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Benchmarking Inpatient Mortality Using Electronic Medical Record Data: A Retrospective, Multicenter Analytical

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A new model using electronic medical record data accurately predicts hospital mortality by analyzing physiology, comorbidity, and support indices. This approach offers a reliable method for benchmarking patient outcomes across various hospital settings.

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Area of Science:

  • Medical Informatics
  • Health Services Research
  • Clinical Epidemiology

Background:

  • Benchmarking hospital mortality is crucial for quality assessment.
  • Traditional prognostic models often require manual data collection, limiting their widespread use.
  • Electronic medical record (EMR) data offers a rich, accessible resource for developing predictive models.

Purpose of the Study:

  • To develop and validate a novel model for benchmarking mortality in hospitalized patients.
  • To utilize readily available EMR data for risk stratification and outcome prediction.
  • To create a model that is applicable beyond intensive care units (ICUs).

Main Methods:

  • Developed a multivariable logistic regression model using EMR data from adult inpatients.
  • Incorporated three indices: Physiology, Comorbidity, and Support, alongside primary diagnosis.
  • Validated the model on independent cohorts, including a subset of ICU patients.

Main Results:

  • The model accurately predicted hospital mortality in validation and revalidation cohorts.
  • Achieved high discrimination (Area Under the Curve ~0.88-0.90) and good calibration.
  • Demonstrated effectiveness in both general ward and ICU settings.

Conclusions:

  • The developed EMR-based model effectively benchmarks patient mortality across diverse hospital locations.
  • This model eliminates the need for manual data collection, unlike traditional methods like the Acute Physiology and Chronic Health Evaluation (APACHE).
  • Further prospective testing in a larger, representative sample of hospitals is recommended to assess its utility for performance benchmarking.