grootste nadeel van de niet-operatieve behandeling
dat de uitkomst waarschijnlijk van tijdelijk aard was en mogelijk slechts
palliatief. Over het algemeen gaven patiënten aan de leiding te willen hebben
bij het nemen van de beslissing. De handchirurgen gaven de voorkeur aan
gezamenlijke besluitvorming. Patiënten en handchirurgen waren het eens over
de waarde van de ondersteuning van familie en andere zorgverleners bij het
besluitvormingsproces.
Er werden wel enkele verschillen waargenomen tussen de voorkeuren
van patiënten en handchirurgen met betrekking tot het besluitvormingsproces.
Die gingen vooral over de risico’s en voordelen van diagnostische en
therapeutische procedures.
Klinisch relevante informatie, gebaseerd op de meest recente bewijzen
uit de literatuur, zou patiënten kunnen helpen om hun voorkeuren en waarden
beter te begrijpen, keuzestress te verminderen, praktijkvariatie tussen chirurgen
te beperken en gezondheidsuitkomsten te vergroten. Onze studie identificeerde
enkele gebieden waarover patiënten meer informatie wensten: specifieke risico’s
en voor- en nadelen van de verschillende diagnostische en therapeutische opties,
het advies van de zorgverlener en zijn hulp bij de waarde-exploratie.
De studie beschreven in hoofdstuk 6 beoordeelde het verband
tussen de verwachtingen van de patiënt voorafgaand aan het consult, de
patiënttevredenheid en de categorieën binnen de verwachtingen die met de
tevredenheid samenhingen.
86 nieuwe patiënten, waarvan 70% direct door de huisarts naar de
handchirurg waren verwezen, en de meeste met een niet-spoedeisende
zorgvraag, deelden hun verwachting voorafgaand aan het consult (Patient
intention Questionnaire [PIQ]). Direct erna gaven dezelfde patiënten hun
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mening over de vraag in hoeverre aan hun verwachtingen was voldaan
(Expectation Met Questionnaire [EMQ]) en de mate van hun tevredenheid
over het consult (Medical Interview Satisfaction Scale). Door middel van een
multivariate regressie-analyse hebben we vervolgens bepaald in hoeverre de
PIQ- en EMQ-scores verband houden met de mate van tevredenheid.
De resultaten toonden aan dat er wel een relatie is tussen de
tevredenheid en de mate waarin is voldaan aan de verwachtingen, maar niet
met de verwachtingen die patiënten voorafgaand aan het consult hadden.
We hebben vijf categorieën verwachtingen voorafgaand aan een consult
geïdentificeerd. Die verklaarden 50% van de variatie in de PIQ: (1) “Informatie en
uitleg”; (2) “Emotie en begrip”; (3) “Emotionele problemen”; (4) “Diagnostiek”,
en (5) “Comfort”. De enige categorie van ‘beantwoorde verwachtingen’ die
correleerde aan de mate van tevredenheid, was “Informatie en uitleg”.
De mate waarin verwachtingen zijn beantwoord is dus gerelateerd
aan de mate van tevredenheid. Vooral patiënten van wie de verwachtingen
over “Informatie en uitleg” werden beantwoord, waren meer tevreden met het
consult. Om te bepalen wat de meest effectieve methode is om informatie over
te brengen die de patiënt niet verwacht, is meer onderzoek nodig.
Eerdere studies hebben aangetoond dat keuzehulpen tegenstellingen
in de spreekkamer kunnen normaliseren en minder persoonlijk kunnen maken.
Ze geven patiënten informatie waar zij op hun eigen manier en in hun eigen
tempo over na kunnen denken. Bovendien rusten keuzehulpen patiënten toe
om een beslissing te nemen die gebaseerd is op een beter begrip van hun ziekte
of aandoening. In hoofdstuk 7 stelden we de hypothese dat er geen verschil in
keuzestress is tussen patiënten met heup- of knieslijtage die de ondersteuning
van een keuzehulp kregen en patiënten die dat niet kregen. Onze tweede
hypothese was dat er geen verschil is tussen deze twee groepen met betrekking
tot angst, kennis, tevredenheid, de behandeling die de voorkeur kreeg bij
inschrijving, de fysieke functie en kwaliteit van leven op de langere termijn
(26 weken nadat het besluit over de behandeling was genomen).
