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
Cited by
Sorted by citations
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Braunstein, M. L. (2022) · Health informatics · doi:10.1007/978-3-030-91563-6_5
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“Hripcsak G, Duke JD, Shah NH, Reich CG, Huser V, Schuemie MJ, et al. Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers.”
MethodNakayama, M., Feng, H., & Inoue, R. (2022) · Studies in health technology and informatics · doi:10.3233/shti220020
An OHDSI ATLAS Extension to Support Feasibility Requests in a Research Network
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Characterizing Variability of EHR-Driven Phenotype DefinitionsPreprint
Brandt, P., Kho, A., Luo, Y., Pacheco, J. A., Walunas, T. L., Hakonarson, H. H., Hripcsak, G. M., Liu, C., Shang, N., Weng, C., et al. (2022) · medRxiv · doi:10.1101/2022.07.10.22277390
Kim, S. Y. (2022) · The Korean Journal of Helicobacter and Upper Gastrointestinal Research · doi:10.7704/kjhugr.2022.0026
“Observational Medical Outcome PartnersCDM은 각 의료 기관들이 보유한 서로 다른 구조의 전자의무 기록정보를 가능한 표준화된 구조로 변환한 의료데이터 네트워크 이다.(15) 이번에 이용된 KDH-CDM은 단일기관 데이터베이스로 1,689,604명 환자의 2004년부터 2019년까지 16년 동안의 진 료 자료를 포함하고 있으며 이전 연구에도 이용된 바 있다.”
Comparative Analysis of Statistical Modeling and Approaches of Artificial Intelligence
Savchenko-Synyakova, Y. (2022) · Cybernetics and computer engineering · doi:10.15407/kvt208.02.030
BIBLIOGRAPHIC DATA SCIENCE – KONCEPTUALIZACJA OBSZARU BADAWCZEGO
Roszkowski, M. (2022) · Przegląd Biblioteczny · doi:10.36702/pb.914
Ostropolets, A., Hripcsak, G. M., Husain, S. A., Richter, L., Spotnitz, M., Elhussein, A., & Ryan, P. B. (2022) · medRxiv · doi:10.1101/2022.12.27.22283944
Ahmadi, N., Nguyen, Q. V., Sedlmayr, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2546267/v1
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“2, there are two modules in CDM-FL: The Observational Medical Outcomes Partnership (OMOP) CDM and the FL. OMOP is a public-private partnership that is a centerpiece of the Observational Health Data Sciences and Informatics (OHDSI) [18].”
BackgroundStarren, J. B., Rasmussen, L. V., Schneider, D., Nannapaneni, P., & Michelson, K. N. (2023) · Journal of the Society for Clinical Data Management · doi:10.47912/jscdm.234
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Future Directions in Clinical Research Informatics
Embí, P. J., & Richesson, R. (2023) · Health informatics · doi:10.1007/978-3-031-27173-1_25
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Data and databases in cardiovascular medicine and surgery
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Adaptability of Existing Feasibility Tools for Clinical Study Research Data Platforms
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Modelling Adverse Events with the TOP Phenotyping Framework
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“TVT is limited to six-dimensional data, but it uses OMOP concepts [20] instead of raw register data used by LifeTrack.”
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Representing Health Data and Medical Knowledge for Deep Learning
Hansen, E. R. (2023) · VBN Forskningsportal (Aalborg Universitet) · doi:10.54337/aau621651364
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HealthDBFinder: a question-answering task for health database discovery
Almeida, J. R., Silva, J. M., Afonso, L. C., Almeida, T. M., Antunes, R., Jonker, R. A. A., Reis, J. A., Silva, D. L. D. O., Matos, S., & Oliveira, J. L. (2024) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms61543.2024.00025
“The Automated Characterization of Health Information at Large-scale Longitudinal Evidence Systems (ACHILLES) 2 supports the analytical capabilities of ATLAS through comprehensive data characterization [22].”
MethodCuyàs, B., Alvarado‐Tápias, E., Tan, E. H., Golozar, A., Duarte‐Salles, T., Delmestri, A., Ballbe, J. M. A., Man, W. Y., Burn, E., Guarner, C., et al. (2024) · medRxiv · doi:10.1101/2024.08.05.24311466
“Both databases were mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) (10,11).”
