Applying Practice-based QI Methods to Improve Preventive Care

1. Applying Practice-based QI Methods to Improve Preventive Care Robb Malone Greg Randolph

2. Systems �Every system is perfectly designed to get the results it gets� --Corollary: If you want better results, you have to make changes to the system! �The definition of insanity is continuing to do the same thing over and over again and expecting a different result� -Albert Einstein

3. Model for Improvement: Fundamental Questions for Improvement What are we trying to accomplish? How will we know that a change is an improvement? What changes can we make that will result in an improvement?

4. Evidence for Model in Prevention Model used to improve immunization UTD in academic pediatric continuity clinic from 60% to 86% (Mohr et al, Amb Pediatrics, 2003) Model used to improve preventive service rates in NC private practices from 7% to 34% UTD for all 4 services (Margolis et al, BMJ, 2004)

5. Model for Improvement Aim Measures Changes/Evidence-based strategies

6. Model for Improvement AIM: What are we trying to accomplish? MEASURES: How will we know that changes are an improvement? CHANGES: What changes can we make that will result in an improvement?

7. Importance of Aim Statement Captures early decisions Alignment of team Communication with others Touchstone throughout project Key to success

8. What is an Aim Statement? Aim: A written statement of the accomplishments expected from improvement effort Key components: A general description of aim - should answer, �what are we trying to accomplish?� Rationale/importance Some guidance for carrying out the work Specify target population and time period Measurable goals

9. Aim Statement Exercise See handout: Are the key elements of a good aim statement present? Any recommendations to improve the aim statement?

10. Aim Statements Key components: A general description of aim - should answer, �what are we trying to accomplish?� Rationale/importance Some guidance for carrying out the work Specify target population and time period Measurable goals

11. Model for Improvement AIM: What are we trying to accomplish? MEASURES: How will we know that changes are an improvement? CHANGES: What changes can we make that will result in an improvement?

12. Project Measures The question: How will we know that a change is an improvement? - usually requires more than one measure�.need a set of measures

13. Project Measures A set of measures helps assure that the system is improved: Related to aim�s measurable goals Easy to collect Show improvement quickly Include outcome, process, and �balancing� measures (assess for harm) Can display them graphically over time Run charts

14. Example of Measure Set % of 24-30 month old children who were up-to-date on recommended vaccines by 24 months of age % of parents of children < 2 with accurate hand-held immunization records at their well child visits % of visits among children < 2 with a prompting sheet completed total visit times for children < 2

15. Usual Display of Measures

16. QI Measurement: Annotated Run Charts

17. Model for Improvement AIM: What are we trying to accomplish? MEASURES: How will we know that change is an improvement? CHANGES: What changes can we make that will result in an improvement?

18. Changes Changes can come from: Evidence Models that have been shown to work well elsewhere Process mapping Brainstorming Changes need adaptation to local practice setting (PDSAs) Important to have a TEAM representing all parts of the system affected by changes involved in an improvement effort

19. Change Example: CDC Recommendations Measure immunization delivery annually Assessment and prompting at every visit Patient reminder and recall systems Up to date vaccination protocols readily available at point of care Accurate patient held records maintained

20. Adapting Changes Locally Vague, strategic Accurate patient held records maintained Make updating records systematic during preventive visits Develop/document process for nurses Pilot test new process for one day Specific, actionable (use PDSA cycle)

21. UNC General Internal Medicine Diabetes Program Examples from our quality improvement experience

22. GIM Diabetes Program Consists of 3 practitioners and 4 care assistants Utilizes a registry, evidenced based algorithms, and a stepped care approach All patients are categorized by risk Risk calculated on an ongoing basis, in real-time Calculation includes A1c, BP, ASA and statin utilization, and smoking and depression status, and the �x-factors� Goal is to closely follow all high-risk (red zone) patients and the majority of moderate-risk (yellow zone) patients, while leaving low-risk (green zone) patients to the PCP Intervention performed in clinic and by telephone

23. Role of The Diabetes Extenders Patient education Facilitate proactive care based on best evidence Address and co-manage glucose, lipids, hypertension, and depression with primary care provider Address and follow-up other issues identified by PCP Do not serve as primary care providers

24. Role of The Care Assistants Consists of 4 care assistants Care assistants see patients during provider visits Utilize the tools created by the database Assist the physician Facilitate proactive care, encourage intervention Address patient barriers, adherence, glucose monitoring, provide smoking cessation counseling, screen for depression

25. Description of Current Population Currently co-manage 1744 patients Average A1c 7.5% Information systems categorize patients by risk Green zone or �low risk� = 40% Yellow zone or �moderate risk� = 35% Red zone or �high risk� = 25% Risk determines intervention

26. Program Needs Assessment How can we identify new patients who need to be in the registry? Do we need to formalize an intervention for �low risk� patients? Are we happy with our medication utilization rates? Are we ready to specifically target lipid values and treatment targets? How do we increase the residents involvement in our program and quality improvement initiatives? Are we happy with how we address �other� quality markers?