In deze gerandomiseerde onderzoeken met controlegroepen, die in
diverse instellingen werden gehouden, werden alleen patiënten met heup- of
knieslijtage betrokken die niet al eens met deze klacht bij een orthopedisch
chirurg waren geweest. Gedurende het eerste consult kregen de patiënten uit
de controlegroep de standaardzorg. De patiënten uit de interventiegroep kregen
de beschikking over een keuzehulp. Direct na het consult en nog eens na
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26 weken werden de mate van keuzestress (DCS), angst (PASS-20), kennis, fysieke
functie (HOOS/KOOS), pijn (NRS), tevredenheid en kwaliteit van leven (EQ-5D)
gemeten.8-13
De resultaten toonde aan dat keuzehulpen patiënten helpen hun diagnose
en behandelopties te begrijpen, aan te geven wat zij belangrijk vinden en
keuzestress te verminderen. Toekomstig onderzoek op grote schaal is nodig om te
beoordelen wat de invloeden zijn van keuzehulpen op gezondheidsuitkomsten, de
betrokkenheid van patiënten gedurende hun herstel en praktijkvariatie.
Sommige zorgverleners zijn van mening dat patiënten met een
spoedeisende hulpvraag minder in staat zijn deel te nemen aan gezamenlijke
besluitvorming dan patiënten met een niet-spoedeisende hulpvraag. In
hoofdstuk 8 hebben we getoetst of deze patiënten een andere voorkeur hebben
voor gezamenlijke besluitvorming dan patiënten met een niet-spoedeisende
zorgvraag.
Aan deze prospectieve, cohortvergelijkende studie hebben 133 patiënten
met een spoedeisende- of niet-spoedeisende aandoening aan de bovenste
extremiteit deelgenomen. Elke patiënt vulde een vragenlijst in over zijn
voorkeursrol in het gezamenlijke besluitvormingsproces (controle preferences
scale [CPS])14, symptomen van depressiviteit (Patients’ Health Questionnaire)11,15
en over zijn coping-strategie (Pain self-efficacy)16. Patiënten vulden ook een
vragenlijst in over hun fysieke functie (Disability Arm, Shoulder and Hand
Questionnaire) en de mate van pijn die zij ervoeren17.
De resultaten toonden aan dat er geen verschil was in de mate van
voorkeur tussen patiënten die een spoedeisende- of niet-spoedeisende
zorgvraag hadden. Patiënten met een hogere opleiding gaven meer dan
lageropgeleide patiënten de voorkeur aan een actievere rol. Hun fysieke functie,
depressiviteit en coping-strategie vertoonden geen relatie met verschillende
voorkeursrollen in het besluitvormingsproces.
De resultaten laten zien dat patiënten met een spoedeisende
zorgvraag een vergelijkbare hoge mate van voorkeur voor een actieve rol in
het besluitvormingsproces hebben als patiënten met een niet-spoedeisende
zorgvraag. Hogeropgeleide patiënten gaven de voorkeur aan een actievere rol
in de besluitvorming, maar er was geen verband met verschillen in pijn en
functioneren, depressie en coping-strategie. Patiënten met een spoedeisende
zorgvraag hebben dus over het algemeen dezelfde voorkeur voor gezamenlijke
besluitvorming als patiënten met een niet-spoedeisende zorgvraag.
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Conclusie mogelijk toekomstig onderzoek
Dit proefschrift beschrijft diverse mogelijkheden om de betrokkenheid van de
patiënt in het besluitvormingsproces over zijn behandeling te vergroten, de
tevredenheid over de geleverde zorg te verhogen, keuzestress te verminderen en
praktijkvariatie te beperken. Die verdienen allemaal aanvullende studie.
Ten eerste concluderen we dat objectieve klinische testen, van grotere
invloed zouden moeten zijn op de aanbevelingen van de zorgverlener dan
symptomen, mindset, diagnoses, omstandigheden en verwachtingen van de
patiënt.
