The diffusion of an evidence-based disease guidance system for managing stroke

International Journal of Medical Informatics 51 (1998) 107–116

The diffusion of an evidence-based disease guidance system formanaging stroke

Francis Lau a,*, Andrew Penn b, Deborah Wilson c, Tom Noseworthy f,Douglas Vincent d, Sandra Doze e

a Business Faculty, Uni6ersity of Alberta, 3-30D Business Building, Edmonton, Alberta T6G 2R6, Canadab Synapse Publishing, 8440–112 St., Edmonton, Alberta, Canada

c Alberta Medical Association, 12230–106 A6e., Edmonton, Alberta, Canadad Infoward, 1980 Mannlife Place 10180–101 St., Edmonton, Alberta, Canada

e Crossroads Health Region, 5610–40 A6e., Westakiwin, Alberta, Canadaf Public Health Sciences, Uni6ersity of Alberta, 13–103 Clinical Sciences Building, Edmonton, Alberta, Canada

Abstract

This paper describes the diffusion of an evidence-based stroke guidance system (SGS) in a field setting throughparticipatory research. SGS enables physicians to review relevant evidence-based literature, from which patient ordersare generated for managing cerebrovascular accident. The paper focuses on the question ‘what are the barriers andenablers to adopting SOS?’ The research site consisted of eight hospitals within two health regions in Alberta, with47 physicians as the intended users. The data sources consisted of surveys, education sessions, design feedback, fieldobservations, and usage logs. Preliminary results revealed an initial slow rate of adoption that gradually improvedwith the influence of clinical champions, more effective communication, sustained education, round-the-clock supportand continued system refinement. These initial findings suggest that models of technological diffusion can help usbetter understand the complexities of changing physician practice behaviors. © 1998 Elsevier Science Ireland Ltd. Allrights reserved.

Keywords: Diffusion of innovations; Stroke management; Evidence-based practice

1. Introduction

In recent years, health organizations haveturned to disease management protocols in

the form of care maps, critical paths andpractice guidelines to enhance their standardof clinical practice and patient outcomes [1–3]. A 1993 survey by Lumsdon [4] of 581hospital executives revealed that 57% hadformal initiatives to monitor and manageclinical processes, and nearly 60% were in

* Corresponding author. Tel.: +1 403 4925828; fax: +1403 4923325; e-mail: [email protected]

1386-5056/98/$19.00 © 1998 Elsevier Science Ireland Ltd. All rights reserved.

PII S1386-5056(98)00108-7

F. Lau et al. / International Journal of Medical Informatics 51 (1998) 107–116108

early stages of implementing some type ofdisease management protocols. Of the hos-pitals surveyed, 37% had plans to automatethese protocols. This trend towards automa-tion is likely to escalate as paperbasedmethods become unmanageable with the in-clusion of more complex diseases and het-erogeneous patient groups such as strokeand epilepsy [5–7].

There has also been steady growth insupport of evidence-based decision making,a process of using best available evidence toinform clinical practice [8–10]. In parallelwith this has been the development of newinformation tools for the dissemination ofclinical trials and synthesis of research find-ings over the Internet [11–14]. The emerg-ing challenges to use these tools effectivelyto organize the relevant literature and de-liver it as best available evidence, withguidelines for use by practitioners to man-age diseases. While there is an abundanceof literature on the development, use andevaluation of disease management protocols[15–17] and evidence-based decision making[18–21], less has been written on the diffu-sion of these tools as innovations and theireffects on individual and organizational per-formance.

From the perspective of technological in-novation, this paper examines the imple-mentation of an evidence-based strokeguidance system (SGS) for managing cere-brovascular accident. First, different modelsof diffusion are reviewed. Next, our re-search design in terms of the approach, in-terventions, site/subjects and data sources ispresented. Preliminary findings to date arereported. This is followed by a discussionon the barriers and enablers identified, andthe use of diffusion models as an alternativeapproach, when studying new technologiessuch as the SGS in the field setting.

