Prodigy™: An AI Engine for Clinical Data Abstraction
Quality teams need registry data that is complete, accurate, and defensible, without adding to an already stretched clinical data abstraction workload. Prodigy™ is the AI engine MRO built to help meet that need. It reads clinical documentation, identifies relevant evidence, and generates values for abstraction fields used in registry reporting, accreditation submissions, and research. Prodigy was designed from the start around the specific demands of clinical data work, pairing AI-driven review with the clinical expertise needed to keep results accurate and defensible.
How Does Prodigy Process Clinical Documentation?
Prodigy reviews clinical documentation through a structured process:
- Task instructions and curated clinical data are provided to our secure, frontier large language model gateway
- The AI models there extract, reason over, and generate values for each required data element
- Our proprietary confidence models evaluate the output before it moves forward
- Confidence-labeled results are routed to MRO’s clinical experts for validation
Prodigy delivers 60% of data elements at our highest confidence level today. MRO’s clinical experts review these results and complete any remaining work that requires additional review, registry knowledge, or clinical judgment—keeping automation and clinical expertise working together at every step.
Prodigy vs. Point Solution AI Tools in Healthcare
Most point-solution AI tools in healthcare are thin wrappers around a chatbot, built to prove a concept rather than to hold up across the full range of documentation a health system produces day to day. Clinical records vary widely between facilities, arrive in different formats, and often include gaps or inconsistencies that a narrow, single-purpose AI solution isn’t built to handle.
Prodigy is built around a robust pipeline of technologies and methods, not a single model call. The surrounding system—data preparation, confidence checks, and clinical review—works to keep results dependable and defensible across real-world documentation, not just in a pilot demo.
How Prodigy Scales Across Registries and Health Systems
Prodigy is designed to expand in two directions at once. Within a given registry, ongoing use improves accuracy and the quality of supporting evidence over time. Across registries, learnings from one area can inform how the system approaches an adjacent one, without mixing data between clients or facilities. Data belonging to one client is never used to shape results for another, so improvements can scale across the business without any single client’s information being reused elsewhere.
The goal behind Prodigy is simple: faster, more defensible registry data without sacrificing clinical judgment. See exactly how that plays out in practice in this conversation with MRO’s software engineering and AI architecture leadership in the video below and contact our experts to learn more.
Frequently Asked Questions About Prodigy™
What kind of data does Prodigy™ work with?
Prodigy is built to process clinical documentation used in registry reporting and quality programs, including narrative text, scanned records, and structured data.
Does Prodigy™ replace clinical judgment?
No. Prodigy is built to handle documentation review and generate suggested data points. Clinical experts remain a vital part of the review workflow before that data moves forward.
How is Prodigy™ different from a general purpose AI model?
Prodigy pairs frontier large language models with a data pipeline and confidence modeling system built specifically for clinical documentation, rather than applying a general model without the additional structure clinical data requires
