ArticleNPJ digital medicine2019
Medical device surveillance with electronic health records.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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Who cites it
26 citing papers in PubMed.
- A tri-scale in silico framework integrating pharmacovigilance and mechanistic modeling suggests tepotinib-associated acute kidney injury risk.Renal failure · 2026Article
- The role of medical equipment reliability assessment in improving healthcare service quality: a scoping review.BMC health services research · 2026Article
- Machine Learning for Revision Joint Arthroplasty: A Systematic Review of Distinct Challenges, Current Performance, and Clinical Readiness.Arthroplasty today · 2026Article
- Latent profile analysis of health-related quality of life and its associated factors in postoperative aortic dissection patients: a cross-sectional study.Health and quality of life outcomes · 2025Article
- AXpert: human expert facilitated privacy-preserving large language models for abdominal X-ray report labeling.JAMIA open · 2025Article
- Capacity assessment for EHR-based medical device post-market surveillance for synthetic mid-urethral slings among women with stress urinary incontinence: a NEST consortium study.BMJ surgery, interventions, & health technologies · 2025Article
- AI-Driven Decision Support Framework for Preventing Medical Equipment Failure and Enhancing Patient Safety: A New Perspective.Journal of multidisciplinary healthcare · 2025Article
- Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study.JMIR medical informatics · 2024Article
- Evaluation of patient health outcomes of a student-run free clinic in East Harlem.BMC medical education · 2024Article
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- Distinguishing cardiac catheter ablation energy modalities by applying natural language processing to electronic health records.Journal of comparative effectiveness research · 2024Article
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- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.Medical & biological engineering & computing · 2023Review
- Validation of the first computerized indicator for orthopaedic surgical site infections in France: ISO-ORTHO.Antimicrobial resistance and infection control · 2023Article
- Diverse patients' attitudes towards Artificial Intelligence (AI) in diagnosis.PLOS digital health · 2023Article
- Inconsistent Surgical Implant Documentation: A Case Study in Total Knee and Hip Arthroplasty.Health services insights · 2023Article
- Labels in a haystack: Approaches beyond supervised learning in biomedical applications.Patterns (New York, N.Y.) · 2021Review
- Article
- ACE: the Advanced Cohort Engine for searching longitudinal patient records.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14;
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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.