Artificial Intelligence–Driven Pharmacotherapy Optimization in Chronic Kidney Disease: Bridging Clinical Pharmacology and Urology
DOI:
https://doi.org/10.65327/kidneys.v14i4.569Keywords:
Chronic Kidney Disease, Artificial Intelligence, Pharmacotherapy Optimization, Renal Dosing, Interdisciplinary Nephrology–Urology CareAbstract
Chronic kidney disease (CKD) poses a constant threat to pharmacotherapy because of variable renal clearance, polypharmacy, and high-risk potential of intoxication with drugs. Although the clinical attentiveness has improved, safe and personalized dosing and especially of patients with interdisciplinary nephrology-urology care needs still have gaps. Artificial intelligence (AI) has become one of the possible ways to improve the precision in therapy, but it has not been widely integrated into clinical practice. The study was a qualitative descriptive study that examined the attitudes of 500 clinicians, nephrologists, urologists, and clinical pharmacologists, towards AI-driven pharmacotherapy optimization in CKD. Semi-structured interview questionnaires were used to collect the data, and it was tabulated in a structured Excel template. Based on the thematic analysis, which was done according to the framework of Braun and Clarke, it was possible to determine some of the major patterns, barriers, and facilitators relevant to the adoption of AI. There were 5 key themes: persistent dosing and polypharmacy issues; urology-related issues in CKD; the desire to use AI to support dosing and prediction and checking interactions, barriers (such as the lack of trust, workflow mismatch, and a lack of transparency), and facilitating factors (including seamless integration of EMR, interdisciplinary collaboration, and mechanisms to check interactions in real-time). Clinicians highlighted the necessity of AI systems to accommodate changes in renal and integrate cross-specialty data. According to the findings, there is a significant clinical need of AI-enhanced pharmacotherapy applications that can positively influence the safety, customization, and interdisciplinary CKD care coordination. These findings are used to develop future transparent, workflow-compatible, and clinically based AI frameworks to optimize medication management in nephrology and urology.
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ISSN 2307-1265
