The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index; none of the 7 sources its claims cite is checked for support yet.
Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers
Open accessAbstract
The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
OHDSI is an international open collaborative that builds on OMOP to create an open-source, federated network using the OMOP Common Data Model for large-scale observational health research. It reports that 58 existing OMOP databases had converted 682 million patient records and estimates over 200 million patients available in the OHDSI Research Forum.
Key findings
- OHDSI grew out of the five-year Observational Medical Outcomes Partnership (OMOP) and adopted/maintains the OMOP Common Data Model (CDM), requiring data holders to map their data to the CDM so that a query can be executed at any site without modification.
- A survey of current OMOP data model users found that 58 existing OMOP databases had collectively converted 682 million patient records to the OMOP CDM; this includes duplicated patients and sources and databases not currently participating in the OHDSI Research Forum. The paper estimates that the actual number of patients currently available in the OHDSI Research Forum is over 200 million.
- OHDSI’s first annual meeting at Columbia University Medical Center on 16–17 October 2014 had 58 participants; over 90 participants from around the world have joined the collaborative, with a vision to access a network of one billion patients.
- OHDSI tools include ACHILLES for data characterization and quality visualization, HERMES for vocabulary browsing, PLATO for patient-level prediction, HERACLES for cohort exploration, and HOMER for risk identification and comparative effectiveness studies. The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study.
Methods
- Descriptive program/vision paper, not an empirical study; it narrates OHDSI’s infrastructure, governance, software tools, and research network.
- Data/sample: a survey of current OMOP data model users, reporting 58 databases and 682 million converted patient records; the first annual meeting had 58 participants.
- Main analysis: narrative synthesis of a federated model in which data are retained locally, analyses are run locally, and de-identified aggregate results are transmitted centrally; standard terminologies include SNOMED for diagnoses, RxNorm for drug ingredients, and LOINC for laboratory results. Results may be reported without aggregation, summarized, or aggregated if sufficient homogeneity can be demonstrated.
Limitations
- The paper does not present a formal limitations section. It acknowledges that the 682 million records include duplicated patients and sources and databases not currently participating in the OHDSI Research Forum, so the count is not a unique-patient count.
- It notes that OMOP previously demonstrated databases from different sources can give vastly different answers, and that blind aggregation may increase variance due to heterogeneity instead of decreasing it due to sample size.
- Evident from the text: no formal evaluation or outcome data for OHDSI tools or network performance are reported; the paper is descriptive and vision-oriented.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
10 Own interpretation · 7 Sources not found · 6 No basis given · 1 Own result
In the paper
“The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study, assessing whether drugs with similar function or structure cause similar side effects [10]”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
What the sources say
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They found that 58 existing OMOP databases have collectively converted 682 million patient records to the OMOP CDM.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
plainn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enables multicenter, global analyses to be executed rapidly and efficiently using applications or programs developed at a single site.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A blind aggregation of the results may increase variance due to heterogeneity instead of decreasing it due to sample size.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
What the sources say
- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The existence of multiple very large databases that use the OMOP Common Data Model demonstrates that a worldwide conversion of clinical data on all patients is in fact feasible.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Discussion and Conclusion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, OHDSI vision is, in fact, feasible today.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
boostedn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The existing OMOP databases and conversion experience are sufficient to make the OHDSI vision of accessing a network of one billion patients feasible today.
In the paper
“The team previously found that simply merging the databases is likely to give poor answers because of the heterogeneity [6].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
- Probably not a substantial claim. 8
- It may state two separate claims as one. 10
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“OHDSI held its first annual meeting at Columbia University Medical Center in New York City on 16 and 17 of October 2014.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, we estimate that the actual number of patients currently available in the OHDSI Research Forum is over 200 million.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Fifty eight participants reviewed the vision and goals giving rise to the creation of OHDSI and formed working groups to address the common data model, vocabulary, knowledge bases, estimation methods, phenotype generation, clinical characterization, and cohort definition.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Results
plaindirectn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Other investigators have used ACHILLES to do an initial assessment of whether the database is likely to hold a sufficient number of cases of interest to be worth investigating further.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data owners have used ACHILLES to assess the quality of their database, looking for gaps that may signify upload errors.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This large number includes both patients and sources that are duplicated across databases and also includes databases that are not currently participating in the OHDSI Research Forum.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
plainn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nevertheless, the total count demonstrates the feasibility of imposing a strong information model and executing a CDM conversion on a number of records that would represent a significant fraction of the world's population.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
boosteddirectn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 682 million patient records converted to the OMOP CDM represent a significant fraction of the world's population.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In return, this approach creates a unique opportunity of implementing a number of existing data exploration and evidence generation tools and participating in world-wide studies because any given query can be executed at any site without modification.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Any given query can be executed at any site without modification.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Previous success in algorithm development, software distribution, and evidence generation points to potential success in the overall evidencegenerating OHDSI project.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Discussion and Conclusion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“The fiveyear project developed new methods in observational research and established an observational research laboratory.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ACHILLES enables the characterization, quality assessment, and visualization of observational data and provides users with an interactive, exploratory framework to assess patient demographics and the prevalence of all conditions, drugs, procedures, and observations stored in the dataset.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP) [4], which was a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“OHDSI's choice of standard terminologies does not limit who may participate because mappings and tools are supplied to translate from other terminologies.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Mappings and tools supplied by OHDSI are sufficient to translate from all other terminologies so that choice of standard terminologies does not limit participation.
