IA Dossier Médical Chronologies ngir dosiye yu Yoon

Medical records arrive in messy form: faxed PDFs, scanned handwritten notes, EHR printouts with repeated headers, billing ledgers and imaging reports. The same visit often appears several times from different providers. A chronology tool first runs optical character recognition (OCR) on image pages. It then splits the file into separate documents, removes exact and near duplicates, and identifies each document's type, provider, facility and date of service. A language model then summarizes each encounter: complaints, findings, diagnoses, medications, procedures, work restrictions and recommendations. The output is a table sorted by date, usually with a page reference for each row. Vendors in this space include personal-injury platforms such as EvenUp and Supio and record-review companies such as Wisedocs. General legal AI tools can also produce chronologies when given records. Features vary, so firms should test any tool on their own files. Two kinds of error matter. Omissions happen when OCR fails on handwriting or a poor fax, when a date is misread, when a short but important note (a nurse's fall report, a refusal of treatment) gets folded into a longer summary, or when deduplication wrongly merges two different visits. Invented or distorted entries happen when the model fills gaps. Examples include saying a test was performed when it was only ordered, putting a finding on the wrong date, or mistaking a record's print date for its date of service. EHR copy-forward text, where earlier notes are pasted into later ones, can make an old finding look current. A common misconception is that a citation proves accuracy. A page reference only helps if someone opens it. Good practice is to verify every entry that supports a claim and sample the rest. Also run a gap check for missing date ranges and compare the chronology against billing records, which list every billed date of service.