Colombian ICU Doctor Finds Critical Flaws in AI-to-FHIR Medical Data Pipeline

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A critical care physician in Colombia ran an experiment mapping clinical pipeline output to FHIR health data standards, discovering significant errors in the process. Out of 38 resources in the first test run, two were rejected by the server, including a record for norepinephrine, a key drug in intensive care. Further analysis revealed that nursing timestamps were lost due to missing schema fields, duplicate lab values created phantom data points, and a negation note was incorrectly converted into a positive clinical finding. The physician also found that 15% of LOINC codes assigned with clinical judgment were incorrect when checked against official NLM records. The author concluded that human oversight remains essential in medical NLP pipelines, particularly for terminology assignment, and has published the full findings and code on GitHub.

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