ArticleScientific reports2024
Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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.
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Who cites it
21 citing papers in PubMed.
- From electronic health records to real-world evidence: Savana Next-Generation Registry digital solution.ESMO real world data and digital oncology · 2026Article
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Associations Between Systemic Diseases and Dental Pulp Diagnoses: A Retrospective Chart Study Using a Locally Deployed Large Language Model.Dentistry journal · 2026Article
- Article
- Impact of Structured Data Validation on Case Completeness and Key Quality Indicators in the SWISS Implant Registry (SIRIS Spine): A SINGLE-CENTER Audit.Global spine journal · 2026Article
- RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.medRxiv : the preprint server for health sciences · 2026Article
- AI-Assisted Clinical Data Abstraction From Electronic Health Records: Retrospective Concordance Study.JMIR formative research · 2026Article
- Reproducibility of natural-language-driven statistical workflows compared with conventional software for diagnostic metrics in prostate cancer research.Investigative and clinical urology · 2026Article
- Integrating GPT-4o Into Data Mining in Neurosurgery: Feasibility and Proof-of-Concept Study.JMIR formative research · 2026Article
- AI assistance in tumor multidisciplinary teams.ESMO real world data and digital oncology · 2026Review
- Impella vs. IABP in Non-emergent High-Risk PCI: Outcomes and LVEF Recovery from a Large US EHR Study.Cardiology and therapy · 2026Article
- Privacy-preserving augmentation of structured telehealth activity data in diabetes patients using natural language processing.Frontiers in digital health · 2026Article
- Integrating rule-based NLP and large language models for statin information extraction from clinical notes.International journal of medical informatics · 2026Article
- Between the scalpel and algorithms: how AI is redesigning Surgery.Revista do Colegio Brasileiro de Cirurgioes · 2026Article
- Ghost in the Machine: Leveraging artificial intelligence in the trauma bay.Trauma surgery & acute care open · 2026Review
- Leveraging Machine Learning and Robotic Process Automation to Identify and Convert Unstructured Colonoscopy Results Into Actionable Data: Proof-of-Concept Study.JMIR medical informatics · 2025Article
- Article
- Confidence-linked and uncertainty-based staged framework for phenotype validation using large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- The Evolution of Radiology Image Annotation in the Era of Large Language Models.Radiology. Artificial intelligence · 2025Review
- Artificial intelligence for surgical data management and decision support: lessons from wound care.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.
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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.