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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Weaver, J. C., Voss, E. A., Cafri, G., Beyrau, K. K., Nashleanas, M., & Suruki, R. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02428-7 · Cited by 5
The PICU Data Collaborative: A Novel, Multi-Institutional, Pediatric Critical Care Dataset
Farris, R. W. D., Shah, S. S., Bennett, T. D., Brown, S. R., Cornell, T. T., Dziorny, A. C., Flynn, A., Grunwell, J. R., Heneghan, J. A., Kennedy, C. E., et al. (2025) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000003758 · Cited by 6
Association Between Neighborhood-Level Social Vulnerability and Hypertension Outcomes
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Tong, J., Reps, J., Luo, C., Lu, Y., Li, L., Ramírez‐Anguita, J. M., Brand, M. T., DuVall, S. L., Falconer, T., Fuentes, A. M., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01846-1 · Cited by 5
“Other decentralized initiatives aimed at facilitating international studies include The Consortium for Clinical Characterization of COVID-19 by Electronic Health Records (4CE) 20,21 ,akeyplayerinCOVID-19research,andtheObservationalHealth Data Sciences and Informatics (OHDSI) community 22 , a key stakeholder in the Eur…”
InfluentialOstropolets, A., Ryan, P. B., Schuemie, M. J., et al. (2022) · JMIR Public Health and Surveillance · doi:10.2196/33099 · Cited by 5
“Both data sources were mapped to the Observational Medical Outcomes Partnership Common Data Model [14].”
MethodKim, C., Lee, Y., Lee, E., You, S. C., Jang, J.-H., Park, R. W., & Park, H. (2022) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2022.06.009 · Cited by 5
Bräuner, K. B., Tsouchnika, A., Mashkoor, M., Williams, R. D., Rosen, A. W., Hartwig, M. F. S., Bulut, M., Dohrn, N., Rijnbeek, P. R., & Gögenür, I. (2024) · International Journal of Colorectal Disease · doi:10.1007/s00384-024-04607-w · Cited by 5
Trinh, N. T., Houghtaling, J., Bernal, F. L. M., et al. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105602 · Cited by 5
Ahmadi, F., Chalabianloo, N., McArthur, E., Omrani, M. A., Abdullah, S. S., García‐Bournissen, F., & Muanda, F. T. (2025) · Annals of Pharmacotherapy · doi:10.1177/10600280251320690 · Cited by 5
“18 To further enrich the data set, we added RxNorm Concept Unique Identifiers (RxCUIs), generic names, and Anatomical Therapeutic Chemical (ATC) classification system codes.”
The OHDSI Network in Ophthalmology—The Promise of Observational Health Data
Hribar, M. R., Cai, C. X., Goetz, K., & Hripcsak, G. M. (2025) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2025.0399 · Cited by 5
Wu, Q., Reps, J., Li, L., Zhang, B., Lu, Y., Tong, J., Zhang, D., Lumley, T., Brand, M. T., Zandt, M. V., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01781-1 · Cited by 5
Kim, Y., Ahn, I., Cho, H. N., Gwon, H., Kang, H. J., Seo, H., Choi, H., Kim, K., Jun, T. J., & Kim, Y. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106866 · Cited by 5
An interactive fitness-for-use data completeness tool to assess activity tracker data
Cho, S., Ensari, I., Elhadad, N., Weng, C., Radin, J. M., Bent, B., Desai, P. M., & Natarajan, K. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac166 · Cited by 4
Accelerating Food Allergy Research: Need for a Data Commons
Gupta, R. S., Sehgal, S., Wlodarski, M., Bilaver, L., Wehbe, F., Spergel, J. M., Wang, J., Ciaccio, C. E., Nimmagadda, S., Assa’ad, A., et al. (2023) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2023.02.003 · Cited by 6
External validity in distributed data networks
Webster‐Clark, M., Toh, S., Arnold, J., McTigue, K. M., Carton, T. W., & Platt, R. W. (2023) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5666 · Cited by 5
“…World Interrogation Network (DARWIN-EU) in Europe 10 ; a network built by the Observational Health Data Sciences and Informatics (OHDSI) community 11 ; the Asian pharmacoepidemiology network (AsPEN) 12 ; the Vaccine Safety Datalink (VSD) 13 ; and a European distributed network created for…”
BackgroundLee, Y. K., Lim, H. S., Choi, Y. I., et al. (2023) · Pharmaceuticals · doi:10.3390/ph16091213 · Cited by 5
“The OMOP-CDM represents healthcare data from a diverse source in a consistent and standardized manner [40].”
