Title: Analyzing Hospital Discharge Data
1Analyzing Hospital Discharge Data
David Madigan Rutgers University
2Comparing 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.
3Data
- 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.
4SYSID 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
5Risk 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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7Hospital Responses
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9p-value computation
- n463 suppose actual number of deaths40
- e29.56
- p-value
p-value lt 0.05
10Concerns
- Ad-hoc groupings of strata
- Adequate risk adjustment for outcomes other
mortality? Sensitivity analysis? Hopeless? - Statistical testing versus estimation
- Simpsons paradox
11Risk 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
12Hierarchical Model
- Patients -gt physicians -gt hospitals
- Build a model using data at each level and
estimate quantities of interest
13Bayesian Hierarchical Model
MCMC via WinBUGS
14Goldstein and Spiegelhalter, 1996
15Discussion
- 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?