Enhancing diagnostic safety: addressing knowledge gaps for using human factors tools in the safe and effective use of AI - a proposed research agenda.
Authors
Affiliations (21)
Affiliations (21)
- Department of Mechanical and Industrial Engineering, Marieb Center for Nursing and Engineering Innovation, University of Massachusetts Amherst, Amherst, MA, USA.
- Department of Radiology, Brown University Health and Warren Alpert Medical School, Brown University, Providence, RI, USA.
- Department of Medicine, Harvard University, Boston, MA, USA.
- Independent Healthcare Expert and Consultant, Rockville, MD, USA.
- Department of Anesthesia and Perioperative Medicine, College of Medicine, Medical University of South Carolina, Charleston, SC, USA.
- Department of Emergency Medicine, Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA.
- Department of Medicine, Stony Brook University, New York, US; and Community to Improve Diagnosis, Plymouth, MA, USA.
- Armstrong Institute Center for Health Care Human Factors and Anesthesiology and Critical Care, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Radiology and Imaging Sciences, School of Medicine, Emory University, Atlanta, GA, USA.
- Department of Information Sciences and Technology, Penn State University, College Park, PA, USA.
- Pennsylvania Patient Safety Authority, Harrisburg, PA, USA.
- Department of Emergency Medicine, National Center for Human Factors in Healthcare, Medstar Health, Georgetown University, Washington, DC, USA.
- Department of Radiology, Penn State Health Milton S. Hershey Medical Center, Hershey, PA, USA.
- Department of Health Systems and Implementation Science, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA.
- Department of Emergency Medicine, University of Florida Gainesville, Gainesville, FL, USA.
- Department of Radiology, Great Ormond Street Hospital, London, UK.
- Aidoc, Plain City, OH, USA.
- Institute of Medical Education Research Rotterdam (iMERR), Erasmus, Netherlands.
- Kaiser-Permanente Bernard J. Tyson School of Medicine and the Southern California Permanente Medical Group, Pasadena, CA, USA.
- University of Maryland, Baltimore, MD, USA.
Abstract
Identify knowledge gaps in applying artificial intelligence in clinical settings, using medical imaging as a primary use case to enhance diagnostic efficacy, efficiency, and patient and provider safety. We convened a two-day workshop with 18 interdisciplinary experts from three countries. Experts represented quality and patient safety, human factors and systems engineering, radiology and other medical specialties, nursing, medical informatics, cognitive and perceptual psychology, psychometrics and statistics, and machine learning, drawn from academia, industry, health systems, and government. We identified by consensus six major knowledge-gap domains, with specific research questions for each domain: development, validation, integration and sustainability; redesign of existing healthcare systems; human and team augmentation; deployment of adaptive-learning "Foundation Models;" and balancing innovation, standardization, and regulatory oversight. We recommend employing a multidisciplinary collaborative approach in future research to leverage transformational AI capabilities anticipated in the next 5-7 years for each of the identified knowledge-gap domains, including ensuring that clinical AI supports diagnostic decision-making, integrates into clinical workflows, and mitigates risks related to automation bias, overreliance, fragmentation of care, and unintended consequences.