MethodWebster‐Clark, M., Filion, K. B., & Platt, R. W. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae015 · Cited by 5
Transfer learning via random forests: A one-shot federated approach
Xiang, P., Zhou, L., & Tang, L. (2024) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2024.107975 · Cited by 5
Sibert, N. T., Soff, J., Ferla, S. L., Quaranta, M., Kremer, A., & Kowalski, C. (2024) · Cancers · doi:10.3390/cancers16112069 · Cited by 5
“…the OMOP initiative, the OHDSI platform was built in 2014 as an open-sciences, multi-stakeholder collaborative to enhance the use of the OMOP CDM for large-scale “real-world evidence” research projects [24], and is up until now in charge of the maintenance and further development of the OMOP CDM.”
MethodDomalpally, A., Fickweiler, W., Levine, S. R., Goetz, K., VanderBeek, B. L., Lee, A., Sundstrom, J. M., Markel, D. S., & Sun, J. K. (2024) · Translational Vision Science & Technology · doi:10.1167/tvst.13.10.4 · Cited by 5
Quon, J. C., Long, C. P., Halfpenny, W., Chuang, A., Cai, C. X., Baxter, S. L., Daketi, V., Schmitz, A., Bahroos, N., Xu, B. Y., et al. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100666 · Cited by 5
Annotating Cohort Data Elements with OHDSI Common Data Model to Promote Research Reproducibility
Zhao, Y., Wang, Y., Wang, H. E., Yan, B., Shen, F., Peterson, K. J., Rocca, W. A., Sauver, J. L. S., & Liu, H. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621269 · Cited by 6
What is the first choice for blood pressure treatment?
Ives, C. W., & Oparil, S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)32461-4 · Cited by 5
Kim, S.-J., Jeong, C., Kim, T., et al. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-55190-2_54 · Cited by 5
Common Data Model-based Analysis of Selective Leukoreduction Protocol Compliance at Three Hospitals
Choi, S., Choi, S. J., Shin, J. W., et al. (2022) · Annals of Laboratory Medicine · doi:10.3343/alm.2023.43.2.187 · Cited by 5
“A CDM is an informational model that allows the transformation of data contained in different databases to a common format, in which all coding and vocabulary are prespecified and standardized [7].”
MethodRisks of complicated acute appendicitis in patients with psychiatric disorders
Kim, J., Yang, C., Joo, H. J., Park, R. W., Kim, G. E., Kim, D., Choi, J., Lee, J. H., Kim, E., Park, S., et al. (2022) · BMC Psychiatry · doi:10.1186/s12888-022-04428-7 · Cited by 5
“Data curation Observational Health Data Sciences and Informatics (OHDSI) is an international collaboration whose goal is to create and apply open-source data analytic solutions to an extensive network of health databases to improve human health and wellbeing [27].”
BackgroundTemporal Poisson Square Root Graphical Models.
Geng, S., Kuang, Z., Peissig, P. L., & Page, D. (2018) · PubMed · Cited by 6
Ontologies as nested facet systems for human–data interaction
Zhang, G., Tao, S., Zeng, N., et al. (2019) · Semantic Web · doi:10.3233/sw-190378 · Cited by 5
Cloud-based data pipeline orchestration platform for COVID-19 evidence-based analytics
Alarcon, M. L., Oruche, R., Pandey, A., & Calyam, P. P. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-90054-6.00003-9 · Cited by 5
Future Directions in Health Information Exchange
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A Knowledge Graph-based Approach for Exploring the U.S. Opioid EpidemicPreprint
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Evaluation of crowdsourced mortality prediction models as a framework for assessing AI in medicinePreprint
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Statistical approaches in ambispective cohort studies: Challenges and methodological insights
Machiraju, P. R., & Jeyapal, G. P. (2025) · Perspectives in Clinical Research · doi:10.4103/picr.picr_190_25 · Cited by 5
“[37,38] To further account for heterogeneity, meta-regression and mixed-model techniques allow for analysis across subgroups defined by multiple biomarkers or variables.”