Ten tweede, De gemeten invloed van patiëntgebonden factoren op het
advies van chirurgen toont aan dat het vooroordeel van de chirurg buitensporige
invloed kan hebben.
Ten derde, het gegeven dat chirurgen terugvallen op hun comfort zone,
ongeacht factoren gerelateerd aan het perspectief van de patiënt, suggereert
dat als chirurgen minder moeite zouden hebben om uit hun comfort zone te
treden, zij bij gebrek aan bewijs voor een optimale behandeling de voorkeur van
patiënten vaker zouden honoreren.
Ten vierde, reële verwachtingen over de uitkomst van een behandeling
kunnen worden bevorderd door patiënten evidence-based informatie in
een duidelijke en betekenisvolle vorm te geven (bijvoorbeeld in de vorm van
keuzehulpen), en door begeleiding.
Tot slot, als er sprake is van een gebrek aan bewijs en als uitkomsten
van behandelingen door patiënten verschillend kunnen worden gewaardeerd,
dan kunnen keuzehulpen keuzestress verminderen, het besluitvormingsproces
bevorderen, totdat één van de behandelopties de voorkeur heeft, de kwaliteit van
zorg bevorderen doordat patiënten een actievere rol vervullen in hun herstel en
de mate van tevredenheid bevordert.
Door patiënten actief te laten participeren in het besluitvormingsproces
kan ongewenste praktijkvariatie tussen chirurgen worden teruggebracht en
kunnen de veiligheid, efficiëntie en duurzaamheid van de zorg worden bevorderd.
Ondanks dat de bevindingen van dit proefschrift suggereren dat
keuzehulpen effectief kunnen zijn in de orthopedische praktijk, is toekomstig
onderzoek op grote schaal nodig om te beoordelen wat de invloeden zijn van
keuzehulpen op gezondheidsuitkomsten, de betrokkenheid van patiënten en
praktijkvariatie.
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and 2009: perspectives from Medicare provider claims. J Am Coll Radiol 2012;9:474-9.3. Fanuele J, Koval KJ, Lurie J, Zhou W, Tosteson A, Ring D. Distal radial fracture treatment:
what you get may depend on your age and address. The Journal of bone and joint surgery
American volume 2009;91:1313-9.4. Bruinsma WE, Guitton TG, Warner JJ, Ring D, Science of Variation G. Interobserver reliability of
classification and characterization of proximal humeral fractures: a comparison of two and
three-dimensional CT. J Bone Joint Surg Am 2013;95:1600-4.5. Foroohar A, Tosti R, Richmond JM, Gaughan JP, Ilyas AM. Classification and treatment of
proximal humerus fractures: inter-observer reliability and agreement across imaging
modalities and experience. Journal of orthopaedic surgery and research 2011;6:38.6. Thomas G, Pring R. Evidence-Based Practise in Education. Youblishercom 2004.7. O’Connor AM S, D, & Jacobsen MJ. Ottawa Decision Support Tutorial (ODST): Improving
Practitioners’ Decision Support Skills Ottawa Hospital Research Institute: Patient Decision
Aids, 2011. Web. 2011 Nov 30.8. Aaronson NK, Muller M, Cohen PD, et al. Translation, validation, and norming of the Dutch
language version of the SF-36 Health Survey in community and chronic disease populations.
J Clin Epidemiol 1998;51:1055-68.9. de Groot IB, Favejee MM, Reijman M, Verhaar JA, Terwee CB. The Dutch version of the Knee
Injury and Osteoarthritis Outcome Score: a validation study. Health and quality of life
outcomes 2008;6:16.10. EuroQol G. EuroQol--a new facility for the measurement of health-related quality of life.
