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Predicting and improving patient-level antibiotic adherence

Author

Listed:
  • 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)

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.

Suggested Citation

  • Isabelle Rao & Adir Shaham & Amir Yavneh & Dor Kahana & Itai Ashlagi & Margaret L. Brandeau & Dan Yamin, 2020. "Predicting and improving patient-level antibiotic adherence," Health Care Management Science, Springer, vol. 23(4), pages 507-519, December.
  • Handle: RePEc:kap:hcarem:v:23:y:2020:i:4:d:10.1007_s10729-020-09523-3
    DOI: 10.1007/s10729-020-09523-3
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    References listed on IDEAS

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    1. Chan, David Chimin & Avorn, Jerry Lewis & Solomon, Daniel Hal & Brookhart, Alan & Choudhry, Niteesh K & Cutler, David M. & Jan, Saira & Fischer, Michael Adam & Liu, Jun & Shrank, William H., 2010. "Patient, Physician, and Payment Predictors of Statin Adherence," Scholarly Articles 5343023, Harvard University Department of Economics.
    2. Rabia Khan & Karolina Socha-Dietrich, 2018. "Investing in medication adherence improves health outcomes and health system efficiency: Adherence to medicines for diabetes, hypertension, and hyperlipidaemia," OECD Health Working Papers 105, OECD Publishing.
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    Cited by:

    1. Roberto Aringhieri & Patrick Hirsch & Marion S. Rauner & Melanie Reuter-Oppermanns & Margit Sommersguter-Reichmann, 2022. "Central European journal of operations research (CJOR) “operations research applied to health services (ORAHS) in Europe: general trends and ORAHS 2020 conference in Vienna, Austria”," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 1-18, March.
    2. Kılıç, Hakan & Güneş, Evrim Didem, 2024. "Patient adherence in healthcare operations: A narrative review," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).

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