Predicting and improving patient-level antibiotic adherence

Isabelle Rao, Adir Shaham, Amir Yavneh, Dor Kahana, Itai Ashlagi, Margaret L. Brandeau () and Dan Yamin
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Isabelle Rao: Stanford University
Adir Shaham: Tel Aviv University
Amir Yavneh: Tel Aviv University
Dor Kahana: Tel Aviv University
Itai Ashlagi: Stanford University
Margaret L. Brandeau: Stanford University
Dan Yamin: Tel Aviv University

Health Care Management Science, 2020, vol. 23, issue 4, No 3, 507-519

Abstract: Abstract Low adherence to prescribed medications causes substantial health and economic burden. We analyzed primary data from electronic medical records of 250,000 random patients from Israel’s Maccabi Healthcare services from 2007 to 2017 to predict whether a patient will purchase a prescribed antibiotic. We developed a decision model to evaluate whether an intervention to improve purchasing adherence is warranted for the patient, considering the cost of the intervention and the cost of non-adherence. The best performing prediction model achieved an average area under the receiver operating characteristic curve (AUC) of 0.684, with 82% accuracy in detecting individuals who had less than 50% chance of purchasing a prescribed drug. Using the decision model, an adherence intervention targeted to patients whose predicted purchasing probability is below a specified threshold can increase the number of prescriptions filled while generating significant savings compared to no intervention – on the order of 6.4% savings and 4.0% more prescriptions filled for our dataset. We conclude that analysis of large-scale patient data from electronic medical records can help predict the probability that a patient will purchase a prescribed antibiotic and can provide real-time predictions to physicians, who can then counsel the patient about medication importance. More broadly, in-depth analysis of patient-level data can help shape the next generation of personalized interventions.

Keywords: Medication adherence; Prediction; Machine learning; Decision model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10729-020-09523-3

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