Health policy 1990;16:199-208.11. Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item
depression screener. Medical care 2003;41:1284-92.12. McCracken LM, Dhingra L. A short version of the Pain Anxiety Symptoms Scale (PASS-20):
preliminary development and validity. Pain research & management : the journal of the
Canadian Pain Society = journal de la societe canadienne pour le traitement de la douleur
2002;7:45-50.13. van Oldenrijk J, Sierevelt IN, Haverkamp D, Harmse IW, Poolman RW. Re: Validation of the
Dutch version of the Hip disability and Osteoarthritis Outcome Score (HOOS). Osteoarthritis
Cartilage 2009;17:133-4.14. Degner LF, Sloan JA, Venkatesh P. The Control Preferences Scale. The Canadian journal of
nursing research = Revue canadienne de recherche en sciences infirmieres 1997;29:21-43.15. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure.
J Gen Intern Med 2001;16:606-13.16. Nicholas MK. The pain self-efficacy questionnaire: Taking pain into account. Eur J Pain
2007;11:153-63.17. Gummesson C, Ward MM, Atroshi I. The shortened disabilities of the arm, shoulder and hand
questionnaire (QuickDASH): validity and reliability based on responses within the full-length
DASH. BMC musculoskeletal disorders 2006;7:44.
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PhD-portfolio
Name PhD-student: Michiel G.J.S. Hageman
PhD-period: November 2011 - July 2014
Name PhD-supervisor: Prof. dr. D. Ring, Prof. dr. C.N. van Dijk
1 PhD-training
General coursesThe Principles and Practice of Clinical Research Data
management (MGH). Ethics and Clinical Research Protocols (MGH).IRB and QI Roundtable Series: Consent Form Writing (MGH).How to Give a Presentation (MGH).What does the IRB Really Want? How to Write Human
Studies Protocol (MGH).Specific coursesBasic Biostatistics to Clinical Research (MGH/HMS).Introduction to Clinical Investigation Training Course (HMS).Design and Implementation of Clinical Trials (HMS).Design and Conduct of Clinical Trials (MGH/HMS).Certificate in Applied Biostatistics: (HMS).Applied Biostatistics for Clinical Trials (MGH/HMS).An Introduction to the Enhanced RPDR Query Tool (MGH).Seminars, workshops and master classesClinical Research 101.Orientation Program: Clinical research at MGH.Dr. Baratz visiting MGH.Dr. Szabo visiting MGH.Dr. Morrey visiting MGH.PROMIS/Assessment Center Course.Redcap Course.Podium presentations/ international conferencesNVT: Proximal Humeral Fractures: Operative versus
Conservative treatment. M. Hageman, D. Meijer, S. Stufkens, J. Ultee, J. Doornberg, E. Steller.
Smith Day: Variation in recommendations for operative treatment for compressive neuropathy. M. Hageman, S. Becker, A. Bot, T. Guitton, D. Ring.
Year
2011
2011201220122012
2011201120122012201220132013
2011201120122012201220122012
2012
2013
2013
Workload(Hours/ECTS)
0.1
0.20.10.10.1
112225
0.5
0.20.20.20.20.10.10.1
1
1
1
continue >
131
Smith Day: Spectrum and Trends in Complaints to the Patient Advocate. P. van Dijk, M. Hageman, J. King, C. Overbeek, D. Ring.
NEHS: How surgeons make decisions when the evidence is inconclusive.
Hageman MG, Guitton TG, Ring D; Science of Variation Group.Harvard Orthopaedic Trauma day: Predictors of Readmission
within 30 days of Orthopaedic Surgery. M. Hageman, T. Voskuyl, J. Bossen, J. Blauth, M. Smith, D. Ring.
AAHS: How surgeons make decisions when the evidence is inconclusive. Naples Florida, USA. Hageman MG, Guitton TG, Ring D; Science of Variation Group.
NOV: The effect of decion aids on patient with hip or knee osteoarthritis. M. Hageman, R. Poolman, J. Du Long, T. Vervest, D. Haverkamp.