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Towards cross-application model-agnostic federated cohort discovery
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Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data
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HERALD: A domain-specific query language for longitudinal health data analytics
Baum, L., Johns, M., Müller, A., Attieh, H. A., & Praßer, F. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105646
Big Data Technology and Healthcare Informatics
Li, J., Tian, Y. H., & Zhou, T. (2024) · Innovative Medical Devices. · doi:10.1007/978-981-97-9551-2_9
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Leveraging Transformers-based models and linked data for deep phenotyping in radiology
Hurtado, L.-F., Marco-Ruiz, L., Segarra, E., Castro-Bleda, M. J., Bustos, A., de la Iglesia-Vayá, M., & Vallalta-Rueda, J. F. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2024.108567
A common data model for oral anticoagulants-related risk of spontaneous intracranial hemorrhage
Hong, N., Ko, Y.-H., Park, J. H., et al. (2025) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2025.111039
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Identifying patients with neurofibromatosis type 1 related optic pathway glioma using the OMOP CDM
Dhaenens, B. A. E., Moinat, M., Didden, E., Ammour, N., Oostenbrink, R., & Rijnbeek, P. R. (2025) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2025.105011
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“Although multicenter studies have demonstrated robust and convincing results, they remain underre-presented due to methodological challenges, including data standardization [101,102] and privacy protection [103,104].”
Català, M., Li, X., Jödicke, A. M., Prats‐Uribe, A., Delmestri, A., Verhamme, K. M. C., Mosseveld, M., Brash, J. T., Vojinović, D., Cros, A. P., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-6579059/v1
A Generalized Tool to Assess Algorithmic Fairness in Disease Phenotype Definitions.
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Leveraging SNOMED CT for patient cohort identification over heterogeneous EHR data.
Hao, X., Huang, Y., Cui, L., et al. (2025) · PubMed
Lu, B., Pham, D., Chang, T., Lovette, M. A., Bui, T., & Ma, S. (2025) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc64641.2025.00021
Mohyeldin, M., Mohamed, F. O., Molina, M., Towfig, M. F., Mustafa, A., Elhussein, A. H., Alamin, F., Khaja, M., & Jadhav, P. (2025) · Cureus · doi:10.7759/cureus.87907 · Cited by 3
“Multi-center collaborations are essential for developing robust and generalizable AI models, representing a feasible short-term goal, given existing consortium structures [75].”
Rappoport, N., Livne, G., Cohen, N. P., Makover, N., Eshel-Geva, H., Kapach, H., Hadad, T., Alon, Y., Rubin, R., Chai, S., et al. (2025) · medRxiv · doi:10.1101/2025.07.04.25330663
“…clinical data models have been developed and implemented, including the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) [4], the Patient-Centered Outcomes Research Network (PCORnet) Common Data Model, the Sentinel Common Data Model [5], and the i2b2 (Informatics for…”
From data to knowledge: an introduction to biomedical informatics
Payne, P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-800684-9.00010-1
BIG DATA PROCESSING AND ANALYTICS IN HEALTHCARE FINANCE MANAGEMENT
Sulakadze, K. (2025) · Theoretical & Applied Science · doi:10.15863/tas.2025.07.147.5
Relationship between canagliflozin use and sarcopenia: Real-world data from the United States
Yuan, Ž., Hardin, J., Gilbert, J. P., Mehawej, J., Ali, S. R., Jeffcoat, C., & Fonseca, S. (2025) · Diabetes Epidemiology and Management · doi:10.1016/j.deman.2025.100280
Digital Health Maturity Stage 4: Optimizing platform and services
Wilkins-Wong, Z. S. Y., & Liaw, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95260-6.00004-6
Methodologies for quality measurement, monitoring, and evaluation
Liaw, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95260-6.00006-x
Jeong, E., Shendre, A., Su, Y., Guo, X., Li, L., & Chen, Y. (2025) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.70058
“The SD database is a de-identified EHR repository structured in the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) [29], facilitating standardized analyses across diverse clinical datasets, including demographics, diagnoses, medications, laboratory results, clinical notes,…”
Biggin, F., White, L. M., Ashcroft, Q., et al. (2025) · BMJ Neurology Open · doi:10.1136/bmjno-2025-001202
Islam, M. M., Foraker, R. E., Hossain, M. S., Arnold, W. D., Dandachi, D., & Mosa, A. S. M. (2026) · JMIR Formative Research · doi:10.2196/83028
“Cohort definitions were implemented in ATLAS version 2.12 (Observational Health Data Sciences and Informatics [OHDSI]) [64,65] and executed in R version 4.4.1 (R Foundation for Statistical Computing) using the OHDSI packages CohortMethod version 4.2.3 [66,67], FeatureExtraction version 3.7.2 [68],…”
InfluentialHuang, C., Wei, K., Liu, Y., et al. (2025) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2025.2571686
“There have been several successful multi-sites networks, such as the Observational Health Data Sciences and Informatics (OHDSI) consortium and the National Pediatric Learning Health System (PEDSnet) [2,3].”
