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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.
Background:
The Centers for Medicare and Medicaid Services (CMS) use colon surgical site infection (SSI) rates to rank hospitals and apply financial penalties. The CMS' risk-adjustment model omits potentially impactful variables that might disadvantage hospitals with complex surgical populations.
Methods:
We analyzed adult patients who underwent colon surgery within facilities associated with HCA Healthcare from 2014 to 2016. SSIs were identified from National Health Safety Network (NHSN) reporting. We trained and validated 3 SSI prediction models, using (1) current CMS model variables, including hospital-specific random effects (HCA-adapted CMS model); (2) demographics and claims-based comorbidities (expanded-claims model); and (3) demographics, claims-based comorbidities, and NHSN variables (claims-plus-electronic health record [EHR] model). Discrimination, calibration, and resulting rankings were compared among all models and the current CMS model with published coefficient values.
Results:
We identified 39 468 colon surgeries in 149 hospitals, resulting in 1216 (3.1%) SSIs. Compared to the HCA-adapted CMS model, the expanded-claims model had similar performance (c-statistic, 0.65 vs 0.67, respectively), while the claims-plus-EHR model was more accurate (c-statistic, 0.70; 95% confidence interval, .67-.73; P = .004). The sampling variation, due to the low surgical volume and small number of infections, contributed 74% of the total variation in observed SSI rates between hospitals. When CMS model rankings were compared to those from the expanded-claims and claims-plus-EHR models, 18 (15%) and 26 (22%) hospitals changed quartiles, respectively, and 10 (8.3%) and 12 (10%) hospitals changed into or out of the lowest-performing quartile, respectively.
Conclusions:
An expanded set of variables improved colon SSI risk predictions and quartile assignments, but low procedure volumes and SSI events remain a barrier to effectively comparing hospitals.
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