In the paper
“A centerpiece of the OMOP project was the development of the OMOP Common Data Model (CDM) [4] which represents healthcare data from diverse sources in a consistent and standardized way (see Table 1).”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data are retained at the participant's site, simplifying patient and business privacy issues.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“At the conclusion of this five-year project, the OMOP research investigators initiated the OHDSI effort, and the research laboratory moved to the Reagan-Udall Foundation for the FDA under the Innovation in Medical Evidence Development and Surveillance (IMEDS) Program [5].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The paper relies on this without support.
The OMOP CDM is a 'strong' information model, in which the encoding and relationships among concepts are explicitly and formally specified.
“This CDM is a”
The paper relies on this without support.
The 682 million patient records converted to the OMOP CDM represent a significant fraction of the world's population.
“a number of records that would represent a significant fraction of the world's population”
The paper relies on this without support.
The existing OMOP databases and conversion experience are sufficient to make the OHDSI vision of accessing a network of one billion patients feasible today.
“In other words, OHDSI vision is, in fact, feasible today.”
The paper relies on this without support.
Mappings and tools supplied by OHDSI are sufficient to translate from all other terminologies so that choice of standard terminologies does not limit participation.
“mappings and tools are supplied to translate from other terminologies”
The paper relies on this without support.
Any given query can be executed at any site without modification.
“because any given query can be executed at any site without modification”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The OHDSI network and its open-source analytic tools have been previously used to generate reliable evidence in medicine, including ophthalmology.”
From Semaglutide and Nonarteritic Anterior Ischemic Optic Neuropathy (2025) · cites this paper as [12]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The Strengthening the Reporting of Observational Studies in Epidemiology guideline was followed.11”
From Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022. (2024) · cites this paper as [11]
Experimental — accuracy not yet measured
Citations
Cites
Launching PCORnet, a national patient-centered clinical research network
Fleurence, R., Curtis, L. H., Califf, R. M., Platt, R., Selby, J. V., & Brown, J. S. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002747 · Cited by 654
Validation of a common data model for active safety surveillance research
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000376 · Cited by 548 · cited 2×
“OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP) [4], which was a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.”
BackgroundMethodEvaluating the Impact of Database Heterogeneity on Observational Study Results
Madigan, D., Ryan, P. B., Schuemie, M. J., Stang, P., Overhage, J. M., Hartzema, A. G., Suchard, M. A., DuMouchel, W., & Berlin, J. A. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt010 · Cited by 186 · cited 1×
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
BackgroundA ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care
Longhurst, C., Harrington, R. A., & Shah, N. H. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0099 · Cited by 159 · cited 1×
“We envision a future where observational studies will inform clinical practice—by providing practice-based evidence— using the unprecedented amount of available patient data and the use of computerized systems to process the data [11].”
MethodMedication‐Wide Association Studies
Ryan, P. B., Madigan, D., Stang, P., Schuemie, M. J., & Hripcsak, G. M. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.52 · Cited by 59 · cited 1×
“The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study, assessing whether drugs with similar function or structure cause similar side effects [10].”
MethodEstimating Prognosis with the Aid of a Conversational-Mode Computer Program
Feinstein, A. R., RUBINSTEIN, J. F., & Ramshaw, W. A. (1972) · Annals of Internal Medicine · doi:10.7326/0003-4819-76-6-911 · Cited by 42 · cited 1×
“initiated the idea of using data on 678 lung cancer patients as an electronic ‘library of clinical experience’ to obtain personalized prognosis [12].”
Method
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Aryal, D., Beane, A., Dondorp, A. M., et al. (2021) · Wellcome Open Research · doi:10.12688/wellcomeopenres.16486.1 · Cited by 37
Pedrera-Jiménez, M., García-Barrio, N., Cruz-Rojo, J., Terriza-Torres, A. I., López-Jiménez, E. A., Calvo-Boyero, F., Jiménez-Cerezo, M. J., Blanco-Martínez, A., Roig-Domínguez, G., Cruz-Bermúdez, J. L., et al. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103697 · Cited by 32
“Advanced healthcare information systems and clinical data warehouses such as i2b2 and OMOP [13,14], implement a dual paradigm, which separates the data model and the concept model.”
DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models
Luo, C., Islam, M. N., Sheils, N. E., Buresh, J., Reps, J., Schuemie, M. J., Ryan, P. B., Edmondson, M. J., Duan, R., Tong, J., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29160-4 · Cited by 39
“Similarly, the additional terms in (6) can also be reconstructed by plugging in (8) and (4).”
