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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Rider, N. L. (2020) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000000963 · Cited by 3
Computer Informatics for Infection Control
Lin, M. Y., & Trick, W. E. (2021) · Infectious Disease Clinics of North America · doi:10.1016/j.idc.2021.04.010 · Cited by 3
Payne, P., Embí, P. J., & Cimino, J. J. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_27 · Cited by 4
Combining heterogeneous patient-level data into tranSMART to support multicentre studies
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“Currently, this organisation supports an ecosystem with several open-source solutions to perform medical product safety surveillance using observational databases [3].”
BackgroundEcosystem of Patient-Centered Research and Information System Design
Hsueh, P.-Y. S. (2022) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-07696-1_16 · Cited by 3
Schüttler, C., Zerlik, M., Gruendner, J., et al. (2023) · JMIR Human Factors · doi:10.2196/43782 · Cited by 3
“…for Integrating Biology and the Bedside (i2b2) [28] (SUS score=59.83); ATLAS, developed by Observational Health Data Sciences and Informatics (OHDSI) [29] (SUS score=27.81); and the GBA Sample Locator [30] (SUS score=77.03), whose user-friendliness was examined in a study published in 2021 [3].”
InfluentialBebbington, E., Miles, J., Peck, M. D., Singer, Y. M., Dunn, K., & Young, A. E. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-066512 · Cited by 3
Sharathkumar, A. A., Wendt, L. H., Ortman, C., Srinivasan, R., Chute, C. G., Chrischilles, E. A., Takemoto, C. M., Wilcox, A. B., Lee, A. M., Graves, A., et al. (2023) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2023.04.040 · Cited by 3
Banda, J. M., Shah, N. H., & Periyakoil, V. S. (2023) · JAMIA Open · doi:10.1093/jamiaopen/ooad043 · Cited by 4
“Lastly, our case study phenotypes were evaluated on a single dataset, and, in future work, we plan to fully leverage the OHDSI community to conduct a network study examining racial bias of phenotypic algorithms.(62,63) One major item to note is that self-reported race is usually error prone and very often incomplete (…”
Methodde Groot, R., Puttmann, D., Fleuren, L. M., et al. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105200 · Cited by 3
Towards a Bavarian Oncology Real World Data Research Platform
Ziegler, J., Gruendner, J., Rosenau, L., et al. (2023) · Studies in health technology and informatics · doi:10.3233/shti230696 · Cited by 3
Kleinert, P., Gebhardt, M. L., Buckow, K., et al. (2024) · Studies in health technology and informatics · doi:10.3233/shti240751 · Cited by 3
Gutiérrez‐Sacristán, A., Makwana, S., Dionne, A., Mahanta, S., Dyer, K. J., Serrano, F., Watrin, C. H., Pages, P., Mousavi, S., Degala, A. K., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105337 · Cited by 9
“The adoption of universal common data models, such as ODHSI OMOP, 23 i2b2 40 or PIC-SURE 41 could speed up the design and implementation of automatic data approaches like the one described in this study.”
MethodLöffler, L., Mashkoor, M., Gögenür, I., & Gögenur, M. (2024) · International Journal of Colorectal Disease · doi:10.1007/s00384-024-04735-3 · Cited by 3
Justesen, T. F., Orhan, A., Rosen, A. W., Gögenur, M., & Gögenür, I. (2024) · Annals of Surgery Open · doi:10.1097/as9.0000000000000499 · Cited by 4
Current status of anti-obesity medications and performance, an EHR based survey
Ruan, X., Li, R., Wang, L., Lu, S., Wen, A., Murali, S. B., & Liu, H. (2026) · BMC Endocrine Disorders · doi:10.1186/s12902-026-02423-7 · Cited by 3
Basile, A. O., Verma, A., Tang, L. A., Serper, M., Scanga, A. E., Farrell, A., Destin, B., Carr, R. M., Anyanwu‐Ofili, A., Rajagopal, G., et al. (2024) · Clinical and Translational Science · doi:10.1111/cts.70105 · Cited by 3
“16,17 OHDSI maintains the OMOP CDM, 18 a data standard that normalizes observational data to enable efficient, reproducible analyses.”
