Conceptualizing Machine Learning for Dynamic Information Retrieval of Electronic Health Record Notes
This addresses clinician efficiency and burnout in healthcare by reducing time spent on information retrieval, though it is incremental as it builds on existing EHR and machine learning concepts.
The paper tackled the problem of clinician burnout from sifting through electronic health record notes by proposing a machine learning framework for dynamic retrieval of relevant notes during documentation, achieving an AUC of 0.963 for predicting note relevance in emergency department settings.
The large amount of time clinicians spend sifting through patient notes and documenting in electronic health records (EHRs) is a leading cause of clinician burnout. By proactively and dynamically retrieving relevant notes during the documentation process, we can reduce the effort required to find relevant patient history. In this work, we conceptualize the use of EHR audit logs for machine learning as a source of supervision of note relevance in a specific clinical context, at a particular point in time. Our evaluation focuses on the dynamic retrieval in the emergency department, a high acuity setting with unique patterns of information retrieval and note writing. We show that our methods can achieve an AUC of 0.963 for predicting which notes will be read in an individual note writing session. We additionally conduct a user study with several clinicians and find that our framework can help clinicians retrieve relevant information more efficiently. Demonstrating that our framework and methods can perform well in this demanding setting is a promising proof of concept that they will translate to other clinical settings and data modalities (e.g., labs, medications, imaging).