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Overview of Quality Improvement
Focus on Designing Reliable Interventions
Greg Maynard MD, MSProfessor of Clinical Medicine and Chief,
Division of Hospital MedicineUniversity of California, San Diego
Quality Improvement: Bridging the Implementation
Gap
Implementation Gap
Scientific understanding
Patient care
Pro
gres
s
Time
Working harder isn’t always the answer……
The Evolving Culture of Medicine • 20th Century
Characteristics– Autonomy– Solo Practice– Continuous learning– Infallibility– Individual Knowledge
• 21st Century Characteristics– Teamwork & systems– Group practice– Continuous
improvement– Multidisciplinary
problem solving– Change
Shine, KI. Acad.Med. 2002;77:91-99
How Do We Close the Gap? Essential Elements
• Institutional support and multidisciplinary teams • Standardized order sets
– Infusion– Subcutaneous which promote basal / bolus regimens
• Algorithms / protocols / policies– Address dosing– Nutritional intake– Special situations: TPN, enteral tube feedings,
perioperative insulin, steroids– Safety issues– Transitions in care and discharge planning
• Metrics: How will you know you’ve made a difference?
• Comprehensive educational program
Traditional Quality Assurance
worsebetterworsebetter Quality Quality
threshold
Before After
outliers
Quality Improvement
worsebetter worsebetter Quality Quality
AfterBefore
Quality Improvement is…• Focus on processes of care
• Reduced variation by shifting entire practice
• A change in the design of care
Quality Improvement is NOT…
• Forcing people to work harder / faster / safer
• Traditional QA or peer review
• Creating order sets or protocols without monitoring use or effect
Good Teamwork is Essential
Features of a Good Team
• Safe – no ad hominem attacks
• Inclusive – open to all potential contributors – values diverse views; not a clique
• Open– considers all ideas fairly
• Consensus seeking– finds a solution all members can support
Models for Improvement
• In use around the globe for decades• Success in many fields of endeavor
– Healthcare late to the game!
• Alternative to the usual:– Predictable breakdowns in reliability leading to
common problems– Ignoring improvement concepts & trying the
first thing that comes to mind– Not measuring effectiveness of implementation
outcomes or process until bad events happen…..again
Establishing MeasuresTeams use quantitative measures to determine if a specific change actually leads to an improvement.
Setting Aims Improvement requires setting aims. The aim should be time-specific and measurable, with a defined population.
Selecting ChangesAll improvement requires making changes, but not all changes result in improvement. Organizations therefore must identify the changes that are most likely to result in improvement. Testing ChangesThe Plan-Do-Study-Act (PDSA) cycle is shorthand for testing a change in the real work setting — by planning it, trying it, observing the results, and acting on what is learned. This is the scientific method used for action-oriented learning.
A Model for Improvement
Features of Good Aim Statements
• Specific
• Measurable
• Aggressive yet Achievable
• Relevant
• Time-bound
Sample Aim Statements:• Glycemic Control on the Wards
– Within 6 months the use of sliding scale only regimens will be reduced by half.
– Within 12 months the % of patients with POC glucose testing achieving a mean glucose of < 200 mg/dL will improve from 65% to 85%.
– Within 12 months the % of our patients suffering from hypoglycemic events will be reduced from 11% to 6%.
Measurement Principles
• Seek usefulness, not perfection • Integrate measurement into daily routine • Use qualitative and quantitative data • Use sampling• Plot data over time
Use a balanced set of measures for all improvement efforts
A Blend of Measures• Structure
– Do you have a multidisciplinary steering committee?
– Do your SQIO sets include a prompt for A1c?
• Process– % of SQIO written using your order form– % with basal insulin
• Outcomes– LOS, Mortality: Glycemic control,
Hypoglycemia
Picabo Street and Communication
Olympic Gold Medal Winner….AND a Critical Care Nurse!
“Picabo, ICU”
Hierarchy of Reliability
No protocol* (“State of Nature”)
Decision support exists but not linked to order writing, or prompts within orders but no decision support
Protocol well-integrated (into orders at point-of-care)
Protocol enhanced (by other QI and high Protocol enhanced (by other QI and high reliability strategies)reliability strategies)
Oversights identified and addressed in Oversights identified and addressed in real timereal time
Level
4
1
2
3
5
Predicted
Success rate
40%
50%
65-85%
90%
95+%
Order sets w/ embedded insulin orders:
Standardization?