2. Models of diffusion

Different models of diffusion have beendescribed in literature over the past 30 years.The classical model by Rogers [22] depictsthe diffusion of innovations in terms of theinnovation itself, transmitted through chan-nels of communication, over time, amongmembers of a social system. The model alsodistinguishes the stages of diffusion as a S-shaped curve made up of early adopters, lateadopters and laggards. Tornatzky and Fleis-cher [23] emphasize innovation as a processof many discrete decisions and behaviors thatunfold slowly over time involving social unitsat many different levels of aggregation.Viewed from a technological diffusion per-spective, Kwon and Zmud [24] describe theinformation technology (IT) implementationprocess as a staged organizational effort todiffuse appropriate IT within a user commu-nity. The stages in this model consist of ini-tiation, adoption, adaptation, acceptance,routinization and infusion that are equivalentto Lewin’s change model of unfreezing,changing and freezing [25]. These stages canalso be mapped to five contextual factorscomprising the characteristics of users, theirorganization, technology used, task involvedand social environment [24,26].

In this study, a simplified model for tech-nological diffusion is used as a conceptualscheme to help understand the process ofimplementing the SGS in a clinical setting.This simplified model comprises adoption,use and evolution as the relevant stages ofdiffusion, to be considered within the contextof the user, task, technology and conditions.While adoption refers to the process of de-ploying SGS in the regions, use and evolutionpertain to users learning the tool/process andmaking refinements to its usage over time.The characteristics of users, task, technologyand conditions are contextual factors similar

F. Lau et al. / International Journal of Medical Informatics 51 (1998) 107–116 109

to those described by Kwon and Zmud [24],except that conditions cover both the socialand organizational dimensions.

3. Study design

3.1. Approach

This is a descriptive study through partici-patory research [27–29] to explore the diffu-sion of SGS in a clinical setting. This was aniterative process in that SGS was adopted,used and evolved over time, based on thedeliberations among the researchers, design-ers and users as active participants.

Our overall objectives are to address threequestions: (a) what are the barriers and en-ablers to implementing SGS?; (b) Can itchange clinician practice behaviors?; (c) Doesit have any effect on patient outcomes? Forthis paper we limit the focus to issues relatedto the implementation of SGS in the clinicalsetting.

3.2. Site and subjects

The initial research site in 1996 consistedof the emergency room (ER) and the neurol-ogy wards of a university teaching hospital inan urban health region of Canada. All neu-rologists and neurology residents from thehospital were invited to participate as users.In the summer of 1997, the initiative ex-panded to include the ERs and ER physi-cians in four other hospitals in the sameregion, as well as three health facilities in arural region involving most of the familyphysicians.

3.3. Inter6entions

The primary intervention introduced wasSGS as a windows-based software tool run-

ning on a networked PC environment. Thetool assists physicians with routine tasks suchas generating patient orders and dischargesummaries. In addition, it provides links tosupporting literature, as the source of bestavailable evidence to guide the managementof acute stroke. Use of the tool was voluntaryand not part of the routine existing tasks forphysicians. Additional interventions imple-mented over time included an integrator soft-ware called CLINT [30] with otherevidence-based information resources, educa-tion sessions, physician champions, user feed-back and design changes, andround-the-clock user support.

3.4. Data sources

Multiple data sources were employed, com-prising physician surveys, transcripts of edu-cation sessions, feedback to designers, fieldobservations, and system usage logs. Specifi-cally, a baseline survey was used to determinethe physicians’ prior experience with comput-ers, and an interval/exit survey to monitortheir progress after being trained on SGS.Feedback was gathered and discussed period-ically among the designers (i.e. faculty,1 de-velopers and champions), researchers andusers to refine the tool and process. Fieldobservations from interactions with physi-cians and other staff were recorded as ongo-ing events. The SGS database was examinedfor actual usage over time. The analyses con-sisted of frequency tabulations and contentanalysis of textual comments collectedthrough surveys and field observations.

1 An international academic faculty maintains the strokeguidance system and its underlying evidence through a consen-sus process based on critical appraisal of relevant literature.


4. Preliminary results

4.1. Chronology of e6ents

The SGS was first introduced in a formaleducation session at the university hospital inJune 1996 and implemented in October thatyear in the ER and on two neurology wards.CLINT was implemented in June 1997 fol-lowed by an upgrade of SGS in October.SGS and CLINT were implemented in theERs of four other hospitals in the regionduring the period of October 1997 and Janu-ary 1998. Education sessions were providedthroughout October 1996 and January 1998.

