Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.

Large 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. To evaluate the ability to use natural language processing (NLP) to identify ACP documention in clinical notes from patients participating in a multisite trial. Patients 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. 435 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). NLP is more efficient and as accurate as manual chart review for identifying ACP documentation in studies with large patient cohorts.

Lindvall C ，Deng CY ，Moseley E ，Agaronnik N ，El-Jawahri A ，Paasche-Orlow MK ，Lakin JR ，Volandes A ，Tulsky TAIJA ... -  《-》

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome.

Lee RY ，Kross EK ，Torrence J ，Li KS ，Sibley J ，Cohen T ，Lober WB ，Engelberg RA ，Curtis JR ... -  《JAMA Network Open》

Bolstering Advance Care Planning Measurement Using Natural Language Processing.

Zupanc SN ，Durieux BN ，Walling AM ，Lindvall C ... -  《-》

Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability.

To assess the utility of applying natural language processing (NLP) to electronic health records (EHRs) to identify individuals with chronic mobility disability. We used EHRs from the Research Patient Data Repository, which contains EHRs from a large Massachusetts health care delivery system. This analysis was part of a larger study assessing the effects of disability on diagnosis of colorectal cancer. We applied NLP text extraction software to longitudinal EHRs of colorectal cancer patients to identify persons who use a wheelchair (our indicator of mobility disability for this analysis). We manually reviewed the clinical notes identified by NLP using directed content analysis to identify true cases using wheelchairs, duration or chronicity of use, and documentation quality. EHRs from large health care delivery system PARTICIPANTS: Patients (N=14,877) 21-75 years old who were newly diagnosed with colorectal cancer between 2005 and 2017. Not applicable. Confirmation of patients' chronic wheelchair use in NLP-flagged notes; quality of disability documentation. We identified 14,877 patients with colorectal cancer with 303,182 associated clinical notes. NLP screening identified 1482 (0.5%) notes that contained 1+ wheelchair-associated keyword. These notes were associated with 420 patients (2.8% of colorectal cancer population). Of the 1482 notes, 286 (19.3%, representing 105 patients, 0.7% of the total) contained documentation of reason for wheelchair use and duration. Directed content analysis identified 3 themes concerning disability documentation: (1) wheelchair keywords used in specific EHR contexts; (2) reason for wheelchair not clearly stated; and (3) duration of wheelchair use not consistently documented. NLP offers an option to screen for patients with chronic mobility disability in much less time than required by manual chart review. Nonetheless, manual chart review must confirm that flagged patients have chronic mobility disability (are not false positives). Notes, however, often have inadequate disability documentation.

Agaronnik ND ，Lindvall C ，El-Jawahri A ，He W ，Iezzoni LI ... -  《-》

Advanced Care Planning Content Encoding with Natural Language Processing.

Knoll BC ，Gunderson M ，Rajamani G ，Wick EC ，Colley A ，Lindemann E ，Rizvi R ，Diethelm M ，Hultman G ，Pierce L ，Zhang R ，Melton GB ... -  《-》