Knowledge Acquisition of Consumer Medication Adherence

  • Elena Vlahu-Gjorgievska
  • Harith Hassan
  • Khin Than WinEmail author
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)


Medication nonadherence is an important health consideration that affects the patient’s overall well-being and healthcare costs. This study conducts the literature review on medication adherence and presents the recent trends in measuring, predicting, and improving adherence for nonadherent patients using advanced analytical methods. A combination of advanced medication adherence metrics employing information technology capabilities and using analytical methods can help healthcare providers to discover future patterns, knowledge, and insights about the patient situation, at the same time enabling to shape a specific intervention to improve adherence to medication.


Medical adherence Knowledge acquisition Medication nonadherence Patient well-being Healthcare costs 


  1. Bjarnadóttir, M. V., Malik, S., Onukwugha, E., Gooden, T., & Plaisant, C. (2016). Understanding adherence and prescription patterns using large-scale claims data. PharmacoEconomics, 34, 169–179.CrossRefGoogle Scholar
  2. Chang, D. S., Friedman, D. S., Frazier, T., Plyler, R., & Boland, M. V. (2013). Development and validation of a predictive model for nonadherence with once-daily glaucoma medications. Ophthalmology, 120, 1396–1402.CrossRefGoogle Scholar
  3. Cheng, L. I., Durden, E., Limone, B., Radbill, L., Juneau, P. L., Spangler, L., Mirza, F. M., & Stolshek, B. S. (2015). Persistance and compliance with osteroporosis therapies among women in a commercially insured population in the United States. Journal of Managed Care Pharmacy, 21, 824–833.CrossRefGoogle Scholar
  4. Coletti, D. J., Stephanou, H., Mazzola, N., Conigliaro, J., Gottridge, J., & Kane, J. M. (2015). Patterns and predictors of medication discrepancies in primary care. Journal of Evaluation in Clinical Practice, 21, 831–839.CrossRefGoogle Scholar
  5. Curtis, J. R., XI, J., Westfall, A. O., Cheng, H., Lyles, K., Saag, K. G., & Delzell, E. (2009). Improving the prediction of medication compliance: The example of bisphosphonates for osteoporosis. Medical Care, 47, 334.CrossRefGoogle Scholar
  6. Davis, N. A., & Kendrick, D. C. (2014). An analysis of medication adherence of sooner health access network SoonerCare choice patients. In AMIA annual symposium proceedings (p. 457). American Medical Informatics Association.Google Scholar
  7. Dixon, B. E., Jabour, A. M., Phillips, E. O. K., & Marrero, D. G. (2014). An informatics approach to medication adherence assessment and improvement using clinical, billing, and patient-entered data. Journal of the American Medical Informatics Association, 21, 517–521.CrossRefGoogle Scholar
  8. Eby, E. L., Van Brunt, K., Brusko, C., Curtis, B., & Lage, M. J. (2015). Dosing of U-100 insulin and associated outcomes among Medicare enrollees with type 1 or type 2 diabetes. Clinical Interventions in Aging, 10, 991.PubMedPubMedCentralGoogle Scholar
  9. Farr, A. M., Sheehan, J. J., Curkendall, S. M., Smith, D. M., Johnston, S. S., & Kalsekar, I. (2014). Retrospective analysis of long-term adherence to and persistence with DPP-4 inhibitors in US adults with type 2 diabetes mellitus. Advances in Therapy, 31, 1287–1305.CrossRefGoogle Scholar
  10. Franklin, J. M., Shrank, W. H., Pakes, J., Sanfélix-Gimeno, G., Matlin, O. S., Brennan, T. A., & Choudhry, N. K. (2013). Group-based trajectory models: A new approach to classifying and predicting long-term medication adherence. Medical Care, 51, 789–796.CrossRefGoogle Scholar
  11. Franklin, J. M., Krumme, A. A., Shrank, W. H., Matlin, O. S., Brennan, T. A., & Choudhry, N. K. (2014). Predicting adherence trajectory using initial patterns of medication filling. The American Journal of Managed Care, 21, e537–e544.Google Scholar
  12. Franklin, J. M., Shrank, W. H., LII, J., Krumme, A. K., Matlin, O. S., Brennan, T. A., & Choudhry, N. K. (2016). Observing versus predicting: Initial patterns of filling predict long-term adherence more accurately than high-dimensional modeling techniques. Health Services Research, 51, 220–239.CrossRefGoogle Scholar