For the rural region, SGS was imple-mented in October 1997, with education ses-sions provided as part of the implementationand follow-up process. For both the ruraland urban regions, the baseline surveys weremostly done during the education sessions.The interval/exit surveys were mostly done aspersonal interviews. Field observations wererecorded throughout the study when interact-ing with physicians and other staff.

4.2. User subjects

Sixty-one physicians were invited in thisanalysis, during September 1996 and January1998. These included six neurologists, 34 resi-dents, ten ER physicians, ten family physi-cians and one student intern. Forty-sevenphysicians participated in different educa-tional sessions held throughout 1996 and 97.The initial training plan in 1996 includedonly neurologists and residents. This waslater expanded in 1997 to include other physi-cians and clinical staff.

4.3. Education sessions

During September 1996 and January 1998,14 education sessions were recorded. These

consisted of one grand round, four rounds/lectures, and nine hands-on sessions in differ-ent sites with audiences that ranged from 2 to20+ individuals. Most sessions were targetedfor physicians, but some were also attendedby clinical staff such as nurses and therapists.The types of education ranged from broaddiscussions in grand rounds such as usingSGS in rural settings, to search strategies forliterature in rounds/lectures, to practical tipson combining knowledge and experience inhands-on sessions. Attendance in any of theseeducation sessions was not mandatory. Sev-eral physician champions emerged over timeto promote evidence-based practice and useof the tool. An in-depth interview of onechampion indicated the need for more pro-tected time when training residents.

4.4. Baseline sur6eys

Baseline surveys were conducted on 28physicians during July and December 1997(Table 1). Many of the physicians reportedlittle prior computer training, regarded them-selves as novices, with varying levels of satis-faction with computers. Time to learncomputers, accessibility, availability of rele-vant literature, range of applications avail-able (such as e-mail, lab/X-ray results andhome access), ease of use and user-friendli-ness were cited as factors that would encour-age/discourage their use of computers atwork. Example comments included ‘time tofind relevant info and availability of re-sources’, ‘not knowledgeable about the areas’and ‘inability to access, having to wait to usea terminal’.

4.5. Inter6al/exit sur6eys

Surveys were collected on 11 residentsfrom the urban region after they had beentrained on SGS (Table 2). Many of the resi-

F.

Lau

etal./

InternationalJournal

ofM

edicalInform

atics51

(1998)107

–116

111

Table 1Summarized results of baseline surveys conducted on 28 physicians between July and December of 1997

Time spent on computers at work Very dissatisfied Beginner to average 7 (30.4%) Average to ad- Very Satisfied 23 (100%)Very low Low Average High Very high Total12 (42.9%) 7 (25%) 5 (17.9%)Prior computer training 2 (7.1%) 2 (7.1%) 28 (100%)

Very dissatisfied Dissatisfied Neutral Satisfied Very Satisfied Total1 (3.6%) 4 (14.3%)Use of computers 9 (32.1%) 11 (39.3%) 3 (10.7%) 28 (100%)

B1 h 1–2 h \2–4 h \4 h N/A TotalTime spent on computers at work 16 (57.2%) 9 (32.1%) 2 (7.1%) 1 (3.6%) 28 (100%)

Beginner Beginner to average Average Average to ad- TotalAdvancedvanced

6 (26.1%) 8 (34.8%)Skill level 7 (30.4%) 2 (8.7%) 23 (100%)

Factors that encourage/discourage use of computers at work Total responses (%)Time needed to learn and use computers 12 out of 21 responses or57.1%Accessibility and availability of relevant literature 7 out of 21 responses or 33.3%Range of technologies and applications available e.g. email, lab/x-ray results, home access 7 out of 21 responses or 33 3%Ease of use and user-friendliness 3 out of 21 responses or 14.3%


Table 2Summarized results of interval/exit surveys conducted on 11 residents between July 1997 and February 1998

\15 min Total11–15 min5–10 minN/A B5 min3 (27.3%) 3 (27.3%) 2 (18.1%)Time to complete a ses- 11 (100%)3 (27.3%)

sion of SGS

Minor prob- TotalN/A Major prob- No problemslems lems

2 (18.1%) 11 (100%)Did you encounter prob- 3 (27.3%) 4 (36.5%)2 (18.1%)lems using SGS?

