Using language models to integrate clinical decision support and note taking: a qualitative study

<bold>Introduction:</bold> Prior to designing a novel user interface which would integrate clinical decision support from the Acute COPD Exacerbation Prediction Tool (ACCEPT) with physician note-taking, we conducted an exploratory qualitative study to understand physician attitudes, experiences, and expectations for this kind of functionality. <bold>Methods:</bold> We conducted semi-structured interviews with practicing physicians recruited through convenience sampling from five teaching hospitals in Canada and the US. We asked questions about how formal risk assessment is used, experience and trust in prediction models, EHR integration, and dictation practices. We qualitatively analyzed interview transcripts using affinity diagrams to produce themes highlighting relevant topics. <bold>Results:</bold> We interviewed ten physicians (70% female, 4 respirologists, 4 cardiologists, 2 residents) whose clinical experience ranged from 2 to 38 years. Our affinity analysis revealed five high-level themes. All participants were routinely but infrequently using prediction models that required manual data entry outside of the EHR system, mostly to determine malignancy risk for pulmonary nodules, pre-operative mortality risk assessment, or statins eligibility. Reasons for using models included risk communication with patients or colleagues, and automating mundane tasks. Frustration with data organization, retrieval, and letter writing in the EHR system was common. <bold>Conclusions:</bold> Participants were receptive to decision support software interfaces that would simplify data retrieval and letter writing, but were also willing to accept some inconvenience to use a truly useful risk score, especially if they only used it occasionally.