Professional · Issue 2 · 2026-10-08
Using the Electronic Health Record and Artificial Intelligence to Guide Clinical Decision-Making
Editorially reviewed research summaries, context, and limitations.
Renee M Cholyway, William C Kethman
Summary: This narrative review in Clinics in Colon and Rectal Surgery surveys how electronic medical records and artificial intelligence are being applied to clinical decision-making, covering prehabilitation, sepsis detection, protocol-driven care, large language models for documentation, and patient triage. The authors highlight both the expanding capabilities of AI-augmented EMRs and acknowledged limitations including data scarcity for rare conditions and opacity in algorithm development. No original data are presented; this is a descriptive synthesis aimed at a surgical subspecialty audience.
Why it matters: For clinicians and healthcare informatics professionals, the review consolidates a wide landscape of EMR-AI applications into a single readable reference. The explicit acknowledgment that data scarcity poses problems for rare diseases and uncommon complications is a meaningful point often underemphasised in enthusiastic AI literature. However, because the piece does not advance methodology, generate new evidence, or rigorously evaluate existing evidence, its contribution is primarily educational rather than scientific.
Expert perspective: Narrative reviews of EMR and AI capabilities are proliferating nearly as fast as the technologies themselves, and this one is competent but not distinctive. The article covers well-trodden territory — sepsis early-warning systems, LLMs reducing documentation burden, patient portal engagement — without critically appraising the underlying evidence base for any of these applications. Readers should note that many AI clinical decision-support tools carry substantial implementation challenges: alert fatigue, workflow disruption, algorithmic bias, and performance degradation when models are deployed outside the populations on which they were trained. These concerns receive only passing acknowledgment.
The mention of data scarcity as a barrier to identifying rare complications is particularly relevant to rare-disease research communities, where small patient populations, heterogeneous genotypes, and complex multi-system disease make AI model training genuinely difficult. Registry data and federated learning approaches are natural responses to this challenge, but they are not discussed.
The claim that AI has "vastly broadened" EMR capabilities warrants scepticism. Many AI-embedded tools show promise in controlled settings but modest or mixed results at scale. The review does not distinguish between tools with robust prospective validation and those that remain largely aspirational. Transparency in algorithm development — cited as a limitation — is a patient safety and regulatory concern that deserves deeper treatment than it receives here.
The absence of a systematic search strategy, quality appraisal, or evidence grading means conclusions should be treated as expert opinion rather than synthesised evidence. Worth reading as a primer; not a definitive reference.
Relevance: Clinical Informatics and Health Data Methods
Source: https://pubmed.ncbi.nlm.nih.gov/42553947/
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