AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH026170039
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ORIGINAL RESEARCH ARTICLE

Reducing cognitive load in polypharmacy: A prototype clinical decision support system

Insiya Fatma1 ,  Mary A. Dolansky2 ,  Evelyn G. Duffy2 ,  Colin K. Drummond1*
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1 Department of Biomedical Engineering, School of Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America
2 Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, Ohio, United States of America
AIH, 026170039 https://doi.org/10.36922/AIH026170039
Received: 23 April 2026 | Revised: 29 May 2026 | Accepted: 12 June 2026 | Published online: 23 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Polypharmacy, defined as the concurrent use of five or more medications, presents a significant health risk for older adults, particularly when managing complex “as-needed” (PRN) regimens. While healthcare providers establish treatment plans, the operational complexity of managing daily regimens often exceeds the support structures available to the patient, increasing the risk of unintended medication errors. This paper describes the development of a prototype clinical decision support tool designed to enable exploration of automated systems with the potential to reduce this cognitive load by integrating natural language processing with dynamic prescription transcription. The prototype utilizes a Retrieval-Augmented Generation (RAG) architecture, merging targeted information retrieval from a clinical knowledge base with the generative capabilities of Large Language Models (LLMs). This allows the system to interpret natural language queries and provide contextually relevant safety data. At this stage of development, the prototype does not test cognitive load explicitly, rather provides preliminary technical feasibility through retrieval performance, guardrail behavior, and prototype feasibility. Preliminary results are encouraging; the prototype effectively processed complex inquiries regarding NSAID usage, accurately identifying drug-drug interactions (e.g., with lithium) and flagging gastrointestinal risks. Furthermore, the system demonstrated essential safety guardrails by identifying its own data limitations and directing users to professional consultation, but the system has not been clinically validated for such use. These initial findings suggest that RAG-based prototypes offer a feasible pathway toward reducing the cognitive demands of polypharmacy, providing a foundation for future systems that integrate patient-specific health data to further minimize clinical ambiguity.

Keywords
Polypharmacy
Retrieval-Augmented Generation
Medication adherence
Large Language Models
Clinical decision support systems
Geriatric health informatics
Funding
Research reported in this publication was internally funded by Case Western Reserve University Department of Biomedical Engineering.
Conflict of interest
The authors declare no conflicts of interest.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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