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Adherence to medical guidelines

Adherence to medical guidelines is a global issue, specially on chronic patients. AI helps medical professionals and patients to follow guidelines.

Problem

The medical guides are documents that offer recommendations for treating patients with certain health conditions. They are created by teams of experts who base their work on the available scientific evidence. Its goal is to assist healthcare professionals in making informed, high-quality clinical decisions.

However, a recent study revealed that adherence to medical guidelines is a global issue that was published in the journal The Lancet [1]. 57% of patients, on average, receive the recommended treatment, according to the researchers' findings.

Size of the Problem

  • It affects 50–70% of patients with chronic diseases [2].
  • A 2003 study on the level of compliance with clinical practice recommendations showed that only 55 percent of all recommended measures were ultimately applied to patients [3].
  • Medication adherence appeared highest in cancer patients (80%), about 75% in many other diseases [4].

Why it matters

Adherence to medical guidelines is crucial for a variety of reasons. In the first place, it can help to improve the quality of medical care. When medical professionals follow guidelines, it is more likely that patients will receive appropriate treatment for their conditions.

In second place, adherence to medical guidelines may help to reduce healthcare costs. When patients receive appropriate treatment, it is less likely that they may require hospitalization or costly medical procedures. In third place, adherence to medical guidelines can help to improve patient satisfaction. Patients receiving the necessary treatment will more likely be satisfied with their medical care.

Solution

● Mobile Applications Approach: Mobile applications based on artificial intelligence (AI) have been evaluated as tools to assess and promote medication adherence in patients with non-communicable chronic diseases (NCDs). A study conducted by Labovitz et al. [5] developed an AI application to measure adherence in stroke patients, achieving an absolute improvement of 67% in adherence in the group monitored with the application compared to the control group. A 100% adherence rate was observed in the intervention group, assessed through drug plasma concentration levels. In another study by Bain et al. [6], a real-time monitoring method on an AI platform for patients with schizophrenia and cognitive impairment showed a 17.9% higher adherence compared to the control group under directly observed therapy.

● Automated Reminders: AI has been used to deploy health communication and automated reminders to encourage adherence. A reminder system based on artificial intelligence using text messages (SMS) demonstrated significantly higher medication refill rates in older patients with NCDs. Additionally, projects like ChronologyMD for Crohn's disease utilized artificial intelligence to enable patients to track their adherence and symptoms, facilitating communication with healthcare providers.

● Patient Empowerment with AI: AI has shown indirect benefits in adherence by empowering patients. A chatbot named "Vik" provided personalized medical information about breast cancer, improving adherence by 20% in patients who used the medication reminder feature. Robotic assistants have also been employed to enhance self-control in patients with diabetes.

● Integration of Care with AI: Integrated care programs, aiming to improve clinical outcomes and patient experience, can be supported by AI technologies. These programs involve multidisciplinary networks and electronic records, and AI can assist in managing unstructured medical data and optimizing prescriptions, improving medication reconciliation.

Datasources

  • Electronic Health Records (EHR) Databases: These databases are essential as they contain detailed information about physicians' clinical practices, including treatment decisions and prescriptions. They are fundamental for assessing whether physicians are following recommended guidelines.
  • Pharmacovigilance Databases: These databases are crucial for understanding prescription patterns and the safety of medications used in healthcare. They can help identify potential areas for improvement in adherence to medical guidelines.
  • Clinical Research Databases: Data from clinical trials and research studies provide solid evidence of the effectiveness of different treatments and medical practices. Utilizing this data can help identify best practices and address discrepancies in adherence to medical guidelines.

Citations

  1. Zhou, Bin, et al. "Global, regional, and national levels of adherence to guideline-recommended cardiovascular disease prevention and management interventions in 184 countries." The Lancet 396.10248 (2020): 1734-1747. DOI: 10.1016/S0140-6736(20)30673-6
  2. Lam WY, Fresco P. Medication Adherence Measures: An Overview. Biomed Res Int. 2015;2015:217047. doi: 10.1155/2015/217047. Epub 2015 Oct 11. PMID: 26539470; PMCID: PMC4619779.
  3. EA McGlynn, SM Asch, J. Adams, J. Keesey, J. Hicks, A. DeCristofaro, et al .La calidad de la atención médica brindada a adultos en los Estados Unidos.N Engl J Med, 348 (2003), págs. 2635-2645. http://dx.doi.org/10.1056/NEJMsa022615
  4. Brown, Jessica, et al. "Effect of adherence to diabetes guidelines on long-term outcomes." Diabetes Care 40.12 (2017): 2077-2085. DOI: 10.2337/dc16-2383
  5. Labovitz DL, Shafner L, Reyes Gil M, Virmani D, Hanina A. Using artificial intelligence to reduce the risk of nonadherence in patients on anticoagulation therapy. Stroke. (2017) 48:1416–9. doi: 10.1161/STROKEAHA.116.016281
  6. Bain EE, Shafner L, Walling DP, Othman AA, Chuang-Stein C, Hinkle J, et al. Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia. JMIR Mhealth Uhealth. (2017) 5:e18. doi: 10.2196/mhealth.7030

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