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Validation of a Natural Language Processing-Assisted Chart Review Method for Identifying Advance Care Planning
Emily J Upham1,2, Suzanne R Gouda1,2,3, Charlotta Lindvall2,3
1Division of Medical Critical Care, Department of Pediatrics, Boston Children's Hospital, Boston, MA.
Objectives:
Reliable measurement of advance care planning (ACP) is essential for evaluating both communication practices and quality of care in pediatric critical illness. However, most ACP documentation is embedded in free-text clinical notes and cannot be captured using administrative data, while manual chart review is time-intensive and difficult to scale. We sought to validate a rule-based natural language processing (NLP) approach to identify ACP documentation in the electronic health record (EHR) compared with manual chart review.
Design:
Retrospective cohort study.
Setting:
Single-center, quaternary PICU.
Patients:
Children younger than 21 years admitted to the PICU for greater than 24 hours following out-of-hospital cardiac arrest (OHCA) from 2012 to 2024.
Interventions:
None.
Measurements And Main Results:
A rule-based NLP approach incorporating semi-automated chart review was developed through iterative refinement of previously validated keyword libraries to identify ACP documentation across prespecified domains in the EHR. In the validation cohort (n = 95), NLP-assisted chart review showed complete agreement with manual chart review (F1 = 1.0) while substantially reducing abstraction time, enabling analysis of over 31,479 clinical notes in 3 days compared with 6 months for manual review of 43,179 notes. Among 125 children with OHCA, 65% had documented ACP discussions (n = 81). Goals-of-care conversations were most common (99%), followed by limitations of life-sustaining treatment (78%), subspecialty palliative care involvement (28%), preferred location of death (9%), and hospice discussions (7%).
Conclusions:
NLP-assisted chart review can efficiently and accurately identify ACP documentation in pediatric critical illness for retrospective research and quality improvement efforts. This semi-automated workflow substantially reduced manual abstraction burden while maintaining excellent agreement with manual chart review, enabling effective abstraction of communication processes not captured in structured data.
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