BackgroundMethodYi, W., Kim, B. H., Kim, M., Kim, J., Im, M., Ryang, S., Kim, E. H., Jeon, Y. K., Kim, S. S., & Kim, I. J. (2022) · Thyroid · doi:10.1089/thy.2021.0634 · Cited by 36
“21,22 The entire code used to perform this study can be shared in any data-base in the format of the OMOP CDM to allow analysis by applying the same method used in this study.”
Comparative safety and effectiveness of alendronate versus raloxifene in women with osteoporosis
Kim, Y.-S., Tian, Y., Yang, J., Huser, V., Jin, P., Lambert, C. G., Park, H., You, S. C., Park, R. W., Rijnbeek, P. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-68037-8 · Cited by 34
Papež, V., Moinat, M., Payralbe, S., Asselbergs, F. W., Lumbers, R. T., Hemingway, H., Dobson, R., & Denaxas, S. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab001 · Cited by 40
“This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC; (4) the National Institute for Health Research University College London Hospitals Biomedical…”
BackgroundMethodInfluentialLeveraging the Electronic Health Record to Address the COVID-19 Pandemic
Satterfield, B. A., Dikilitas, O., & Kullo, I. J. (2021) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2021.04.008 · Cited by 42
Lenert, L. A., Ilatovskiy, A. V., Agnew, J., Rudisill, P., Jacobs, J., Weatherston, D., & Deans, K. R., Jr. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab108 · Cited by 37
“N3C’s preferred data model for accepting results is the Observational Medical Outcomes Partnership (OMOP) model maintained by the Observational Health Data Sciences and Informatics (OHDSI) collaborative.(4) However, N3C accepts data in a variety of formats.”
MethodSeo, S. I., Park, C. H., Kim, T. J., Bang, C. S., Kim, J., Lee, K. J., Kim, J., Kim, H. H., You, S. C., & Shin, W. G. (2021) · Cancer Medicine · doi:10.1002/cam4.4514 · Cited by 35
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Kamdar, M. R., Fernández, J. D., Polleres, A., Tudorache, T., & Musen, M. A. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0162-5 · Cited by 36
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Junior, J. F. R., Gutierrez, M. A., Spadon, G., Brandoli, B., & Amer-Yahia, S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.09.024 · Cited by 37
“The data model of InCor follows the OMOP Common Data Model (CDM) [17] to 205”
MethodShoaib, A., Cepeda, M. S., Murray, G., & Ochs-Ross, R. (2021) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-021-05289-x · Cited by 39
“Use of the common data model allowed for consistent and precise replication across each of the two databases (Hripcsak et al., 2015).”
MethodZhou, D., Gan, Z., Shi, X., Patwari, A., Rush, E. N., Bonzel, C.-L., Panickan, V. A., Hong, C., Ho, Y.-L. A., Cai, T. A., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104147 · Cited by 36
Zhou, Y., Shi, J., Stein, R., Liu, X., Baldassano, R. N., Forrest, C. B., Chen, Y., & Huang, J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad066 · Cited by 33
Mateus, P., Moonen, J., Beran, M., Jaarsma, E., van der Landen, S. M., Heuvelink, J., Birhanu, M., Harms, A. G. J., Bron, E., Wolters, F. J., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104661 · Cited by 31
The Stanford Medicine data science ecosystem for clinical and translational research
Callahan, A., Ashley, E. A., Datta, S., Desai, P., Ferris, T., Fries, J., Halaas, M., Langlotz, C. P., Mackey, S., Posada, J., et al. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad054 · Cited by 43
“A common data model to organize clinical data STARR 19 uses the OHDSI Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), 25 which provides interoperability across research centers and data sources 25–27 to enable OHDSI network studies.”
Ahmad, F., Ricket, I., Hammill, B., Eskenazi, L., Robertson, H. R., Curtis, L., Dobi, C. D., Girotra, S., Haynes, K., Kizer, J., et al. (2020) · Circulation. Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.119.006292 · Cited by 30
Harnessing Real-World Data for Regulatory Use and Applying Innovative Applications
Zou, K. H., Li, J. Z., Imperato, J., et al. (2020) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s262776 · Cited by 34
“The drive for more standardization has been led by organizations such as the Observational Health Data Sciences and Informatics group (OHDSI).(26) The OHDSI has Dovepress Zou et al”
MethodKim, J. H., Lee, S. W., Yon, D. K., Ha, E. K., Jee, H. M., Sung, M., Sim, H. J., Yoon, J. W., Choi, S. H., Shin, Y. H., et al. (2020) · Pediatric Allergy and Immunology · doi:10.1111/pai.13391 · Cited by 37
Roel, E., Pistillo, A., Recalde, M., Fernández-Bertolín, S., Aragón, M., Soerjomataram, I., Jenab, M., Puente, D., Prieto-Alhambra, D., Burn, E., et al. (2021) · International Journal of Cancer · doi:10.1002/ijc.33846 · Cited by 17
“In addition, SIDIAP has been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), allowing us to apply common analytical tools developed by the open-science Observational Health Data Sciences and Informatics (OHDSI) network.(16)”
MethodA methodology for cohort harmonisation in multicentre clinical research
Almeida, J. R., Silva, L. B., Bos, I., Visser, P. J., & Oliveira, J. L. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100760 · Cited by 34
Validation of an electronic frailty index with electronic health records: eFRAGICAP index
Orfila, F., Carrasco-Ribelles, L. A., Abellana, R. M., Roso‐Llorach, A., Lombardo, F. C., Reyes, C., & Violán, C. (2022) · BMC Geriatrics · doi:10.1186/s12877-022-03090-8 · Cited by 34
“This will facilitate the subsequent development of an international standard to measure frailty [32].”