Poster presentationASSH: Internet Self-diagnosis in Hand Surgery. M. Hageman,
J. Anderson, R. Blok, J. Bossen, D. Ring.
Year
2013
2013
2013
20132013
2016
2013
Workload(Hours/ECTS)
1
1
1
11
1
1
continued
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2 Teaching
3 Parameters of Esteem
Tutoring, mentoring and supervisingJillian Gruber John King Pim van Dijk Jeroen Bossen Charlotte HoogstinsChristiaan SwellengrebelJan Paul BriëtSjoerd NotaMariano MenendezJade AndersonAhmet KinaciMark van SuchtelenStijn BekkersRobin BlokDennis MakarawungTimothy VoskuijlThijs OosterhoffSilke SpitEmily Thornton Joost StrookerAnne-Carolin DöringCeleste OverbeekPrakash JayakumarNick WickramasingheJos MellemaStein JanssenTeun TeunisDirk ter MeulenSaroj GolayRajesh ReddyJsamijn du LongAlexander RakicDick Vuijk
GrantsStichting Anna fonds| NOREFStichting Marti Keuning Eckhardt fondsStichting Achmea Gezondheidszorg
Year
201220122012201220122012201220122013201320132012201220122012201220122012201320132013201320132013201320132013201320132013201420142014
Year
201120112013
Workload
0.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.50.5
133
List of Publications
1. Barber LA, Hageman MG, King JD, Bekkers S, Bot AG, Ring D. The influence of patients’
participation in research on their satisfaction. J Hand Surg Am. 2014;39:1591-1594 e1593.
2. Becker SJ, Briet JP, Hageman MG, Ring D. Death, Taxes, and Trapeziometacarpal Arthrosis. Clin
Orthop Relat Res. 2013.
3. Beulen L, van den Berg M, Faas BH, Feenstra I, Hageman M, van Vugt JM, Bekker MN. The effect
of a decision aid on informed decision-making in the era of non-invasive prenatal testing: a
randomised controlled trial. Eur J Hum Genet. 2016.
4. Bossen JK, Hageman MG, King JD, Ring DC. Does Rewording MRI Reports Improve Patient
Understanding and Emotional Response to a Clinical Report? Clin Orthop Relat Res. 2013.
5. Briet JP, Bot AG, Hageman MG, Menendez ME, Mudgal CS, Ring DC. The Pain Self-Efficacy
Questionnaire, Validation of an Abbreviated Two-Item Questionnaire. Psychosomatics. 2014.
6. Briet JP, Hageman MG, Blok R, Ring D. When do patients with hand illness seek online health
consultations and what do they ask? Clin Orthop Relat Res. 2014;472:1246-1250.
7. Briet JP, Hageman MG, Overbeek CL, Mudgal C, Ring DC, Vranceanu AM. Factors Associated
With Met Expectations in Patients With Hand and Upper Extremity Disorders: A Pilot Study.
Psychosomatics. 2016;57:401-408.
8. Bruinsma W, Kodde I, de Muinck Keizer RJ, Kloen P, Lindenhovius AL, Vroemen JP, Haverlag
R, van den Bekerom MP, Bolhuis HW, Bullens PH, Meylaerts SA, van der Zwaal P, Steller
PE, Hageman M, Ring DC, den Hartog D, Hammacher ER, King GJ, Athwal GS, Faber KJ,
Drosdowech D, Grewal R, Goslings JC, Schep NW, Eygendaal D. A randomized controlled trial
of nonoperative treatment versus open reduction and internal fixation for stable, displaced,
partial articular fractures of the radial head: the RAMBO trial. BMC Musculoskelet Disord.
2014;15:147.
9. Doring AC, Nota SP, Hageman MG, Ring DC. Measurement of upper extremity disability
using the Patient-Reported Outcomes Measurement Information System. J Hand Surg Am.
2014;39:1160-1165.
10. du Long J, Hageman M, Vuijk D, Rakic A, Haverkamp D. Facing the decision about the
treatment of hip or knee osteoarthritis: What are patients’ needs? Knee Surg Sports
Traumatol Arthrosc. 2016;24:1710-1716.
11. Finger A, Teunis T, Hageman MG, Thornton ER, Neuhaus V, Ring D. Do patients prefer optional
follow-up for simple upper extremity fractures: A pilot study. Injury. 2016.
12. Gruber JS, Hageman M, Neuhaus V, Mudgal CS, Jupiter JB, Ring D. Patient activation and
disability in upper extremity illness. J Hand Surg Am. 2014;39:1378-1383 e1373.
