Quality Measurement

Apply to Join NCQA’s AI Learning Collaborative

AI-Enabled Quality Measurement Defining What Good Looks Like for AI in Quality Measurement Workflows

AI is rapidly transforming quality measurement, from chart abstraction and gap identification to digital quality submission. But quality measurement is high-stakes work. HEDIS®, Star Ratings and other performance programs influence payment, performance and accountability, making accuracy, transparency and essential.

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NCQA’s AI Learning Collaborative brings health plans and quality measurement vendors together to develop a shared understanding of what responsible, high-quality AI implementation looks like in practice. Through a structured cohort experience, participants learn from peers, evaluate outcomes, contribute to emerging leading practices and benchmark their progress against standardized metrics.

What Is the AI Learning Collaborative?

The AI Learning Collaborative is a cohort-based program focused on high-impact healthcare AI use cases. Participants work together to identify leading practices, evaluate outcomes and accelerate responsible implementation through peer learning, expert guidance and structured assessment.

The Quality Measurement cohort will focus on AI-enabled workflows across the quality measurement lifecycle, helping organizations understand where they are today, how their performance compares with peers and what actions can strengthen implementation quality and outcomes. Cohort participants move through the program together over approximately six to eight months, contributing to a practical playbook and outcomes framework that reflects real-world implementation experience.

Benefits of Program Participation

By participating in NCQA’s AI Learning Collaborative, your organization will:

Help shape and gain early access to a leading-practice playbook for AI-enabled quality measurement workflows.
Contribute to the development of an outcomes-based framework for evaluating AI implementation quality.
Receive a confidential, de-identified assessment of your organization’s implementation and benchmark results against peer participants.
Learn directly from health plans and vendors addressing similar measurement challenges.
Reduce implementation risk through stronger governance, transparency and outcomes-monitoring approaches.
Help define the industry’s understanding of high-quality AI implementation rather than adapting to definitions created by others.

Information collected as part of the Learning Collaborative will be de-identified, used in aggregate and treated as confidential in accordance with participation agreements.

Use Case Focus: AI-Enabled Workflows for Quality Measurement

Quality measurement is among healthcare’s most data-intensive and operationally complex workflows. Organizations must assemble complete and accurate data, interpret measure specifications, and navigate reporting requirements that directly influence performance and payment.

AI has the potential to improve multiple stages of this process, including event detection, data gathering via external sources, data normalization, measure calculation and digital quality reporting. However, without consistent evaluation and governance, organizations may struggle to demonstrate accuracy and transparency.

How the Collaborative Works

1

Structured Learning and Peer Exchange

Join facilitated discussions focused on real-world AI-enabled measurement implementations.
Learn from organizations at varying stages of AI maturity.
Share implementation challenges, lessons learned and emerging practices in a confidential environment.

2

Playbook and Outcomes Framework

Help build and gain early access to a leading-practice implementation playbook.
Apply a standardized framework for assessing implementation quality.
Align with peers on what constitutes high-quality AI implementation in measurement workflows.

3

Outcomes Evaluation and Benchmarking

Contribute baseline and post AI implementation data.
Receive de-identified benchmarking and comparative insights.
Identify opportunities for improvement and future scale.

Ways to Participate

Plans and vendors may participate independently or together.

Health Plan, In-House Measurement

Participate using your own implementation and data.

Health Plan Using a Vendor

Coordinate data access and participation with your vendor partner.

Vendor, Independent Participation

Participate through an agreement with a health plan supplying implementation data.

Plan and Vendor Together

Collaborate jointly and determine participation responsibilities together.

Who Should Apply

Health Plans

This cohort is designed for health plans that are:

Exploring AI solutions for quality measurement.
Piloting AI for chart review, data extraction, gap closure or related activities.
Scaling AI-enabled measurement and seeking stronger governance and performance evaluation.
Looking for practical implementation guidance, not just high-level AI principles.

Quality Measurement Vendors

This cohort is also designed for vendors that:

Provide AI-enabled quality measurement platforms or measure engines.
Support AI-enabled digital quality measurement, reporting or measure calculation workflows.
Offer AI-enabled capabilities that improve portions of the measurement process.
Want to help define industry leading practices and demonstrate measurable impact.

Data Participation Requirement

All participants must contribute baseline and post-implementation data for comparative evaluation. Securing the rights and permissions to use this data is the responsibility of the participating organization. Synthetic data is not accepted.