The Limited Utility of Ranking Hospitals Based on Their Colon Surgery Infection Rates

Daniel A Caroff1, Rui Wang1, Zilu Zhang1

  • 1Department of Population Medicine, Harvard Medical School and the Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA.

Insights

Improving hospital rankings for colon surgical site infections (SSIs) requires more data. Including electronic health record data enhances prediction accuracy but low surgical volumes still pose challenges for accurate hospital comparisons.

Area of Science:

  • Healthcare analytics
  • Surgical outcomes research
  • Public health surveillance

Background:

  • Centers for Medicare and Medicaid Services (CMS) utilize colon surgical site infection (SSI) rates for hospital performance evaluation and financial penalties.
  • The current CMS risk-adjustment model may not fully account for patient complexity, potentially disadvantaging hospitals with sicker surgical populations.

Purpose of the Study:

  • To evaluate the impact of incorporating additional variables into SSI risk-prediction models.
  • To compare the accuracy and hospital ranking performance of different risk-adjustment models for colon surgery SSIs.

Main Methods:

  • Analysis of adult colon surgery patients from HCA Healthcare facilities (2014-2016), with SSIs identified via National Health Safety Network (NHSN) reporting.
  • Development and validation of three SSI prediction models: HCA-adapted CMS model, expanded-claims model, and claims-plus-electronic health record (EHR) model.
  • Comparison of model discrimination, calibration, and resulting hospital rankings against the current CMS model.

Main Results:

  • The claims-plus-EHR model demonstrated superior accuracy (c-statistic 0.70) compared to the HCA-adapted CMS model (c-statistic 0.65).
  • Hospital rankings shifted significantly when using the expanded-claims (15% quartile change) and claims-plus-EHR (22% quartile change) models.
  • Low surgical volume and infection rates contributed substantially (74%) to observed inter-hospital variation in SSI rates.

Conclusions:

  • Expanding the variable set in risk-adjustment models improves colon SSI prediction and hospital quartile assignment.
  • Despite improvements, low procedure volumes and SSI event counts remain limitations for robust hospital performance comparisons.
  • Further refinement of risk-adjustment models is necessary to accurately assess and compare hospital performance in preventing colon SSIs.
Abstract

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