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Home Healthcare, Hospice, and Information Technology Innovations Conference

Innovations in Home Healthcare, Hospice, and Information Technology

A forum towards achieving evidence-based diffusion and implementation of innovations

Fri, Nov 3, 2017
Friday, Nov 3, 2017, Washington - D.C.
H3IT: Home Healthcare, Hospice, and Information Technology Conference Nashville, TN, 2015
Using Natural Language Processing to
Automatically Identify Wound Information in
Narrative Clinical Notes: Application Development
and Testing.
Maxim Topaz RN, PhD
1, 2
, Dawn Dowding RN, PhD
3, 4
, Victor J. Lei BA
, Anna Zisberg RN, PhD
, Kathryn H. Bowles
2, 6
, Li Zhou MD, PhD
1, 1
his study developed and validated one of the rst automated natural language processing (NLP) 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=1,100) of clinical notes (including 50% discharge summaries and 50% outpatient notes, including homecare
notes), identied wound cases, and created a gold standard dataset. We then trained and tested our NLP system (known as
MTERMS) to process the wound information. Finally, we assessed our automated approach by comparing system-generated
ndings against the gold standard. We also compared the prevalence of wound cases identied 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 =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.
Conclusion: The NLP 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, a fact that highlights the
signicance of using NLP 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. We are also conducting
a validation and further system development with homecare notes from the Visiting Nurse Services of New-York and will
share the results at the H3IT conference.
Brigham and Women’s Hospital, Boston, MA, USA
Harvard Medical School, Boston, MA, USA
Visiting Nurse Service of New York, NY, USA
School of Nursing, Columbia University, NY, USA
The Cheryl Spencer Department of Nursing, Haifa University, Israel
School of Nursing, University of Pennsylvania, PA, USA
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