Detecting Unplanned Care From Clinician Notes in Electronic Health Records

Abstract

Purpose: Reduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcome measures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review. Methods:We studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or thoracic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms documented in emergency department (ED) notes.Results: Combined reporting increased the identification of patients with one or more unplanned care visits by 32% (15% using coded data; 20% using all the data) among patients with 3 months of follow-up and by 21% (23% using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35% of all ED encounter notes. Conclusion:The text-mining methods we describe can be applied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing.

Leveraging the electronic health record to improve quality and safety in rheumatology

“…[27,28,29,30] Outside of rheumatology, researchers have developed text data extractors for dementia diagnoses and cancer staging, urinary incontinence-related PROs, identification of out-of-network emergent care encounters, disease phenotyping, and extraction of documentation of advanced directives. [31,32,33,34] This work has shown that important healthcare outcomes are being captured in EHRs as free text and that although challenges exist, NLP and machine learning methods may be increasing feasible options for accurately and efficiently identifying health outcomes.…”

New Paradigms for Patient-Centered Outcomes Research in Electronic Medical Records: An example of detecting urinary incontinence following prostatectomy

“…We customized our approach for identifying cases of urinary incontinence documented in free text using an approach that has been previously applied to develop task-specific extractors. 30 With the aim of improving sensitivity, we enhanced the annotator’s terminology to include additional terms relevant to urinary incontinence (e.g., “wears adult diapers”). In addition, we extended the basic set of rules provided by NegEx to consider additional contextual information such as the following: hypothetical terms, e.g., “at risk for” (urinary incontinence); historical terms, e.g., “past history of” (urinary incontinence); and discussion terms, e.g., “discussed complications such as” (urinary incontinence).…”

Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records

“…To leverage the scale and richness of EHR for clinical decisions, particularly in emergencies and in the absence of evidence from randomized control trials, timely and accurate synthesis of evidence through automated methods is necessary. Toward that vision, advances have been made with automatic cohort selection via electronic phenotyping [ 13 ], natural language processing [ 66 , 67 ], patient similarity [ 8 ] and automatic confounder control by PS methods [ 17 , 19–22 , 24–27 ]. This study supplements automation efforts by demonstrating that there exist automated alternatives to expert-based PS, including non-PS-based methods, for confounder control.…”