Detecting clinically relevant new information in clinical notes across specialties and settings

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Abstract

Background: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is useful for clinicians and researchers involved in patient care and clinical research, respectively. We evaluated methods to automatically identify clinically relevant new information in clinical notes, and compared the quantity of redundant information across specialties and clinical settings. Methods: Statistical language models augmented with semantic similarity measures were evaluated as a means to detect and quantify clinically relevant new and redundant information over longitudinal clinical notes for a given patient. A corpus of 591 progress notes over 40 inpatient admissions was annotated for new information longitudinally by physicians to generate a reference standard. Note redundancy between various specialties was evaluated on 71,021 outpatient notes and 64,695 inpatient notes from 500 solid organ transplant patients (April 2015 through August 2015). Results: Our best method achieved at best performance of 0.87 recall, 0.62 precision, and 0.72 F-measure. Addition of semantic similarity metrics compared to baseline improved recall but otherwise resulted in similar performance. While outpatient and inpatient notes had relatively similar levels of high redundancy (61% and 68%, respectively), redundancy differed by author specialty with mean redundancy of 75%, 66%, 57%, and 55% observed in pediatric, internal medicine, psychiatry and surgical notes, respectively. Conclusions: Automated techniques with statistical language models for detecting redundant versus clinically relevant new information in clinical notes do not improve with the addition of semantic similarity measures. While levels of redundancy seem relatively similar in the inpatient and ambulatory settings in the Fairview Health Services, clinical note redundancy appears to vary significantly with different medical specialties.

Original languageEnglish (US)
Article number68
JournalBMC medical informatics and decision making
Volume17
DOIs
StatePublished - Jul 5 2017

Bibliographical note

Funding Information:
This research was supported and publication costs were covered by the Agency for Healthcare Research & Quality grant (#1R01HS022085) (GM), National Center for Complementary and Integrative Health, National Institutes of Health (#1R01AT009457) (RZ) and the University of Minnesota Clinical and Translational Science Award (#8UL1TR000114) (Blazer).

Publisher Copyright:
© 2017 The Author(s).

Keywords

  • Clinical specialty
  • Electronic health records
  • Natural language processing
  • New information
  • Redundancy
  • Semantic similarity
  • Statistical language models

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