High Reliability Design Solutions (as applied to Insulin Protocol)
• Standardize insulin choices for common situations
• MD must “opt out” of default choices (not opt in)• Prompts for basal insulin if over glycemic
target, prompts for HgA1c, etc.• Scheduled assessments of glycemic control /
insulin regimen • Redundant responsibility to maintain glycemic
target
CAUTION!!!!
Be Sure to Insert a Brain Between Protocol and Patient!
• Education for broad range of providers
• Consider special team of focused providers
Engineering Change: Hints for Success
• Empower nursing• Expedite passage through medical staff
committees• Better to implement an imperfect,
compromise change than no change at all• Provide hot line or support for difficult
situations• Follow metrics continuously as you implement
Engineering Change: Hints for Success
• Measure, learn, and over time eliminate variation arising from professionals; retain variation arising from patients
• Keep big picture in mind• Negotiate ‘speed bumps’
– Time delays in getting data– Incomplete buy-in– Go around obstacles instead of through them
(can always go back to them later)– Some who disagree with you may be correct– Make changes painless as possible: make it
easy to do the right thing
PDSA: Plan-Do-Study-Act
– The use of PDSA has been referred to as the “democratization of the scientific method.” (Paul Miles, MD)
– Do small scale tests of change.
– Everyone can do it!
Act Plan
Study Do
Benefits of rapid cycle change:
• Increases belief that change will result in improvement
• Allows opportunities for “failures” without impacting performance
• Provides documentation of improvement• Adapts to meet changing environment• Evaluates costs and side-effects of the change• Minimizes resistance upon implementation
Examples: integration of best practice
• A1c level within last 30 days.
• Specify hyperglycemic diagnosis
• Each patient should have a glycemic target.
A1c Level
• Incorporate prompt for A1c level in insulin order sets and protocols.
• Ordering can be accomplished with checkbox
• Monitor performance, feedback to providers
• Glycemic control team obtains it
Proper diagnosis
Diagnosis:Uncontrolled –or– Controlled
Diabetes type: 1 2 Gestational –or–
Secondary to another cause;Specify
–or– Stress/situational hyperglycemia
Improves reimbursement: define “uncontrolled DM” and monitor coding accuracy
Order set docmentation translates into ICD-9
Identify non-critical care glycemic target
• Preprandial target 90–130 mg/dL; maximum random glucose < 180 mg/dL (ADA/AACE consensus target)
• 80–150 mg/dL
• Preprandial target 90–130 mg/dL for most patients, 90–150 mg/dL if hypoglycemia risk factors
“Actionable” Glycemic Target
The “what” is common to all institutions: push for changes in regimens when glycemic target not being met.
Variable by institution:Glycemic target definitionHow to generate reportWho acts on report
Putting this in place moves you up hierarchy of reliability.
Opportunity to Learn from variation!
Hierarchy of Reliability
No protocol* (“State of Nature”)
Decision support exists but not linked to order writing, or prompts within orders but no decision support
Protocol well-integrated (into orders at point-of-care)
Protocol enhanced (by other QI and high Protocol enhanced (by other QI and high reliability strategies)reliability strategies)
Oversights identified and addressed in Oversights identified and addressed in real timereal time
Level
4
1
2
3
5
Predicted
Success rate
40%
50%
65-85%
90%
95+%
•eliminate variation arising from professionals; retain variation arising from patients
Setting• Academic teaching
medical centers with over 400 beds
• Adult inpatients on non-critical care wards with POC glucose testing.
• Nov 2002 – Dec 2005
• Excluded:– Critical care, OB, Psych,
Senior Behavioral Health
Questions • What is current state? Baseline Nov ’02-Oct ’03.
– Insulin Use Patterns– Glycemic Control – Hypoglycemia– Other
• What is effect of implementing a standardized SQIO set?
Main Intervention #1 Nov ’03-May ‘05
• What is the incremental effect of an insulin management protocol?
Main Intervention #2 May ’05-Dec ‘05
Intervention #1 (Nov 2003):A Basic Subcutaneous Insulin Order Set
• Basal / Nutritional / Correction dose terminology introduced
• Multiple correction dose scales available, based on total insulin dose required.