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Novel Hemodynamic Markers and Kidney Function in Patients with Acute Decompensated Heart Failure
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“Study data were extracted from a commercial EHR and transformed to the Observational Medical Outcomes Partnership (OMOP) common data model maintained by the Observational Health Data Sciences and Informatics consortium.(26) Weill Cornell Medicine Internal Review Board JAMIA Open, 2020, Vol.”
MethodA semi-automatic methodology for analysing distributed and private biobanks
Almeida, J. R., Pratas, D., & Oliveira, J. L. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104180 · Cited by 4
Moreno‐Martos, D., Verhamme, K. M. C., Ostropolets, A., Kostka, K., Duarte‐Salles, T., Prieto‐Alhambra, D., Alshammari, T. M., Alghoul, H., Ahmed, W., Blacketer, C., et al. (2022) · Wellcome Open Research · doi:10.12688/wellcomeopenres.17403.1 · Cited by 4
“All data for were standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) 8,9 .”
MethodImplementation of a Data Warehouse in Primary Care: First Analyses with Elderly Patients
Fruchart, M., Quindroit, P., Patel, H., et al. (2022) · Studies in health technology and informatics · doi:10.3233/shti220510 · Cited by 4
“By using the OMOP-CDM, we can also integrate data from other software editors for further collaborations in a multisite network.”
MethodInfluentialAn ordinal severity scale for COVID-19 retrospective studies using Electronic Health Record data
Khodaverdi, M., Price, B. S., Porterfield, J. Z., Bunnell, H. T., Vest, M. T., Anzalone, A. J., Harper, J. R., Kimble, W., Moradi, H., Hendricks, B. M., et al. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac066 · Cited by 5
“Source data for N3C are harmonized into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) which was designed by Observational Health Data Sciences and Informatics (OHDSI) specifically for conducting research and analyses, facilitating patient classification in an interoperable fashion from…”
MethodMoreno‐Martos, D., Verhamme, K. M. C., Ostropolets, A., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.17403.3 · Cited by 4
Wu, Q., Schuemie, M. J., Suchard, M. A., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104476 · Cited by 4
Cano, I., Arismendi, E., & Borrat, X. (2023) · European Respiratory Society eBooks · doi:10.1183/2312508x.10000623 · Cited by 4
How Are Leading Research Institutions Engaging with Data Sharing Tools and Programs?
Hall, E. S., Melton, G. B., Payne, P., Dorr, D. A., & Vawdrey, D. K. (2023) · PubMed Central · Cited by 4
Innovations in pharmacovigilance studies of medicines in older people
Ellett, L. M. K., Janetzki, J. L., Lim, R., Laba, T., & Pratt, N. L. (2024) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.16049 · Cited by 7
“…Drug Effect Studies (CNODES), 70 Sentinel (US Food and Drug Administration), 71,72 the Observational Health Data Sciences and Informatics (ODHSI) network 73 and the Asian Pharmacoepidemiology Network (AsPEN, a special interest group of the International Society of Pharmacoepidemiology).”
NLP Applications—Clinical Documents
Wang, Y. (2024) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-55865-8_12 · Cited by 4
Tracking provenance in clinical data warehouses for quality management
Johns, M., Baum, L., & Praßer, F. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105690 · Cited by 9
Development of machine learning-based mpox surveillance models in a learning health system
Nieva, H. R., Zucker, J. E., Tucker, E., McLean, J., DeLaurentis, C., Gunaratne, S. H., & Elhadad, N. (2025) · Sexually Transmitted Infections · doi:10.1136/sextrans-2024-056382 · Cited by 5
DiSantostefano, R. L., Berlin, J. A., Chuang‐Stein, C., Quartey, G., Eichenbaum, G. M., & Levitan, B. S. (2016) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2016.1225596 · Cited by 4
Explorative Analyses of Nursing Research Data
Kim, H., Jang, I., Quach, J., Richardson, A., Kim, J., & Choi, J. (2016) · Western Journal of Nursing Research · doi:10.1177/0193945916673815 · Cited by 4
Sanchez-Pinto, L. N., & Dziorny, A. C. (2020) · Pediatric Critical Care Medicine · doi:10.1097/pcc.0000000000002366 · Cited by 4
“…allow users from different institutions to share digital resources (e.g., the markdown scripts for a statistical analysis or the programming code to calculate a severity of illness score) and can facilitate the merging of multicenter data and the development of federated research networks (6, 7).”