13. Hageman MG, Anderson J, Blok R, Bossen JK, Ring D. Internet self-diagnosis in hand surgery.
Hand (N Y). 2015;10:565-569.
14. Hageman MG, Bossen JK, King JD, Ring D. Surgeon confidence in an outpatient setting. Hand
(N Y). 2013;8:430-433.
15. Hageman MG, Bossen JK, Neuhaus V, Mudgal CS, Ring D, Science of Variation G. Assessment of
Decisional Conflict about the Treatment of carpal tunnel syndrome, Comparing Patients and
Physicians. Arch Bone Jt Surg. 2016;4:150-155.
16. Hageman MG, Bossen JK, Smith RM, Ring D. Predictors of readmission in orthopaedic trauma
surgery. J Orthop Trauma. 2014;28:e247-249.
17. Hageman MG, Briet JP, Bossen JK, Blok RD, Ring DC, Vranceanu AM. Do Previsit Expectations
Correlate With Satisfaction of New Patients Presenting for Evaluation With an Orthopaedic
Surgical Practice? Clin Orthop Relat Res. 2014.
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18. Hageman MG, Briet JP, Oosterhoff TC, Bot AG, Ring D, Vranceanu AM. The Correlation of
Cognitive Flexibility with Pain Intensity and Magnitude of Disability in Upper Extremity
Illness. Journal of hand and microsurgery. 2014;6:59-64.
19. Hageman MG, Jayakumar P, King JD, Guitton TG, Doornberg JN, Ring D, Science of Variation
G. The factors influencing the decision making of operative treatment for proximal humeral
fractures. J Shoulder Elbow Surg. 2014.
20. Hageman MG, Reddy R, Makarawung DJ, Briet JP, van Dijk CN, Ring D. Do Upper Extremity
Trauma Patients Have Different Preferences for Shared Decision-making Than Patients With
Nontraumatic Conditions? Clin Orthop Relat Res. 2015.
21. Hageman MG, Ring DC, Gregory PJ, Rubash HE, Harmon L. Do 360-degree Feedback Survey
Results Relate to Patient Satisfaction Measures? Clin Orthop Relat Res. 2014.
22. Janssen SJ, Ter Meulen DP, Hageman MG, Earp BE, Ring D. Quantitative 3-dimensional CT
analyses of fractures of the middle phalanx base. Hand (N Y). 2015;10:210-214.
23. Janssen SJ, Ter Meulen DP, Nota SP, Hageman MG, Ring D. Does verbal and nonverbal
communication of pain correlate with disability? Psychosomatics. 2015;56:338-344.
24. Janssen SJ, Teunis T, ter Meulen DP, Hageman MG, Ring D. Estimation of base of middle
phalanx size using anatomical landmarks. J Hand Surg Am. 2014;39:1544-1548.
25. Kortlever JT, Janssen SJ, Molleman J, Hageman MG, Ring D. Discrete Pathophysiology is
Uncommon in Patients with Nonspecific Arm Pain. Arch Bone Jt Surg. 2016;4:213-219.
26. Mellema JJ, O’Connor CM, Overbeek CL, Hageman MG, Ring D. The effect of feedback
regarding coping strategies and illness behavior on hand surgery patient satisfaction and
communication: a randomized controlled trial. Hand (N Y). 2015;10:503-511.
27. Menendez ME, Bot AG, Hageman MG, Neuhaus V, Mudgal CS, Ring D. Computerized adaptive
testing of psychological factors: relation to upper-extremity disability. J Bone Joint Surg Am.
2013;95:e149.
28. Neuhaus V, King J, Hageman MG, Ring DC. Charlson comorbidity indices and in-hospital
deaths in patients with hip fractures. Clin Orthop Relat Res. 2013;471:1712-1719.
29. Nota SP, Spit SA, Oosterhoff TC, Hageman MG, Ring DC, Vranceanu AM. Is Social Support
Associated With Upper Extremity Disability? Clin Orthop Relat Res. 2016;474:1830-1836.
