On-Demand Learning Lab: AI 101 for the Quality Professional-Recording AI 101 for the Quality Professional Knowledge, Skills, and Abilities To Thrive

Recording AI 101 for the Quality Professional Knowledge, Skills, and Abilities To Thrive

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Video Summary

NACU hosted a Live Learning Lab webinar, “AI 101 for the Quality Professional,” featuring Rich Greenhill, who framed AI’s growing role in healthcare and what quality leaders need to thrive. He emphasized that AI is essentially math- and statistics-driven tools that mimic human tasks like reasoning and problem solving, and he distinguished predictive AI from newer generative AI (large language models). Greenhill outlined how algorithms are trained and tested, noting that deep learning can become a “black box,” raising challenges for transparency and informed consent.<br /><br />He warned that healthcare often overlays technology on flawed workflows—citing EHR frustrations—and urged organizations to start with process improvement (PDSA, Lean, Six Sigma where appropriate) before adopting AI. Using the quintuple aim as context, he highlighted pressures from administrative costs, workforce shortages, declining patient trust, and serious equity gaps (e.g., maternal mortality). Key risks include poor data quality, lack of interoperability, and multiple forms of bias (systemic, computational, human), which can worsen inequities.<br /><br />Quality professionals can contribute through data governance and analytics (descriptive to prescriptive), selecting low-risk use cases first, and advancing “responsible AI” with ethics, governance, explainability, diverse teams, and ongoing monitoring. He encouraged learning prompt engineering and deeply understanding organizational data.

Keywords

AI in healthcare

quality improvement

predictive vs generative AI

large language models

responsible AI governance

algorithm bias and equity

data quality and interoperability

explainable AI and transparency