BackgroundHurwitz, E. G., Butzin-Dozier, Z., Master, H., O’Neil, S. T., Walden, A., Holko, M., Patel, R. C., & Haendel, M. A. (2024) · JMIR mhealth and uhealth · doi:10.2196/54622 · Cited by 37
“The first branch of the 3-fold approach to creating a cohort of women with PPD was conducted using Observational Medical Outcomes Partnership concept IDs in the condition table based on the Observational Health Data Sciences and Informatics initiative in Multimedia Appendix 1 [27,28].”
MethodBig data and precision medicine: challenges and strategies with healthcare data
Kraus, J. M., Lausser, L. M., Kühn, P., Jobst, F., Bock, M., Halanke, C., Hummel, M., Heuschmann, P. U., & Kestler, H. A. (2018) · International Journal of Data Science and Analytics · doi:10.1007/s41060-018-0095-0 · Cited by 35
Shin, S. J., You, S. C., Park, Y. R., Roh, J., Kim, J.-H., Haam, S., Reich, C., Blacketer, C., Son, D., Oh, S., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/13249 · Cited by 33
Ryu, B., Yoon, E., Kim, S., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/18526 · Cited by 33
“OHDSI is an open-science community that aims to improve health by empowering the community to collaboratively generate evidence that promotes better health decisions and care [14].”
BackgroundLuo, G., Nau, C., Crawford, W., Schatz, M. X., Zeiger, R. S., Rozema, E. J., & Koebnick, C. (2020) · JMIR Medical Informatics · doi:10.2196/22689 · Cited by 34
“This data model and its linked standardized terminologies [44] standardize administrative and clinical attributes from at least 10 large US health care systems [45,46].”
MethodPedrera‐Jiménez, M., Barrio, N. G., Frid, S. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/48702 · Cited by 32
“…a core OpenEHR repository based on Better Platform technology [47], along with others relying on standardized models for real-world data research, such as Informatics for Integrating Biology and the Bedside (i2b2) and Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) [48,49].”
MethodSharing sensitive data in life sciences: an overview of centralized and federated approaches
Rujano, M. A., Boiten, J., Ohmann, C., Canham, S., Contrino, S., David, R., Ewbank, J. J., Filippone, C., Connellan, C., Custers, I., et al. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae262 · Cited by 32
“…estimates,drug and biomarker evaluations and patient-level predictions while maintaining data locality [51, 53], as protocols and analysis code can be executed across the network, with only aggregate sum-mary statistics (not patient-level data) shared among collaborating researchers [48, 54].”
BackgroundEHR-based phenotyping: Bulk learning and evaluation
Chiu, P.-H., & Hripcsak, G. M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.009 · Cited by 32
Comprehensive Real-World Assessment of Marketed Medications to Guide Parkinson’s Drug Discovery
Cepeda, M. S., Kern, D. M., Seabrook, G. R., & Lovestone, S. (2019) · Clinical Drug Investigation · doi:10.1007/s40261-019-00830-4 · Cited by 32
Transforming Two Decades of ePR Data to OMOP CDM for Clinical Research
de Lima, D. M., Junior, J. F. R., Traina, A. J. M., et al. (2019) · Studies in health technology and informatics · doi:10.3233/shti190218 · Cited by 32
Using the Data Quality Dashboard to Improve the EHDEN Network
Blacketer, C., Voss, E. A., DeFalco, F., Hughes, N., Schuemie, M. J., Moinat, M., & Rijnbeek, P. R. (2021) · Applied Sciences · doi:10.3390/app112411920 · Cited by 32
ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data
Luo, C., Duan, R., Naj, A. C., Kranzler, H. R., Bian, J., & Chen, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-09069-0 · Cited by 33
Pfohl, S., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J., & Shah, N. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3531146.3533166 · Cited by 30
“[95] George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, Johan Van Der Lei, Nicole Pratt, G Niklas Norén, Yu-Chuan Chuan Li, Paul E Stang, David Madigan, and Patrick B Ryan.”
BackgroundSelf-citationHadidchi, R., Wang, S. H., Rezko, D., Henry, S. S., Coyle, P. K., & Duong, T. Q. (2024) · Multiple Sclerosis and Related Disorders · doi:10.1016/j.msard.2024.105613 · Cited by 31
Seinen, T. M., Kors, J. A., van Mulligen, E. M., & Rijnbeek, P. R. (2024) · Journal of Medical Internet Research · doi:10.2196/66910 · Cited by 34
“The database is standardized using the Observational Medical Outcomes Partnership Common Data Model [28].”
