Abstract: Patient summarization is essential for clinicians to provide coordinated care and practice effective communication. Automated summarization has the potential to save time, standardize notes, aid clinical decision making, and reduce medical errors. Here we provide an upper bound on extractive summarization of discharge notes and develop an LSTM model to sequentially label topics of history of present illness notes. We achieve an F1 score of 0.876, which indicates that this model can be employed to create a dataset for evaluation of extractive summarization methods.

Introduction: Summarization of patient information is essential to the practice of medicine. Clinicians must synthesize information from diverse data sources to communicate with colleagues and provide coordinated care. Examples of clinical summarization are abundant in practice; patient handoff summaries facilitate provider shift change, progress notes provide a daily status update for a patient, oral case presentations enable transfer of information from overnight admission to the care team and attending, and discharge summaries provide information about a patient's hospital visit to their primary care physician and other outpatient providers BIBREF0 . Informal, unstructured, or poor quality summaries can lead to communication failures and even medical errors, yet clinical instruction on how to formulate clinical summaries is ad hoc and informal. Non-existent or limited search functionality, fragmented data sources, and limited visualizations in electronic health records (EHRs) make summarization challenging for providers BIBREF1 , BIBREF2 , BIBREF3 . Furthermore, while dictation of EHR notes allows clinicians to more efficiently document information at the point of care, the stream of consciousness-like writing can hinder the readability of notes. Kripalani et al. show that discharge summaries are often lacking key information, including treatment progression and follow-up protocols, which can hinder communication between hospital and community based clinicians BIBREF4 . Recently, St. Thomas Hospital in Nashville, TN stipulated that discharge notes be written within 48 hours of discharge following incidences where improper care was given to readmitted patients because the discharge summary for the previous admission was not completed BIBREF5 . Automated summary generation has the potential to save clinician time, avoid medical errors, and aid clinical decision making. By organizing and synthesizing a patient's salient medical history, algorithms for patient summarization can enable better communication and care, particularly for chronically ill patients, whose medical records often contain hundreds of notes. In this work, we explore the automatic summarization of discharge summary notes, which are critical to ensuring continuity of care after hospitalization. We (1) provide an upper bound on extractive summarization by assessing how much information in the discharge note can be found in the rest of the patient's EHR notes and (2) develop a classifier for labeling the topics of history of present illness notes, a narrative section in the discharge summary that describes the patient's prior history and current symptoms. Such a classifier can be used to create topic specific evaluation sets for methods that perform extractive summarization. These aims are critical steps in ultimately developing methods that can automate discharge summary creation.

Question: what topics did they label?
Answer: Demographics, Diagnosis History, Medication History, Procedure History, Symptoms, Labs, Procedures, Treatments, Hospital movements, and others