  13. Gartner IT Glossary. (2017). Big data analytics – Gartner Tech definitions. [online] Available at: Accessed Jan 2017.
  14. Georga, E., Protopappas, V., Guillen, A., Fico, G., Ardigo, D., Arredondo, M. T., et al. (2009, September). Data mining for blood glucose prediction and knowledge discovery in diabetic patients: The METABO diabetes modeling and management system. In 2009 annual international conference of the IEEE engineering in medicine and biology society (pp. 5633–5636). IEEE.Google Scholar
  15. George, J., Mackinnon, A., Kong, D. C., & Stewart, K. (2006). Development and validation of the Beliefs and Behaviour Questionnaire (BBQ). Patient Education and Counseling, 64, 50–60.CrossRefGoogle Scholar
  16. Gill, C. J., Desilva, M. B., Hamer, D. H., Keyi, X., Wilson, I. B., & Sabin, L. (2015). Novel approaches for visualizing and analyzing dose-timing data from electronic drug monitors, or “how the ‘broken Window’Theory pertains to ART adherence”. AIDS and Behavior, 19, 2057–2068.CrossRefGoogle Scholar
  17. Horne, R., & Weinman, J. (2002). Self-regulation and self-management in asthma: Exploring the role of illness perceptions and treatment beliefs in explaining non-adherence to preventer medication. Psychology and Health, 17, 17–32.CrossRefGoogle Scholar
  18. Kozma, C. M., Phillips, A. L., & Meletiche, D. M. (2014). Use of an early disease-modifying drug adherence measure to predict future adherence in patients with multiple sclerosis. Journal of Managed Care Pharmacy, 20, 800–807.CrossRefGoogle Scholar
  19. Lafeuille, M.-H., Grittner, A. M., Lefebvre, P., Ellis, L., Mckenzie, R. S., Slaton, T., & Kozma, C. (2014). Adherence patterns for abiraterone acetate and concomitant prednisone use in patients with prostate cancer. Journal of Managed Care Pharmacy, 20, 477–484.CrossRefGoogle Scholar
  20. Lo-Ciganic, W.-H., Donohue, J. M., Thorpe, J. M., Perera, S., Thorpe, C. T., Marcum, Z. A., & Gellad, W. F. (2015). Using machine learning to examine medication adherence thresholds and risk of hospitalization. Medical Care, 53, 720.CrossRefGoogle Scholar
  21. Mabotuwana, T., Warren, J., & Kennelly, J. (2009). A computational framework to identify patients with poor adherence to blood pressure lowering medication. International Journal of Medical Informatics, 78, 745–756.CrossRefGoogle Scholar
  22. Malik, S., Shneiderman, B., Du, F., Plaisant, C., & Bjarnadottir, M. (2016). High-volume hypothesis testing: Systematic exploration of event sequence comparisons. ACM Transactions on Interactive Intelligent Systems (TIIS), 6, 9.Google Scholar
  23. Maulucci, R. A., & Somerville, D. (2011). An automated medication adherence tool. Engineering in Medicine and Biology Society, EMBC. In 2011 annual international conference of the IEEE (pp. 1165–1168). IEEE.Google Scholar
  24. Mcdonald, M. V., Peng, T. R., Sridharan, S., Foust, J. B., Kogan, P., Pezzin, L. E., & Feldman, P. H. (2013). Automating the medication regimen complexity index. Journal of the American Medical Informatics Association, 20, 499–505.CrossRefGoogle Scholar
  25. Molfenter, T. D., Bhattacharya, A., & Gustafson, D. H. (2012). The roles of past behavior and health beliefs in predicting medication adherence to a statin regimen. Patient Preference and Adherence, 6, 643–651.CrossRefGoogle Scholar
  26. Pavel, M., Jimison, H., Hayes, T., Larimer, N., Hagler, S., Vimegnon, Y., et al. (2010). Optimizing medication reminders using a decision-theoretic framework. Studies in Health Technology and Informatics, 160(Pt 2), 791–795.Google Scholar
  27. Petersen, M. L., Ledell, E., Schwab, J., Sarovar, V., Gross, R., Reynolds, N., Haberer, J. E., Goggin, K., Golin, C., & Arnsten, J. (2015). Super learner analysis of electronic adherence data improves viral prediction and may provide strategies for selective HIV RNA monitoring. Journal of Acquired Immune Deficiency Syndromes (1999), 69, 109.CrossRefGoogle Scholar