Schuemie, M. J., Ryan, P. B., Pratt, N. L., Chen, R., You, S. C., Krumholz, H. M., Madigan, D., Hripcsak, G. M., & Suchard, M. A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa124 · Cited by 31
Abidi, M. Z., Aboulafia, D. M., Accordino, M. K., Acoba, J. D., Ahluwalia, M. S., Ahmad, S. A., Ajmera, A., Alimohamed, S. I., Altman, J. K., Angevine, A. H., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.10.022 · Cited by 31
Gruendner, J., Gülden, C., Kampf, M. O., Mate, S., Prokosch, H., & Zierk, J. (2021) · JMIR Medical Informatics · doi:10.2196/25645 · Cited by 34
“…with appropriate analysis environments, which have been used extensively and received increasing support, are the OHDSI OMOP common data model [1], which has been designed to facilitate observational research, and Informatics for Integrating Biology and the Bedside (i2b2) [2], which focuses…”
Bayless, N. L., Bluestone, J. A., Bucktrout, S. L., Butterfield, L. H., Jaffee, E. M., Koch, C. A., Roep, B. O., Sharpe, A. H., Murphy, W. J., Villani, A., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002627 · Cited by 32
NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study
Reese, J., Coleman, B., Chan, L., Blau, H., Callahan, T. J., Cappelletti, L., Fontana, T., Bradwell, K. R., Harris, N. L., Casiraghi, E., et al. (2022) · Virology Journal · doi:10.1186/s12985-022-01813-2 · Cited by 40
“COVID-19 positive patients whose drug era [33] for any of the 12 NSAIDs began on or before the initial date of COVID-19 diagnosis and continued for at least one day after COVID-19 diagnosis were included in the NSAID treated group.”
Sınacı, A. A., Gençtürk, M., Teoman, H. A., Ertürkmen, G. B. L., Álvarez-Romero, C., Martínez-García, A., Poblador‐Plou, B., Carmona‐Pírez, J., Löbe, M., & Parra-Calderón, C. L. (2023) · Journal of Medical Internet Research · doi:10.2196/42822 · Cited by 34
“…clinical research networks enforcing different CDMs for their participants: the Observational Health Data Sciences and Informatics (formerly Observational Medical Outcomes Partnership [OMOP]) [3,4], Informatics for Integrating Biology and the Bedside (i2b2) [5], PCORnet [6], and Sentinel [7].”
Lu, J., Buczek, A., Fleysher, R., Musheyev, B., Henninger, E. M., Jabbery, K., Rangareddy, M., Kanawade, D., Nelapat, C., Soby, S., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e15277 · Cited by 31
Web services for data warehouses: OMOP and PCORnet on i2b2
Klann, J. G., Phillips, L. C., Herrick, C. D., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy093 · Cited by 31
Reproducibility in Machine Learning for HealthPreprint
McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Ghassemi, M., & Foschini, L. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.01463 · Cited by 34
Evidence appraisal: a scoping review, conceptual framework, and research agenda
Goldstein, A., Venker, E., & Weng, C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx050 · Cited by 30
Advancing Medical Imaging Research Through Standardization
Jeon, K., Park, W. Y., Kahn, C. E., Nagy, P. G., You, S. C., & Yoon, S. H. (2024) · Investigative Radiology · doi:10.1097/rli.0000000000001106 · Cited by 34
Integrated Data Repository Toolkit (IDRT)
Baum, B., Christoph, J., Engel, I., Löbe, M., Mate, S., Stäubert, S., Drepper, J., Prokosch, H. U., Winter, A., Sax, U., et al. (2015) · Methods of Information in Medicine · doi:10.3414/me15-01-0082 · Cited by 30
Ensari, I., Pichon, A., Gorman, S. L., Henry, S. B., & Elhadad, N. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1718755 · Cited by 30
Kaur, N., Bhattacharya, S., & Butte, A. J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00439-x · Cited by 33
“Several commonly used data models are supported by organizations such as integrating biology in the bedside, Observational Health Data Sciences and Informatics, Sentinel and Patient-Centered Outcomes Research Network 19,20,46 .”
