Abstract
Venous thromboembolism (VTE), including deep venous thrombosis and pulmonary embolism, is a significant cause of preventable morbidity and mortality, 1 with rising incidence due to increased rates of obesity, surgery, and cancer.2 Guidelines advocate for personalized VTE risk assessment, 3,4 and current strategies include provider education, medical admission order sets with VTE prophylaxis, and electronic alerts for high-risk patients.5 Despite these guidelines, VTE prophylaxis remains inaccurately used, 6,7 and both diagnosis and management of VTE are areas that need improvement.8,9 One underexplored strategy is the use of artificial intelligence (AI) and its subbranch machine learning (ML).AI/ML is flexible and scalable, bypassing traditional statistical assumptions (eg, type of error distribution or proportional hazards assumption), which is advantageous for risk stratification, diagnosis, and survival predictions.10 ML algorithms can also analyze different types of data (ie, laboratory/imaging data and physician notes) to incorporate into predictions.11 Major ML applications in VTE include machine vision for detecting VTE on imaging, natural language processing (NLP) for identifying VTE diagnoses in the medical chart, and predictive modeling for estimating clot risk.12 Although recent metaanalyses show AI/ML tools are effective, their clinical implementation remains limited, requiring further research to bridge this gap.13 We have previously conducted studies to assess clinicians' acceptability and views with regard to the integration of AI/ML in VTE prevention and management.14 However, successful application of computational technologies requires the inputs of subject matter experts such as informaticians for AI/ML.To harness the expertise of informaticians specializing in AI/ML, interviews were conducted and thematically analyzed for insights on using these tools for VTE.