27. Lipid Management

28. Status of Lipid Management March 2005 62% of patients had total cholesterol tested annually Approximately 68% were prescribed statins A standing order was in place for program extenders Average total cholesterol = 185 mg/dl Average LDL = 99 mg/dl

29. Model for Improvement: Lipids March 2005 Aim: Heart disease is common among patients with diabetes. Evidence exists re benefits. We aim to increase lipid testing rates and prescription of statins Measures: Percent patients receiving �adequate� yearly testing and prescription of statin therapy. Lipids should be checked once a year. Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Our goal for each measure is 90%. We also want 90% of patients taking a statin.

30. March 2005 Developed mechanism for physician prompting: The �green sheet� Background Per Division meeting 3/31/05 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. We would pilot test a �green lab sheet� with necessary information already filled out. This sheet recommends ordering Total Cholesterol and HDL. Per automated algorithm, all patients scheduled to receive a POC A1c would receive the �green sheet� if no lipid results were available within the past 11 months. Background Per Division meeting 3/31/05 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. We would pilot test a �green lab sheet� with necessary information already filled out. This sheet recommends ordering Total Cholesterol and HDL. Per automated algorithm, all patients scheduled to receive a POC A1c would receive the �green sheet� if no lipid results were available within the past 11 months.

31. Lipid Testing Follow-up 8/25/2005 Background Per Division meeting 3/31/05 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. We would pilot test a �green lab sheet� with necessary information already filled out. This sheet recommends ordering Total Cholesterol and HDL. Per automated algorithm, all patients scheduled to receive a POC A1c would receive the �green sheet� if no lipid results were available within the past 11 months. We reviewed and discussed this during the 7/28/05 Division Meeting.� Agreement with the above recommendations was confirmed.� The consensus was that returning to automated lipid testing that was done PRIOR to the provider visit was the best option to attain our goal. �Although this recommendation was made, we planned to make one more intervention prior to considering this change.� Starting Monday 8/8/05 we separated lipid testing recommendations from POC A1c testing.� This would increase the number of recommendations (�green sheets�) for lipid testing (non-HDL). � Increase provider awareness of these recommendations and the intent of the �green sheet�. � TC/HDL Ordered 7/28/05- 11%, 8/25/05 20% Assessed reasons for not ordering� LDL Ordered, Recent dose change of lipid med, started or planned to start med, nonadherence or off med, lipids recently checkedBackground Per Division meeting 3/31/05 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. We would pilot test a �green lab sheet� with necessary information already filled out. This sheet recommends ordering Total Cholesterol and HDL. Per automated algorithm, all patients scheduled to receive a POC A1c would receive the �green sheet� if no lipid results were available within the past 11 months. We reviewed and discussed this during the 7/28/05 Division Meeting.� Agreement with the above recommendations was confirmed.� The consensus was that returning to automated lipid testing that was done PRIOR to the provider visit was the best option to attain our goal. �Although this recommendation was made, we planned to make one more intervention prior to considering this change.� Starting Monday 8/8/05 we separated lipid testing recommendations from POC A1c testing.� This would increase the number of recommendations (�green sheets�) for lipid testing (non-HDL). � Increase provider awareness of these recommendations and the intent of the �green sheet�. � TC/HDL Ordered 7/28/05- 11%, 8/25/05 20% Assessed reasons for not ordering� LDL Ordered, Recent dose change of lipid med, started or planned to start med, nonadherence or off med, lipids recently checked

32. Model for Improvement: Lipids Sept 2005 Aim: Heart disease is common among patients with diabetes. Evidence exists re benefits. We aim to increase TC testing rates and prescription of statins Measures: Percent patients receiving yearly TC testing and prescription of statin therapy. TC should be checked once a year. Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Our goal for each measure is 90%. Prompting can not reach this goal. We still want 90% of patients taking a statin.

33. Developed mechanism for automated ordering: Sept 2005 Summary: Of the patients who showed for their appointment this week, 15 patients were in need of a POC A1c AND total cholesterol/HDL.� Of these patients, 11 (73%) had the POC A1c as recommended and 13 (87%) had a total cholesterol/HDL obtained. 6 patients successfully had other labs added to the venipuncture sample. �1 patient went to phlebotomy again for a CBC which could not be added on to the TC/HDL sample. Assessment: The process works and appears to be rather easy for providers. It appears that only 1 patient required addition venipuncture. Our rate of obtaining POC A1c needs to improve. �In previous weeks we were above 95%. This process will be revisited again in a November division meeting. �Please provide feedback, comments, or concerns as they arise. Summary: Of the patients who showed for their appointment this week, 15 patients were in need of a POC A1c AND total cholesterol/HDL.� Of these patients, 11 (73%) had the POC A1c as recommended and 13 (87%) had a total cholesterol/HDL obtained. 6 patients successfully had other labs added to the venipuncture sample. �1 patient went to phlebotomy again for a CBC which could not be added on to the TC/HDL sample. Assessment: The process works and appears to be rather easy for providers. It appears that only 1 patient required addition venipuncture. Our rate of obtaining POC A1c needs to improve. �In previous weeks we were above 95%. This process will be revisited again in a November division meeting. �Please provide feedback, comments, or concerns as they arise.