MethodEligulashvili, A., Gordon, M., Lee, J. S., Lee, J., Mehrotra-Varma, S., Mehrotra‐Varma, J., Hsu, K. A., Hilliard, I., Lee, K., Li, A., et al. (2024) · PLoS Medicine · doi:10.1371/journal.pmed.1004263 · Cited by 35
“OMOP CDM represents healthcare data from diverse sources, which are stored through standard vocabulary concepts [25], allowing for the systematic analysis of disparate observational databases, including data from the EMR, administrative claims, and disease classifications systems (e.g.,…”
BackgroundWang, L., Rastegar-Mojarad, M., Ji, Z., et al. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.00875 · Cited by 30
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasksPreprint
Pang, C., Jiang, X., Kalluri, K. S., Spotnitz, M., Chen, R., Perotte, A., & Natarajan, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08585 · Cited by 43
Ji, H., Kim, S., Yi, S., Hwang, H., Kim, J.-W., & Yoo, S. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103459 · Cited by 29
Biology, Social Environment, and Personalized Medicine
Horwitz, R. I., Conroy, A. H., & Singer, B. H. (2016) · Psychotherapy and Psychosomatics · doi:10.1159/000452134 · Cited by 31
CodeMapper: semiautomatic coding of case definitions. A contribution from the ADVANCE project
Becker, B. F. H., Avillach, P., Romio, S., et al. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4245 · Cited by 29
Exploring completeness in clinical data research networks with DQe-c
Estiri, H., Stephens, K. A., Klann, J. G., & Murphy, S. N. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx109 · Cited by 28
Yu, Y., Ruddy, K. J., Hong, N., Tsuji, S., Wen, A., Shah, N. D., & Jiang, G. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103119 · Cited by 29
Bradwell, K. R., Wooldridge, J. T., Amor, B., Bennett, T. D., Anand, A., Bremer, C., Yoo, Y. J., Qian, Z., Johnson, S., Pfaff, E., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac054 · Cited by 29
“Quantitative data in the National COVID Cohort Collaborative (N3C) originate from data partners who submit electronic health record (EHR) data via different Common Data Models (CDMs), which are then harmonized into the Observational Medical Outcomes Partnership (OMOP) CDM.”
MethodInfluentialA new paradigm for accelerating clinical data science at Stanford MedicinePreprint
Datta, S., Posada, J., Olson, G., Li, W., O’Reilly, C., Balraj, D., Mesterhazy, J., Pallas, J. I., Desai, P., & Shah, N. H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.10534 · Cited by 73
Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact
Balagopalan, A., Baldini, I., Celi, L. A., Gichoya, J. W., McCoy, L. G., Naumann, T., Shalit, U., van der Schaar, M., & Wagstaff, K. L. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000474 · Cited by 34
“A notable and successful effort in this direction is the work done by the OHDSI consortium [81].”
Barclay, N. L., Burn, E., Delmestri, A., Duarte‐Salles, T., Golozar, A., Man, W. Y., Tan, E. H., Tietzova, I., N’Dow, J., Witjes, W. P. J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-69006-1 · Cited by 38
“CPRD GOLD and Aurum have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to facilitate replication of analyses 45,46 .”
MethodEHR-Independent Predictive Decision Support Architecture Based on OMOP
Unberath, P., Prokosch, H. U., Gruendner, J., Erpenbeck, M., Maier, C., & Christoph, J. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1710393 · Cited by 29
Lee, K. A., Jin, H. Y., Kim, Y. J., Im, Y., Kim, E., & Park, T. S. (2021) · Journal of Korean Medical Science · doi:10.3346/jkms.2021.36.e230 · Cited by 29
Kim, S., Bang, J.-I., Boo, D., et al. (2022) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-05779-9 · Cited by 28
“The Observational Health Data Sciences and Informatics (OHDSI) program is an international collaborative to create analytic solutions for a large network of health databases [21].”
BackgroundLiu, S., Wang, Y., Wen, A., Wang, L., Hong, N., Shen, F., Bedrick, S. D., Hersh, W., & Liu, H. (2020) · JMIR Medical Informatics · doi:10.2196/17376 · Cited by 35
Ahn, I., Na, W., Kwon, O., Yang, D. H., Park, G., Gwon, H., Kang, H. J., Jeong, Y. U., Yoo, J., Kim, Y., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01392-2 · Cited by 26
Health-Analytics Data to Evidence Suite (HADES): open-source software for observational research.
Schuemie, M., Reps, J., Black, A., Defalco, F., Evans, L., Fridgeirsson, E., Gilbert, J. P., Knoll, C., Lavallee, M., Rao, G. A., et al. (2024) · Studies in Health Technology and Informatics · doi:10.3233/shti231108 · Cited by 28
Centralized and Federated Models for the Analysis of Clinical Data
Li, R., Romano, J. D., Chen, Y., & Moore, J. H. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122220-115746 · Cited by 29
“Major research communities like OHDSI (13, 83) and the International Agency for Research on Cancer (https://www.iarc.who.int/) have adopted some of these methods, which are expected to make a lasting impact on biomedical studies and public health by facilitating more efficient and inclusive data sharing for multi-inst…”
InfluentialDixon, B. E., Chen, W., French, T., Williams, J. L., Duke, J. D., & Grannis, S. J. (2020) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2019-100054 · Cited by 28
Gülden, C., Blasini, R., Nassirian, A., Stein, A., Altun, F. B., Kirchner, M., Prokosch, H., & Boeker, M. (2020) · JMIR Medical Informatics · doi:10.2196/20470 · Cited by 29
“Similar to our attempts to harmonize data from heterogeneous trial registries, the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) is used to store and analyze observational health data from disparate source databases [28].”
