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Related Experiment Video

Updated: Dec 6, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Predicting and improving patient-level antibiotic adherence.

Isabelle Rao1, Adir Shaham2, Amir Yavneh2

  • 1Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.

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Summary

Predicting antibiotic purchasing adherence using electronic health records can improve patient outcomes. Targeted interventions for low-adherence patients can increase filled prescriptions and reduce healthcare costs.

Keywords:
Decision modelMachine learningMedication adherencePrediction

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Area of Science:

  • Health Informatics
  • Pharmacoeconomics
  • Public Health

Background:

  • Low medication adherence significantly impacts patient health and incurs substantial economic costs.
  • Antibiotic non-adherence presents a growing challenge in healthcare, contributing to treatment failures and antimicrobial resistance.
  • Electronic medical records (EMRs) offer a rich source of data for understanding patient behavior regarding medication adherence.

Purpose of the Study:

  • To predict the likelihood of patients purchasing prescribed antibiotics using EMR data.
  • To develop a decision model for evaluating the cost-effectiveness of interventions aimed at improving antibiotic purchasing adherence.
  • To assess the potential impact of targeted interventions on prescription fulfillment rates and healthcare savings.

Main Methods:

  • Analysis of primary data from 250,000 electronic medical records from Maccabi Healthcare services (2007-2017).
  • Development and validation of a predictive model to identify patients with a low probability of purchasing prescribed antibiotics.
  • Implementation of a decision model incorporating intervention costs and non-adherence costs to guide intervention strategies.

Main Results:

  • The best prediction model achieved an AUC of 0.684, with 82% accuracy in identifying individuals unlikely to purchase antibiotics.
  • A targeted adherence intervention strategy demonstrated potential for 6.4% cost savings and a 4.0% increase in filled prescriptions within the analyzed dataset.
  • The decision model indicated that targeted interventions are warranted for patients with a predicted purchasing probability below a specific threshold.

Conclusions:

  • Large-scale EMR data analysis can effectively predict antibiotic purchasing probability, enabling real-time physician insights.
  • Personalized interventions, informed by predictive analytics, can enhance medication adherence and optimize healthcare resource allocation.
  • This approach provides a foundation for developing next-generation, data-driven personalized healthcare interventions.