]96–8 Total55N/A 011 (100%)4 (36.3%)SGS has met your expec- 1 (9.1%)1 (9.1%) 1 (9.1%) 4 (36.4%)

tations? (0–10)

YesN/A No Not sure Total3 (27.3%) 11 (100%)SGS has influenced diag- 5 (45 5%) 1 (9 1%)2 (18.1%)

nostic thinking?

YesN/A No Not sure Total11 (100%)3 (27.3%)SGS has influenced man- 1 (9 1%)4 (36.3%)3 (27.3%)

agement decisions?TotalN/A No Not sure

SGS has changed patient 11 (100%)5 (45.5%) 2 (18.1%) 4 (36.4%)outcomes?

TotalNot sureN/A NoWould you use SGS 11 (100%)10 (90.9%) 1 (9 1%)

again?

dents reported it took up to 15 min or theyhad problems in completing a session, butrated the system as having met their expecta-tions. While many were not sure or did notthink SGS had influenced their diagnosticthinking, they admitted not having used itenough to judge. Some thought it was useful‘in thinking about prognosis and implica-tions’ and felt ‘the literature was good, help-ful and informative’.

Many of the residents were not sure, or didnot think, SGS had any influence on patientmanagement decisions, while admitting theydid not use it enough to know. Some resi-dents mentioned the literature was helpful asreminders on basic orders. It also identifiednew practice patterns to them, such as con-trol of temperature in the acute phase ofstroke, which they were unaware of despite

directives being present in published guideli-nes. Example comments included ‘the litera-ture has given me the opportunity to learnmore about stroke and the patients I treat’,and ‘I learned a great deal about antithrom-bolytic therapy and changed my way of treat-ing based on the literature in SGS’. Many ofthe residents were not sure whether SGS hadchanged patient outcomes. The main reasoncited was not being able to track the patientsonce they were discharged. Almost all resi-dents responded ‘yes’ when asked if theywould use SGS again.

In the rural region, family physicians didnot use SGS to generate patient orders. How-ever, some did use the computers to accessInternet web sites for ER and disease litera-ture. Main reasons given for non-use werelack of time and need for a quiet office near


ER to learn the tool. The uncertainty withpending reorganization in that region hadalso led to apathy among staff knowing thatchange was ahead.

4.6. Feedback to designers

Initially, an international academic facultywas responsible for synthesizing the support-ing evidence and designing the interface forSGS. The faculty consisted of four to sixsenior neurologists (varied over time), dis-persed geographically across North Americaand Europe, who met twice a year as aneditorial board to review the latest evidenceand issues regarding interface design. WhenSGS was first implemented, many technicalproblems were encountered and had to beresolved quickly by the local design team.Two local physician champions took an ac-tive part in reporting problems and eventu-ally participated in the faculty meetings torefine the tool. The process of logging prob-lems also had to be formalized as the numberof users and issues increased over time. Thesoftware design had also grown more sophis-ticated in order to integrate with existinginformation systems (e.g. ML-7) and be cus-tomizable in different settings.

4.7. Field obser6ations

A total of 33 field observations were madeon the interactions among the researchers,designers, and users during June 1996 andDecember 1997. The transcripts revealed fourmajor types of events that emerged as anongoing part of ‘life around SGS’. Theseconsisted of education, facilitation, technicalsupport and communication. Education tookplace in different forms ranging from formallectures to impromptu hands-on sessions in-volving physicians, clinical staff and even pa-tients. Facilitation involved helping

physicians access literature and use variousinformation tools. Technical support becamea round-the-clock service to help physiciansto install hardware/software and solve prob-lems. Communication was a deliberation pro-cess with senior physicians to seek buyin andresources to adopt the tool in the regions.

4.8. Actual usage

As of January 1998, there were 67 strokecases recorded in the SGS database. With anaverage of 300 strokes a year at the universityhospital, the rate of usage is around 22.3%.The pattern of usage revealed a slightly in-creasing trend with most cases entered duringJanuary, June, July, September, Novemberand December of 1997. These periods looselycorresponded with the release of new soft-ware versions (Jan., Jun. and Oct.), arrival ofthe physician champions and education ses-sions for residents.

5. Discussion

5.1. Barriers and enablers

The baseline surveys revealed our physi-cians did not rate themselves as very knowl-edgeable or satisfied with computers ingeneral. Their previous level of computerusage at work had been minimal. These aresimilar to earlier reports [31,32] on ‘computeranxiety’ factors such as unfamiliarity withcomputers, low self-rated skills and typingability that would affect user attitudes towardcomputers.

