Harnessing Natural Language Processing to Identify Documentation of Serious Illness Communication for Patients With Decompensated Cirrhosis.

Abstract

Given the high mortality of patients with decompensated cirrhosis (DC), there is increasing focus on improving serious illness communication (SIC) for this population. However, SIC documentation in the electronic health record (EHR) is often unstructured and difficult to find. We aimed to evaluate the ability to use natural language processing (NLP) to identify SIC documentation in clinical notes from patients with DC. In a single-center cohort of adult patients with DC who were evaluated for liver transplantation between January 1, 2010 and December 31, 2017 and died by June 30, 2018, we developed a semiautomated NLP approach to identify SIC documentation in clinical notes. All inpatient and outpatient notes from 1 year until 3 days before death were extracted from the EHR. NLP software with semiautomated chart review was applied to identify SIC documentation across 4 domains: goals of care conversations, code status limitations, specialist palliative care involvement, and hospice assessment. The performance of NLP was compared with gold standard manual chart review. One hundred ninety-six unique patients with 14,062 notes were included in the study. In the gold standard data set, NLP achieved F1 scores ranging from 0.91 to 1.0 across all 4 SIC domains. Identification of SIC documentation required 6.8 minutes per patient using NLP, compared with 41.5 minutes per patient using manual chart review. Forty-eight percent of patients had no SIC documentation. NLP is more efficient and as accurate as manual chart review for identifying SIC documentation in the EHR for patients with DC and can be used at scale for quality improvement initiatives and clinical trials.