Maxim Topaz1, Kenneth Lai2, Dawn Dowding3, Victor J Lei2, Anna Zisberg4, Kathryn H Bowles5, Li Zhou6. 1. Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: mtopaz80@gmail.com. 2. Brigham and Women's Hospital, Boston, MA, USA. 3. Visiting Nurse Service of New York, NY, USA; School of Nursing, Columbia University, NY, USA. 4. The Cheryl Spencer Department of Nursing, Haifa University, Israel. 5. Visiting Nurse Service of New York, NY, USA; School of Nursing, University of Pennsylvania, PA, USA. 6. Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Abstract
BACKGROUND: Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care. OBJECTIVE: This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes. METHODS AND DESIGN: First, two human annotators manually reviewed a purposeful training sample (n=360) and random test sample (n=1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data. RESULTS: The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system's accuracy=92.7%), with best results for wound treatment (F-measure=95.7%) and poorest results for wound size (F-measure=81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis. CONCLUSIONS: The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data. Copyright Â
BACKGROUND: Electronic health records are being increasingly used by nurses with up to 80% of the health data recorded as free text. However, only a few studies have developed nursing-relevant tools that help busy clinicians to identify information they need at the point of care. OBJECTIVE: This study developed and validated one of the first automated natural language processing applications to extract wound information (wound type, pressure ulcer stage, wound size, anatomic location, and wound treatment) from free text clinical notes. METHODS AND DESIGN: First, two human annotators manually reviewed a purposeful training sample (n=360) and random test sample (n=1100) of clinical notes (including 50% discharge summaries and 50% outpatient notes), identified wound cases, and created a gold standard dataset. We then trained and tested our natural language processing system (known as MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated findings against the gold standard. We also compared the prevalence of wound cases identified from free-text data with coded diagnoses in the structured data. RESULTS: The testing dataset included 101 notes (9.2%) with wound information. The overall system performance was good (F-measure is a compiled measure of system's accuracy=92.7%), with best results for wound treatment (F-measure=95.7%) and poorest results for wound size (F-measure=81.9%). Only 46.5% of wound notes had a structured code for a wound diagnosis. CONCLUSIONS: The natural language processing system achieved good performance on a subset of randomly selected discharge summaries and outpatient notes. In more than half of the wound notes, there were no coded wound diagnoses, which highlight the significance of using natural language processing to enrich clinical decision making. Our future steps will include expansion of the application's information coverage to other relevant wound factors and validation of the model with external data. Copyright Â
Authors: Kyungmi Woo; Victoria Adams; Paula Wilson; Li-Heng Fu; Kenrick Cato; Sarah Collins Rossetti; Margaret McDonald; Jingjing Shang; Maxim Topaz Journal: J Am Med Dir Assoc Date: 2021-01-09 Impact factor: 4.669
Authors: Sebastian Gehrmann; Franck Dernoncourt; Yeran Li; Eric T Carlson; Joy T Wu; Jonathan Welt; John Foote; Edward T Moseley; David W Grant; Patrick D Tyler; Leo A Celi Journal: PLoS One Date: 2018-02-15 Impact factor: 3.240