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Overview of Quality Improvement Focus on Designing Reliable Interventions Greg Maynard MD, MS Professor of Clinical Medicine and Chief, Division of Hospital.

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Presentation on theme: "Overview of Quality Improvement Focus on Designing Reliable Interventions Greg Maynard MD, MS Professor of Clinical Medicine and Chief, Division of Hospital."— Presentation transcript:

1 Overview of Quality Improvement Focus on Designing Reliable Interventions Greg Maynard MD, MS Professor of Clinical Medicine and Chief, Division of Hospital Medicine University of California, San Diego

2 Quality Improvement: Bridging the Implementation Gap Implementation Gap Scientific understanding Patient care Progress Time

3 Working harder isnt always the answer……

4 The Evolving Culture of Medicine 20 th Century Characteristics –Autonomy –Solo Practice –Continuous learning –Infallibility –Individual Knowledge 21 st Century Characteristics –Teamwork & systems –Group practice –Continuous improvement –Multidisciplinary problem solving –Change Shine, KI. Acad.Med. 2002;77:91-99

5 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 youve made a difference? Comprehensive educational program

6 Traditional Quality Assurance outliers

7 Quality Improvement worse better worse better Quality After Before

8 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

9 Good Teamwork is Essential

10 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

11 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

12 Establishing Measures Establishing Measures Teams use quantitative measures to determine if a specific change actually leads to an improvement. Setting Aims Setting Aims Improvement requires setting aims. The aim should be time-specific and measurable, with a defined population. Selecting Changes Selecting Changes All 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 Changes Testing Changes The 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

13 Features of Good Aim Statements Specific Measurable Aggressive yet Achievable Relevant Time-bound

14 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%.

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16 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

17 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

18 Picabo Street and Communication Olympic Gold Medal Winner….AND a Critical Care Nurse!

19 Picabo, ICU

20 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 reliability strategies) Oversights identified and addressed in real time Level Predicted Success rate 40% 50% 65-85% 90% 95+%

21 Order sets w/ embedded insulin orders: Standardization?

22 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

23 CAUTION!!!! Be Sure to Insert a Brain Between Protocol and Patient! Education for broad range of providers Consider special team of focused providers

24 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

25 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

26 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! ActPlan StudyDoDo

27 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

28 Examples: integration of best practice A1c level within last 30 days. Specify hyperglycemic diagnosis Each patient should have a glycemic target.

29 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

30 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

31 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

32 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 definition How to generate report Who acts on report Putting this in place moves you up hierarchy of reliability. Opportunity to Learn from variation!

33 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 reliability strategies) Oversights identified and addressed in real time Level Predicted Success rate 40% 50% 65-85% 90% 95+% eliminate variation arising from professionals; retain variation arising from patients

34 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

35 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

36 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

37 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

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39 The Use of Basal Insulin Increases (sliding scale only regimens decline) patients sampled per month, no formal analysis done, results sustained

40 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

41 62 % 69% 73% 5800 patients w/ > 8 POC glucose values, day 1-14 values: p value <.02 (Pearson chi-square statistic)

42 1 st order set BaselineOrder set Algorithm

43 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)

44 Clinical Inertia Improves with Order Set and Algorithm

45 Oh no! What about…… HYPOGLYCEMIA !

46 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

47 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

48 Approximately 100 fewer patients with Hypoglycemia per year Month

49 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

50 The first time subcutaneous insulin is ordered, the prescriber is asked for an actionable glycemic target. A prompt to order HbA1C is also presented.

51 The weight and markers of insulin sensitivity are elicited, as well as the form of the patients nutritional intake. (in this case, the patient is an obese 80 kg woman eating regular meals)

52 The Total Daily Dose (TDD) is calculated for the clinician, based on the information provided on the patients obesity and weight. The TDD can be adjusted by the physician. Alternate methods of calculating the TDD are also presented.


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