Automated identification of wound information in clinical notes of patients with heart diseases: Developing and validating a natural language processing application.
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
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.
Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: mtopaz80@gmail.com.
Maxim
M
Topaz
Brigham and Women's Hospital, Boston, MA, USA.
Kenneth
K
Lai
Visiting Nurse Service of New York, NY, USA; School of Nursing, Columbia University, NY, USA.
Dawn
D
Dowding
Brigham and Women's Hospital, Boston, MA, USA.
Victor J
VJ
Lei
The Cheryl Spencer Department of Nursing, Haifa University, Israel.
Anna
A
Zisberg
Visiting Nurse Service of New York, NY, USA; School of Nursing, University of Pennsylvania, PA, USA.
Kathryn H
KH
Bowles
Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Li
L
Zhou
D001331:Automation; D001330:Electronic Data Processing / Q000379:methods*; D006331:Heart Diseases / Q000150:complications*; D006751:Hospital Information Systems; D006801:Humans; D009323:Natural Language Processing; D019095:Point-of-Care Systems; D014947:Wounds and Injuries
Electronic health records; Medical informatics; Natural language processing; Nursing informatics; Pressure ulcer; Wound