30. Nota SP, Spit SA, Voskuyl T, Bot AG, Hageman MG, Ring D. Opioid Use, Satisfaction, and Pain
Intensity After Orthopaedic Surgery. Psychosomatics. 2015;56:479-485.
31. Overbeek CL, Nota SP, Jayakumar P, Hageman MG, Ring D. The PROMIS Physical Function
Correlates With the QuickDASH in Patients With Upper Extremity Illness. Clin Orthop Relat
Res. 2014.
32. Strooker JA, Nota SP, Hageman MG, Ring DC. Patients With Greater Symptom Intensity and
More Disability are More Likely to be Surprised by a Hand Surgeon’s Advice. Clin Orthop Relat
Res. 2014.
33. Ten Have IA, van den Bekerom MP, van Deurzen DF, Hageman MG. Role of decision aids in
orthopaedic surgery. World J Orthop. 2015;6:864-866.
34. ter Meulen DP, Janssen SJ, Hageman MG, Ring DC. Quantitative three-dimensional computed
tomography analysis of glenoid fracture patterns according to the AO/OTA classification.
J Shoulder Elbow Surg. 2016;25:269-275.
35. Ter Meulen DP, Nota SP, Hageman MG, Ring DC. Progression of Heterotopic Ossification
around the Elbow after Trauma. Arch Bone Jt Surg. 2016;4:228-230.
36. Ubbink DT, Hageman MG, Legemate DA. Shared Decision-Making in Surgery. Surgical
technology international. 2015;26:31-36.
37. Voskuijl T, Hageman M, Ring D. Higher Charlson Comorbidity Index Scores are associated with
135
readmission after orthopaedic surgery. Clin Orthop Relat Res. 2014;472:1638-1644.
38. Vranceanu AM, Hageman M, Strooker J, ter Meulen D, Vrahas M, Ring D. A preliminary RCT of a
mind body skills based intervention addressing mood and coping strategies in patients with
acute orthopaedic trauma. Injury. 2015;46:552-557.
39. Wickramasinghe NR, Duckworth AD, Clement ND, Hageman MG, McQueen MM, Ring D. Acute
Median Neuropathy and Carpal Tunnel Release in Perilunate Injuries Can We Predict Who
Gets a Median Neuropathy? Journal of hand and microsurgery. 2015;7:237-240.
Acknowledgements
I have been undeservedly lucky to work with people who are incredibly talented,
who were willing to share their wisdom and gracefulness and pass it off as my
own. Many people helped me carrying out scientific endeavors and supported
me while living abroad. Besides all the patients, who participated I would like to
say special thanks to:
Prof. David Ring, the mentor, editor and principal investigator of this
thesis. David Ring provided me the ideal research factory, where he patiently
gave me the opportunity to grow and make me fall in love with carrying out
research. Everyone should have a teacher, coach as good and as generous as
David. David wrote long and extraordinary critiques of the early drafts of the
manuscript. I am thankful that I had a chance to know him, work with him, and
learn from him.
A big thank you to my prof. Niek van Dijk, who deftly and thoughtfully
guided me and shared his academic and orthopaedic expertise. I always looked
forward to our meetings in Amsterdam to discuss the progress of this thesis.
I am also very grateful for our elaborate conversations about my future and your
guidance in making the right decisions.
Dr. Daniel Haverkamp, Prof. Gino Kerkhoffs, Dr. Rudolf Poolman and
Dr. Ton Vervest thank you for your support in carrying out the first decision aids
study. We have been able to achieve a lot in a short time frame.
Many thanks also to the talented and supportive colleagues at the
Orthopaedic Hand and Upper Extremity Service: Johann Blauth, Arjan Bot,
Stephanie Becker and Valentin Neuhaus who taught me statistics, helped
136
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urg
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improve the various manuscripts and supported the long afternoons at the back
office. We had a great time. I owe an enormous debt to my many co-authors
and colleagues (Jade Anderson, Stijn Bekkers, Robin Blok, Jeroen Bosen, Jan Paul
Briët, Pim van Dijk, Anne Caroline Döring, Saroj Golay, Jillian Gruber, Charlotte
Hoogstins, Stein Janssen, Prakash Jayakumar, Ahmet Kinaci, John King, Jasmijn
du Long, Dennis Makrawung, Mariano Menendez, Jos Mellema, Dirk ter Meulen,
Sjoerd Nota, Thijs Oosterhoff, Celeste Overbeek, Rajesh Reddy, Alexander Rakic,
Silke Spit, Joost Strooker, Mark van Suchtelen, Christiaan Swellengrebel, Teun
Teunis, Emily Thornton, Timothy Voskuijl, Dick Vuijck and Nick Wickramasinghe),
whose great ideas fill this manuscript, and to all the kind people who have taken
the time to teach me what I know about carrying out scientific research.
