Plain-language · 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 review article surveys the ways electronic health records and AI are being used to support clinical decisions. Topics covered include early detection of sepsis, tools that reduce the time clinicians spend writing documentation, and systems that help prioritize patients who need attention. The authors note both the growing capabilities of these tools and real limitations — including the fact that AI systems are harder to train when data on a particular condition are scarce, and that the inner workings of many algorithms are not made transparent to the people using them. No new data are presented; this is a descriptive overview written for a surgical specialist audience.
Why it matters: For people new to these tools, the review brings together a broad landscape of AI applications into one readable summary. Its acknowledgment that data scarcity is a genuine obstacle — particularly for rare conditions — is an important point that enthusiastic accounts of AI often skip. Understanding this limitation matters for anyone involved in registries or research networks built around conditions that affect relatively small numbers of patients.
Expert perspective: This review covers familiar ground competently but does not critically examine whether the tools it describes actually work well in practice. Many AI clinical decision-support tools perform promisingly in controlled settings but show mixed results when used more broadly. Problems such as alert overload, disruption to clinical workflows, and the tendency of algorithms to perform poorly outside the populations they were originally trained on receive only passing attention here. The lack of a formal evidence-appraisal process means the review's conclusions reflect expert opinion rather than a systematic weighing of the available evidence. It is a reasonable starting point for someone new to the field, but not a definitive reference for those already working in health informatics or rare-disease data research.
Relevance: Clinical Informatics and Health Data Methods
Source: https://pubmed.ncbi.nlm.nih.gov/42553947/
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