34. Lipid Testing Follow-up 11/17/2005 Lipid Testing Follow-up 11/17/2005 Background In August 2005 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Per automated algorithm, all patients scheduled to receive a POC A1c within the past 11 months would have total cholesterol and HDL In September of 2005 we eliminated inactive patients from our registry to obtain a more accurate denominator. In January and February 2006 we redoubled efforts to ensure CBAs follow TC/HDL ordering protocols. We set a goal for ordering accuracy per protocol at 90%. In February of 2006 we decrease the protocol ordering interval from 11 to 10 months. TC/HDL Ordered 7/28/05 11% 8/25/05 20% 9/23/05 87% 11/11/05 60% 2/10/06 100% Additional phlebotomy required 7-10% Summary An increase in Lipid testing has been noted, however remains significantly under our goal of 90%. Will reevaluate interventions to improve CBA TC/HDL ordering per protocol over the next 3 months.Lipid Testing Follow-up 11/17/2005 Background In August 2005 we adopted lipid testing guidelines for patients with diabetes: Lipids should be checked once a year. We set our goal for testing at 90% (HEDIS 88%). Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Per automated algorithm, all patients scheduled to receive a POC A1c within the past 11 months would have total cholesterol and HDL In September of 2005 we eliminated inactive patients from our registry to obtain a more accurate denominator. In January and February 2006 we redoubled efforts to ensure CBAs follow TC/HDL ordering protocols. We set a goal for ordering accuracy per protocol at 90%. In February of 2006 we decrease the protocol ordering interval from 11 to 10 months. TC/HDL Ordered 7/28/05 11% 8/25/05 20% 9/23/05 87% 11/11/05 60% 2/10/06 100% Additional phlebotomy required 7-10% Summary An increase in Lipid testing has been noted, however remains significantly under our goal of 90%. Will reevaluate interventions to improve CBA TC/HDL ordering per protocol over the next 3 months.

35. Model for Improvement: Lipids Jan 2006 Aim: Heart disease is common among patients with diabetes. Evidence exists re benefits. We aim to increase TC testing rates and prescription of statins Measures: Percent patients receiving yearly TC testing and prescription of statin therapy. TC should be checked once a year. Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Our goal for each measure is 90%. Automated ordering will work but requires front-desk and triage intervention. We still want 90% of patients taking a statin.

36. Lipid testing year to date To go from 85 to 90% would require seperation of the POc A1c and TC/HDL process. We can not do this at this time due to capacity of our POC lab.To go from 85 to 90% would require seperation of the POc A1c and TC/HDL process. We can not do this at this time due to capacity of our POC lab.

37. Model for Improvement: Lipids Jan 2006 Aim: Heart disease is common among patients with diabetes. Evidence exists re benefits. We aim to increase TC testing rates and prescription of statins Measures: Percent patients receiving yearly TC testing and prescription of statin therapy. TC should be checked once a year. Non-HDL cholesterol is an adequate measure to initiate, titrate, and monitor statin therapy. Our goal for each measure is 90%. Automated ordering, staff evaluation, and provider prompting are keys to success. We still want 90% of patients taking a statin.

38. Developed mechanism for prompting providers re statin prescription: June 2006 Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher

