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Medication Adherence AI model: Enhancing Treatment Outcomes

Poor medication adherence causes 125k deaths, 10% hospital admissions, costs $300B yearly. AI can boost adherence, reduce risks.

Problem

Poor medication adherence is a major challenge in chronic disease care, with approximately 50% to 70% of patients affected. Although there are written prescriptions, 20% remain unfilled, and even when they are filled, only half are taken as directed (1). This non-compliance is costly, both in human and economic terms, contributing to approximately 125,000 deaths and 10% of hospitalizations annually, resulting in up to $300 billion in costs (2-4). Studies reveal that adherence rates vary by condition, with cancer patients demonstrating the highest levels at 80%, while other chronic diseases have approximately 75% adherence (4).

Addressing adherence can lead to substantial healthcare savings. For example, every dollar spent on prescription medications for certain commercial populations results in a $3 to 10 decrease in medical costs (5), and high adherence correlates with 8 to 26 percent fewer hospitalizations and 3 to 10 percent fewer hospitalizations. 12% fewer emergency room visits. (6). Medication adherence is multifaceted and influenced by various factors specific to each patient, provider, medication, and medical condition (7,8), indicating the need for personalized strategies to improve adherence rates and outcomes.

Why it matters

  • Poor medication adherence affects 50% to 70% of patients, with 20% of prescriptions remaining unfilled and only half of filled prescriptions taken as directed.
  • Non-compliance leads to approximately 125,000 deaths, 10% of hospitalizations annually, and costs up to $300 billion.
  • Improving adherence can reduce medical costs by $3 to $10 per dollar spent on medications, and leads to 8-26% fewer hospitalizations and 12% fewer emergency room visits.

Solution

An AI-based model known as “MediComply AI” has been created to anticipate medication adherence levels. This model evaluates various clinical and demographic factors to detect patients who are more likely to deviate from their treatment plans, allowing healthcare professionals to implement targeted and timely interventions.

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Datasources

The synthetic database for the model emulates real-world conditions and was created with insights from a range of medication adherence literature, including analyzes by Brown and Bussell (2), medication adherence impact studies by NEHI (3), cost and use assessments by Roebuck et al. (4)(5), risk assessments related to cost-related nonadherence by Briesacher et al. (6), and broader reviews of adherence intervention strategies by Viswanathan et al. (8) and Conn et al. (9). These sources guide the range and dynamics of the variables used to predict adherence, ensuring the accuracy and relevance of the model.

Citations

  1. Neiman, Andrea B., et al. "CDC Grand Rounds: Improving Medication Adherence for Chronic Disease Management: Innovations and Opportunities." MMWR. Morbidity and Mortality Weekly Report, vol. 66, no. 45, November 17, 2017, pp. 1248-1251, doi:http://dx.doi.org/10.15585/mmwr.mm664522. Accessed February 24, 2021.
  2. Brown, Marie T., Bussell, Jennifer K. "Medication Adherence: WHO Cares?" Proceedings of the Mayo Clinic, vol. 86, no. 4, April c2011, pp. 304-314, DOI:10.4065/mcp.2010.0575. Accessed February 24, 2021.
  3. NEHI “Taking Stock: Patient Medication Adherence and Chronic Disease Management.” Network for Excellence in Health Innovation, June 10, 2020. Accessed February 24, 2021.
  4. Roebuck MC, Liberman JN, Gemmill-Toyama M, Brennan TA. Medication adherence leads to lower healthcare utilization and costs despite increased medication spending. Health Affairs. 2011:30(1):91-99. doi:10.1377/hlthaff.2009.1087
  5. Roebuck MC, Kaestner RJ, Dougherty JS. Impact of medication adherence on health care utilization in Medicaid. Medical attention. 2018;56(3):1. doi:10.1097/mlr.00000000000000870
  6. BA Briesacher, et al; Patients at risk for cost-related medication nonadherence: a literature review. J Gen Intern Med. 2007,22:864-71.
  7. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic diseases; NEHI Research Brief, August 2009.
  8. M. Viswanathan, et al; Interventions to improve adherence to self-administered medications; Ann InterMed; September 2012.
  9. Conn VS, Ruppar TM, Enriquez M, Cooper P. Medication adherence interventions aimed at subjects with adherence problems: systematic review and meta-analysis. Research in Social and Administrative Pharmacy. 2016;12(2):218-246. doi:10.1016/j.sapharm.2015.06.001

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