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

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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

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

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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

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ORClinical Measures

IT or Other Structural Measures

Patient Experience and Satisfaction Measures

Admin data

Specialized clinical data collection

PEP-C Scores

Survey Tools and Documentation

Data Aggregator - Produces one set

of scores per hospital

Reportto

Hospitals

Report toHealthPlansand

Purchasers

Reportto

Public

How CHART Might Play Out

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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

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Tough Decision #1:Collaboration vs. Competition?

• Among health plans

• Among providers

• With legislators and regulators

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Tough Decision #1:Collaboration vs. Competition?

• Among health plans

• Among providers

• With legislators and regulators

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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

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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

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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?

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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

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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?

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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?

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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??

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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

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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)

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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

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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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Probability Distribution of Risk Adjusted Mortality Rate for Mean Hospital in Each Sub-Group

17.1%12.2%8.6%

7.6% 16.6%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

0% 5% 10% 15% 20% 25% 30%

Risk Adjusted Mortality Rate

Probability Distribution Mortality Outcome

Poor Qu ality Hospitals

Good Quality Hospitals

Superior Qua lity

HospitalsAll Hospitals in Mod el

Low Trim Point

High Trim Point

Poor Hospital Mean

Good Hospital Mean

Superior HospitalMean

Scenario #3: 200 patients per hospital; trim points calculated using normal distribution around population mean, 2 tails, each with 2.5% of distribution contained beyond trim points.

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3 Groups of Hospitals with Repeated Measurements (3 Years)

Predictive Values

3 Year Star Scores

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

3 4 5 6 7 8 9

Hospital Star Score

Proportion Total Hospitals

Superior Quality Hospital

Expected Quality Hospital

Poor Quality Hospital

Scenario #3

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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

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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