A governance framework for medical code standardization to enhance multi-institutional data quality
Yamashita, T., Shibata, S.-I., Takada, A., Hotta, T., Izukura, R., Kang, D., & Nakashima, N. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03397-1 · Cited by 2
“OMOP CDM is an open science community standard developed and widely used within the Observational Health Data Sciences and Informatics (OHDSI) network to support large-scale observational studies [31, 32].”
Rowlands, E. J., Campanile, C., Junaid, H., Lane, J. C. E., Rahman, U., Eaton, B., Griffin, X. L., South, A., Cavalcante, A., Cartner, D., et al. (2026) · European Journal of Public Health · doi:10.1101/2025.11.26.25340975
An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System
Marteau, B. L., Hornback, A. M., Tan, S. M., Lowson, C., Woloff, J., & Wang, M. D. (2025) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi67747.2025.11269482 · Cited by 1
Goyal, J., Ng, D. Q., Zhang, K., Chan, A., Lee, J., Zheng, K., Hurley‐Kim, K., Nguyen, L., He, L., Nguyen, M. H., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26596367
Wirth, F. N., Kussel, T., Müller, A., Hamacher, K., & Prasser, F. (2024) · Figshare · doi:10.6084/m9.figshare.26552593
Wirth, F. N., Kussel, T., Müller, A., Hamacher, K., & Prasser, F. (2024) · Figshare · doi:10.6084/m9.figshare.26552596
Patoliya, J., Thaker, K., & Joshi, R. G. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898812164125080004
Hogenboom, J., Gouthamchand, V., Cairns, C., Janssen, S. H. M., Way, K. M., Dekker, A. L. A. J., van der Graaf, W. T. A., Darlington, A., Husson, O., Wee, L., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00177
“In comparison with other data models, such as the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), 31 our approach bene fi ts from Flyover ’ s on-read approach, 9 allowing the STRONG AYA knowledge graph and schema to adapt without modifying the data.”
Prieto, B. B., Arribas, D. P., Moreno-Parro, I., et al. (2026) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2025-1668
“Once data are mapped to the common structure, OMOP enables the de fi nition and sharing of reproducible cohorts and phenotypes across databases, facilitating consistent analytical approaches in multicentre studies [4].”
Mapping the Danish National Health Registries to the OMOP Common Data Model (Preprint)Preprint
Dobreva, J., Yaqoob, M., Alves, F. S., Dahl, S. C., Vesteghem, C., Nielsen, P. B., Hose, K., Bøgsted, M., & Sagi, T. (2026) · doi:10.2196/preprints.93317
dos Santos, P. V. B., Alvino, C. G., Olsen, D. R., et al. (2025) · doi:10.5753/latinoware.2025.16569
S, P. K. C. (2026) · International Journal of Latest Technology in Engineering Management & Applied Science · doi:10.51583/ijltemas.2026.15020000117 · Cited by 1
Kim, C., Bu, F., Blacketer, C., Ostropolets, A., Duarte‐Salles, T., Viernes, B., Falconer, T., Pistillo, A., Li, J., Yin, C., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-71307-0 · Cited by 3
“We conducted a retrospective observational cohort study using internationaldatabases standardized tothe Common Data Model (CDM) 13 .”
InfluentialMachine learning for large observational datasets in healthcare
Morel, M. (2021) · theses.fr (ABES) · doi:10.70675/4c309e60zdba1z4d73za007zbd512e238110
Lee, J. H., Ryu, B., & Lee, Y. (2026) · Scientific Reports · doi:10.1038/s41598-026-46000-3
“The OMOP-CDM standardizes various Electronic Health Record (EHR) data into a unified structure and terminology, thereby providing a common analytic framework that facilitates multicenter analyses 28,29 .”
