Conceptualizing Machine Learning for Dynamic Information Retrieval of
  Electronic Health Record Notes
Sharon Jiang Shannon Shen Monica Agrawal Barbara Lam Nicholas Kurtzman Steven Horng David Karger David Sontag
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).