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Medical Evaluation Readiness Information Toolset (MERIT): Developing a Data-driven Decision Support Tool to Augment

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The Medical Evaluation Readiness Information Toolset (MERIT) uses AI to predict military service members

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Area of Science:

  • Military Medicine
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Military readiness is a key concern, with policy mandating referral to the Disability Evaluation System (DES) for predictable recovery paths or within one year of diagnosis.
  • The top 24 medical conditions account for over 90% of DES referrals, highlighting the need for efficient identification and management.

Purpose of the Study:

  • To develop and validate an AI-driven clinical decision support tool, the Medical Evaluation Readiness Information Toolset (MERIT).
  • To predict service members' likelihood of referral to the DES for the most common medical conditions leading to separation.

Main Methods:

  • Utilized a 19-year dataset of over 3 million Army service members.
  • Developed a "Disease Severity Index" (DSI) using a Gamma window function to prioritize recent medical data.
  • Employed logistic regression and random forest models incorporating DSI and personnel records to predict DES referral for 24 key medical conditions.

Main Results:

  • Achieved high-performing classification models with an area under the receiver operating characteristics curve exceeding 0.919 for all conditions using logistic regression.
  • Demonstrated MERIT's capability to generate accurate individual risk profiles for service members.

Conclusions:

  • MERIT accurately predicts service members' likelihood of DES referral using ICD codes and personnel data.
  • The toolset provides a robust method for developing individual risk profiles, aiding in medical readiness management.