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.
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References
Brown MT, Bussell JK (2011) Medication adherence: WHO cares? Mayo Clin Proc 86(4):304–314
Rosenbaum L, Shrank WH (2013) Taking our medicine--improving adherence in the accountability era. N Engl J Med 369(8):694–695
Osterberg L, Blaschke T (2005) Adherence to medication. N Engl J Med 353(5):487–497
McDonnell PJ, Jacobs MR (2002) Hospital admissions resulting from preventable adverse drug reactions. Ann Pharmacother 36(9):1331–1336
Khan R, Socha-Dietrich K (2018) Investing in medication adherence improves health outcomes and health system efficiency: adherence to medicines for diabetes, hypertension, and hyperlipidaemia. OECD Health Working Papers, No. 105, OECD Publishing. Available at https://EconPapers.repec.org/RePEc:oec:elsaad:105-en
McGillicuddy JW, Taber DJ, Mueller M et al (2015) Sustainability of improvements in medication adherence through a mobile health intervention. Prog Transplant 25(3):217–223
Morrissey EC, Durand H, Nieuwlaat R et al (2017) Effectiveness and content analysis of interventions to enhance medication adherence and blood pressure control in hypertension: a systematic review and meta-analysis. Psychol Health 32(10):1195–1232
West LM, Cordina M (2019) Educational intervention to enhance adherence to short-term use of antibiotics. Res Social Adm Pharm 15(2):193–201
Viswanathan M, Golin CE, Jones CD et al (2012) Interventions to improve adherence to self-administered medications for chronic diseases in the United States: a systematic review. Ann Intern Med 157(11):785–795
World Health Organization (2003) Adherensce to Long Term Therapies: Evidence for Action. Available at https://www.who.int/chp/knowledge/publications/adherence_report/en/
Chan DC, Shrank WH, Cutler D et al (2010) Patient, physician, and payment predictors of statin adherence. Med Care 48(3):196–202
Brown MT, Bussell J, Dutta S et al (2016) Medication adherence: truth and consequences. Am J Med Sci 351(4):387–399
Ho PM, Bryson CL, Rumsfeld JS (2009) Medication adherence: its importance in cardiovascular outcomes. Circulation 119(23):3028–3035
Kumamaru H, Lee MP, Choudhry NK et al (2018) Using previous medication adherence to predict future adherence. J Manag Care Spec Pharm 24(11):1146–1155
Lauffenburger JC, Franklin JM, Krumme AA et al (2018) Predicting adherence to chronic disease medications in patients with long-term initial medication fills using indicators of clinical events and health behaviors. J Manag Care Spec Pharm 24(5):469–477
Feehan M, Morrison MA, Tak C et al (2017) Factors predicting self-reported medication low adherence in a large sample of adults in the US general population: a cross-sectional study. BMJ Open 7(6):e014435
Pirdehghan A, Poortalebi N (2016) Predictors of adherence to type 2 diabetes medication. J Res Health Sci 16(2):72–75
Franklin JM, Gopalakrishnan C, Krumme AA et al (2018) The relative benefits of claims and electronic health record data for predicting medication adherence trajectory. Am Heart J 197:153–162
Cohen R, Rabin G, National Insurance Institute Research and Planning Administration (2017) Membership in Sick Funds 2016 [Hebrew]. Available at http://www.btl.gov.il
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system. Presented at Proceedings of the 22nd ACM SIGKKD International Conference on Knowledge Discovery and Data Mining: ACM
Costa E, Giardini A, Savin M et al (2015) Interventional tools to improve medication adherence: review of literature. Patient Prefer Adherence 9:1303–1314
Kini V, Ho PM (2018) Interventions to improve medication adherence: a review. JAMA 320(23):2461–2473
Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X (2008) Interventions for enhancing medication adherence. Cochrane Database Syst Rev 2:CD000011
Sorensen SV, Baker T, Fleurence R et al (2009) Cost and clinical consequence of antibiotic non-adherence in acute exacerbations of chronic bronchitis. Int J Tuberc Lung Dis 13(8):945–954
Yamin D, Gavious A, Davidovitch N, Pliskin JS (2014) Role of intervention programs to increase influenza vaccination in Israel. Isr J Health Policy Res 3:13
Martin LR, Williams SL, Haskard KB, Dimatteo MR (2005) The challenge of patient adherence. Ther Clin Risk Manag 1(3):189–199
Holmes AH, Moore LS, Sundsfjord A et al (2016) Understanding the mechanisms and drivers of antimicrobial resistance. Lancet 387(10014):176–187
Nathan C, Cars O (2014) Antibiotic resistance--problems, progress, and prospects. N Engl J Med 371(19):1761–1763
World Health Organization (2018) Antibiotic resistance. Available at https://www.who.int/news-room/fact-sheets/detail/antibiotic-resistance
Regev-Yochay G, Raz M, Dagan R et al (2011) Reduction in antibiotic use following a cluster randomized controlled multifaceted intervention: the Israeli judicious antibiotic prescription study. Clin Infect Dis 53(1):33–41
Wilf-Miron R, Ron N, Ishai S et al (2012) Reducing the volume of antibiotic prescriptions: a peer group intervention among physicians serving a community with special ethnic characteristics. J Manag Care Pharm 18(4):324–328
Jackevicius CA, Li P, Tu JV (2008) Prevalence, predictors, and outcomes of primary nonadherence after acute myocardial infarction. Circulation 117(8):1028–1036
Goldman DP, Joyce GF, Zheng Y (2007) Prescription drug cost sharing: associations with medication and medical utilization and spending and health. JAMA 298(1):61–69
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This work was supported by a Koret Foundation gift for Smart Cities and Digital Living.
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Rao, I., Shaham, A., Yavneh, A. et al. Predicting and improving patient-level antibiotic adherence. Health Care Manag Sci 23, 507–519 (2020). https://doi.org/10.1007/s10729-020-09523-3
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DOI: https://doi.org/10.1007/s10729-020-09523-3