ArticlePerspectives in health information managemen2021
Electronic Health Record (EHR) Abstraction.
Article in Perspectives in health information managemen, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis.The Journal of international medical research · 2024Pooled it
- Using natural language processing to analyze unstructured patient-reported outcomes data derived from electronic health records for cancer populations: a systematic review.Expert review of pharmacoeconomics & outcomes research · 2024Pooled it
- Natural language processing with machine learning methods to analyze unstructured patient-reported outcomes derived from electronic health records: A systematic review.Artificial intelligence in medicine · 2023Pooled it
- [A Transformer-based multimodal model for predicting hospital-acquired infections using imaging and clinical laboratory data].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Exploring Technological Solutions for Interoperability Between Patient Electronic Medical Records and Clinical Registries: Scoping Review.Journal of medical Internet research · 2026Article
- Evaluating Large Language Models for Transparent Quality-of-Care Measurement in Children with ADHD.medRxiv : the preprint server for health sciences · 2026Article
- Nephrotic syndrome genomic discovery in the Mass General Brigham Biobank identifies monoallelic MEFV variants as a risk factor for focal segmental glomerulosclerosis.Kidney international · 2026Article
- A practical guide to the implementation of AI in orthopaedic research part 8: Resource management checklist for AI-driven research projects in orthopaedics.Journal of experimental orthopaedics · 2026Review
- "Assessment of an EMR-integrated Onco-Insight hospital-based cancer registry system for data completeness and data entry turnaround time at a tertiary care cancer center in India".Frontiers in oncology · 2026Article
- Telemedicine and Digital Tools in Dentistry: Enhancing Diagnosis and Remote Patient Care.Medicina (Kaunas, Lithuania) · 2025Review
- Automating surgical procedure extraction for society of surgeons adult cardiac surgery registry using pretrained language models.JAMIA open · 2024Article
- Based on Medicine, The Now and Future of Large Language Models.Cellular and molecular bioengineering · 2024Review
- Next-generation study databases require FAIR, EHR-integrated, and scalable Electronic Data Capture for medical documentation and decision support.NPJ digital medicine · 2024Article
- Selective prediction for extracting unstructured clinical data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Sustained low catheter related infection (CRI) incidence in an observational follow-up study of 9924 catheters using automated data scripts as quality assurance for central venous catheter (CVC) management.Infection prevention in practice · 2023Article
- Physicians' Perspectives on a Multi-Dimensional Model for the Roles of Electronic Health Records in Approaching a Proper Differential Diagnosis.Journal of personalized medicine · 2023Article
- Data Challenges for Externally Controlled Trials: Viewpoint.Journal of medical Internet research · 2023Article
- Telemedicine Utilization Among Transgender and Gender-Diverse Adolescents Before and After the COVID-19 Pandemic.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2023Article
- Using health insurance claims data to assess long-term disease progression in a prostate cancer cohort.The Prostate · 2022Article
- Digital health technology-specific risks for medical malpractice liability.NPJ digital medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of electronic health record (EHR) abstraction includes collection of data related to administrative coding functions, quality improvement, clinical registry functions and clinical research. This article examines the different abstraction methods, such as manual abstraction, simple query, and natural language processing (NLP). It also discusses the advantages and disadvantages of each of those methods. The process used for successful EHR abstraction is also discussed and includes the scope and resources needed (time, budget, type of healthcare professionals RHIA, RHIT, etc.). The relationship between EHRs and the clinical registry is also examined with a focus on validity of the data extracted. Future research in this area to examine abstraction methods across hospitals who do data abstraction are being finalized for a future publication.
Indexed as
Identifiers
34035788PMC8120673What OpenQuestion holds
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.