BackgroundKim, H. I., Park, S. Y., & Shin, H. P. (2021) · Scientific Reports · doi:10.1038/s41598-021-86197-z · Cited by 4
Fife, D., Blacketer, C., Knight, K., & Weaver, J. C. (2021) · Drugs - Real World Outcomes · doi:10.1007/s40801-021-00267-2 · Cited by 6
“The original CCAE database has been converted to the observational medical outcomes partnership Common Data Model [25, 26] to allow for consistent analyses using standardized analytic and statistical software [27–29].”
MethodLi, X., Liu, H., Kury, F. S. P., Yuan, C., Butler, A. M., Sun, Y., Ostropolets, A., Xu, H., & Weng, C. (2021) · PubMed Central · Cited by 5
Kim, T., Noh, S.-H., Kim, Y. R., et al. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104759 · Cited by 4
Hip Fracture Risk After Treatment with Tramadol or Codeine: An Observational Study
Voss, E. A., Ali, S. R., Singh, A., Rijnbeek, P. R., Schuemie, M. J., & Fife, D. (2022) · Drug Safety · doi:10.1007/s40264-022-01198-9 · Cited by 4
Pratt, N., Camacho, X., Vajdic, C. M., et al. (2021) · Minerva Access (University of Melbourne) · doi:10.23889/ijpds.v6i1.1726 · Cited by 4
“This will allow us to make use of open-source software and analytic code that are available through the large community of practice using the OMOP CDM under the auspices of the OHDSI network.”
BackgroundMethodInfluentialDahlweid, F.-M., Rausch, D., Hinske, L. C., et al. (2022) · Studies in health technology and informatics · doi:10.3233/shti220919 · Cited by 4
Glyph-based visualization of health trajectories
Siirtola, H., Tabuenca, J. G., Raisamo, R., et al. (2022) · International Conference Information Visualisation (IV) · doi:10.1109/iv56949.2022.00075 · Cited by 4
“This has been later accomplished in OHDSI project 3 [10], [11].”
BackgroundMethodSong, I., Kim, M., Choi, H., Kim, J. H., Lim, K. H., Yoon, H. S., Rah, Y. C., Park, E., Im, G. J., Song, J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-39316-x · Cited by 5
Generalization of finetuned transformer language models to new clinical contexts
Xie, K., Terman, S. W., Gallagher, R. S., Hill, C. E., Davis, K. A., Litt, B., Roth, D., & Ellis, C. A. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad070 · Cited by 5
“Furthermore, EHRs from multiple institutions can be pooled together to drive large-scale multicenter research.(5) Much of the information in the EHR is stored in the raw text of clinic notes written by healthcare providers during and following an office visit or procedure.”
BackgroundPark, J. K., Kim, M. H., & Lee, H. W. (2023) · Neurocritical Care · doi:10.1007/s12028-023-01889-w · Cited by 4
“…from January 1, 2001, to January 1, 2018, consisting of a standardized structure of patient-level data, such as personal characteristics (age, sex, and residence) and medical records (diagnoses, prescribed drugs, medical procedures, laboratory test results, and self-reported medical history) [16].”
Integrated Real-World Data Warehouses Across 7 Evolving Asian Health Care Systems: Scoping Review
Shau, W., Santoso, H., Jip, V., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/56686 · Cited by 4
“Hence, open data initiatives and international collaborations such as the Observational Health Data Sciences and Informatics and the HTAsiaLink, respectively, are crucial in this regard [40,41].”
BackgroundData discovery with DATS: exemplar adoptions and lessons learned
Gonzalez-Beltran, A. N., Campbell, J. P., Dunn, P., Guijarro, D., Ionescu, S., Kim, H., Lyle, J. A., Wiser, J. A., Sansone, S., & Rocca‐Serra, P. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx119 · Cited by 4
Federated Learning for Data Mining in Healthcare
Sharma, S., Kesarwani, A., Maheshwari, S., & Rai, B. K. (2022) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-030-85559-8_16 · Cited by 4
Lavallee, M., Yu, T. V., Evans, L., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01765-1 · Cited by 4
“Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance.”
