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Predicting COVID-19-Related Health Care Resource Utilization Across a Statewide Patient Population: Model Development

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Summary
This summary is machine-generated.

This study developed predictive models for healthcare resource use during COVID-19, showing high accuracy but also performance disparities across different patient groups. Addressing these biases is crucial for equitable health system planning.

Keywords:
COVID-19decision modelsdigital healthepidemiologyhealth care utilizationhealth datahealth disparitieshealth informaticshealth informationhealthcare resourcesmachine learningpandemicpopulation healthpublic health

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Area of Science:

  • Health Informatics
  • Epidemiology
  • Machine Learning in Healthcare

Background:

  • The COVID-19 pandemic exposed weaknesses in health systems' analytical capabilities for policy and patient care.
  • Systemic health disparities based on demographics and location were exacerbated during the pandemic.

Purpose of the Study:

  • To assess the feasibility of using statewide health data for population health decisions.
  • To develop analytical models predicting COVID-19 healthcare resource utilization.

Main Methods:

  • Utilized Indiana's Health Information Exchange data for model training.
  • Employed decision forest models to predict patient-level healthcare resource needs.
  • Evaluated model performance on subpopulations stratified by age, race, gender, and location.

Main Results:

  • Developed models with >70% precision, >80% accuracy, and >90% sensitivity for predicting healthcare resource utilization.
  • Identified statistically significant performance variations across demographic and geographic subpopulations.
  • Trained models on 96,026 patients using 100 impactful features.

Conclusions:

  • Demonstrated the potential of statewide data for predictive health models with strong overall performance.
  • Highlighted significant biases in model performance across different patient subgroups.
  • Emphasized the need for further research to identify and rectify performance disparities.