The barriers/enablers identified from oursurveys include accessibility of computers,time to learn and use them, availability ofrelevant literature, range of useful applica-tions, and ease of use. These findings areconsistent with major implementation factors


identified by others that include hardwareavailability [33], complexity of the tool [34]and access to up-to-date literature [35].

In her review of physician entry of com-puter-based patient records, Kaplan [36] hassuggested the barriers to be increased time,shifted cost burden, work pattern changes,and threatened professional values. Our ini-tial findings are similar in that time and taskchanges were major concerns for the physi-cians. The voluntary nature of using SGS hasalso affected the level of its use under thesecircumstances.

From the interval/exit surveys, most resi-dents did not feel or were uncertain SGS hadchanged their diagnostic thinking, manage-ment decisions or patient outcomes. How-ever, this resulted largely from their lack oftime to learn and use the tool. Many hadthought SGS provided useful literature onstroke management. Comments from the ed-ucation sessions and field observations alsosuggested SGS to be of value as an educa-tional tool. These initial findings concur withDetmer and Friedman [35], that physiciansvalue self education and access to latest liter-ature, but differ from Fish et al. [37], whereresidents of another teaching hospitalthought SGS would be useful in diagnosingstroke and choosing appropriate diagnostictests.

Other contextual barriers/enablers noted inour study are education, facilitation, commu-nication, and round-the-clock support. Theseare consistent with systems level implementa-tion factors reported in literature such asearly and intensive training and support [38],as well as rewards, planning, champions, ad-ministration support and medical administra-tion involvement [33,34]. Such contextualfactors must be addressed in order to providethe necessary incentives, leadership and sup-port when changing physician practice.

5.2. A broader approach to e6aluation

Over the years, there have been repeatedpleas to the medical informatics communityto take a broader approach when studyingthe implementation and use of health infor-mation systems [39–41]. While such experi-mental methods as randomized control trialshave been advocated [42], the complex inter-actions of people, organizational and socialissues often render these methods unfeasible[43–46].

The participatory research approachadopted in this study provides an alternativemeans to understand the effects of technol-ogy by taking a broader perspective that alsoincludes the individual, task, technology andcondition. Our simplified model of diffusionhelps us understand the adoption, use andevolution of SGS observed over time.

For example, the initial slow rate of adop-tion can be explained by the physicians’ lackof familiarity with computers and their un-certainty on how SGS could improve theirwork. The usage of SGS gradually increasedonly when various individual, task, technol-ogy and social/organizational conditionswere addressed such as introducing champi-ons, communication, design changes, educa-tion and user support as different adaptationsof the initial innovation.

The need for a broader approach to evalu-ating technological innovations is apparent asdemonstrated through our study. We believeonly when there is a critical mass of users forthe innovation can the questions of whetherit changes clinical practice and patient out-comes be addressed satisfactorily.

Acknowledgements

This project is partly funded by HEALNet,a Network of Centres of Excellence for


Health Research, funded by the Medical Re-search Council and the Social Sciences andHumanities Research Council of Canada. Wealso wish to thank: Dr Robert Hayward andInfoward for the use of CLINT; Synapse foruse of StrokeNet; Drs Bruce Fisher, MaryLou Myles, Ken Makus and Curtis Johnstonas champions for StrokeNet/CLINT; the In-formation Systems Departments at the Capi-tal Health Authority and CrossroadsRegional Health Authority for technical sup-port; the Neuroscience program at the Uni-versity of Alberta Hospital for its support.

References

[1] R.J. Coffey, et al., An introduction to criticalpaths, Qual. Manag. Health Care 1 (1) (1991)45–54.

[2] P.A. Hofmann, Critical path method: an impor-tant tool for coordinating clinical care, J. Qual.Improv. 199 (7) (1993) 235–246.

[3] D.C. Hampton, Implementing a managed careframework through care maps, JONA 23 (5)(1993) 21–27.

[4] K. Lumsdon, M. Hagland, Mapping care, Hosp.Health Netw. 10 (1993) 2440.

[5] C. Daus, A framework for team treatment: casemanagement at Baptist Memorial Hospital. J.Resp. Care Pract. Dec/Jan (1994) 115–120.

