QualityBased Purchasing: Challenges, Tough Decisions, and Options

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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 weve seen

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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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How CHART Might Play Out
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Tough Decisions General Ideas and Our
Experience in CHART

• Not because weve done it correctly in CHART, but
  just as a basis for discussion

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Tough Decision 1Collaboration vs. Competition?

• Among health plans
• Among providers
• With legislators and regulators

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Tough Decision 1Collaboration vs. Competition?

• Among health plans
• Among providers
• With legislators and regulators

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Tough Decision 1AWho 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 2Moving 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 2Moving 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
  winnersand many providers doubt theyll be
  winners initially (or ever)
• So, who picks the measures?

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Tough Decision 3Same 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 3Same Incentives for Everyone?

• Monopolies? Weve 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 4Encourage 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 6Use Outcomes Data?

• Especially important issue as sample sizes get
  small
• If we cant fix the sample size issue, well be
  forced to use general measures only (e.g.,
  patient experience measures)

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Outcome Reports

• 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)

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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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3 Groups of Hospitals with Repeated Measurements
(3 Years)
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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
  measuresneed to figure out how to fund data
  collection and auditing of new measures
• Consider varying incentives across providers