MethodInfluentialFelmeister, A. S., Waanders, A. J., Leary, S. E. S., et al. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217983 · Cited by 4
Characterizing non-heroin opioid overdoses using electronic health records
Averitt, A., Slovis, B., Tariq, A. A., Vawdrey, D., & Perotte, A. J. (2019) · JAMIA Open · doi:10.1093/jamiaopen/ooz063 · Cited by 3
Data Consult Service: Can we use observational data to address immediate clinical needs?
Ostropolets, A., Zachariah, P., Ryan, P. B., Chen, R., & Hripcsak, G. M. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab122 · Cited by 4
Yan, Z., Zachrison, K. S., Schwamm, L. H., Estrada, J. J., & Duan, R. (2022) · medRxiv · doi:10.1101/2022.03.07.22271469 · Cited by 4
The Role of Pharmacoepidemiology in Industry
Gatto, N. M., Sobel, R. E., Geier, J. L., Mo, J., Bate, A., & Reynolds, R. F. (2019) · Pharmacoepidemiology · doi:10.1002/9781119413431.ch7 · Cited by 6
Pedrera‐Jiménez, M., Spanish Expert Group on EHR standards, Kalra, D., et al. (2022) · doi:10.36227/techrxiv.19746484.v1 · Cited by 4
Big Data in Laboratory Medicine – FAIR Quality for AI?Preprint
Blatter, T. U., Witte, H., Nakas, C. T., & Leichtle, A. B. (2022) · Preprints.org · doi:10.20944/preprints202204.0078.v2 · Cited by 3
Sharing and Reusing Computable Phenotype DefinitionsPreprint
Visweswaran, S., Zhang, L. Y., Bui, K., Sadhu, E. M., Samayamuthu, M. J., & Morris, M. M. (2023) · medRxiv · doi:10.1101/2023.09.17.23295681 · Cited by 4
“The OMOP Common Data Model is a widely used common data model that is provided by the Observational Medical Outcomes Partnership (OMOP) and is used to harmonize longitudinal patient data with standardized medical terminologies (6).”
MethodWang, Y., Hilsman, J., Li, C., Morris, M. M., Heider, P. M., Fu, S., Kwak, M. J., Wen, A., Applegate, J. R., Wang, L., et al. (2025) · medRxiv · doi:10.1101/2025.01.24.25321096 · Cited by 4
Turki, H., Chebil, K., Dossou, B. F. P., Emezue, C. C., Owodunni, A. T., Taieb, M. A. H., & Aouicha, M. B. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38448 · Cited by 3
“…of data models, such as those utilized by OHDSI for standardizing and harmonizing electronic health record data, plays a crucial role in ensuring interoperability and facilitating large-scale observational studies and comparative effectiveness research across diverse healthcare settings [26].”
MethodMasison, J., Lehmann, H. P., & Wan, J. (2024) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2024.08.025 · Cited by 3
Data harmonization framework for neonatal hypoxic-ischemic encephalopathy studies
Hsiao, C., Foster, A. N., McDonald, S. A., Vyas, R., Ashraf, A., Bao, R., Tran, L., Kesri, A., Darzidehkalani, E., Soldatelli, M. D., et al. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf086 · Cited by 4
“…such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) by Observational Health Data Sciences and Informatics (OHDSI), 31 can be integrated with electronic healthcare records (EHR) systems, such as Epic’s analytic database storing as a Microsoft SQL database (Clarity)…”
No mathematical shortcuts for standardization or harmonization of laboratory measurements
Winter, C., Ganslandt, T., & Bietenbeck, A. (2018) · Journal of Laboratory Medicine · doi:10.1515/labmed-2017-0122 · Cited by 3
Park, S., Bong, J. W., Park, I., et al. (2022) · Applied Sciences · doi:10.3390/app122111250 · Cited by 3
Bui, M.-H., Lee, D. Y., Park, S. J., et al. (2023) · Journal of Korean Medical Science · doi:10.3346/jkms.2023.38.e174 · Cited by 3
“20 The OMOP-CDM provides a comprehensive ability to capture data in the same manner across places which enhance scalability of studies and to analyze data in a rigorous way which enables reproducible research.”