Why we need a representative organization of academic PRM in Europe, and why we need it now
Stucki, G., Stam, H. J., Gimigliano, F., et al. (2025) · Journal of Rehabilitation Medicine · doi:10.2340/jrm.v57.42369 · Cited by 3
Finding a constrained number of predictor phenotypes for multiple outcome prediction
Reps, J., Wong, J., Fridgeirsson, E. A., et al. (2025) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2024-101227 · Cited by 3
“These outcomes were chosen because the Observational Health Data Science and Informatics (OHDSI) collaboration 13 has previously created phenotypes for them.”
Towards Assigning Diagnosis Codes Using Medication History
Sagi, T., Hansen, E. R., Hose, K., et al. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59137-3_19 · Cited by 3
Automatic Data Transfer from OMOP-CDM to REDCap: A Semantically-Enriched Framework
Girani, E., Gabetta, M., Alloni, A., et al. (2021) · Studies in health technology and informatics · doi:10.3233/shti210805 · Cited by 3
“OMOP/OHDSI (Observational Medical Outcomes Partnership - Observational Health Data Sciences and Informatics) is an ideal middleware to be interposed between EHR and eCRF, in order to decouple the complexity of the clinical sources from the target eCRF [2].”
BackgroundMaking EHRs Reusable: A Common Framework of Data Operations
Pedrera‐Jiménez, M., Garcia, N., Rubio, P., et al. (2021) · Studies in health technology and informatics · doi:10.3233/shti210831 · Cited by 3
“They have not been modeled for a single purpose, but as a data warehouse for multiple secondary uses, e.g., i2b2 (tranSMART Foundation) [13], used in TriNetX Platform (federated network for clinical trials) [14], and OMOP CDM (OHDSI) [15], used in EHDEN Consortium (federated network for observational research) [16].”
MethodHan, S., Son, M., Choi, B., et al. (2021) · BioMed Research International · doi:10.1155/2021/5504873 · Cited by 3
Justesen, T. F., Gögenur, M., Clausen, J. S. R., Mashkoor, M., Rosen, A. W., & Gögenür, I. (2023) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2023.03.223 · Cited by 3
On Mapping Textual Queries to a Common Data Model
Liu, S., Wang, Y., Hong, N., et al. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.63 · Cited by 3
Computer-aided Big Healthcare Data (BHD) Analytics
Shaikh, T. A., & Ali, R. (2020) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-83909-099-820201010 · Cited by 3
Automated Generation of Individual and Population Clinical Pathways with the OMOP Common Data Model
Boudis, F., Clément, G., Bruandet, A., et al. (2021) · Studies in health technology and informatics · doi:10.3233/shti210152 · Cited by 3
“It allows to standardize the data structure and vocabulary of data, which are originally collected by different software and characterized by heterogeneous vocabularies, depending on the country [7].”
BackgroundDistributed learning and prediction modelling in radiation oncology
Deist (2019) · doi:10.26481/dis.20190405td · Cited by 3
Intelligent Artificial Intelligence
Kao, D. P. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.119.006021 · Cited by 3
Maro, J. C., & Toh, S. (2022) · American Journal of Epidemiology · doi:10.1093/aje/kwac096 · Cited by 3
Pedrera‐Jiménez, M., Spanish Expert Group on EHR standards, Kalra, D., et al. (2022) · doi:10.36227/techrxiv.19746484 · Cited by 3
Remaki, A., Playe, B., Bernard, P., Vittoz, S., Doutreligne, M., Châtellier, G., Audureau, É., Kempf, E., Porcher, R., & Bey, R. (2023) · medRxiv · doi:10.1101/2023.08.17.23294220 · Cited by 4
“[31] Data are integrated into the CDW on a daily basis, and this study was conducted on July 31 st , 2023.”
