{
  "abstract": "Description This session highlights the results of an Institute for Healthcare Improvement (IHI) 90-day Innovation cycle exploring the implications of artificial intelligence (AI) for quality and safety in healthcare, particularly in quality management. Inputs included a comprehensive literature scan; collaborative design with experts; and validation with the Leadership Alliance’s AI Accelerator.Key findings highlight the potential of AI tools to support quality management, address common failure nodes, and improve data interpretation. We map various AI tools to two complementary quality management approaches: IHI’s Whole System Quality framework and the Care Operating System (CareOS) method. The research highlights the importance of rigorous measurement, data collection, and multidisciplinary governance to ensure responsible AI tool adoption, and offers guidance in these areas. We also offer test assessment criteria for AI tools for quality and safety leaders and parameters for the use of AI tools in quality planning cycles.",
  "authors": [
    {
      "affiliations": [
        "Institute for Healthcare Improvement"
      ],
      "name": "Jeff Rakover"
    },
    {
      "affiliations": [
        "Health Catalyst"
      ],
      "name": "Jason Jones"
    },
    {
      "affiliations": [
        "Institute for Healthcare Improvement"
      ],
      "name": "Pierre Barker"
    },
    {
      "affiliations": [
        "Union Healthcare Insight"
      ],
      "name": "Marina Renton"
    }
  ],
  "title": "2 How can AI support quality management: practical guidance for quality leaders",
  "uid": "0b80e4c0-497c-5fa2-8929-0aa8a5f06f77"
}