NCQA’s AI Learning Collaborative
Advancing Responsible AI in Healthcare
Artificial Intelligence (AI) is rapidly reshaping health plan operations—from supporting timely, consistent prior authorization decisions to accelerating quality measurement and improving data quality. While some organizations are actively piloting or scaling AI solutions, others are still determining where to begin. Regardless of where an organization sits on the AI adoption curve, scaling AI responsibly, transparently, and effectively is foundational.
Without clear guidance or strategy on which use‑cases to prioritize, seamless workflow integration, and meaningful evaluation of outcomes, AI implementations can become fragmented and misaligned with their intended goals leading to operational inefficiencies in the workflow, wasted effort and resources, and increased risk to performance, consistency, and quality.
To help the industry move forward with clarity and confidence, NCQA is launching a series of paid AI Learning Collaboratives focused on quality-specific use cases, offering peer learning, leading practice guidance, and evolving, outcomes-oriented playbooks to enable responsible implementation and clarity on how to evaluate impact.
Learn more about the program
What Is the AI Learning Collaborative?
The AI Learning Collaborative is a paid use case based program designed to support responsible, effective AI use in healthcare helping participants understand where they are today, and how they can scale responsibly. It brings healthcare organizations together to:
- Engage in peer learning on real-world AI implementation.
- Strengthen governance, accountability, and outcomes measurement frameworks for specific use cases.
- Share anonymized outcomes metrics and evaluate implementation effectiveness and variances against peers.
- Contribute to and access evolving use-case specific implementation playbooks and outcomes monitoring.
Tailored to high impact uses cases:
Prior Authorization
Prior authorization is one of healthcare’s most visible and complex operational workflows. As health plans explore AI-enabled approaches, organizations face important questions around governance, compliance, performance measurement and outcomes.
The first cohort centers this high-risk, high-impact workflow that directly affects access to care and clinical decisions while supporting readiness for evolving requirements, including CMS-0057.
Apply NowAI-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, identify care gaps and support reporting requirements that directly influence performance and payment.
AI has the potential to improve multiple stages of this process, including event detection, extraction of information from unstructured data, data normalization, measure calculation and digital quality reporting. However, without consistent evaluation and governance, organizations may struggle to demonstrate accuracy, transparency and audit readiness.
Apply NowAI-Empowered Care Management
A peer learning forum where health plans can share experiences, learn from one another, and help define what effective AI adoption looks like across the industry.
Successful population health management requires wrangling high volumes of data, evaluating individual and population-level risk, engaging patients, and tailoring interventions to meet complex needs, often with limited resources
How the Learning Collaborative Works
Participants move through the program as a cohort focusing on a specific high-impact use case, with the initial cohort centered on prior authorization and future cohorts expanding into additional domains. Key elements include:
Structured Learning Sessions
Facilitated discussions focused on specific AI use cases, implementation challenges, and governance considerations.
Outcomes Evaluation and Continuous Improvement
Evaluate current prior authorization workflows, measure AI’s role and impact on outcomes, and identify opportunities for continuous improvement and scale—while providing visibility into how efforts align across peers.
Leading Practice Playbooks
Contribute to and access evolving playbooks that define effective implementation, governance, and provide a framework for evaluating impact and outcomes.
The learning collaborative emphasizes moving beyond process and system or model specific measures alone to include outcomes-specific monitoring-demonstrating that AI is not only functioning within workflows but demonstrably improving quality, efficiency, and experience.
Who Should Participate?
The AI Learning Collaborative for Prior Authorization is designed for health plans and vendors that are:
- Exploring or already using AI in core healthcare operations.
- Seeking applicable workflow level guidance and metrics to evaluate impact—not just high‑level principles.
- Interested in peer learning and shared problem-solving.
- Committed to shaping emerging industry leading practices by building a shared understanding of what high‑quality, responsible AI implementation looks like in prior authorization.
- Focused on building public trust and reputation by adhering to clearly defined safeguards.
Participants span a range of AI maturity levels—from early-stage, exploratory efforts and initial pilots to more advanced implementations embedded across prior authorization workflows.
Why This Work Matters
AI adoption in healthcare is accelerating faster than shared understanding of its risks, benefits, and improving outcomes. Organizations report common challenges:
Fragmented AI Implementation
Deployment of AI tools without consistent safety, equity, transparency, or accountability standards, and without outcomes-specific monitoring and metrics, results in added workflow burden rather than improved efficiency.
Operational and Regulatory Risk
Lack of standardization introduces risks in clinical decisions and patient outcomes using AI across the healthcare ecosystem.
Limited Real-World Evidence
The scarcity of scalable, sustainable evidence for use-case specific implementations hampers confidence in AI solutions across populations.
Lack of Trust and Reputational Risk
Unclear AI performance and governance can erode trust with members, clinicians, regulators, and the public.
Need for Coordinated Action
Addressing adoption requires collaboration, real-world testing, and trusted evaluation mechanisms.
The AI Learning Collaborative creates space for organizations to learn together while keeping quality, equity, transparency, and measurable impact at the center.
Benefits of Participation
By participating in NCQA’s Learning Collaborative, your organization will gain:
- A structured approach to evaluate AI implementation and maturity relative to peers.
- Insights into AI-enabled workflow performance through standardized metrics.
- Reduced risk and improved public trust through stronger governance, transparency, and monitoring frameworks.
- Access to implementation and monitoring leading practices and implementation guidance.
- Reduced efforts on AI pilots through a standardized, high-quality approach to implementations.
Participation also signals a commitment to thoughtful, accountable AI use in healthcare.
Looking Ahead
The AI Learning Collaborative is part of NCQA’s broader effort to support safe, effective innovation in health care. Insights from this work will help inform future use cases and leading practices, grounded in real‑world experience rather than theoretical, high-level frameworks.
