A Human(e) Factor in Clinical Decision Support Systems

• Published in: Journal of medical Internet research Vol. 21; no. 3; p. e11732
• Main Authors: Bezemer, Tim, de Groot, Mark Ch, Blasse, Enja, Ten Berg, Maarten J, Kappen, Teus H, Bredenoord, Annelien L, van Solinge, Wouter W, Hoefer, Imo E, Haitjema, Saskia
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 19-03-2019; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications
• Subjects: Abbreviations; Algorithms; Artificial intelligence; Bias; Big Data; Clinical decision making; Clinical medicine; Computers; Data entry; Data Science; Decision making; Decision support systems; Decision Support Systems, Clinical - standards; Deep learning; Diabetic retinopathy; Electronic health records; Humans; Interdisciplinary aspects; Interoperability; Jargon; Knowledge; Laboratories; Medical advice systems; Medical personnel; Noise; Patient communication; Patients; Professional ethics; Professionals; Teams; Viewpoint; deep learning; expert systems; data science; clinical decision support; precision medicine; big data; health care providers; machine learning; artificial intelligence
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/11732

The overwhelming amount, production speed, multidimensionality, and potential value of data currently available-often simplified and referred to as big data -exceed the limits of understanding of the human brain. At the same time, developments in data analytics and computational power provide the opportunity to obtain new insights and transfer data-provided added value to clinical practice in real time. What is the role of the health care professional in collaboration with the data scientist in the changing landscape of modern care? We discuss how health care professionals should provide expert knowledge in each of the stages of clinical decision support design: data level, algorithm level, and decision support level. Including various ethical considerations, we advocate for health care professionals to responsibly initiate and guide interprofessional teams, including patients, and embrace novel analytic technologies to translate big data into patient benefit driven by human(e) values.