Haug, M., Ilves, N., Umov, N., et al. (2026) · medRxiv · doi:10.64898/2026.04.22.26351461
Ng, H. X., Ortiz-Ricketts, T., Sandhu, T., et al. (2026) · doi:10.2196/preprints.98133
Tracking Temporal Variations in Predictive Models for Healthcare Claims Data
Ghantasala, K. K. (2025) · 2025 Artificial Intelligence x Humanities, Education, and Art (AIxHEART) · doi:10.1109/aixheart65685.2025.00015
Babli, S. A., Gairola, P., Ogbu, C. E., Longa, F. A., Rautaray, A., Alamu, O. S., & Rizvi, S. F. H. (2026) · Cureus · doi:10.7759/cureus.108550
“Third, we tested exclusively on publicly available, well-characterized datasets within established observational health data networks [30,31]; performance on proprietary institutional EHR systems with more heterogeneous quality issues, novel data schemas, or non-English clinical documentation may…”
Mohajerani, S., Leb, I., Gruendner, J., Overton, P., & Ganslandt, T. (2026) · JMIR Human Factors · doi:10.2196/preprints.103570
Baser, O. (2026) · Journal of health economics and outcomes research · doi:10.36469/jheor.2026.162896
Kesselmeier, M., Thalheim, T., Schmidt, F., Peschel, T., Palm, J., Strübing, A., Medek, A., Przybilla, J., Wermund, A. M., Maas, R., et al. (2026) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-026-03550-w
“…applied at the DIC might be the OMOP (Observational Medical Outcomes Partnership) common data model from the OHDSI (Observational Health Data Sciences and Informatics) consortium, which is an open community data standard, which is tailored to provide observational data for efficient analyses [74].”
Invited Commentary: Transforming Occupational Therapy Research With Artificial Intelligence
Wolpert, R., Liew, S., Pickett, K. A., & Wu, C.-Y. (2026) · OTJR Occupational Therapy Journal of Research · doi:10.1177/15394492261464096
Xu, T., & Leung, H. (2026) · Conference proceedings - Canadian Conference on Electrical and Computer Engineering · doi:10.1109/ccece68150.2026.11610166
“• Schema alignment (ATC vs. free-text): To reconcile extreme interoperability gaps between sites, we implemented a robust schema alignment process within the OMOP Common Data Model (CDM) framework [9], [18].”
InfluentialHealth Data Analysis: Definition, Evolution, and Future Prospects
Liu, B., & Natale, M. (2026) · IEEE Cloud Summit · doi:10.1109/cloudsummit68932.2026.00034
“As digital records proliferated, common data models were developed to standardize EHR and observational health data across institutions, enabling multi-site studies and population-level inference that had not previously been achievable [4], [5].”
Baskaran, B. S., Chalabianloo, N., Eddin, L. E., & Muanda, F. T. (2026) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/cjpp-2025-0331
Knowledge Graph–Guided Domain Generalization for Computational Phenotyping: A Tutorial
Yan, Z., Huang, Z., Wang, F., et al. (2026) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi69079.2026.00198
“In practice, high-level concept sets or standardized vocabularies can further improve stability across coding systems [7], [8].”
Global Adoption of openEHR Clinical Data Repositories: A Vendor and Community SurveyPreprint
Kohler, S., Meyer-Eschenbach, F., Vegas, X. M., et al. (2026) · medRxiv · doi:10.64898/2026.08.27.26361529
Bellucci, G., Gu, W., Rose, P. W., & Baranzini, S. E. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1883853 · Cited by 1
“ATLAS and CIRCE for cohort definition and the HADES packages for population-level estimation and prediction (Hripcsak et al., 2015; The Book of OHDSI, 2021) remain the community standard for validated, reproducible observational analysis.”
Lee, J., Jung, W., An, D., et al. (2026) · doi:10.2196/preprints.111367
Kanyike, F., Nanungi, A., Dickinson, H. A., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1907568
Yao, S., & Chen, X. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1962508
Big data: from signal to action
Youens, K. E., & Priest, E. (2026) · Baylor University Medical Center Proceedings · doi:10.1080/08998280.2026.2731826
What does good governance of federated biomedical data reuse look like? A scoping review protocol.
Maxwell, L., Stecher, M., Panagiotopoulou, M., & Falk, M. (2026) · Open Research Europe · doi:10.12688/openreseurope.24344.1
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).
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