Analyzing Hospital Discharge Data

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Analyzing Hospital Discharge Data
David Madigan Rutgers University
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Comparing Outcomes Across Providers

• Florence Nightingale wrote in 1863

In attempting to arrive at the truth, I have
applied everywhere for information, but in
scarcely an instance have I been able to obtain
hospital records fit for any purposes of
comparisonI am fain to sum up with an urgent
appeal for adopting some uniform system of
publishing the statistical records of hospitals.
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Data

• Data of various kinds are now available e.g.
  data concerning all medicare/medicaid hospital
  admissions in standard format UB-92 covers gt95
  of all admissions nationally
• Considerable interest in using these data to
  compare providers (hospitals, physician groups,
  physicians, etc.)
• In Pennsylvannia, large corporations such as
  Westinghouse and Hershey Foods are a motivating
  force and use the data to select providers.

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SYSID DCSTATUS PPXDOW CANCER1
YEAR LOS SPX1DOW CANCER2
QUARTER DCHOUR SPX2DOW MDCHC4
PAF DCDOW SPX3DOW MQSEV
HREGION ECODE SPX4DOW MQNRSP
MAID PDX SPX5DOW PROFCHG
PTSEX SDX1 REFID TOTALCHG
ETHNIC SDX2 ATTID NONCVCHG
RACE SDX3 OPERID ROOMCHG
PSEUDOID SDX4 PAYTYPE1 ANCLRCHG
AGE SDX5 PAYTYPE2 DRUGCHG
AGECAT SDX6 PAYTYPE3 EQUIPCHG
PRIVZIP SDX7 ESTPAYER SPECLCHG
MKTSHARE SDX8 NAIC MISCCHG
COUNTY PPX OCCUR1 APRMDC
STATE SPX1 OCCUR2 APRDRG
ADTYPE SPX2 BILLTYPE APRSOI
ADSOURCE SPX3 DRGHOSP APRROM
ADHOUR SPX4 PCMU MQGCLUST
ADMDX SPX5 DRGHC4 MQGCELL
ADDOW  
Pennsylvannia Healthcare Cost Containment
Council. 2000-1, n800,000
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Risk Adjustment

• Discharge data like these allow for comparisons
  of, e.g., mortality rates for CABG procedure
  across hospitals.
• Some hospitals accept riskier patients than
  others a fair comparison must account for such
  differences.
• PHC4 (and many other organizations) use indirect
  standardization
• http//www.phc4.org

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Hospital Responses
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p-value computation

• n463 suppose actual number of deaths40
• e29.56
• p-value

p-value lt 0.05
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Concerns

• Ad-hoc groupings of strata
• Adequate risk adjustment for outcomes other
  mortality? Sensitivity analysis? Hopeless?
• Statistical testing versus estimation
• Simpsons paradox

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Risk Cat. N Rate Actual Number Expected Number
Low 800 1 8 8 (1)
High 200 8 16 10 (5)
A
SMR 24/18 1.33 p-value 0.07
Low 200 1 2 2 (1)
High 800 8 64 40 (5)
B
SMR 66/42 1.57 p-value 0.0002
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Hierarchical Model

• Patients -gt physicians -gt hospitals
• Build a model using data at each level and
  estimate quantities of interest

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Bayesian Hierarchical Model
MCMC via WinBUGS
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Goldstein and Spiegelhalter, 1996
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Discussion

• Markov chain Monte Carlo compute power enable
  hierarchical modeling
• Software is a significant barrier to the
  widespread application of better methodology
• Are these data useful for the study of disease?