ResultFu, S., Wang, L., Moon, S., Zong, N., He, H., Pejaver, V. R., Relevo, R., Walden, A., Haendel, M. A., Chute, C. G., et al. (2022) · Clinical and Translational Science · doi:10.1111/cts.13463 · Cited by 30
“66 PCORnet, 67 Observational Health Data Sciences and Informatics (OHDSI), 68 and Precision Medicine Initiative (PMI), 69 and bring positive impacts to future workforce development.”
BackgroundVoss, E. A., Shoaibi, A., Lai, L. Y. H., Blacketer, C., Alshammari, T. M., Makadia, R., Haynes, K., Sena, A. G., Rao, G., Sandijk, S. V., et al. (2023) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.101932 · Cited by 34
Methods Used in the Development of Common Data Models for Health Data: Scoping Review
Ahmadi, N., Zoch, M., Kelbert, P., Noll, R., Schaaf, J., Wolfien, M., & Sedlmayr, M. (2023) · JMIR Medical Informatics · doi:10.2196/45116 · Cited by 24
“An example is the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) developed by the Observational Health Data Science and Informatics (OHDSI) community, which ensures homogeneous storage of observational health care data across different databases with similar formats and terminologies [7].”
Khera, R., Dhingra, L. S., Aminorroaya, A., Li, K., Zhou, J. J., Arshad, F., Blacketer, C., Bowring, M. G., Bu, F., Cook, M. B., et al. (2023) · BMJ Medicine · doi:10.1136/bmjmed-2023-000651 · Cited by 26
Dugas, M., Blumenstock, M. C., Dittrich, T., Eisenmann, U., Feder, S. C., Fritz, F., Kessler, L. J., Klass, M., Knaup, P., Lehmann, C. U., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00994-6 · Cited by 29
“The Observational Health Data Sciences and Informatics (OHDSI) 30 community has very successfully demonstrated the power of open-source systems for large-scale health data analytics in observational settings.”
BackgroundGuo, L. L., Morse, K., Aftandilian, C. C., Steinberg, E., Fries, J., Posada, J., Fleming, S. L., Lemmon, J., Jessa, K., Shah, N. H., et al. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02449-8 · Cited by 25
“The data have been mapped to the standard concept identifiers and structure of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) 13 , resulting in a dataset named STARR-OMOP.”
MethodExpanding transplant outcomes research opportunities through the use of a common data model
Cho, S., Mohan, S., Husain, S. A., et al. (2018) · American Journal of Transplantation · doi:10.1111/ajt.14892 · Cited by 27
Advancing Toward a Common Data Model in Ophthalmology
Cai, C. X., Halfpenny, W., Boland, M. V., Lehmann, H. P., Hribar, M., Goetz, K. E., & Baxter, S. L. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100391 · Cited by 24
“12 Despite the advantages of CDMs, ophthalmology data are often not well represented in data models.”
BackgroundMethodClaire, R., Elvidge, J., Hanif, S., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1289365 · Cited by 26
“To explore the current landscape and prospects of incorporating RWE in HTA, a multi-stakeholder workshop titled “ Advancing Real-World Evidence in Health Technology Assessment ” was convened by the Innovative Medicines Initiative (IMI) funded European Health Data and Evidence Network (EHDEN) project (ehden.eu) (IMI, 2…”
MethodInfluentialShang, Y., Tian, Y., Lyu, K., Zhou, T., Zhang, P., Chen, J., & Li, J. (2024) · Journal of Medical Internet Research · doi:10.2196/54263 · Cited by 38
“Collaborative research networks such as the Observational Health Data Sciences and Informatics (OHDSI) offer valuable insights into addressing this issue [27,28].”
Recalde, M., Pistillo, A., Fernández‐Bertolín, S., Roel, E., Aragón, M., Freisling, H., Prieto‐Alhambra, D., Burn, E., & Duarte‐Salles, T. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab546 · Cited by 26
“The latter allowed the data to be structured in a standardized format and analytical tools to be applied as developed by the open-science Observational Health Data Sciences and Informatics network (13).”
MethodLinking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository
Kapsner, L., Mang, J. M., Mate, S., Seuchter, S., Vengadeswaran, A., Bathelt, F., Deppenwiese, N., Kadioglu, D., Kraska, D., & Prokosch, H. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1733847 · Cited by 26
Mazzotti, D. R., Haendel, M. A., McMurry, J. A., Smith, C. J., Buysse, D. J., Roenneberg, T., Penzel, T., Purcell, S., Redline, S. S., Zhang, Y., et al. (2022) · SLEEP · doi:10.1093/sleep/zsac002 · Cited by 27
Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS
Prieto‐Alhambra, D., Duarte‐Salles, T., Duarte‐Salles, T., Prats‐Uribe, A., Sena, A. G., Pistillo, A., Khalid, S., Lai, L., Alshammari, T. M., Shammari, T. M. A., et al. (2022) · Clinical Epidemiology · doi:10.2147/clep.s323292 · Cited by 28
“Here we present the global Observational Health Data Sciences and Informatics (OHDSI) community, an international open-science initiative of more than 3500 collaborators from 34 countries, response to the COVID-19 pandemic.(3) Founded in 2015, the OHDSI data network enabled a rapid baseline understanding of COVID-19 i…”
BackgroundEdmondson, M. J., Luo, C., Islam, M. N., Sheils, N. E., Buresh, J., Chen, Z., Bian, J., & Chen, Y. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104097 · Cited by 26
National health information technology priorities for research: A policy and development agenda
Zayas‐Cabán, T., Chaney, K., & Rucker, D. W. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa008 · Cited by 25
Reps, J., Kim, C., Williams, R. D., Markus, A. F., Yang, C. B., Duarte‐Salles, T., Falconer, T., Jonnagaddala, J., Williams, A. E., Fernández‐Bertolín, S., et al. (2021) · JMIR Medical Informatics · doi:10.2196/21547 · Cited by 27
“The Observational Health Data Science and Informatics (OHDSI) collaboration is a group of researchers who are collaborating to develop best practices for analyzing observational health care data [9].”
