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Knowledge Acquisition of Consumer Medication Adherence

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Delivering Superior Health and Wellness Management with IoT and Analytics

Part of the book series: Healthcare Delivery in the Information Age ((Healthcare Delivery Inform. Age))

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Abstract

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.

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References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    CAS  PubMed  PubMed Central  Google Scholar 

  • 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.

    Article  CAS  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Gartner IT Glossary. (2017). Big data analytics – Gartner Tech definitions. [online] Available at: http://www.gartner.com/it-glossary/analytics/. Accessed Jan 2017.

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Pharmacy Guild of Australia. (2008). MedsIndex: A medicines compliance indicator. Canberra: The Pharmacy Guild of Australia. http://www.guildlink.com.au/guildcare/products/guildcare-programs/patient-adherence-programs/

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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Correspondence to Khin Than Win .

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Vlahu-Gjorgievska, E., Hassan, H., Win, K.T. (2020). Knowledge Acquisition of Consumer Medication Adherence. In: Wickramasinghe, N., Bodendorf, F. (eds) Delivering Superior Health and Wellness Management with IoT and Analytics. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-030-17347-0_15

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  • DOI: https://doi.org/10.1007/978-3-030-17347-0_15

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