Fairview Health Systems Internal Reporting Quality Of Diabetes Care 2006-2009

1. 1 Fairview Health SystemsInternal Reporting Quality Of Diabetes Care2006-2009 Christopher A. Foley, MD Director, Preventive Vascular Services Minnesota Vascular Clinic Internal Medicine Staff Physician Fairview Oxboro Clinic Fairview Health Services

3. Fairview Health Systems Overview Ten Hospitals Statewide Three Academic, Seven Community 40 Primary Care Clinics 230 Employed Primary Care Physicians 132 Family Practice 36 Internal Medicine 26 Pediatricians 14 IM / Peds 22 OB / Gyn

4. Fairview Health Systems Overview Multiple Care Systems Throughout Minnesota Previously Local Care System Management Leading To: Local innovators Significant clinical variability within the overall organization Moving Forward Group Practice Accountability Model Streamline operations Borrow clinical operational methods from the best local sites , raising the bar for those who lag behind standards

5. Fairview Health Systems Overview Patient Demographics 173,000 Unique Patients 12,300 Diagnosed Diabetics Presumed approx 16,500 total diabetics Presumed approx 43,000 metabolic syndrome Diagnosed diabetics comprise 7.1% of system population 12.3% of total outpatient visits 30-40% hospitalizations

6. Fairview Health Systems Overview Minnesota Ranks Among Best in Nation In Quality of Diabetic Care Three FV Clinics Rank Among Best In MN FV Oxboro Clinic Ranked Number One In MN By Bridges To Excellence 2006 2007 2008 FV Maple Grove Clinic FV Rogers Clinic

7. 7 Provider And Clinic Level Variation in Internal Fairview Data Reporting for Diabetes Metabolic Control (2006) Christopher A. Foley, MD Director, Preventive Vascular Services Minnesota Vascular Clinic Internal Medicine Staff Physician Fairview Oxboro Clinic Fairview Health Services Q3 2006

8. 8 Objectives To examine progress in quality outcomes for three Fairview specialties FP, IM, IM/Peds: Diabetes All Seven To observe how quality outcomes vary by care system, department, and provider, and size of patient population

9. Seven Variables Followed A1C < 7 % A1C checked in last six months LDL-C < 100 mg/dl LDL-C checked in last 12 months BP < 130/80 Taking aspirin 81 mg daily or greater if indicated for other diagnoses Tobacco free

10. 10 Timeframes forDiabetes Results Care System - 2006 by Quarter Department - 2006 by Quarter Provider – 2006 YTD Provider and Department- 2006 YTD Scott: Look at relationship between volumes and variation % of time seeing patients with diabetes # of patients seen per year # of visits per year Etc.Scott: Look at relationship between volumes and variation % of time seeing patients with diabetes # of patients seen per year # of visits per year Etc.

11. 11 How does data vary by care system. This graph shows quarterly results for the diabetes all seven by care system. The specialty total is listed to the far right. Bar Graphs help to visualize performance over time. Y Vertical axis shows the level of performance X horizontal axis show performance over time; each bar represents a quarter. Each care system is displayed along with its corresponding quarterly results. How does data vary by care system. This graph shows quarterly results for the diabetes all seven by care system. The specialty total is listed to the far right. Bar Graphs help to visualize performance over time. Y Vertical axis shows the level of performance X horizontal axis show performance over time; each bar represents a quarter. Each care system is displayed along with its corresponding quarterly results.

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13. 13 How does data vary by provider? This graph is called a histogram. Histograms help us understand a population or distribution of data at a glance. Data is summarized into intervals, and the frequency of observations falling in the interval is counted. So I looked at results for 154 providers. Example. 0-2.5% 0%, 2.2% 12.5 to 15%. 47.5 to 50% 48.9% You can see I’ve excluded providers with small populations to reduce variation. Once we’ve graphed all data points, we can get a sense of the shape, center, and spread of the data distribution Center: Mean (average), Median (point where distribution is cut in two) Spread: if normal distribution, 68.3% of observations will fall within 1 standard deviation of mean. Shape: Skews to right. Median is lower than mean, not a normal distribution. Comment on performance: Min, Max, Target. How does data vary by provider? This graph is called a histogram. Histograms help us understand a population or distribution of data at a glance. Data is summarized into intervals, and the frequency of observations falling in the interval is counted. So I looked at results for 154 providers. Example. 0-2.5% 0%, 2.2% 12.5 to 15%. 47.5 to 50% 48.9% You can see I’ve excluded providers with small populations to reduce variation. Once we’ve graphed all data points, we can get a sense of the shape, center, and spread of the data distribution Center: Mean (average), Median (point where distribution is cut in two) Spread: if normal distribution, 68.3% of observations will fall within 1 standard deviation of mean. Shape: Skews to right. Median is lower than mean, not a normal distribution. Comment on performance: Min, Max, Target.

14. 14 This is a dot plot or frequency plot. This helps us to understand the dispersion of data points with categories. The Y axis lists departments. Within each department, I’ve graphed the results for each provider. Each dot is the providers %. Example. At Chisago Lakes, we have a cluster of providers in the 7 to 10 range, and one over 30 % I’ve displayed the median of all observations so you can get a sense of the top and bottom half. Data points in red are below the target for the initiative.This is a dot plot or frequency plot. This helps us to understand the dispersion of data points with categories. The Y axis lists departments. Within each department, I’ve graphed the results for each provider. Each dot is the providers %. Example. At Chisago Lakes, we have a cluster of providers in the 7 to 10 range, and one over 30 % I’ve displayed the median of all observations so you can get a sense of the top and bottom half. Data points in red are below the target for the initiative.