[6] L. DiJerome, The nursing case management com-puterized system: meeting the challenge of healthcare delivery through technology, Comput. Nurs.10 (6) (1992) 250–258.

[7] P.A. Hofmann, Critical path method: an impor-tant tool for coordinating clinical care, J. Qual.Improv. 19 (7) (1993) 235–246.

[8] R.B. Haynes, R.S.A. Hayward, J. Lomas, Bridgesbetween health care research evidence and clinicalpractice, J. Am. Med. Inform. Assoc. 2 (5) (1995)342–350.

[9] Evidence-Based Medicine Working Group, Evi-dence-based medicine: a new approach to teachingthe practice of medicine, J. Am. Med. Assoc. 268(17) (1992) 2420–2425.

[10] National Health Forum, Evidence-Based DecisionMaking, Ottawa, 1997.

[11] H.J. Lowe, E.C. Lomax, S.E. Polonkey, The worldwide web: a review of an emerging Internet-basedtechnology for the distribution of biomedical infor-mation, J. Am. Med. Inform. Assoc. 3 (1) (1996)1–14.

[12] T.B. Patrick, G.K. Springer, J.A. Mitchell, M.E.Sievert, Virtual shelves in a digital library: a frame-work for access to network information sources, J.Am. Med. Inform. Assoc. 2 (6) (1995) 383–390.

[13] A. Penn, M. Maclure, The Synapse project: just-in-time knowledge, Ann. RCPSC 28 (2) (1992) 94–96.

[14] M. Barnes, G.O. Barnett, An architecture for adistributed guideline server, in: R.M. Gardner(Ed.), Proc. Symp. on Computer Applications inMedical Care, November 1995, pp. 275–279.

[15] S. DeWoody, J. Price, A systems approach tomultidimensional critical paths, Nurs. Manag. 25(11) (1994) 47–51.

[16] R.V. Barnes, L. Lawton, D. Briggs, Clinicalbenchmarking improves clinical paths: experiencewith coronary artery bypass grafting, J. Qual. Im-prov. 20 (5) (1994) 267–276.

[17] J.A. Falconer et al., The critical path method instroke rehabilitation: lessons from an experimentin cost containment and outcome improvement,QRB 19 (1) (1993) 8–16.

[18] J. Newton, D. Knight, G. Woolhead, Generalpractitioners and clinical guidelines: a survey ofknowledge, use and beliefs, Brit. J. Gen. Pract. 46(1996) 513–517.

[19] J. Lomas, G.M. Anderson, K. Domnick-Pierre, E.Vayda, M.W. Enkin, W.J. Hannah, Do practiceguidelines guide practice? The effect of a consensusstatement on the practice of physicians, New Engl.J. Med. 321 (19) (1989) 1306–1311.

[20] R.S.A. Hayward, M.C. Wilson, S.R. Tunis, E.B.Bass, G. Guyatt, Users’ guides to the medicalliterature: how to use clinical practice guidelines—are the recommendations valid?, J. Am. Med. As-soc. 274 (7) (1995) 570–574.

[21] W.S. Richardson, A.S. Detsky, Users’ guides tothe medical literature: how to use clinical practiceguidelines—what are the recommendations andwill they help you in caring for your patients?, J.Am. Med. Assoc. 274 (20) (1995) 1630–1632.

[22] E.M. Rogers, Diffusion of Innovations, 3rd ed.,The Free Press, New York, 1983.

[23] K.G. Tornatzky, M. Fleischer, The Process oftechnological Innovation, Lexington Books, 1990.

[24] T.H. Kwon, R.W. Zmud, Unifying the fragmentedmodels of information systems implementation, in:


Boland, Hirschheim (Eds.), Critical Issues in Infor-mation Systems Research, Wiley, New York, 1987.

[25] K. Lewin, Group decision and social change, in:Newcombe, Hartley (Eds.), Readings in SocialPsychology, Henry Holt, New York, 1952, pp.459–473.

[26] R.B. Cooper, R.W. Zmud, Information technologyimplementation research: a technological diffusionapproach, Manag. Sci. 36 (2) (1990) 123–139.

[27] C. Pava, Redesigning sociotechnical systems de-sign: concepts and methods for the l990s, J. Appl.Behav. Sci. 22 (3) (1986) 201–221.