I am forever thankful to Stefan Breugem, Job Doornberg and Gerard
Schaap, who gave me my first insights in carrying out research, shared their
enthusiasm for orthopaedic surgery and recommended to start my PhD-
program as research assistant at the Hand and Upper Extremity Service. I also
owe a debt to the many people who were generous in sharing their time to
improve my academic and orthopaedic endeavors, like Jakob van Oldenrijk and
Inger Sierevelt.
My “paranimfen” Frederick Mansell en Christophe Wijffels are wonderful
friends and sources of inspiration. I am also very grateful for the Danes: Sjoerd
Nota, Jan Paul Briët, Peter Paul Zwetsloot and Olvert Berkhemer for having such
a great time in Boston, Somerville, throwing BBQ parties and your friendship.
I would like to thank one person in particular: Nicoline, whose love,
support, guidance, critical view, intelligence and most of all friendship make
every day a joy.
I am blessed to have family, friends and team mates who contributed
indirectly to this thesis, by supporting me while I was abroad, who showed
me it was okay to make sacrifices and made my return to the Netherlands feel
as if had never been away. My parents, Gerard and Merel, and sisters, Lois en
Annemijn, encouraged and supported me to achieve my dreams.
Curriculum Vitae
Michiel Hageman was born in Al Jubail, Saudi Arabia on April 7th, 1985. After a
short interlude in The Netherlands, Michiel lived with his family in Malaysia until
1992. Back in the Netherlands, after graduating from high school (VWO, Den Haag)
in 2004, he studied at the medical school of the University of Amsterdam. During
his study Michiel worked for the Bio-Implant Service (BIS) the Netherlands as
orthopaedic tissue-donation surgeon. In his final year of his bachelor he conducted
a research internship at the department of orthopaedic surgery of the Academic
Medical Center Amsterdam (prof. dr. C.N. van Dijk). The experiences at BIS, his
research internship and clinical internship at the AMC made him enthusiastic to
continue working in the medical field of orthopaedic surgery. After obtaining the
medical doctor’s degree in 2011, he worked as PhD student at the department of
Orthopaedic Hand and Upper Extremity of the Massachusetts General Hospital,
Boston – United States as well as the Slotervaart Ziekenhuis in Amsterdam, which
finally resulted in this thesis. During his time in Boston Michiel developed a special
interest in “Shared Decision Making” and “Decision Aids” to facilitate the decision-
making. Together with his friend and colleague Teun Teunis, Michiel launched
PATIENT+, dedicated to support shared decision-making with digital decision
aids. Subsequently Michiel and Teun wrote the book SAMEN Beslissen: waarom
moeilijk doen als het SAMEN kan? and were awarded the best value best health care
initatieve of 2017 (Doelmatigheidsprijs 2017).
In 2014, Michiel started his training for orthopaedic surgery at the department
of general surgery at the Onze Lieve Vrouwe Gasthuis (dr. M.Gerhards).
He continued his residency at the department of orthopaedic surgery at the AMC
(prof. dr. C.N. van Dijk) and Slotervaart Ziekenhuis (dr. H. van der Vis). During his
clinical work, Michiels’ interests to innovate and develop products to improve
health care further increased. At the end of 2017 he decided to focus solely on
PATIENT+. Michiel will lead and support the team of PATIENT+ to develop, integrate
and evaluate decision aids into innovative health care delivery systems.
ISBN 978 94 91549 88 5
omsl.proefschrift.Hageman.indd 1 26-02-18 14:06
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