1. Does Medicare Managed Care Affect Diabetic Patient Cost, Use and Quality Associated with Different Medical Labor Inputs? Bianca K. Frogner Stephen T. Parente Lisa Tomai Nawal Lutfiyya Frank Cerra Presentation at ASHEcon, University of Pennsylvania, 2016 DRAFT as of May 28, 2016 – DO NOT QUOTE/DISTRIBUTE

2. Overview The Medicare market has changed dramatically since the passage of the Medicare Modernization Act (MMA) in 2003. The managed care share of the Medicare population has nearly tripled from 13% in 2004 to 30% in 2014 (Kaiser Family Foundation, 2015). At the same time, the production of medical care for patients with chronic conditions has altered significantly as the use of non-physician medical labor has increased.

3. Approach In this paper we use claims data for the diabetic population from 2011 to 2013 to compare how health care demand, cost and quality differ between fee-for-service (FFS) and Medicare Advantage (MA) plans. Furthermore, we examine whether there are annual differences in these measures for nurse practitioners (NPs) and primary care physicians (PCPs).

4. Data The data for this analysis come from two sources. – For the traditional FFS Medicare program, we use the 5% Standard Analytic File of claims and beneficiary data representing roughly 1.7 M covered lives. – For the MA population, we use claims and enrollment data furnished by the Health Care Cost Institute (HCCI), which represents claims from several large insurers operating across multiple states.

5. Study Cohort Parameters Patient in Medicare Advantage v. Medicare Fee-for- Service Majority care provided by Nurse Practitioner v. Primary Care Physician (i.e., family practice, internal medicine) Three-category health status measure using a medical productivity index (MPI).

6. Medical Productivity Index (MPI) MPI = Health Status / Medical Input (lagged) Where: – Health Status: Inverse of illness burden as measured by the number of and severity of medical diagnoses you have as a patient (i.e. count of ACGs) – Medical Input: Medical/physician effort on medical procedures in all settings (e.g., inpatient, outpatient, long term care, office) measures by physician time units by reimbursable procedure codes – heart of Medicare $$

7. Medical Productivity Index (MPI) Given the high variability in the ratio between provider charges and payment, the use of a simple set of non-cost variables permits robust comparison of clinical input and output at the patient level without relying on negotiated provider prices for services as a potential biased metric of resource consumption. A key design feature of the MPI is the use of common variables found in insurance claims data used by all public and private insurers. How deployed: – Prototype using faculty licensed Medicare data – Use health insurance claims – Calculated by insured person and summed up – Health is current – Medical input is 90 day lagged – 3 categories MPI40 = Person MPI is at 40 th percentile or less of health distribution (not healthy) MPI80 = Person MPI is at 80 th percentile or higher of health distribution (healthy) MPI4080 = in between

8. Estimation Strategy LHS Variables – Cost (allowed amount)  GLM regression with logged dollars – Utilization  GLM Regression – Quality Care for Diabetes  Probit for prevalence of any occurrence. RHS Variables – Patient age, gender, MPI strata for diabetes as proxy for case-mix, state and year dummies

9. Ex Ante Study Hypotheses H1: Medicare Advantage will yield – Better outcomes – Lower cost H2: Nurse Practitioners on teams with yield – Better outcomes – Lower cost

10. Descriptive Results

11. Outcomes Metrics at Baseline

12. Cost Results

13. Utilization Results

14. Quality Results

15. Ex Post (Early) Results H1: Medicare Advantage will yield – Better outcomes OK. Good Process. Reduced mortality. – Lower cost Fail. Generally higher costs – likely due to high reimbursement for physician and outpatient hospital. H2: Nurse Practitioners on teams with yield – Better outcomes Mixed. OK on process and fail or mortality. – Lower cost OK. Solid on all except hospital outpatient.

16. See if 2013 $$ bump are a real post recession effect using 2014 FFS data Replace MPI with traditional case-mix adjuster to see if results vary. State-specific analysis when sample size permits. Prior unpublished work showed substantial state- specific variation. Would be good to investigate for greater insight RE policy implications and welfare analysis. State-specific scope of practice analysis extension. Try as a 3 year closed panel study. Next Steps

17. December 11-13, 2016, British Virgin Islands. Best 8 papers get 3 days accommodation and $1k travel allowance. Deadline for abstract/paper submission is 9/1/2016 to ches2016@terramedica.co.uk ches2016@terramedica.co.uk Look for email invite Consider CHES 2016 2 nd Caribbean Health Economics Sympos ium

18. Special Thanks National Center for Interprofessional Education for financing. HCCI staff especially Eric Barrette and Amanda Frost RESDAC American Action Forum Senate Finance Committee