[28] P. Reason, Three approaches to participative in-quiry, in: Denzin, Lincoln (Eds.), Handbook ofQualitative Research, Sage Publications, London,1993, pp. 324–339.

[29] F. Lau, A review of action research in IS studies,in: Lee, Liebenau, DeGross (Eds.), InformationSystems and Qualitative Research, IFIP Press,1997.

[30] K.B. Langton, R.S.A. Hayward, S.A.Ross, A clin-ical information network (CLINT) for a healthscience centre, in: J.R. Moehr, F. Lau (Eds.),COACH Conf. 21 Scientific Program Proc., 1996,69–71.

[31] S. Brown, R. Coney, Changes in physicians’ com-puter anxiety and attitudes related to clinical infor-mation system use, J. Am. Med. Inform. Assoc. 1(5) (1994) 381–394.

[32] M.C. Mazzoleni, P. Baiardi, I. Giorgi, G. Franchi,R. Marconi, M. Cortesi, Assessing users’ satisfac-tion through perception of usefulness and ease ofuse in the daily interaction with a hospital infor-mation system, Proc. 20th Am. Med. Inform. As-soc. Symposium (1996) 752–756.

[33] C. Weir, M. Lincoln, D. Roscoe, C. Turner, G.Moreshead, Dimensions associated with successfulimplementation of a hospital based integrated or-der entry system, Proc. 18th Am. Med. Inform.Assoc. Symposium (1994) 653–657.

[34] J. Ash, Cross-site study of the implementation ofinformation technology innovations in health sci-ences centres, Proc. 19th Am. Med. Inform. Assoc.Symposium (1995) 795–799.

[35] W.M. Detmer, C.P. Friedman, Academic physi-cians’ assessment of the effects of computers onhealth care, Proc. 18th Am. Med. Inform. Assoc.Symposium (1994) 558–562.

[36] B. Kaplan, Reducing barriers to physician dataentry for computer-based patient records, TopHealth Inform. Manag. 15 (1) (1994) 24–34.

[37] A. Fish, K. Rajkumar, F.L. Silver, Perceived use-fulness of decision support: the StrokeNet diseaseguidance system, Proc. 21st Am. Med. Inform.Assoc. Symposium (1997) 937.

[38] C. Weir, M. Lincoln, D. Roscoe, G. Moreshead,Successful implementation of an integrated physi-cian order entry application: a systems perspective,Proc. 19th Am. Med. Inform. Assoc. Symposium(1995) 790–794.

[39] D.E. Forsythe, B.G. Buchanan, Broadening ourapproach to evaluating medical information sys-tems, Proc. Am. Med. Inform. Assoc. (1994) 8–12.

[40] J.G. Anderson, C.E. Aydin, S.J. Jay (Eds.), Evalu-ating Health Care Information Systems: Ap-proaches and Applications, Sage Publications,Thousand Oaks, CA, 1994.

[41] B. Kaplan, Addressing organizational issues intothe evaluation of medical systems, J. Am. Med.Inform. Assoc. 4 (2) (1997) 94–101.

[42] W.M. Tierney, J.M. Overhage, C.J. McDonald, Aplea for controlled trials in medical informatics—editorial comments, J. Am. Med. Inform. Assoc. 1(4) (1994) 353–355.

[43] F.C.G. Southon, C. Sauer, C.H.G. Dampney, In-formation technology in complex health services:organizational impediments to successful technol-ogy transfer and diffusion, J. Am. Med. Inform.Assoc. 4 (2) (1997) 112–124.

[44] N.M. Lorenzi, R.T. Riley, A.J.C. Blyth, G.Southon, B.J. Dixon, Antecedents of the peopleand organizational aspects of medical informatics:review of the literature, J. Am. Med. Inform.Assoc. 4 (2) (1997) 79–93.

[45] J. Ash, Organizational factors that influence infor-mation technology diffusion in academic healthscience centres, J. Am. Med. Inform. Assoc. 4 (2)(1997) 102–111.

[46] B.L. Rotman, et al., A randomized controlled trialof a computer-based physician workstation in anoutpatient setting: implementation barriers to out-come evaluation, J. Am. Med. Inform. Assoc. 6 (3)(1996) 340–348.

.

Documents

The diffusion of an evidence-based disease guidance system for managing stroke