ArticleApplied clinical informatics2015
Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 2 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
65 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Development and Application of an Active Pharmacovigilance Framework Based on Electronic Healthcare Records from Multiple Centers in Korea.Drug safety · 2023Pooled it
- Pooled it
- Association of Statin Use with Reduced Primary Liver Cancer Risk, Independent of Age and Cirrhosis Protection in MASLD.Cancers · 2026Article
- Transforming unstructured breast cancer pathology reports into the Observational Medical Outcomes Partnership Common Data Model.BMC medical informatics and decision making · 2026Article
- Visualizing the Complexity of the PCORnet® Infrastructure and Implications for Designing PCORnet® Studies.Medical care · 2026Article
- Age-specific protective effects of statins against cirrhosis in patients with chronic liver enzyme elevation associated with metabolic dysfunction: a retrospective cohort study of electronic health records.EClinicalMedicine · 2025Article
- Success and Challenges Querying OMOP-transformed EHR Data from Different Healthcare Organizations.ACI open · 2025Article
- Exploring the Possibility of Medical Device Surveillance in Patients on Peritoneal Dialysis Using a Common Data Model.Medicina (Kaunas, Lithuania) · 2025Article
- Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study.Journal of medical Internet research · 2025Observational
- Assessing the harmonization of structured electronic health record data to reference terminologies and data completeness through data provenance.Learning health systems · 2025Article
- Mapping Emergency Medicine Data to the Observational Medical Outcomes Partnership Common Data Model: A Gap Analysis of the American College of Emergency Physicians Clinical Emergency Data Registry.Journal of the American College of Emergency Physicians open · 2025Article
- Assessing and improving research readiness in PCORnet®.Journal of clinical and translational science · 2025Article
- An Implemented Real-World-Data Pipeline for Standardization of Electronic Health Records in Precision Oncology.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Mini-review of clinical data service platforms in the era of artificial intelligence: A case study of the iHi data platform.BioMedicine · 2025Review
- The challenges and opportunities of continuous data quality improvement for healthcare administration data.JAMIA open · 2024Article
- MENDS-on-FHIR: leveraging the OMOP common data model and FHIR standards for national chronic disease surveillance.JAMIA open · 2024Article
- Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.Translational psychiatry · 2024Article
- Early Detection of Atrial Fibrillation in Chronic Obstructive Pulmonary Disease Patients.Medicina (Kaunas, Lithuania) · 2024Review
- Impact of metformin on cardiovascular and kidney outcome based on kidney function status in type 2 diabetic patients: a multicentric, retrospective cohort study.Scientific reports · 2024Observational
- Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.Frontiers in immunology · 2024Article
5 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
8 authors.
Funding
Abstract
backgroundAdoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs.
objectivesIn this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM.
methodsThe project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules.
resultsRecord counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM.
conclusionsThe data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.
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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.