Episodes of Care: Background and Issues

1. Episodes of Care: Background and Issues James M Naessens, ScD Division of Health Care Policy & Research Mayo Clinic

2. Outline Episodes of Care Background Approaches Current Issues with Episodes CMS Health Affairs Sept/Oct 2009 Mayo Clinic Studies Referral Practice Chronic Disease Cohorts

3. Episodes of Care Concept first introduced in 1960�s by Solon J, et al.^ Advanced by Hornbrook M, et al.* �series of temporally contiguous health care services related to treatment of a given spell of illness or provided in response to a specific request by the patient�

4. Episode of Care Uses Provide measurement and treatment guidelines for physicians Define boundaries of reimbursement Determine risk adjustment For health care utilization analysis Operational aspects of health care delivery  (Mayo Clinic medical record  management)

5. Episode of Care Current Basis for Payment Projects Geisinger - Cardiac Surgery �guarantee� Medicare Acute Care Demonstration Project � bundling for ortho and CV procedures Medicare Physician Hospital Collaboration demonstration � immediate post hospital period

6. Our ProblemOutpatient Care Analysis Capitated model / primary care Patient Fee for service model Encounter Service Referral care Episode (??)

7. Billing Data (Input into MEG)

8. Example: one patient�s visits for one month

9. Episode Groupers Rosen and Mayer-Oakes* compared four major episode grouper programs: Episode Treatment Groups (ETG) Clinical Episode Groups (CEG) Physician Review System CareTrend With no distinctly superior product

10. Episode Groupers:Methodological Issues Starting Point (diagnosis, symptom or visit) End Point (defined length or �clean period�) Comprehensiveness of Services (concurrent episodes?) Clinical Complexity (chronic disease with flare-ups, unrelated acute illness, multiple comorbidities) Provider Attribution

11. CMS Episode Grouper Listening Session November 10, 2009 CMS intends on using input to write RFP on developing a transparent software for episodes of care for Medicare beneficiaries Multiple Chronic Conditions Post-acute Care Length of Chronic Episode Physician Services Risk Adjustment

12. Health Affairs Sept/Oct 2009 issue Episode-Based Performance Measurement And Payment: Making It A Reality Peter S. Hussey et al. From Volume To Value: Better Ways To Pay For Health Care Harold D. Miller Measurement Of And Reward For Efficiency In California�s Pay-For-Performance Program James C. Robinson et al.

13. Hussey article Applies ETGs and MEGs to Medicare part A & B data for 3 states, 2004-6. Identifies Issues with: Defining Episodes Different settings Single- vs. multi-condition focus Within group heterogeneity Attributing responsibility Calls for more empirical work

14. Miller article Suggests that each of 4 methods: FFS, Episodes, Capitation, Comprehensive care payments (condition-adjusted capitation) has role Issues to address: Bundling challenges Setting payment amounts Assuring quality Aligning incentives

15. Robinson article Reviews the California Integrated Healthcare Association Pay for Performance experience addressing efficiency using episodes (MEG) Issues: Small numbers of patients/episode Incomplete data Weights (standard or actual costs)

16. Mayo Cardiovascular Referral Practice Study Goals Do Medstat�s Episodes provide a useful management tool to help understand a multi-specialty group practice? Can we use MEG as a basis to understand different use patterns between rural and urban patients?

17. Methods Patients All patients seen in 2003 For outpatient service By a cardiovascular provider N=102,406 Setting Mayo Clinic, Rochester, Minnesota

18. Comparisons of Interest Primary care vs. referral Mayo Health System Local vs. regional vs. national

19. Episode Outcomes Cardiovascular intensity Low Diagnostic Cardiovascular E & M High Diagnostic Therapy Procedures Hospitalization Cost

20. Statistical Methodology Outcome models Do the types of episodes differ? Are the outcomes (average cost, hospitalization, and cardiovascular intensity) different between rural vs. urban patient after incorporating episode type, severity of episode and comorbidity?

21. Statistical Methodology Logistic and linear regression models developed to account for impacts of Mayo primary care, distance traveled, age, gender, pay source, and physician vs. self-referred. Impact of rural-urban influence added to adjusted model.

22. Summary Findings 96,601 patients with CV provided service in 2003 287,162 outpatient CV visits and 29,369 hospitalizations in 464,067 episodes (90,922 CV episodes)

23. Most Frequent Episodes with Cardiologist E & M Visit

24. Episodes withCardiologist E & M Visit 14 conditions had 1000+ episodes 22 conditions had 500 - 999 episodes 74 conditions had 100 - 499 episodes 62 conditions had 50 - 99 episodes 450 conditions had episodes

25. Influence of Distance and Primary Care

26. Cardiovascular IntensityEpisodes with CV E & M

27. Cardiovascular IntensityEpisodes with CV E & M

28. Mean Charge per Episode 

29. SummaryEpisodes in Specialty Practice Episodes of care were able to categorize both primary care and referral patients. However, after adjustment mean costs per episode differed significantly between the two groups for many types of episodes. Episodes developed for managed care practices may have limited utility for referral specialty practices. Further assessment needed on the differences between primary care and referral practice episodes.

30. Mayo Chronic Disease Cohort Study Goal How well do various systems capture and characterize the health care costs of people with chronic disease?

31. Methods Patients Mayo employees/dependents with continuous health benefit enrollment from 2003-2006 Cohort 1: Meet HEDIS definitions for diabetes in 2000-2003 Cohort 2: Meet HEDIS definitions for CAD in 2000-2003 Data Source Medical and Pharmacy Claims

32. Methods Generate Total Costs for 2003-2006 Apply Prometheus Models to Cohort Apply ETGs to Cohort WORK IN PROGRESS!

33. Diabetes Cohort

34. CAD Cohort

35. SummaryEpisodes in Chronic Disease Cohorts Different schemes identify different patients in disease cohorts. ETGs and Prometheus capture only a portion of costs of Diabetes and CAD cohorts. ETG hierarchy influences what they consider as disease-related costs.