AI-Driven Solutions Promote Medication Adherence


High nonadherence in the US leads to avoidable mortality.

Artificial intelligence -- Image credit: Kaikoro |

Image credit: Kaikoro |

About the Authors

Eric Pulice, MPA, DBA candidate, is an assistant professor of health care management, College of Business and Aviation, Fairmont State University in Fairmont, West Virginia.

Alberto Coustasse, MD, DrPH, MBA, MPH, is a professor of health care management and administration, Lewis College of Business, Marshall University in Huntington, West Virginia.

A study conducted in 2019 showed that approximately 50% of the 187 million patients in the US health care system did not follow their medication plan as prescribed.1 This means they failed to adhere to the drug regimen or to take the medication for the entire prescribed duration. Adherence rates for most medications used to treat chronic conditions, such as diabetes and hypertension, usually fall in the range of 50% to 60%, even with patients who have good insurance and drug benefits.2 This rate of adherence leads to an estimated 125,000 avoidable deaths each year and $100 billion annually in preventable health care costs.1 The World Health Organization identified economic, social, health care, patient, provider, and therapy-related factors for nonadherence.3 However, medication nonadherence is not considered a significant issue by most practicing physicians.4,5

Technological Advancements in Medication Adherence

Traditional methods, such as pill counts, have been unreliable in accurately measuring patient medication adherence.6 Current technologies addressing medication adherence include electronic pill bottles, ingestible sensors, and video-based systems. These technologies offer diverse ways to monitor medication adherence with expectations of accuracy and increased patient adoption because of a lack of obtrusiveness for patients; however, successful clinical implementation remains infrequently documented. Further, telephonic-based e-health interventions to promote medication adherence have been implemented with mixed results.4,7 Nevertheless, the global digital medication adherence market is projected to reach $6.8 billion by 2026, indicating substantial investment and potential for growth.4

The inverse correlation between medication adherence and quality of life underscores the importance of adherence in improving overall patient well-being.8 Nonadherent patients have been found to have a higher risk of major cardiovascular events, emphasizing the impact of adherence on disease-specific outcomes.9,10 Nonadherence has also been linked to potentially inappropriate increases in treatment intensity, indicating the broader impact on health care utilization and patient safety.11 The absence of successful interventions to enhance medication adherence, coupled with the crucial role of adherence in achieving better patient outcomes, underscores the necessity to investigate alternative approaches.

The Role of AI in Medication Adherence

Artificial intelligence (AI) and machine learning are valuable tools for predicting medication adherence for various conditions, such as diabetes and hypertension, for which AI has been shown to achieve 70% to 80% accuracy in identifying nonadherent individuals.12 To date, the FDA has approved over 60 AI-equipped medical devices, indicating a substantial trend in the integration of AI in health care.13 Machine learning, a subset of AI, can analyze large, intricate data sets using sophisticated algorithms and techniques, extracting valuable insights and patterns.14,15 Machine learning has been effectively utilized to predict medication adherence in various medical conditions, such as opioid use disorder, type 2 diabetes, an hypertension.12,16,17 Moreover, machine learning techniques have been employed to forecast the likelihood of nonadherence in individuals with type 2 diabetes, demonstrating the potential to identify patients susceptible to suboptimal medication adherence.18

AI has also been employed to develop tools for providing comprehensive assessments of patients’ adherence behaviors and psychological factors influencing adherence levels. For instance, AI-based tools have been designed to evaluate self-reported adherence to medication, offering insights into patients’ adherence behaviors and barriers.19,20 These interventions were customized to meet the individual needs of patients and could include motivational messages, behavioral interventions, and psychological support for individuals with chronic conditions, such as affective disorders and schizophrenia.21

Challenges and Considerations in AI Integration

Despite its potential, integrating AI into medication adherence faces challenges. For example, health care professionals often face limitations in general knowledge and awareness of available technological-based solutions for medication adherence.22,23 Moreover, variance exists in knowledge and perspectives regarding the utilization of AI among physicians and medical students. These differences can result in differing levels of acceptance and understanding of the value of AI technologies when utilized in health care.24

However, there is a growing understanding that medical education must play a formal role in the adoption and use of AI technologies. Specifically, medical education should, going forward, involve teaching future physicians about the applications and limitations of using AI tools in medical practice.25 Additionally, as a part of that education process, ongoing discussions around the ethical, legal, and societal concerns regarding AI implementation and use will continue to be important.