15. 15 Is there a relationship or association between size of a providers patient population and the health outcomes for that population. This is a scatter diagram, and its helpful in showing the relationship between two corresponding variables. On the vertical axis, Y I’ve displayed the % of a providers population meeting the target. On the X axis, I’ve displayed population size. Each dot is the intersection population size and % meeting target. So, we have providers with about 35 and 50 patients who all meet zero, one with over 300 well over 40%, and a large cluster in 40 to 75 range around 12%. Once graphed, we look for a shape that seems to move in a direction. The scatter here seems to move in a positive direction, as size gets bigger, so does the percentage meeting all criteria. The regression line is a mathematical representation of the direction and strength of the relationship. Its moving in a positive direction, but not very rapidly. The “r” is listed is called a correlation coefficient (pearson’s r). A coefficient is a number that ranges from 0 to zero, positive or negative from -1.0 to 1.0. 0 would be no relationship, a .4 would be a moderate relationship, and a .8 would be a strong relationship. Mary and Chocolate is a close to a perfect correlation; as chocolate goes up, so does Mary’s happiness. We rarely see perfect correlations in life. This .217 indicates a positive relationship, weak to moderate.Is there a relationship or association between size of a providers patient population and the health outcomes for that population. This is a scatter diagram, and its helpful in showing the relationship between two corresponding variables. On the vertical axis, Y I’ve displayed the % of a providers population meeting the target. On the X axis, I’ve displayed population size. Each dot is the intersection population size and % meeting target. So, we have providers with about 35 and 50 patients who all meet zero, one with over 300 well over 40%, and a large cluster in 40 to 75 range around 12%. Once graphed, we look for a shape that seems to move in a direction. The scatter here seems to move in a positive direction, as size gets bigger, so does the percentage meeting all criteria. The regression line is a mathematical representation of the direction and strength of the relationship. Its moving in a positive direction, but not very rapidly. The “r” is listed is called a correlation coefficient (pearson’s r). A coefficient is a number that ranges from 0 to zero, positive or negative from -1.0 to 1.0. 0 would be no relationship, a .4 would be a moderate relationship, and a .8 would be a strong relationship. Mary and Chocolate is a close to a perfect correlation; as chocolate goes up, so does Mary’s happiness. We rarely see perfect correlations in life. This .217 indicates a positive relationship, weak to moderate.

16. 16 This is an easier way to understand relationships between variables. I’ve organized providers into groups or categories based on size of population, than displayed the percentage meeting target. You can see that there is a positive relationship here. Not a prediction of a causal relationship that, its more of an association, high populations tend to have greater scores.This is an easier way to understand relationships between variables. I’ve organized providers into groups or categories based on size of population, than displayed the percentage meeting target. You can see that there is a positive relationship here. Not a prediction of a causal relationship that, its more of an association, high populations tend to have greater scores.

17. Fairview Health Systems2008 Diabetes Outcomes

18. Topics Internally Reported Results Trend 2006 to 2008 Comparison of Fairview and Groups Participating in Minnesota Community Measurement 2008 Variation by Provider and Clinic 2008 Longitudinal results by patient 2006 to 2008

19. Excludes Red Wing and MesabaExcludes Red Wing and Mesaba

20. MNCM Data- Includes Red Wing, Excludes Mesaba and CPMGMNCM Data- Includes Red Wing, Excludes Mesaba and CPMG

28. Do patients who were in control in 2006 maintain control two years later?

35. Factors Having Led To Diabetic Success - FV Oxboro Clinic Highly Motivated “Type A” Individual MDs Altruistic, nonfinancial motivation (doing what’s right for the pt) Electronic Medical Record Every physician “thinks they’re a good MD” When you actually see how good you are (or are not) you tend to improve Fraternal Camaraderie / Friendly Competition Not wanting your colleague to gain bragging rights Interdisciplinary approach Strong working relationship with on site CDEs

36. Factors Having Led To Diabetic Success - FV Oxboro Clinic Cultural Expectation Of Physician To Be Appropriately Medically Aggressive MD willingness to utilize new yet vetted treatment modalities GLP-1 mimetics No fear of TZD Insulin pumps Heavy MD utilization of continuous glucose monitoring Disease State Focus Rather Than Metric Focus Active MD awareness and willingness to treat prediabetic patients from a glycemic and global lipemic perspective to reduce residual cardiometabolic risk above and beyond LDL Physician adipovascular axial centricity MD culture of “overshooting” goals

37. Factors Having Led To Diabetic Success - FV Oxboro Clinic Strong Patient Messaging Making patients recognize need to take ownership of their obesity mediated insulin resistance Heavy Medication Sample Usage Reality is: many patients have difficulty accessing efficacious name nongeneric medications capable of affording persistent glycemic control, attaining lipemic control above and beyond LDL control Active Quality Review Officer on Site NonMD employee who reviews MD quality data at regular intervals to assure maintenance of control

38. Factors Having Led To Decreased Diabetic Control – FV Oxboro Clinic Some MDs / Patients are sprinters, not marathoners Physician fatigue Pt noncompliance through time Generic prescribing pressures lead to decreased persistence of metabolic control

39. Conclusions – Fairview Health System Experience EMR utilization mandatory to achieve initial control Degree of physician / CDE / patient ownership of dietary and pharmacologic management directly correlates with maintenance of control Administrative assistance a plus Allowing MDs to sample pt meds critical to maintenance of control