Predicting and improving patient-level antibiotic adherence
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Predicting and improving patient-level antibiotic adherence Isabelle Rao 1 & Adir Shaham 2 & Amir Yavneh 2 & Dor Kahana 2 & Itai Ashlagi 1 & Margaret L. Brandeau 1
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Dan Yamin 2
Received: 20 January 2020 / Accepted: 22 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
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
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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 2007-2017 to predict whether a patient will purchase a prescribed antibiotic, and 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.
Isabelle Rao, Adir Shaham, Amir Yavneh and Dor Kahana contributed equally to this work. Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10729-020-09523-3) contains supplementary material, which is available to authorized users. * Margaret L. Brandeau [email protected] 1
Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, 69978 Tel Aviv, Israel
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We show that an adherence intervention targeted to patients whose predicted purchasing probability is below a specified threshold can increase the numb
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