• Charlotta Lindvall, Chih-Ying Deng, Edward Moseley, Nicole Agaronnik, Areej El-Jawahri, Michael K Paasche-Orlow, Joshua R Lakin, Angelo Volandes, and The Acp-Peace Investigators James A Tulsky.
• Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute (C.L., CY.D.,E.M., N.A., JR.L., JA.T.), Boston, Massachusetts; Department of Medicine, Brigham and Women's Hospital (C.L., JR.L., JA.T.), Boston, Massachusetts; Harvard Medical School, Boston (C.L., N.A., A.EJ., JR.L., A.V., JA.T.), Massachusetts. Electronic address: charlotta_lindvall@dfci.harvard.edu.
• J Pain Symptom Manage. 2022 Jan 1; 63 (1): e29e36e29-e36.

ContextLarge multisite clinical trials studying decision-making when facing serious illness require an efficient method for abstraction of advance care planning (ACP) documentation from clinical text documents. However, the current gold standard method of manual chart review is time-consuming and unreliable.ObjectivesTo evaluate the ability to use natural language processing (NLP) to identify ACP documention in clinical notes from patients participating in a multisite trial.MethodsPatients with advanced cancer followed in three disease-focused oncology clinics at Duke Health, Mayo Clinic, and Northwell Health were identified using administrative data. All outpatient and inpatient notes from patients meeting inclusion criteria were extracted from electronic health records (EHRs) between March 2018 and March 2019. NLP text identification software with semi-automated chart review was applied to identify documentation of four ACP domains: (1) conversations about goals of care, (2) limitation of life-sustaining treatment, (3) involvement of palliative care, and (4) discussion of hospice. The performance of NLP was compared to gold standard manual chart review.Results435 unique patients with 79,797 notes were included in the study. In our validation data set, NLP achieved F1 scores ranging from 0.84 to 0.97 across domains compared to gold standard manual chart review. NLP identified ACP documentation in a fraction of the time required by manual chart review of EHRs (1-5 minutes per patient for NLP, vs. 30-120 minutes for manual abstraction).ConclusionNLP is more efficient and as accurate as manual chart review for identifying ACP documentation in studies with large patient cohorts.Copyright © 2021 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

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