Computer-assisted abstraction for reporting of quality measures

This invention describes a computer system that helps healthcare providers track how well they are meeting quality standards. It works by taking summarized patient records, identifying a relevant quality measure category, and then using specific keywords and questions to find evidence within those records. The system then generates a report showing which criteria were met and where in the record the evidence was found, specifically for documents related to a single patient visit. It can also alert staff if a deadline for a quality measure is approaching.

Why it matters: Filed before large language models could do the heavy lifting of natural language understanding and abstraction. The core tasks of identifying categories, matching criteria, and generating reports from complex medical text are now far more robust and efficient with modern AI.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedSoftware logic, natural language processing, healthcare data integration

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