Isha Thakur
Independent Researcher
Himachal Pradesh, India
Abstract
Non-compliance with prescribed drug regimens remains one of the most significant barriers to effective healthcare delivery worldwide. Despite numerous interventions, patient adherence rates remain suboptimal across various therapeutic areas. Recent advancements in artificial intelligence (AI) have enabled the development of intelligent systems capable of personalized patient education, aiming to bridge knowledge gaps, reinforce medication routines, and provide contextual alerts. This study investigates the potential impact of AI-based patient education platforms on improving drug compliance, particularly in chronic disease management. Through a comprehensive literature review and simulation of AI-driven interaction models, the manuscript evaluates how early AI methods in natural language processing (NLP), decision trees, and rule-based expert systems were integrated into educational tools to enhance understanding and adherence. The findings suggest a statistically significant improvement in patient outcomes when AI-based education tools are used in conjunction with traditional healthcare services. The study contributes to the understanding of how early-stage AI methods could be practically applied to optimize therapeutic compliance and reduce hospital readmissions.
Keywords
AI, patient education, drug compliance, expert systems, chronic disease management, healthcare adherence
References
- Bates, D. W., Cohen, M., Leape, L. L., Overhage, J. M., Shabot, M. M., & Sheridan, T. (2001). Reducing the frequency of errors in medicine using information technology. Journal of the American Medical Informatics Association, 8(4), 299–308. https://doi.org/10.1136/jamia.2001.0080299
- Bell, D. S., Cretin, S., Marken, R. S., & Landman, A. B. (2004). A conceptual framework for evaluating outpatient electronic prescribing systems based on their functional capabilities. Journal of the American Medical Informatics Association, 11(1), 60–70. https://doi.org/10.1197/jamia.M1424
- Boren, S. A., Gunlock, T. L., Schaefer, J., & Albright, A. (2007). Redesigning diabetes self-management education and support: Recommendations for national standards. The Diabetes Educator, 33(6), 887–894. https://doi.org/10.1177/0145721707308407
- Chomutare, T., Fernandez-Luque, L., Arsand, E., & Hartvigsen, G. (2011). Features of mobile diabetes applications: Review of the literature and analysis of current applications compared against evidence-based guidelines. Journal of Medical Internet Research, 13(3), e65. https://doi.org/10.2196/jmir.1874
- Haynes, R. B., Ackloo, E., Sahota, N., McDonald, H. P., & Yao, X. (2008). Interventions for enhancing medication adherence. Cochrane Database of Systematic Reviews, (2), CD000011. https://doi.org/10.1002/14651858.CD000011.pub3
- Montori, V. M., & Guyatt, G. H. (2001). Progress in evidence-based medicine. JAMA, 286(24), 2980–2982. https://doi.org/10.1001/jama.286.24.2980
- Osterberg, L., & Blaschke, T. (2005). Adherence to medication. New England Journal of Medicine, 353(5), 487–497. https://doi.org/10.1056/NEJMra050100
- Patel, V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M. R., Bellazzi, R., & Abu-Hanna, A. (2009). The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine, 46(1), 5–17. https://doi.org/10.1016/j.artmed.2008.07.017
- Piette, J. D., Lun, K. C., Moura, L. A., Fraser, H. S. F., Mechael, P. N., Powell, J., & Khoja, S. R. (2012). Impacts of e-health on the outcomes of care in low- and middle-income countries: Where do we go from here? Bulletin of the World Health Organization, 90, 365–372. https://doi.org/10.2471/BLT.11.099069
- Shortliffe, E. H. (1976). Computer-based medical consultations: MYCIN. Elsevier/North-Holland Biomedical Press. (Landmark early AI expert system used in clinical settings.)