Quality-Based Purchasing: Challenges, Tough Decisions, and Options

1. Quality-Based Purchasing: Challenges, Tough Decisions, and Options R. Adams Dudley, MD, MBA Support: Agency for Healthcare Research and Quality, California Healthcare Foundation, Robert Wood Johnson Foundation Investigator Award Program, Blue Shield of California Foundation

2. Dudley 2006 2 Outline of Talk A brief description of a real world example of performance measurement Addressing the tough decisions, with reference to some solutions we�ve seen

3. CHART: California Hospital Assessment and Reporting Task Force A collaboration between California hospitals, clinicians, patients, health plans, and purchasers Supported by the California HealthCare Foundation, Blue Shield of California Foundation, and California hospitals and health plans

4. Dudley 2006 4 Participants in CHART All the stakeholders: Hospitals: e.g., CHA, hospital systems, individual hospitals Physicians: e.g., California Medical Association Consumers/Labor: e.g., Consumers Union/California Labor Federation Employers: e.g., PBGH, CalPERS Health Plans: every plan with =3% market share Regulators: e.g., JCAHO, OSHPD, NQF Government Programs: CMS, MediCal

5. Dudley 2006 5 How CHART Might Play Out Point out: How data flows Group options/responsibilities Clarify mix and match on clinical measures Will plans run data for groups not contracted? Point out: How data flows Group options/responsibilities Clarify mix and match on clinical measures Will plans run data for groups not contracted?

6. Dudley 2006 6 Tough Decisions: General Ideas and Our Experience in CHART Not because we�ve done it correctly in CHART, but just as a basis for discussion

7. Dudley 2006 7 Tough Decision #1:Collaboration vs. Competition? Among health plans Among providers With legislators and regulators

8. Dudley 2006 8 Tough Decision #1:Collaboration vs. Competition? Among health plans Among providers With legislators and regulators

9. Dudley 2006 9 Tough Decision #1A:Who can collaborate? Easier to identify partners in urban areas Puget Sound Health Alliance is a good example of a multi-stakeholder coalition In rural areas? Consider medical societies for leadership, as providers are often fragmented

10. Dudley 2006 10 Tough Decision #2:Moving Beyond HEDIS/JCAHO No other measure sets routinely collected, audited If you want public reporting or P4P of new measures, must balance data collection and auditing costs vs. information gained Admin data involves less data collection cost, equal or more auditing costs Chart abstraction much more expensive data collection, equal or less auditing

11. Dudley 2006 11 Tough Decision #2:Moving Beyond HEDIS/JCAHO If plans or a coalition drive the introduction of new quality measurement costs, who pays and how? Some approaches to P4P only reward the winners�and many providers doubt they�ll be winners initially (or ever) So, who picks the measures?

12. Dudley 2006 12 Tough Decision #3:Same Incentives for Everyone? Does it make sense to set up incentive programs that are the same for everyone? This would be unusual in many other industries Providers differ in important ways Baseline performance/potential Preferred rewards (more patients vs. more $) Monopolies and safety net providers

13. Dudley 2006 13 Tough Decision #3:Same Incentives for Everyone? Monopolies? We�ve seen situations in which payers bristle at the idea of paying monopolists more What about providers that are already too busy?

14. Dudley 2006 14 Tough Decision #4:Encourage Investment? Much of the difficulty we face in starting public reporting or P4P comes from the lack of flexible IT that can cheaply generate performance data. Similarly, much QI is best achieved by creating new team approaches to care. Should we explicitly pay for these changes?

15. Dudley 2006 15 Tough Decision #5: Use Only National Measures or Local? Well this is easy, national, right? Hmmm. Have you ever tried this? Is there any �there� there? Are there agreed upon, non-proprietary data definitions and benchmarks? Even with NQF? Maybe you should be leading NQF??

16. Dudley 2006 16 A Local Measure Developed in CHART Consumers wanted C-section rates Hospitals pointed out there is no accepted �appropriate� or �optimal� C-section rate, and that an overall rate should be risk-adjusted Solution: C-section rate for uncomplicated first pregnancies (to give sense of �tendency to do C-section�), without any quality label attached

17. Dudley 2006 17 Tough Decision #6:Use Outcomes Data? Especially important issue as sample sizes get small If we can�t fix the sample size issue, we�ll be forced to use general measures only (e.g., patient experience measures)

18. Dudley 2006 18 Some providers are concerned about random events causing variation in reported outcomes that could: Ruin reputations (if there is public reporting) Cause financial harm (if direct financial incentives are based on outcomes) Outcome Reports

19. Dudley 2006 19 An Analysis of MI Outcomes and Hospital �Grades� � From California hospital-level risk-adjusted MI mortality data: Fairly consistent pattern over 8 years: 10% of hospitals labeled �worse than expected�, 10% �better�, 80% �as expected� Processes of care for MI worse among those with higher mortality, better among those with lower mortality From these data, calculate mortality rates for �worse�, �better�, and �as expected� groups

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21. Dudley 2006 21 3 Groups of Hospitals with Repeated Measurements (3 Years)

22. Dudley 2006 22 Outcomes Reports and Random Variation: Conclusions Random variation can have an important impact on any single measurement Repeating measures reduces the impact of chance Provider performance is more likely to align along a spectrum rather than lumped into two groups whose outcomes are quite similar Providers on the superior end of the performance spectrum will almost never be labeled poor

23. Dudley 2006 23 Conclusions Many tough decisions ahead Avoid paralysis or legislators and regulators will lead Consider collaboration on the choice of measures Everyone frustrated with JCAHO and HEDIS measures�need to figure out how to fund data collection and auditing of new measures Consider varying incentives across providers