Clinical Notes

Overview

The vast majority of the world's actionable clinical knowledge does not exist in neatly organized, easily queried databases. Instead, an estimated 80% of all medical data is trapped within the chaotic, unstructured text of clinical notes. When a patient with a rare, undiagnosed condition finally secures an appointment at a tertiary care center, they rarely arrive with a simple, structured summary. They arrive with a "data dump"—often consisting of hundreds of pages of discharge summaries, progress notes from various unconnected specialists, hastily typed referral letters, and sometimes even decades-old handwritten records that have been scanned into degraded PDFs. For a clinical geneticist, finding the single diagnostic clue hidden within this mountain of text is akin to searching for a needle in a haystack while blindfolded. A subtle observation noted by a pediatric ophthalmologist five years ago—perhaps a passing mention of a slightly dislocated lens—might be the definitive phenotypic link required to diagnose Marfan Syndrome today. Yet, if that note is buried on page 142 of a 300-page PDF, the human physician is highly likely to miss it during a standard 45-minute consultation. The sheer volume of unstructured data acts as a massive operational bottleneck, directly contributing to the multi-year diagnostic odysseys experienced by millions of rare disease patients. Sanjeevni was fundamentally engineered to solve this unstructured data crisis. We recognize that doctors went to medical school to solve complex biological puzzles, not to act as administrative data-miners. By deploying medical-grade artificial intelligence to pre-read and synthesize these vast archives of unstructured text, Sanjeevni liberates the physician to focus entirely on the intellectual task of diagnosis and patient care.