  28. Pharmacy Guild of Australia. (2008). MedsIndex: A medicines compliance indicator. Canberra: The Pharmacy Guild of Australia.
  29. Piette, J. D., Farris, K. B., Newman, S., An, L., Sussman, J., & Singh, S. (2015). The potential impact of intelligent systems for mobile health self-management support: Monte Carlo simulations of text message support for medication adherence. Annals of Behavioral Medicine, 49, 84–94.CrossRefGoogle Scholar
  30. Ritchey, M., Tsipas, S., Loustalot, F., & Wozniak, G. (2016). Use of pharmacy sales data to assess changes in prescription-and payment-related factors that promote adherence to medications commonly used to treat hypertension, 2009 and 2014. PLoS One, 11(7), e0159366.Google Scholar
  31. Sandy, R., & Connor, U. (2015). Variation in medication adherence across patient behavioral segments: A multi-country study in hypertension. Patient Preference and Adherence, 9, 1539.CrossRefGoogle Scholar
  32. Sayner, R., Carpenter, D. M., Blalock, S. J., Robin, A. L., Muir, K. W., Hartnett, M. E., Giangiacomo, A. L., Tudor, G., & Sleath, B. (2015). The accuracy of patient-reported adherence to glaucoma medications on a visual analog scale compared with electronic monitors. Clinical Therapeutics, 37, 1975–1985.CrossRefGoogle Scholar
  33. Serdaroglu, K., Uslu, G., & Baydere, S. (2015). Medication intake adherence with real time activity recognition on IoT. In 2015 IEEE 11th international conference on wireless and mobile computing, networking and communications (WiMob) (pp. 230–237). IEEE.Google Scholar
  34. Shukla, N., Hagenbuchner, M., Win, K. T., & Yang, J. (2018). Breast cancer data analysis for survivability studies and prediction. Computer Methods and Programs in Biomedicine, 155, 199–208.CrossRefGoogle Scholar
  35. Son, Y.-J., Kim, H.-G., Kim, E.-H., Choi, S., & Lee, S.-K. (2010). Application of support vector machine for prediction of medication adherence in heart failure patients. Healthcare Informatics Research, 16, 253–259.CrossRefGoogle Scholar
  36. Steinberg, G. B., Church, B. W., Mccall, C. J., & Scott, A. B. (2014). Novel predictive models for metabolic syndrome risk: A “big data” analytic approach. The American Journal of Managed Care, 20, e221–e228.PubMedGoogle Scholar
  37. Stewart, K., Mc Namara, K. P., & George, J. (2014). Challenges in measuring medication adherence: Experiences from a controlled trial. International Journal of Clinical Pharmacy, 36, 15–19.CrossRefGoogle Scholar
  38. Tucker, C. S., Behoora, I., Nembhard, H. B., Lewis, M., Sterling, N. W., & Huang, X. (2015). Machine learning classification of medication adherence in patients with movement disorders using non-wearable sensors. Computers in Biology and Medicine, 66, 120–134.CrossRefGoogle Scholar
  39. Win, K. T., Hassan, N. M., Oinas-Kukkonen, H., & Probst, Y. (2016). Online patient education for chronic disease management: Consumer perspectives. Journal of Medical Systems, 40(4), 88.Google Scholar
  40. Wu, J.-R., Moser, D. K., Chung, M. L., & Lennie, T. A. (2008). Predictors of medication adherence using a multidimensional adherence model in patients with heart failure. Journal of Cardiac Failure, 14, 603–614.CrossRefGoogle Scholar
  41. Yen, L., Wu, J., Hodgkins, P., Cohen, R. D., & Nichol, M. B. (2012). Medication use patterns and predictors of nonpersistence and nonadherence with oral 5-aminosalicylic acid therapy. Journal of Managed Care Pharmacy, 18, 701–712.CrossRefGoogle Scholar
  42. Yu, W., Liu, T., Valdez, R., Gwinn, M., & Khoury, M. J. (2010). Application of support vector machine modeling for prediction of common diseases: The case of diabetes and pre-diabetes. BMC Medical Informatics and Decision Making, 10, 16.CrossRefGoogle Scholar
  43. Zhang, J. X., & Meltzer, D. O. (2016). Identifying patients with cost-related medication non-adherence: A big-data approach. Journal of Medical Economics, 19, 806–811.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Elena Vlahu-Gjorgievska
    • 1
  • Harith Hassan
    • 1
  • Khin Than Win
    • 1
    Email author
  1. 1.University of WollongongWollongongAustralia

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