longitudinal-ehr-qa

To build a longitudinal EHR timeline, this Skill harmonizes heterogeneous FHIR, OMOP, and mixed CSV/JSON/XML/PDF exports into a visit-centric timeline. It processes your patient data to produce per-visit narratives and evidence-backed outputs.

Yes, you can answer patient-level questions using a harmonized visit-centric timeline. The Skill creates question-specific workspaces with provenance-aware reasoning, ensuring agent-reviewed timing and deterministic sub-tasks for auditable decision-making.

The best way to perform EHR harmonization and provenance tracking is using a Skill that constructs visit-centric timelines with evidence tables. It enforces agent-reviewed timing and produces analysis plans, validation logs, and JSON answers with explicit provenance.

Yes, this EHR timeline approach works with PDF patient records by using the PyMuPDF dependency. It detects and harmonizes mixed CSV, JSON, XML, and PDF inputs into a unified timeline for longitudinal question answering.

You get final deliverables including reports, JSON answers, and evidence tables from patient-level longitudinal EHR question answering. The Skill generates per-question workspaces containing analysis plans, validation logs, and final reports with explicit provenance.

You validate timing and events in an OMOP or FHIR timeline through reflection-driven validation and deterministic Python sub-tasks. The Skill enforces agent-reviewed timing to ensure trustworthiness and produces validation logs within per-question workspaces.