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Area of Science:

  • Health Services Research
  • Health Informatics
  • Machine Learning in Healthcare

Background:

  • Administrative claims data are crucial for healthcare management and payment but have limitations in predicting spending.
  • Existing models struggle to incorporate a large number of diagnoses without incentivizing undesirable coding practices.
  • There is a need for advanced methods that enhance the accuracy and transparency of healthcare spending predictions.

Purpose of the Study:

  • To develop a machine learning (ML) algorithm that automates the creation of clinically credible and transparent predictive models for healthcare spending.
  • The algorithm builds upon Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods.
  • The goal is to provide better tools for policymakers and clinicians.

Main Methods:

  • Diagnostic Items (DXIs) were organized into disease hierarchies with an Appropriateness to Include (ATI) score to address vagueness and gameability.
  • A novel automated DCG algorithm iteratively assigned DXIs to DCGs, identifying dominant DXIs based on regression coefficients.
  • The Merative MarketScan Commercial Claims and Encounters Database (Jan 2016-Dec 2018) was used, with data split for model development (90%) and validation (10%).

Main Results:

  • The developed algorithm implemented 218 clinician-specified hierarchies, outperforming the 64 hierarchies in the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model.
  • The base model, which excluded vague and gameable DXIs, reduced parameters by 80% and achieved an R2 of 0.535.
  • The model predicted spending within 12% of actual costs for rare diseases, significantly outperforming the HHS HCC model, which underpaid this group by 33%.

Conclusions:

  • Automating DXI clustering within clinically specified hierarchies enables the creation of interpretable risk models from large datasets.
  • The algorithm effectively addresses concerns regarding diagnostic vagueness and gameability in predictive modeling.
  • This approach offers a more accurate and transparent method for predicting healthcare spending and outcomes.