Trial reportJournal of pain and symptom management2022
Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial.
Trial report in Journal of pain and symptom management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06786104 (A Multisite RWCT Comparing the Effectiveness of a Goals of Care Video and Navigator Intervention, the VIDEO-PEDS Experience, Versus Usual Care in Pediatric Cancer), which is not on this map. Cited by 47 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Multisite RWCT Comparing the Effectiveness of a Goals of Care Video and Navigator Intervention, the VIDEO-PEDS Experience, Versus Usual Care in Pediatric Cancer
Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- An Intervention to Increase Advance Care Planning Among Older Adults With Advanced Cancer: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Feasibility and acceptability of a nurse-led telehealth intervention (BOLSTER) to support patients with peritoneal carcinomatosis and their caregivers: A pilot randomized clinical trial.Gynecologic oncology · 2024Trial
- Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome.JAMA network open · 2023Trial
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 2026Article
- Large Language Models (LLMs) vs Manual Chart Review for Assessing Goal-Concordant Care: An Exploratory Study.The American journal of hospice & palliative care · 2026Article
- Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.Mayo Clinic proceedings. Digital health · 2026Article
- The Technologies, Applicability, and Trade-Offs of AI in Palliative Care for Older Adults: Scoping Review.Journal of medical Internet research · 2026Article
- Timing Prompts for Advance Care Planning Discussions and Advance Directive Completion.JAMA network open · 2026Article
- Review
- Leveraging Natural Language Processing for Symptom Identification in Acute Myeloid Leukemia Using Clinical Notes from Electronic Health Records.Cancer nursing · 2026Article
- Large Language Models for Summarizing Advance Care Planning Information From Goals of Care Notes in the EHR.Learning health systems · 2026Article
- Characterizing Survivors Living With Likely Incurable Cancer: A Closer Look at an Emerging Population.JCO oncology practice · 2026Review
- A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records.Clinical trials (London, England) · 2026Article
- Assessment of a Zero-Shot Large Language Model in Measuring Documented Goals-of-Care Discussions.Journal of pain and symptom management · 2026Article
- Development and Validation of a Natural Language Processing Algorithm Using Electronic Health Record Data to Identify Breast Cancer Patients With Low Social Support.JCO oncology advances · 2025Article
- A modular pipeline for natural language processing-screened human abstraction of a pragmatic trial outcome from electronic health records.medRxiv : the preprint server for health sciences · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Performance of Predefined Search Patterns for Identifying Documented Goals-of-Care Discussions in Inpatient Electronic Health Records.Journal of palliative medicine · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.