CliniNote Insights
Articles on RWE methodology, clinical NLP engineering, and evidence generation for research teams.
Major adverse cardiovascular events are defined in trial protocols but scattered across progress notes, discharge summaries, and procedure reports. We walk through how structured NLP extraction maps free text to MACE definitions.
Read articleStructured fields in the EHR hold demographics, billing codes, and lab results. The clinical story lives in the notes. Most evidence teams still process notes by hand.
Read articleClinical timelines are implicit. A note from March may describe symptoms that started in November. Getting dates right requires understanding narrative structure, not just extracting date strings.
Read articleInclusion and exclusion criteria are written in protocol language. Patient records are written in clinical shorthand. Closing that gap at scale requires systematic entity extraction.
Read articleGeneral NLP benchmarks do not predict performance on clinical text. Negation handling, abbreviation disambiguation, and section segmentation require domain-specific engineering.
Read articleHIPAA compliance is a legal framework with specific covered-entity requirements. Most clinical AI tools are not covered entities themselves. Here is what responsible data design looks like.
Read articleEvery SOAP note follows a loose structure. Every clinician deviates from it differently. Understanding what goes where in a progress note is the prerequisite to extracting anything useful from it.
Read articleRegulatory agencies now accept RWE for certain label extensions and post-market commitments. This primer covers what RWE is, where it comes from, and the data quality bar required.
Read articleICD billing codes capture diagnoses at a coarse level. Clinical phenotypes require finer discrimination. Bridging structured codes to unstructured note evidence is where most RWE cohort definitions break down.
Read articleF1 score against a gold standard is the standard measure. But what is the gold standard in clinical NLP? How inter-annotator agreement is calculated, and why it sets the ceiling for any automated extraction system.
Read articleLarge language models can summarize a clinical note. They struggle to consistently identify negated findings, handle medication dose ambiguity, or maintain extraction precision across thousands of records.
Read articleManual chart abstraction is slow, expensive, and inconsistently reproducible. The 12 to 30 percent inter-reviewer disagreement rate in published studies is not a personnel problem. It is a structural one.
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