Department of Management Science and Engineering, Stanford University, Stanford, CA, USA.
Department of Industrial Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
Health Care Manag Sci. 2020 Dec;23(4):507-519. doi: 10.1007/s10729-020-09523-3. Epub 2020 Oct 5.
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.
低药物依从性会给患者带来严重的健康和经济负担。我们分析了 2007 年至 2017 年以色列马卡比医疗保健服务的 25 万随机患者的电子病历中的原始数据，以预测患者是否会购买处方抗生素。我们开发了一个决策模型，以评估是否有必要对患者进行干预以提高购买依从性，同时考虑干预成本和不依从的成本。表现最佳的预测模型的平均接受者操作特征曲线（ROC）下面积（AUC）为 0.684，在检测购买药物可能性低于 50%的个体方面准确率为 82%。使用决策模型，针对预测购买概率低于特定阈值的患者进行依从性干预，可以在与不干预相比产生显著节省的情况下增加处方数量——我们的数据集可以节省约 6.4%的费用和增加 4.0%的处方数量。我们的结论是，对电子病历中的大规模患者数据进行分析可以帮助预测患者购买处方抗生素的概率，并可以为医生提供实时预测，以便医生可以就药物重要性对患者进行咨询。更广泛地说，对患者水平数据的深入分析可以帮助塑造下一代个性化干预措施。
