ArticleJAMIA open2023
A certified de-identification system for all clinical text documents for information extraction at scale.
Article in JAMIA open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T cell therapy using large language models.PLOS digital health · 2026Article
- Computational identification of salient cancer care topics and themes in oncology social work notes.JNCI cancer spectrum · 2026Article
- Large language model-generated clinical summaries in emergency departments: A blinded comparison study.PLOS digital health · 2026Article
- Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models.medRxiv : the preprint server for health sciences · 2026Article
- Natural language processing to identify documented pain preceding radiation therapy for bone metastases.JNCI cancer spectrum · 2026Article
- Extracting TNFi switching reasons and trajectories from real-world data using large language models.JAMIA open · 2025Article
- Quantifying Barriers to Diabetic Eye Screening: A Two-Center Study at the University of California.Diabetes care · 2025Observational
- MedVidDeID: Protecting privacy in clinical encounter video recordings.Journal of biomedical informatics · 2025Article
- Harvard Electroencephalography Database: A comprehensive clinical electroencephalographic resource from four Boston hospitals.Epilepsia · 2025Article
- Evaluating large language models for drafting emergency department encounter summaries.PLOS digital health · 2025Article
- Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.Scientific reports · 2025Article
- Capturing Patients' Lived Experiences with Chronic Pain through Motivational Interviewing and Information Extraction.Proceedings of the ... Workshop on Patient-Oriented Language Processing · 2025Article
- Understanding contraceptive switching rationales from real world clinical notes using large language models.NPJ digital medicine · 2025Article
- Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes.JAMIA open · 2024Article
- Economics and Equity of Large Language Models: Health Care Perspective.Journal of medical Internet research · 2024Article
- Evaluating the use of large language models to provide clinical recommendations in the Emergency Department.Nature communications · 2024Article
- A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Using incident reports to diagnose communication challenges for precision intervention in learning health systems: A methods paper.Learning health systems · 2024Article
- Use of a Large Language Model to Assess Clinical Acuity of Adults in the Emergency Department.JAMA network open · 2024Article
- Evaluating large language models as agents in the clinic.NPJ digital medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Clinical notes are a veritable treasure trove of information on a patient's disease progression, medical history, and treatment plans, yet are locked in secured databases accessible for research only after extensive ethics review. Removing personally identifying and protected health information (PII/PHI) from the records can reduce the need for additional Institutional Review Boards (IRB) reviews. In this project, our goals were to: (1) develop a robust and scalable clinical text de-identification pipeline that is compliant with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule for de-identification standards and (2) share routinely updated de-identified clinical notes with researchers. Materials and Methods: Building on our open-source de-identification software called Philter, we added features to: (1) make the algorithm and the de-identified data HIPAA compliant, which also implies type 2 error-free redaction, as certified via external audit; (2) reduce over-redaction errors; and (3) normalize and shift date PHI. We also established a streamlined de-identification pipeline using MongoDB to automatically extract clinical notes and provide truly de-identified notes to researchers with periodic monthly refreshes at our institution. Results: To the best of our knowledge, the Philter V1.0 pipeline is currently the
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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.