ArticleMedical care2013
Caveats for the use of operational electronic health record data in comparative effectiveness research.
Article in Medical care, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 337 papers, 7 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
337 citing papers in PubMed, 7 syntheses or guidelines pooled it.
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- Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Considerations for pooling real-world data as a comparator cohort to a single arm trial: a simulation study on assessment of heterogeneity.BMC medical research methodology · 2023Pooled it
- Factors associated with missed appointments by adults with type 2 diabetes mellitus: a systematic review.BMJ open diabetes research & care · 2021Pooled it
- Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.International journal of environmental research and public health · 2020Pooled it
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Population segmentation based on healthcare needs: a systematic review.Systematic reviews · 2019Pooled it
- Beyond Blind Trust in Hospital Data Warehouses: Evaluating Real-World Data Accountability.Learning health systems · 2026Article
- Availability and Quality of Electronic Health Record Data to Track Physical Function Across a Care Transition.Learning health systems · 2026Article
- Patterns and Determinants of Time to First DMARD Prescription in US Adults with Rheumatoid Arthritis: A Retrospective Cohort Study Using a Large Electronic Health Record Database.Advances in therapy · 2026Article
- Building the adult growth hormone deficiency data mart: a Real-World model of AI-driven clinical data extraction in a single Italian center.Journal of endocrinological investigation · 2026Article
- Primary care physician documentation in oncology electronic health records: prognostic marker or immortal-time bias?ESMO real world data and digital oncology · 2026Article
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- HealthFlow: automating electronic health record analysis via a strategically self-evolving multi-agent framework.NPJ digital medicine · 2026Article
- New to cohort, not new to care: leveraging historical data in HIV clinical cohorts in the United States.American journal of epidemiology · 2026Article
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- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- Mitigating bias in the analysis and inferences from using longitudinal EHR data in disease outcomes research.BMC medical research methodology · 2026Article
- Using large language models to enhance clinically-driven missing data recovery algorithms in electronic health records.JAMIA open · 2026Article
277 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
The growing amount of data in operational electronic health record systems provides unprecedented opportunity for its reuse for many tasks, including comparative effectiveness research. However, there are many caveats to the use of such data. Electronic health record data from clinical settings may be inaccurate, incomplete, transformed in ways that undermine their meaning, unrecoverable for research, of unknown provenance, of insufficient granularity, and incompatible with research protocols. However, the quantity and real-world nature of these data provide impetus for their use, and we develop a list of caveats to inform would-be users of such data as well as provide an informatics roadmap that aims to insure this opportunity to augment comparative effectiveness research can be best leveraged.
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What 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.