Early Disease Detection Using Patient Health Report (500+ Parameters): A Catalogue-Driven Report-Screening Prototype

Patient health reports contain measurements that are difficult to review consistently across formats. This paper presents HealthLens, a Flask-based prototype that extracts report values, matches them to a configurable parameter catalogue, and highlights deviations from configured reference ranges. PDF, image, text, CSV, JSON and selected synthetic-data (Synthea) archives are supported, using text extraction first and local OCR as a fallback. Each recognised value is shown with its unit, configured interval and status, alongside a deterministic review-priority indicator and explanatory summaries. The catalogue architecture has no hard-coded parameter limit and is intended to grow beyond 500 entries; the current implementation contains 15 configured entries. We describe the pipeline, the comparison and scoring rules, and the measurements behind them: 11 automated tests passed in one local run, and one fictional demonstration report produced eight findings. We state plainly that no diagnostic-accuracy or clinical evaluation has been performed. The system supports educational report screening, does not diagnose disease, and requires clinical validation before any healthcare deployment.