• Sliding scale only regimens discouraged• Check box simplicity• Some guidance for dosing and adjustment • Hypoglycemia protocol incorporated• Paper, then CPOE versions
Intervention #2 (May 2005)Insulin Management Protocol
• One page algorithm• Glycemic Target• Prompt for A1C• DC Oral Hypoglycemic Agents• Guidance on dosing• Suggested regimens for eating patient,
NPO patient, patient on enteral nutrition• Guidance on dosing adjustment• Introduced with case based teaching
The Use of Basal Insulin Increases(sliding scale only regimens decline)
Percent Sliding Scale Insulin Only
0
10
20
30
40
50
60
70
80
Per
cent
10/20/03
New Order Set
01/20/04
CPOE - TH
30-90 patients sampled per month, no formal analysis done, results sustained
Glycemic Control• Days 1 – 14 of admission• Exclude patients with < 8 POC tests• 5,800 patients• 37,516 patient days• 111,473 POC tests• By patient stay
– % of patients with mean glucose < 180 mg /dL• By patient day
– % of patient days when all glucose values were between 60 – 180 mg / dL
• Pearson chi-square statistic to compare: – TP 1 (Baseline) Nov ’02 – Oct ‘03– TP2 (Order Set) Nov ‘03 – Apr ’05– TP3 (Algorithm) May ’05 – Dec ’05
Percent of patients with mean glucose < 180 mg/dL
56%
58%
60%
62%
64%
66%
68%
70%
72%
74%
Baseline Order Set Algorithm
62 %
69%
73%
5800 patients w/ > 8 POC glucose values, day 1-14 values: p value < .02 (Pearson chi-square statistic)
Percent of patients with a glucose average <=180
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1st order setBaseline Order set Algorithm
Percent Patient Days w/ all Controlled Glucose(no values < 60 or > 180 mg/dL)
0%
10%
20%
30%
40%
50%
60%
Baseline Order Set Algorithm
44%48%
53%
37,516 Patient Days monitored in 5800 patients with > 8 POC glu tests, day 1-14: (Pearson chi-square statistic p < .001)
Clinical Inertia Improves with Order Set and Algorithm
Glycemic Control "Inertia" Improves with Interventions
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1 2 3 4 5 6 7 8
Hospital Day
Pro
po
rtio
n o
f p
atie
nts
wit
h m
ean
d
aily
glu
< 1
80 m
g/d
L
Baseline
Order set
Algorithm
p < 0.001 for time period
Oh no! What Oh no! What about…… about……
HYPOGLYCEMIA!
Hypoglycemia
• All non critical care patients with POC values• 11,057 patients / 53,466 days / 148,466 POC tests• Hypoglycemia: ≤ 60 mg/dL • Extreme Hypoglycemia: ≤ 40 mg/dL • By patient day
– % of patient days with one or more hypoglycemic events
• Pearson chi-square statistic to compare: – TP 1 (Baseline) Nov ’02 – Oct ‘03– TP2 (Order Set) Nov ‘03 – Apr ’05– TP3 (Algorithm) May ’05 – Dec ’05
Percent of Patient Days with Hypoglycemia / Extreme Hypoglycemia decreased by 30% and 31%, respectively. (Pearson chi square p < .02)
> 53,000 patient days > 148,000 POC glu tests
Percent of patients with any glucose <=60 mg/dl
0%
2%
4%
6%
8%
10%
12%
14%
16%
18%
Complete CPOE implementation of
Approximately 100 fewer patients with Hypoglycemia per year
Month
Summary• Large opportunities for improvement• A safety and quality issue• Systems approach is needed• SHM and others now provide resources to assist
implementation teams with all “essential elements”• Use “Talking Points”, local anecdote, and small
sample data to gain institutional support• Reduced hypoglycemia can be compatible with
improved glycemic control on the wards• Controversy exists, but time for action is now
The first time subcutaneous insulin is ordered, the prescriber is asked for an actionable glycemic target. A prompt to order HbA1C is also presented.
The weight and markers of insulin sensitivity are elicited, as well as the form of the patient’s nutritional intake. (in this case, the patient is an obese 80 kg woman eating regular meals)
The Total Daily Dose (TDD) is calculated for the clinician, based on the information provided on the patient’s obesity and weight. The TDD can be adjusted by the physician. Alternate methods of calculating the TDD are also presented.