39. Annual Diabetes Quality: Highest percentage of statin utilization Highest percentage of statin utilization (%) First place (tie): Tom Miller (100) First place (tie): Amy Weil (100) Runner-up: Marco Aleman (93.75) Honorable mention (tie): Darren Dewalt (93.33) Honorable mention (tie): Stacey Sheridan (93.33) Highest Provider Detail Sheet Participation First place: Marco Aleman Honorable mention: Cristin Colford Lowest AVG SBP (mmHg) First place: Stacey Sheridan (125.8) Honorable mention: Brian Goldstein (127.7) Lowest AVG Total Cholesterol (mg/dl) First place: Tom Keyserling (160.7) Honorable mention: Tom Miller (166.7) Highest percentage of patients with lipids tested in the last 12 months (%) First place: Christopher Klipstein (95.9) Honorable mention: Cristin Colford (94.3) Lowest AVG A1c (%) First place: Stacey Sheridan (6.8) Honorable mention: Paul Chelminski (6.9) Highest percentage of patients with an A1c in the last 6 months (%) First place: Tom Keyserling (88.9) Honorable mention: Darren DeWalt (87.5) Highest percentage of ASA utilization (%) First place: Thomas Keyserling (94.5) Honorable mention: Nancy Henley (92.3) Highest percentage of statin utilization (%) First place: Darren DeWalt (92.8) Honorable mention: Mike Pignone (90.5) Highest percentage of patients with an ophthalmology visit in the last 12 months (%) First place: Darren DeWalt (60.0) Honorable mention: Thomas Miller (51.5) Highest Provider Detail Sheet Participation First place: Marco Aleman Honorable mention: Cristin Colford Lowest AVG SBP (mmHg) First place: Stacey Sheridan (125.8) Honorable mention: Brian Goldstein (127.7) Lowest AVG Total Cholesterol (mg/dl) First place: Tom Keyserling (160.7) Honorable mention: Tom Miller (166.7) Highest percentage of patients with lipids tested in the last 12 months (%) First place: Christopher Klipstein (95.9) Honorable mention: Cristin Colford (94.3) Lowest AVG A1c (%) First place: Stacey Sheridan (6.8) Honorable mention: Paul Chelminski (6.9) Highest percentage of patients with an A1c in the last 6 months (%) First place: Tom Keyserling (88.9) Honorable mention: Darren DeWalt (87.5) Highest percentage of ASA utilization (%) First place: Thomas Keyserling (94.5) Honorable mention: Nancy Henley (92.3) Highest percentage of statin utilization (%) First place: Darren DeWalt (92.8) Honorable mention: Mike Pignone (90.5) Highest percentage of patients with an ophthalmology visit in the last 12 months (%) First place: Darren DeWalt (60.0) Honorable mention: Thomas Miller (51.5)

41. Interventions impact on utilization

42. Interventions impact on lipids

43. Current interventions and assessment Several current projects have been started focusing on medication utilization Statins in DM patients with indication who are over 40 years old ACE/ARB in DM patients with indication who are over 40 years old ASA in DM patients with indication who are over 40 years old

44. Strengths of these cycles Group consensus that impacted care Clear aims and established goals Measurable impact Continued monitoring and intervention Continuing interventions and assessing change this month

45. Nurse-directed planned care: The yellow sheets

46. Interventions June 2006 Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher

47. Process to engage nurses: October 2007 Solidified Divisional support utilization of the intervention Build off of previous success Developed educational session with nurses: Meeting introduction by medical director Revisited intent of the yellow sheets Reiterated the role of the nurse as an integral member of our team Reviewed evidence behind our recommendations Developed rapid means of feedback

48. Build off of previous success! As of September 2007: 94% have been assessed for Pneumovax Status 72% have had a Pneumovax at least once

49. Items to be included in nurse assessment: October 2007 Assess as indicated on the yellow sheet Pneumococcal vaccination Depression screening Smoking assessment and intervention Monofilament testing Eye referrals

50. Interventions October 2007 Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher Introduced 6/2006 Intended to obtain several goals: Facilitate dissemination of collected information in an efficient manner Promote documentation of interventions in the medical record Promote billing at appropriate levels among ATTENDING physicians Baseline: 55% of DM visits with ATTENDINGS billed 99214 or higher Follow-up: 56% of DM visits with ATTENDINGS billed 99214 or higher

51. Depression Screening: Diabetes As of September 2007: 46% have been screened in the past year 62% have been screened at least once 21% screened in the past year are depressed

52. The New Yellow Sheet: Depression

53. Tobacco Data: Diabetes As of September 2007 98% have had smoking status assessed 20% are current tobacco users (cigarettes or smokeless)

54. Yellow Sheet: Smoking

55. Ophthalmology Status � Current Data As of September 2007: We have assessed 98-99% of patients 52% have had a dilated eye exam in past year 70% have had a dilated eye exam in past 2 years

56. The New Yellow Sheet: Ophthalmology

57. Monofilament Data: Diabetes As of September 2007: 49% have been assessed for monofilament exam 48% have had a monofilament exam in the past year

58. The New Yellow Sheet: Monofilament

59. Simple procedure for tracking daily progress

60. Report outcomes: Nurses

61. Report outcomes: Providers

62. Next Steps Continue to focus on nursing and ancillary staff as a means to obtain quality goals Revisit tasks of nurse triage Revise WebCIS Health Maintenance prompting and Q&A

63. QI Resources Institute for Healthcare Improvement: http://www.ihi.org/ihi National Initiative for Children�s Healthcare Quality http://www.nichq.org/nichq The Improvement Guide, Langley et al. 1996 UNC General Internal Medicine, Enhanced Carehttp://www.med.unc.edu/medicine/generalm/resourcepages.html