1. Walsh CA, Cahir C, Tecklenborg S, Byrne C, Culbertson MA, Bennett KE. The association between medication non-adherence and adverse health outcomes in ageing populations: a systematic review and meta-analysis. Br J Clin Pharmacol. 2019;85(11):2464-2478. doi:10.1111/bcp.14075
2. A tough pill to swallow: medication adherence and cardiovascular disease. American Heart Association. June 2016. Accessed December 15, 2023.
3. Maxik K, Kimble C, Coustasse A. Help patients safely handle medications to improve adherence. Pharmacy Times. November 12, 2021. Accessed December 15, 2023.
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12. Warren D, Marashi A, Siddiqui A, et al. Using machine learning to study the effect of medication adherence in opioid use disorder. PLoS One. 2022;17(12):e0278988. doi:10.1371/journal.pone.0278988
13. Hamamoto R, Suvarna K, Yamada M, et al. Application of artificial intelligence technology in oncology: towards the establishment of precision medicine. Cancers (Basel). 2020;12(12):3532. doi:10.3390/cancers12123532
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17. Aziz F, Malek S, Ali AM, Wong MS, Mosleh M, Milow P. Determining hypertensive patients’ beliefs towards medication and associations with medication adherence using machine learning methods. PeerJ. 2020;8:e8286. doi:10.7717/peerj.8286
18. Wu XW, Yang HB, Yuan R, Long EW, Tong RS. Predictive models of medication non-adherence risks of patients with T2D based on multiple machine learning algorithms. BMJ Open Diabetes Res Care. 2020;8(1):e001055. doi:10.1136/bmjdrc-2019-001055
19. Larsen RE, Pripp AH, Krogstad T, Johannessen Landmark C, Holm LB. Development and validation of a new non-disease-specific survey tool to assess self-reported adherence to medication. Front Pharmacol. 2022;13:9813368. doi:10.3389/fphar.2022.981368.
20. Chan AHY, Vervloet M, Lycett H, Brabers A, van Dijk L, Horne R. Development and validation of a self-report measure of practical barriers to medication adherence: the medication practical barriers to adherence questionnaire (MPRAQ). Br J Clin Pharmacol. 2021;87(11):4197-4211. doi:10.1111/bcp.14744
21. Oakey-Neate L, Schrader G, Strobel J, Bastiampillai T, Van Kasteren Y, Bidargaddi N. Using algorithms to initiate needs-based interventions for people on antipsychotic medication: implementation protocol. BMJ Health Care Inform. 2020;27(1):e100084. doi:10.1136/bmjhci-2019-100084
22. van Berkel N, Bellio M, Skov MB, Blandford A. Measurements, algorithms, and presentations of reality: framing interactions with AI-enabled decision support. ACM Trans Comput Hum Interact. 2023;30(2):1-33. doi:10.1145/3571815
23. van Boven JF, Tsiligianni I, Potočnjak I, et al. European Network to Advance Best Practices and Technology on Medication Adherence: mission statement. Front Pharmacol. 2021;12:748702. doi:10.3389/fphar.2021.748702
24. Kansal R, Bawa A, Bansal A, et al. Differences in knowledge and perspectives on the usage of artificial intelligence among doctors and medical students of a developing country: a cross-sectional study. Cureus. 2022;14(1):e21434. doi:10.7759/cureus.21434
25. Mehta N, Harish V, Bilimoria K, et al. Knowledge and attitudes on artificial intelligence in healthcare: a provincial survey study of medical students. MedEdPublish. 2021;10(1):75. doi:10.15694/mep.2021.000075.1
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