Health Leaders Need to Scrutinize How AI Interprets EHR Data
The article examines how AI systems draw conclusions from electronic health record data. It notes that a record may list a medication as active even when a patient stopped taking it weeks earlier, which can lead an AI tool to give a wrong answer with high confidence. A clinician can address that ambiguity by asking a follow-up question.
Electronic records may lag behind reality: a prescription remains flagged active after a patient discontinues it. If an AI model accepts that status as present truth, it may answer confidently but incorrectly. A human clinician can ask clarifying questions; software needs explicit design to recognize and communicate uncertainty.
The source also points to standardization gaps. FHIR, LOINC, RxNorm, and UCUM aim to align exchange, lab names, drug names, and units, yet organizations may map fields unevenly. Conflicting entries—resolved symptoms versus unconfirmed self-reported reversals—require systems that flag discrepancies and support human review.
If health systems adopt AI tools that misread outdated or conflicting records, patients could receive inaccurate guidance, and clinicians may face added burden verifying outputs. Health leaders and vendors may need stronger validation, transparency, and human-review workflows. This could improve safety when uncertainty is surfaced, but may also widen gaps between well-resourced and under-resourced organizations if implementation varies.