In healthcare, quality improvement abstraction is the process of extracting key clinical data from patient records to evaluate and improve care quality. Abstractors review charts to capture specific quality measures—such as cancer screenings or blood pressure control—that reflect how well providers are meeting clinical standards.

 

In my project at Reveleer, I designed the Quality Data Entry platform used by both internal abstractors and health plan customers. This abstraction work supports national quality programs (e.g., HEDIS, Star Ratings) and payer reporting, which in turn influence compliance, care delivery, and incentive-based reimbursement.

 

By integrating AI capabilities directly into the abstraction workflow, the platform now helps abstractors:

• Automatically detect and surface relevant clinical evidence from unstructured medical records using NLP and OCR

• Pre-populate quality measure fields with AI-suggested values to reduce repetitive data entry

• Highlight evidence, confidence and source context, improving trust and decision-making

• Enable human validation and override, ensuring compliance in high-risk, regulated environments

 

A reliable and efficient platform is critical—inaccurate or delayed data entry can impact patient outcomes, financial performance, and regulatory standing.