ReviewClinical and translational science2026
Real-World Clinical Datasets in Practice: Applications for Learners, Clinician-Educators, and Health Services Teams.
Review in Clinical and translational science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Real-world clinical databases offer students, residents, fellows, clinician-educators, librarians, and early-career investigators an accessible entry point into population health, quality improvement, and outcomes research. Leveraging large-scale, de-identified datasets enables the investigation of healthcare delivery, cost, treatment effectiveness, and clinical outcomes without the need for patient recruitment or extensive funding sources. This review provides a synopsis of four widely used real-world clinical datasets: the Healthcare Cost and Utilization Project (HCUP, low-cost with a Data Use Agreement), the Medical Information Mart for Intensive Care (MIMIC-IV, openly available), TriNetX (subscription-based), and Epic Cosmos (institutional access). Highlighting their structure, data types, access requirements, and ideal use cases, the distinguishing features of each database are discussed. Finally, we provide guidance on how learners and research teams can formulate research questions, identify the appropriate dataset, and leverage mentorship resources for database-focused research, allowing them to meaningfully engage in data-driven research and contribute to improving healthcare delivery and patient outcomes.
Indexed as
Identifiers
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.