BackgroundRamakrishnaiah, Y., Maćešić, N., Webb, G. I., Peleg, A. Y., & Tyagi, S. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105816 · Cited by 36
Preserving temporal relations in clinical data while maintaining privacy
Hripcsak, G. M., Mirhaji, P., Low, A., & Malin, B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw001 · Cited by 25
Vogelsang, R. P., Bojesen, R. D., Hölmich, E. R., Orhan, A., Buzquurz, F., Cai, L., Grube, C., Zahid, J. A., Allakhverdiiev, E., Raskov, H., et al. (2021) · BJS Open · doi:10.1093/bjsopen/zrab023 · Cited by 25
Revel, M., Beeker, N., Porcher, R., Jilet, L., Fournier, L. S., Rance, B., Chassagnon, G., Fontenay, M., & Sanchez, O. (2022) · European Radiology · doi:10.1007/s00330-021-08377-9 · Cited by 27
“1 3 them into a clinical data warehouse following Observational Medical Outcomes Partnership (OMOP) common data model [23].”
BackgroundYu, Y., Zong, N., Wen, A., Liu, S., Stone, D. J., Knaack, D., Chamberlain, A. M., Pfaff, E., Gabriel, D. L., Chute, C. G., et al. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104002 · Cited by 25
“Moreover, the encoding and relationships among distinct medical concepts are explicitly and formally specified [30].”
BackgroundWong, J., Prieto‐Alhambra, D., Rijnbeek, P. R., Desai, R. J., Reps, J., & Toh, S. (2022) · Drug Safety · doi:10.1007/s40264-022-01158-3 · Cited by 25
COMMUTE: Communication-efficient transfer learning for multi-site risk prediction
Gu, T., Lee, P. H., & Duan, R. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104243 · Cited by 29
Park, W. Y., Jeon, K., Schmidt, T. S., Kondylakis, H., Alkasab, T. K., Dewey, B. E., You, S. C., & Nagy, P. G. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-00982-6 · Cited by 26
“As of 2022, structured data of 928 million patients have been harmonized into OMOP CDM from EHR, registries, and administrative claims [5].”
Method
Metadata
- Authors
- George, H., Duke Jon, D., Shah Nigam, H., Reich Christian, G., Vojtech, H., Schuemie Martijn, J., Suchard Marc, A., Woong, P. R., Kei, W. I. C., Rijnbeek Peter, R., Johan, V. D. L., Nicole, P., Niklas, N. G., Yu-Chuan, L., Stang Paul, E., David, M., & Ryan Patrick, B.
- Affiliations
- Ajou University (KR)
- Erasmus MC (NL)
- Venue
- Studies in Health Technology and Informatics
- Publisher
- IOS Press
- Published
- 2015
- Volume
- 216
- Pages
- 574-8
- ISSN
- 0926-9630 (ISSN-L)
- PMC
- PMC4815923
- OpenAlex
- W1126991912
- Semantic Scholar
- d5424a5f93f9d410cf17c5393eb5e6a1821dc135
- Cited by
- 1,107
- References
- 6
- Citations by year
- 2026: 40 · 2025: 111 · 2024: 138 · 2023: 136 · 2022: 179 · 2021: 149 · 2020: 123 · 2019: 85 · 2018: 70 · 2017: 40 · 2016: 27 · 2015: 7
- FWCI
- 143.57
- Type
- book chapter
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- public-domain
- Locations
- Studies in health technology and informatics (publishedVersion)
- PubMed (publishedVersion)
- AJOU Open Repository (Ajou University) (submittedVersion, public-domain)
- Europe PMC (PubMed Central) (submittedVersion)
- PubMed Central (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, other-oa)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion)
- PubMed
- Funders
- National Science Foundation (1251151)
- U.S. National Library of Medicine (R01LM006910)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- OHDSI, OMOP, observational studies, real-world data, research infrastructure
- MeSH terms
- Health Services Research, Medical Informatics, Databases, Factual, Models, Organizational, Internationality, Observational Studies as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-15, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-15 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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