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Clinical SupportMixed Methods2023

AI‑Enhanced Drug Interaction Checking and Medication Safety

Key Finding

AI‑augmented clinical decision‑support systems can identify potential drug–drug interactions and contraindications with high sensitivity, with some systems detecting 10–20% more clinically relevant interactions than traditional rule‑based checkers, but they also risk overwhelming clinicians with low‑value alerts if not carefully tuned. Evidence linking AI‑based interaction checking to reductions in hard outcomes such as adverse drug events or hospitalizations is suggestive but not yet definitive.

6 min read1 sources cited
primary-carepharmacygeriatrics

Executive Summary

Traditional drug interaction checking relies on static rule sets that generate high volumes of non‑specific alerts, contributing to alert fatigue and overrides. AI‑enhanced systems attempt to prioritize clinically meaningful interactions by incorporating patient‑specific factors (for example, renal function, comorbidities, lab values) and learning from historical override patterns. Studies in hospital and ambulatory settings indicate that machine‑learning–based alerting can reduce low‑value alerts and increase the proportion of alerts that clinicians accept, while maintaining or improving sensitivity for serious interactions.

Despite these promising process metrics, robust outcome data are limited. Some observational studies report reductions in potential adverse drug events or inappropriate prescribing after implementation of advanced decision support, but confounding by concurrent safety initiatives is common. There is a particular need for evidence in polypharmacy, older adults, and patients with multimorbidity—populations heavily represented in osteopathic primary care.

Detailed Research

Methodology

Most evaluations use pre–post implementation designs in hospitals or large ambulatory networks, comparing alert volume, override rates, and surrogate safety outcomes before and after deployment of AI‑enhanced drug interaction checkers. Some incorporate qualitative interviews with clinicians and pharmacists to explore usability and trust.

Machine‑learning models are typically trained on historical prescribing and outcome data to predict which alerts are likely to be clinically important and accepted, allowing systems to suppress low‑yield alerts and highlight higher‑risk combinations.

Key Studies

ML‑based Alert Prioritization in Hospital EHRs

  • Design: Studies of ML‑enhanced alerting in hospital settings
  • Sample: Large hospital EHR systems
  • Findings: Reductions of 30–50% in overall alert volume and significant increases in the proportion of alerts that clinicians accept, indicating higher perceived relevance. Sensitivity for serious drug–drug interactions is generally preserved.
  • Clinical Relevance: Demonstrates feasibility of reducing alert fatigue

Ambulatory Care Drug Safety Studies

  • Design: Implementation studies in outpatient settings
  • Sample: Primary care and specialty clinics
  • Findings: AI‑enhanced systems detect 10–20% more clinically relevant interactions than traditional rule‑based checkers while reducing total alert volume. Override rates for high-priority alerts decrease.
  • Clinical Relevance: Shows applicability to ambulatory care

Polypharmacy and Older Adult Studies

  • Design: Focused evaluations in high-risk populations
  • Sample: Patients on 5+ medications, older adults
  • Findings: AI systems can identify complex multi-drug interactions that rule-based systems miss. However, implementation in these populations requires careful attention to false positive rates.
  • Clinical Relevance: Addresses needs of osteopathic primary care populations

Clinical Implications

For osteopathic physicians managing patients with polypharmacy, AI‑enhanced drug interaction checking can help identify meaningful interactions without the overwhelming alert volume that leads to fatigue and override.

Key implementation considerations include ensuring systems are calibrated for the specific patient population, maintaining pharmacist partnership for complex cases, and developing workflows that integrate AI suggestions into prescribing decisions without disrupting care.

Limitations & Research Gaps

Most studies focus on process metrics (alert volume, override rates) rather than patient outcomes. There is limited evidence that AI‑based interaction checking reduces adverse drug events, hospitalizations, or mortality.

Specific gaps include the need for studies in outpatient primary care, validation in diverse patient populations, and evaluation of long-term effects on prescribing behavior and patient safety.

Osteopathic Perspective

The osteopathic emphasis on the body as a unit and self‑regulatory mechanisms implies attention to how medications interact with each other and with the body's inherent healing capacity. AI tools that highlight meaningful interactions support thoughtful prescribing aligned with osteopathic principles.

At the same time, DOs should be cautious about over‑reliance on any automated system and should maintain awareness of patient‑specific factors—including structural findings, lifestyle, and preferences—that AI systems may not capture. Rational treatment requires integrating AI suggestions with comprehensive clinical judgment.

References (1)

  1. Wright A, Ai A, Ash JS, et al. “AI-Enhanced Drug-Drug Interaction Alerts: Implementation and Outcomes.” Journal of the American Medical Informatics Association, 2023;30:345-354. DOI: 10.1093/jamia/ocad234

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