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Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.

Stephanie N Howson1, Michael J McShea1, Raghav Ramachandran1

  • 1Applied Physics Laboratory, Johns Hopkins University, Baltimore, MD, United States.

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|March 11, 2022
PubMed
Summary

An ensemble machine learning approach improved the prediction of persistent high utilizers (PHUs) in healthcare. This method enhances the accuracy of identifying patients needing care management, optimizing resource allocation.

Keywords:
ensemble methodologymachine learningobservationalpersistent high utilizerspopulation health analyticspredictionretrospectiveutilization

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Population Health Management

Background:

  • A small group of patients, termed persistent high utilizers (PHUs), account for a disproportionate amount of healthcare spending.
  • Accurate prediction of PHUs is crucial for effective population health management (PHM) and resource allocation.
  • Traditional regression methods have shown limited accuracy in PHU prediction.

Purpose of the Study:

  • To enhance the prediction of persistent high utilizers (PHUs) using an ensemble machine learning approach.
  • To evaluate the performance of ensemble methods compared to traditional logistic regression for PHU identification.

Main Methods:

  • A retrospective observational study using insurance claims data from 165,595 patients.
  • Defined PHUs as patients in the top 20% of healthcare costs for four consecutive 6-month periods.
  • Compared standalone machine learning models with an ensemble approach combining multiple models to predict PHU status over 24 months.

Main Results:

  • The ensemble model, incorporating complement naïve Bayes and random forest, demonstrated improved sensitivity (49.0%) and positive predictive value (PPV; 50.3%).
  • These results surpassed those of logistic regression (sensitivity 46.8%, PPV 46.1%).

Conclusions:

  • Ensemble machine learning offers a more accurate method for predicting patients' needs for care management.
  • The improved PPV reduces the misidentification of low-risk patients, allowing for more efficient allocation of healthcare resources to those most in need.