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Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness using a Personalized
Areej Bayatra1, Rima Nasserat1, Yaron Ilan1
1Department of Medicine, the Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
Current Pharmaceutical Biotechnology
|January 30, 2024
Summary
Low adherence to chronic treatments hinders patient outcomes. This review explores digital solutions, highlighting the need for advanced AI systems beyond simple reminders to improve medication adherence and treatment effectiveness.
Area of Science:
- Medical adherence research
- Digital health interventions
- Artificial intelligence in healthcare
Background:
- Low adherence to chronic treatment regimens is a major obstacle to improving clinical outcomes in patients with chronic diseases.
- Multiple factors contribute to low adherence, necessitating comprehensive solutions.
Purpose of the Study:
- To review existing studies on the prevalence of low adherence to chronic treatments.
- To explore potential solutions for improving patient adherence.
- To describe the development of a second-generation artificial intelligence system for enhanced chronic drug effectiveness.
Main Methods:
- Literature review of studies on adherence prevalence.
- Analysis of current adherence improvement measures.
- Description of a novel artificial intelligence system.
Main Results:
- First-generation digital systems primarily use reminders, proving insufficient for many patients.
- Existing adherence improvement methods require enhancement.
- A second-generation artificial intelligence system is proposed to improve chronic drug effectiveness.
Conclusions:
- Improving clinically meaningful outcome measures and disease parameters can enhance adherence.
- Better patient response to therapy is achievable through improved adherence.
- Advanced digital health solutions, like AI, are crucial for overcoming adherence barriers.
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