1. Analyzing Hospital Discharge Data David Madigan Rutgers University

2. 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 comparison…I am fain to sum up with an urgent appeal for adopting some uniform system of publishing the statistical records of hospitals.”

3. Data Data of various kinds are now available; e.g. data concerning all medicare/medicaid hospital admissions in standard format UB-92; covers >95% 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.

4. SYSIDDCSTATUSPPXDOWCANCER1 YEARLOSSPX1DOWCANCER2 QUARTERDCHOURSPX2DOWMDCHC4 PAFDCDOWSPX3DOWMQSEV HREGIONECODESPX4DOWMQNRSP MAIDPDXSPX5DOWPROFCHG PTSEXSDX1REFIDTOTALCHG ETHNICSDX2ATTIDNONCVCHG RACESDX3OPERIDROOMCHG PSEUDOIDSDX4PAYTYPE1ANCLRCHG AGESDX5PAYTYPE2DRUGCHG AGECATSDX6PAYTYPE3EQUIPCHG PRIVZIPSDX7ESTPAYERSPECLCHG MKTSHARESDX8NAICMISCCHG COUNTYPPXOCCUR1APRMDC STATESPX1OCCUR2APRDRG ADTYPESPX2BILLTYPEAPRSOI ADSOURCESPX3DRGHOSPAPRROM ADHOURSPX4PCMUMQGCLUST ADMDXSPX5DRGHC4MQGCELL ADDOW Pennsylvannia Healthcare Cost Containment Council. 2000-1, n=800,000

5. 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

7. Hospital Responses

9. p-value computation n=463; suppose actual number of deaths=40 e=29.56; p-value = p-value < 0.05

10. Concerns Ad-hoc groupings of strata Adequate risk adjustment for outcomes other mortality? Sensitivity analysis? Hopeless? Statistical testing versus estimation Simpson’s paradox

11. Risk Cat.NRateActual Number Expected Number Low8001%88 (1%) High2008%1610 (5%) Low2001%22 (1%) High8008%6440 (5%) A B SMR = 24/18 = 1.33; p-value = 0.07 SMR = 66/42 = 1.57; p-value = 0.0002

12. Hierarchical Model Patients -> physicians -> hospitals Build a model using data at each level and estimate quantities of interest

13. Bayesian Hierarchical Model MCMC via WinBUGS

14. Goldstein and Spiegelhalter, 1996

15. 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?