Journal article
Harnessing Natural Language Processing and High-Dimensional Clinical Notes to Detect Goals-of-Care and Surrogate-Designation Conversations
Clinical nursing research, Vol.34(7), pp.321-331
09/2025
DOI: 10.1177/10547738241292657
PMID: 39480212
Abstract
Advance care planning, involving goals-of-care and surrogate-designation conversations, is crucial for patient-centered care. However, determining the optimal timing and participants for these conversations remains challenging. This study explored the frequency, timing, and predictors of documenting two advance care planning elements, goals-of-care and surrogate-designation conversations, in clinical notes for patients with advanced illness. In this retrospective observational study, we leveraged high-dimensional data and natural language processing (NLP) to analyze clinical notes and predict the presence or absence of advance care planning conversations. We included notes for patients treated at a Midwestern United States hospital who had advanced chronic conditions and eventually passed away. We manually labeled a gold-standard dataset (
= 913 notes) for the presence or absence of advance care planning conversations at the note level, achieving excellent inter-annotator agreement (90.5%). Training and testing four NLP models to detect goals-of-care and surrogate-designation conversations revealed that a transformer-based model (Bidirectional Encoder Representations from Transformers [BERT]) achieved the highest accuracy, with an F1 score of 93.6. We then deployed the BERT model to a high-dimensional corpus of 247,241 notes for 4,341 patients and detected goals-of-care and surrogate-designation conversations in the records of 85% and 60% of patients, respectively. Temporal analysis revealed that goals-of-care and surrogate-designation conversations were first documented at medians 28 and 8 days before death, respectively. Patient characteristics and referral to specialty palliative care emerged as significant factors associated with documenting these conversations. Our findings demonstrate the potential of NLP, particularly Transformer-based models like BERT, to accurately detect goals-of-care and surrogate-designation conversations in clinical narratives. This study identified significant temporal patterns, including late documentation, and patient characteristics associated with these conversations. It highlights the value of high-dimensional data in enhancing our understanding of advance care planning and offers insights for improving patient-centered care in clinical settings. Future research should explore the integration of these models into clinical workflows to facilitate timely and effective advance care planning discussions.
Details
- Title: Subtitle
- Harnessing Natural Language Processing and High-Dimensional Clinical Notes to Detect Goals-of-Care and Surrogate-Designation Conversations
- Creators
- Alaa Albashayreh - University of IowaKeela Herr - University of IowaWeiguo Fan - University of IowaW Nick Street - University of IowaStephanie Gilbertson-White - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Clinical nursing research, Vol.34(7), pp.321-331
- DOI
- 10.1177/10547738241292657
- PMID
- 39480212
- NLM abbreviation
- Clin Nurs Res
- ISSN
- 1552-3799
- eISSN
- 1552-3799
- Publisher
- SAGE PUBLICATIONS INC
- Grant note
- Institute for Clinical and Translational ScienceBarbara and Richard Csomay Center for Gerontological Excellence at the University of Iowa, Iowa City, IA
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: this study was funded by the Institute for Clinical and Translational Science (AA) and the Barbara and Richard Csomay Center for Gerontological Excellence (AA) at the University of Iowa, Iowa City, IA.
- Language
- English
- Electronic publication date
- 10/31/2024
- Date published
- 09/2025
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics; Internal Medicine
- Record Identifier
- 9984740854302771
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