The draft report offers guidance for developing, selecting, and implementing AI-enabled quality measures for accountability programs.
The National Quality Forum (NQF), an affiliate of the Joint Commission, is seeking public comment on a draft report detailing the use of artificial intelligence (AI) in healthcare quality measurement. The report provides guidance and recommendations for implementing AI-enabled quality measures in programs related to value-based payment, public reporting, and accreditation.
According to the NQF, AI methods such as machine learning and natural language processing can enhance quality measurement by extracting data from clinical notes, lab reports, and external devices like ambient sensors. This approach aims to reduce the burden of data collection and improve the reliability of measures by accessing clinically rich information that has historically been difficult to assess.
“AI has the opportunity to transform data collection and analysis, but using AI in quality measurement effectively and responsibly requires addressing a raft of novel and complex challenges to ensure accuracy, consistency, and adequate transparency, while managing the potential for unintended consequences,” says Dana Gelb Safran, ScD, president and CEO of NQF, in a release. “To realize the full potential of AI for quality measurement, we must establish strong governance and industry consensus.”
Multi-Step Roadmap and Recommendations
To develop the guidance, NQF assembled a national technical expert panel with stakeholders from across the healthcare ecosystem, including experts in clinical informatics, health information technology, payers, and health systems. The panel created a multi-step roadmap and specific recommendations for program owners, measure developers, measured entities, and measure implementation vendors. The recommendations cover the entire lifecycle of a measure, from development and testing to implementation and long-term monitoring.
“Many quality measures rely on claims-based data. A limitation of claims for quality measurement is that they lack the clinical richness and nuance of clinically-sourced data,” says Safran in a release. “AI technology could be the key to unlocking the potential of clinically-sourced data for measuring and improving the quality of healthcare while at the same time easing the measurement burden on care providers.”
This initiative is part of a larger effort by the Joint Commission to promote the responsible use of AI in healthcare. In June, the Joint Commission partnered with the Coalition for Health AI to create AI playbooks, tools, and a new certification program.
Funding for the NQF’s work on AI in quality measures was provided by the Gordon and Betty Moore Foundation. Comments on the draft report must be submitted by Oct 15, 2025, at 6 pm ET.
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