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Artificial Intelligence for Medication Management in Discordant Chronic Comorbidities: An Analysis from Healthcare Provider and Patient Perspectives

Year: 2025

Type: article

Abstract: Recent advances in artificial intelligence (AI) have created opportunities to enhance medical decision-making for patients with discordant chronic conditions (DCCs), where a patient has multiple, often unrelated, chronic conditions with conflicting treatment plans. This paper explores the perspectives of healthcare providers (n = 10) and patients (n = 6) regarding AI tools for medication management. Participants were recruited through two healthcare centers, with interviews conducted via Zoom. The semi-structured interviews (60–90 min) explored their views on AI, including its potential role and limitations in medication decision making and management of DCCs. Data were analyzed using a mixed-methods approach, including semantic analysis and grounded theory, yielding an inter-rater reliability of 0.9. Three themes emerged: empathy in AI–patient interactions, support for AI-assisted administrative tasks, and challenges in using AI for complex chronic diseases. Our findings suggest that while AI can support decision-making, its effectiveness depends on complementing human judgment, particularly in empathetic communication. The paper also highlights the importance of clear AI-generated information and the need for future research on embedding empathy and ethical standards in AI systems.

Source: Information

DOI: 10.3390/info16030237

Authors: Tom Ongwere, Tam V. Nguyen, Zoe Sadowski

Institution: University of Dayton

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,930