ArticleMedical decision making : an international journal of the Society for Medical Decision Making
Natural language processing improves identification of colorectal cancer testing in the electronic medical record.
Article in Medical decision making : an international journal of the Society for Medical Decision Making. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Emerging applications of NLP and large language models in gastroenterology and hepatology: a systematic review.Frontiers in medicine · 2024Pooled it
- Natural Language Processing Algorithm Accurately Classifies Diverticulitis-Related Complications and Predicts Long-Term Outcomes.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2026Article
- Review
- A general framework for developing computable clinical phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Cancer Registry Enrichment via Linkage with Hospital-Based Electronic Medical Records: A Pilot Investigation.Journal of registry management · 2024Article
- Does medication-related osteonecrosis of the jaw affect survival of patients with Multiple Myeloma?: Exploring a large single center database using artificial intelligence.Clinical and experimental medicine · 2023Article
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.Journal of medical Internet research · 2021Article
- Electronic Health Record (EHR) Abstraction.Perspectives in health information managemen · 2021Article
- Natural Language Processing Combined with ICD-9-CM Codes as a Novel Method to Study the Epidemiology of Allergic Drug Reactions.The journal of allergy and clinical immunology. In practice · 2020Article
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- A Deep Learning Approach for Managing Medical Consumable Materials in Intensive Care Units via Convolutional Neural Networks: Technical Proof-of-Concept Study.JMIR medical informatics · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.PloS one · 2018Article
- Population-Based Analysis of Histologically Confirmed Melanocytic Proliferations Using Natural Language Processing.JAMA dermatology · 2018Article
- A Frame-Based NLP System for Cancer-Related Information Extraction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- A Clinical Decision Support System for Monitoring Post-Colonoscopy Patient Follow-Up and Scheduling.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Measuring Preventive Care Delivery: Comparing Rates Across Three Data Sources.American journal of preventive medicine · 2016Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundDifficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates.
objectiveTo use Electronic Health Record (EHR) data to identify patients with prior CRC testing.
designA clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests.
settingLarge academic medical center. PATIENTS: 200 patients ≥ 50 years old who had completed ≥ 2 non-acute primary care visits within a 1-year period. MEASURES: Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources.
resultsFor identification of all CRC tests, recall and precision were as follows: NLP system (recall 93%, precision 94%), chart review (74%, 98%), and billing records review (44%, 83%). Recall and precision for identification of patients in need of screening were: NLP system (recall 95%, precision 88%), chart review (99%, 82%), and billing records (99%, 67%). LIMITATIONS: Small sample size and requirement for a robust EHR.
conclusionsApplying NLP to EHR records detected more CRC tests than either manual chart review or billing records review alone. NLP had better precision but marginally lower recall to identify patients who were due for CRC screening than billing record review.
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