BackgroundSenior Data Consultant, M., & Devineni, S. K. (2023) · Journal of Artificial Intelligence & Cloud Computing · doi:10.47363/jaicc/2023(2)188 · Cited by 3
Lee, D. Y., Kim, C., Kim, J., Yun, J., Lee, Y., Chui, C. S. L., Son, S. J., Park, R. W., & You, S. C. (2024) · BMC Psychiatry · doi:10.1186/s12888-024-05578-6 · Cited by 3
“Specifically, we investigated whether the use of ACE inhibitors increased the risk of schizophrenia compared with the use of angiotensin receptor blockers (ARBs) or thiazide diuretics in the US and Korea across the Observational Health Data Sciences and Informatics (OHDSI) network [10].”
BackgroundWen, A., Wang, L., He, H., Fu, S., Liu, S., Hanauer, D. A., Harris, D. R., Kavuluru, R., Zhang, R., Natarajan, K., et al. (2024) · JMIR Medical Informatics · doi:10.2196/49997 · Cited by 3
Kawakami, Y., Matsuda, T., Hidaka, N., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae094 · Cited by 3
Schulz, N., Carus, J., Wiederhold, A. J., Johanns, O., Peters, F., Rath, N. B., Rausch, K., Holleczek, B., Katalinic, A., the AI-CARE Working Group, et al. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02257-8 · Cited by 7
“Whereas OMOP CDM defines the database structure, specific vocabularies developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative standardize the content of the database [6, 7].”
MethodFu, Y., Sun, H., Wu, Y., et al. (2024) · IEEE Access · doi:10.1109/access.2024.3442372 · Cited by 3
Pyun, J., Lee, I., Lee, K. B., Kim, M. H., Park, C., & Yang, H. (2024) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2024.23.4.202 · Cited by 4
“14,15 Although most Korean hospitals use an electrical health record (EHR) system, numerous Korean diagnoses, medications, and procedural codes are not compatible with the international coding system.”
BackgroundMethodLee, J.-H., Lee, H. W., Lee, H. J., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-95941-8 · Cited by 3
Communication-Efficient Distributed Estimation and Inference for Cox’s Model
Bayle, P., Fan, J., & Lou, Z. (2025) · Journal of the American Statistical Association · doi:10.1080/01621459.2025.2516820 · Cited by 3
“As an illustration, the Observational Health Data Sciences and Informatics (OHDSI) network is an international collaboration that only transmits aggregated information from local health databases to a coordinating center without sharing any patient-level data (Hripcsak et al. 2015).”
Omrani, M. A., Tsobo, C., Chalabianloo, N., Ahmadi, F., Abdullah, S. S., & Muanda, F. T. (2025) · Journal of Psychopharmacology · doi:10.1177/02698811251344684 · Cited by 4
“We used regular expressions to normalize these names during preprocessing and matched them against databases such as RxNorm and Observational Health Data Sciences and Informatics (Hripcsak et al., 2015).”
Wang, Y., Zhang, D., Tong, J., et al. (2025) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaf138 · Cited by 3
Bräuner, K. B., Mashkoor, M., Gögenur, M., Lin, V. A., Oppermann, C., & Gögenür, I. (2025) · Annals of Surgical Oncology · doi:10.1245/s10434-025-18353-y · Cited by 3
Changela, S., Hadidchi, R., Wu, S., Ali, E., Liu, A., Peng, T., & Duong, T. Q. (2025) · Diagnostics · doi:10.3390/diagnostics15202605 · Cited by 3
“Deidentified clinical data were harmonized to the OMOP Common Data Model (CDM) version 6, which maps heterogeneous sources to standardized vocabularies, enabling consistent analyses across electronic health records, claims data, and coding systems such as ICD-10 [33].”
Clinical Research Informatics: Supporting the Research Study Lifecycle
Johnson, S. B. (2017) · Yearbook of Medical Informatics · doi:10.1055/s-0037-1606503 · Cited by 3
A Second Opinion From Observational Data on Second-line Diabetes Drugs
Callahan, A., & Shah, N. H. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.6119 · Cited by 3
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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