The potential use of artificial intelligence for venous thromboembolism prophylaxis and management: clinician and healthcare informatician perspectives

• Published in: Scientific reports Vol. 14; no. 1; pp. 12010 - 9
• Main Authors: Lam, Barbara D., Dodge, Laura E., Zerbey, Sabrina, Robertson, William, Rosovsky, Rachel P., Lake, Leslie, Datta, Siddhant, Elavakanar, Pavania, Adamski, Alys, Reyes, Nimia, Abe, Karon, Vlachos, Ioannis S., Zwicker, Jeffrey I., Patell, Rushad
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 26-05-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 692/308; 692/700; Adult; Artificial Intelligence; Disease prevention; Female; Health care; Humanities and Social Sciences; Humans; Machine Learning; Male; Middle Aged; multidisciplinary; Physicians; Polls & surveys; Preventable deaths; Prevention; Prophylaxis; Risk Assessment; Science; Science (multidisciplinary); Survey; Surveys; Surveys and Questionnaires; Thromboembolism; Venous thromboembolism; Venous Thromboembolism - prevention & control; Survey; Artificial intelligence; Venous thromboembolism; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-62535-9

Venous thromboembolism (VTE) is the leading cause of preventable death in hospitalized patients. Artificial intelligence (AI) and machine learning (ML) can support guidelines recommending an individualized approach to risk assessment and prophylaxis. We conducted electronic surveys asking clinician and healthcare informaticians about their perspectives on AI/ML for VTE prevention and management. Of 101 respondents to the informatician survey, most were 40 years or older, male, clinicians and data scientists, and had performed research on AI/ML. Of the 607 US-based respondents to the clinician survey, most were 40 years or younger, female, physicians, and had never used AI to inform clinical practice. Most informaticians agreed that AI/ML can be used to manage VTE (56.0%). Over one-third were concerned that clinicians would not use the technology (38.9%), but the majority of clinicians believed that AI/ML probably or definitely can help with VTE prevention (70.1%). The most common concern in both groups was a perceived lack of transparency (informaticians 54.4%; clinicians 25.4%). These two surveys revealed that key stakeholders are interested in AI/ML for VTE prevention and management, and identified potential barriers to address prior to implementation.