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Introduction

“Peak demand in hospitals and patient outcomes” Christoph Schwierz, RWI Essen Boris Augurzky, RWI Essen, IZA Bonn Axel Focke, University Duisburg-Essen Jürgen Wasem, University Duisburg-Essen . Research question Sudden surge in demand Quality of treatment of hospital-in-patients?

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Introduction

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  1. “Peak demand in hospitals and patient outcomes”Christoph Schwierz, RWI EssenBoris Augurzky, RWI Essen, IZA BonnAxel Focke, University Duisburg-EssenJürgen Wasem, University Duisburg-Essen

  2. Research question Sudden surge in demand Quality of treatment of hospital-in-patients? Rationale High variation in daily patient volume Legal requirements for staff-to-patient ratios? Introduction

  3. Comparing patient outcomes across hospitals Unobservable hospital differences Unobservable selection of patients Comparing patient outcomes within hospitals Unobservable selection of patients Literature review

  4. Data and samples Key variables: Patient outcomes, demand, selection Estimation strategies Issues

  5. Hospital discharge data 730.000 patient cases From 430 medical departments within 72 acute- care German hospitals Patient characteristics: Age, sex, insurance type Several risk-adjusters: 4-digit diagnosis, patient clinical complexity level, relative diagnosis weight, etc. Data

  6. Samples

  7. Excess length of stay In-hospital mortality Died within 1 day after admission Died within hospital stay Emergency readmission Readmitted as an emergency case within 15 days after last discharge Patient outcomes

  8. Deviation of actual from predicted patient count Result 89% of variation in patient volume is predictable Key variables:Unexpected Demand Variation

  9. Excess share of admissions(Dobkin 2003) Excess weekend mortality due to higher unobservable frailty of patients admitted on weekends With selection index excess mortality on weekends disappears Key variables: Unobservable selection

  10. Estimation strategy Multivariate econometric methods • Dependent variables: • Excess length of stay • Probability of in-hospital death • Probability of the occurrence of an emergency readmission • Explanatory variables • Unexpected demand variation • Index of unobservable selection • Risk-adjusters • Models estimated in 128 specifications

  11. Length of stay increases with unexpected demand for emergency and decreases for elective admissions Emergency high-risk Elective high-risk Emergency low-risk Excess Length of Stay Elective low-risk Unexpected Demand in Percentiles Unexpected Demand in Percentiles

  12. Length of stay increases with excess admission ratio Emergency high-risk Excess Length of Stay Elective high-risk Emergency low-risk Elective low-risk Unobservable Selection in Percentiles Unobservable Selection in Percentiles

  13. After selection correction no significant impact of demand on 1 day mortalityHigh-risk emergency sample Without selection index With selection index

  14. Summary of results

  15. Unobservable selection of patients is an issue also in within-hospital studies Ignoring selection can give misleading results Overall, hospitals do seem to deal well with variation in demand Conclusion

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