AI Decision Support for Renal Dose Adjustment Using Medication Orders and Laboratory Trends Download PDF

Renal impairment is highly dynamic in hospitalized patients, and many medications require dose adjustment to avoid toxicity or therapeutic failure. Conventional renal dose alerts often depend on static thresholds and may not reflect evolving laboratory trajectories. Existing renal dose decision support tools commonly rely on a single creatinine-derived estimate of kidney function. When alerts are broad, repetitive, or poorly contextualized, clinicians may override them despite potential medication safety risk. This article proposes an AI decision support framework that continuously tracks medication orders and serial renal laboratory results. The system would forecast renal function changes and generate patient-specific dose adjustment recommendations at prescribing or pharmacist review. The proposed framework includes real-time data ingestion from electronic health records, a renal function prediction module, a drug-specific dosing rule engine, an alert prioritization layer, and an embedded CPOE interface. Pharmacist verification is incorporated as a human-in-the-loop safeguard. The system would aim to distinguish stable renal impairment from rapidly declining renal function and suppress alerts when the prescribed regimen is already appropriate. Its recommendations would be accompanied by concise explanations linking recent laboratory trends, medication risk, and renal dosing logic. An AI-augmented renal dosing framework could make renal dose adjustment more timely, individualized, and clinically acceptable. Such a system should be implemented only after careful workflow integration, safety review, and prospective clinical evaluation.