Observational studyClinical pharmacology and therapeutics2020
An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma.
Observational study in Clinical pharmacology and therapeutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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.
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2026Pooled it
- Artificial Intelligence Applications for Automated Data Extraction and Secondary Use of Clinical Information in Uro-oncology: A Systematic Review.European urology open science · 2026Review
- Pediatric advance care planning: a mixed-methods evaluation of documentation and sharing in current practice.BMC palliative care · 2026Article
- Treatment of Uterine Fibroid-Related Heavy Menstrual Bleeding: Variations in Clinical Practice at Four Hospitals in the Netherlands.Obstetrics and gynecology international · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Pharmacogenetic Implementation Studies-Lessons Learned From the PREPARE Study.Clinical pharmacology and therapeutics · 2025Article
- Evaluation of a text-mining application for the rapid analysis of free-text wildlife necropsy reports.PloS one · 2025Article
- Automated derivation of diagnostic criteria for lung cancer using natural language processing on electronic health records: a pilot study.BMC medical informatics and decision making · 2024Article
- Riboflavin transporter deficiency, the search for the undiagnosed: a retrospective data mining study.Orphanet journal of rare diseases · 2024Article
- Development and Portability of a Text Mining Algorithm for Capturing Disease Progression in Electronic Health Records of Patients With Stage IV Non-Small Cell Lung Cancer.JCO clinical cancer informatics · 2024Article
- Clinical text mining of the performance status and progression-free survival to facilitate data collection in cancer research: an exploratory study.ESMO real world data and digital oncology · 2024Article
- Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2024Article
- An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.Journal of medical systems · 2024Article
- Women have a higher risk of hospital admission associated with hyponatremia than men while using diuretics.Frontiers in pharmacology · 2024Article
- Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.Cancers · 2023Article
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- External Validation of Prediction Models for Surgical Complications in People Considering Total Hip or Knee Arthroplasty Was Successful for Delirium but Not for Surgical Site Infection, Postoperative Bleeding, and Nerve Damage: A Retrospective Cohort Study.Journal of personalized medicine · 2023Article
- Artificial intelligence in pharmacology research and practice.Clinical and translational science · 2023Review
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Real-world evidence can close the inferential gap between marketing authorization studies and clinical practice. However, the current standard for real-world data extraction from electronic health records (EHRs) for treatment evaluation is manual review (MR), which is time-consuming and laborious. Clinical Data Collector (CDC) is a novel natural language processing and text mining software tool for both structured and unstructured EHR data and only shows relevant EHR sections improving efficiency. We investigated CDC as a real-world data (RWD) collection method, through application of CDC queries for patient inclusion and information extraction on a cohort of patients with metastatic renal cell carcinoma (RCC) receiving systemic drug treatment. Baseline patient characteristics, disease characteristics, and treatment outcomes were extracted and these were compared with MR for validation. One hundred patients receiving 175 treatments were included using CDC, which corresponded to 99% with MR. Calculated median overall survival was 21.7 months (95% confidence interval (CI) 18.7-24.8) vs. 21.7 months (95% CI 18.6-24.8) and progression-free survival 8.9 months (95% CI 5.4-12.4) vs. 7.6 months (95% CI 5.7-9.4) for CDC vs. MR, respectively. Highest F1-score was found for cancer-related variables (88.1-100), followed by comorbidities (71.5-90.4) and adverse drug events (53.3-74.5), with most diverse scores on international metastatic RCC database criteria (51.4-100). Mean data collection time was 12 minutes (CDC) vs. 86 minutes (MR). In conclusion, CDC is a promising tool for retrieving RWD from EHRs because the correct patient population can be identified as well as relevant outcome data, such as overall survival and progression-free survival.
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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.