MethodHurwitz, E. G., Butzin-Dozier, Z., Master, H., O’Neil, S. T., Walden, A., Holko, M., Patel, R. C., & Haendel, M. A. (2023) · medRxiv · doi:10.1101/2023.10.13.23296965 · Cited by 3
Jing, N., Liu, X., Wu, Q., Rao, S., Mejías, A., Maltenfort, M., Schuchard, J., Lorman, V., Razzaghi, H., Webb, R. D., et al. (2024) · medRxiv · doi:10.1101/2024.01.26.24301827 · Cited by 3
The Impact of Evolving Endometriosis Guidelines on Diagnosis and Observational Health StudiesPreprint
Nieva, H. R., Kashyap, A., Voss, E. A., Ostropolets, A., Anand, A., Ketenci, M., DeFalco, F., Choi, Y. S., Li, Y., Allen, M. N., et al. (2024) · medRxiv · doi:10.1101/2024.12.13.24319010 · Cited by 4
Rappoport, N., Livne, G., Perry Cohen, N., Makover, N., Eshel-Geva, H., Kapach, H., Hadad, T., Alon, Y., Rubin, R., Chai, S., et al. (2025) · PLOS One · doi:10.1371/journal.pone.0334848 · Cited by 3
Understanding data differences across the ENACT federated research network
Wang, T. D., Henderson, D. W., Weber, G. M., et al. (2026) · JAMIA Open · doi:10.1093/jamiaopen/ooag038 · Cited by 2
Kim, S., Kim, S., Kim, C., Chang, J., Park, R. W., Kim, K. W., & You, S. C. (2025) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.3614 · Cited by 2
Duftschmid, G., Katsch, F., Ciortuz, G., et al. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02980-2 · Cited by 2
Gap Analysis of Standard Automated Perimetry Concept Representation in Medical Terminologies
Hallaj, S., Halfpenny, W., Radgoudarzi, N., et al. (2025) · Journal of Glaucoma · doi:10.1097/ijg.0000000000002575 · Cited by 2
Feng, L., Wang, W., Yin, C., Li, J., Zhang, X., Chang, X., Feng, Z., Zandt, M. V., You, S. C., Seager, S. L., et al. (2025) · Health Data Science · doi:10.34133/hds.0209 · Cited by 2
“All the databases were standardized to Observational Medical Outcomes Partnership Common Data Model (OMOP CDM version 5.3), which maps international coding systems into standard vocabulary concepts [ 29 ].”
InfluentialAllen, K. S., Park, S., Price, J., Ning, X., & Schleyer, T. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.105971 · Cited by 2
Choi, B., Oh, A. R., Park, J., et al. (2025) · Korean Journal of Anesthesiology · doi:10.4097/kja.24854 · Cited by 2
“Patient-level EMR data were standardized, de-identified into the standard vocabulary of the Observational Medical Outcomes Part-nership Common Data Model [10], and stored at the associated hospital.”
Puca, P., Lopetuso, L. R., Laterza, L., Papa, A., Danese, S., Cesario, A., Damiani, A., Gasbarrini, A., Arcuri, G., & Scaldaferri, F. (2025) · Best Practice & Research Clinical Gastroenterology · doi:10.1016/j.bpg.2025.102050 · Cited by 3
The challenges of integrating diverse data sources: A case study in major depression
Lupton‐Smith, C., Yu, W., Zhao, C., Jeon, K., Ringlein, G., Wang, Q., Lemoto, E., Nguyen, T. Q., Gagliardi, J. P., Zandi, P. P., et al. (2025) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-025-00363-8 · Cited by 2
Jung, G., Lee, J., Gho, S., Han, Y., Choi, B., Cho, J. W., Kim, J., & Lee, G.-H. (2025) · Seizure · doi:10.1016/j.seizure.2025.10.004 · Cited by 2
Lima, B., Hasan, F., Kannan, P., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1679408 · Cited by 2
Hu, F., Tong, J., Gardner, M., Adamson, C. L., Adler, S., Alexander‐Bloch, A. F., Anagnostou, E., Anderson, K. M., Areces-Gonzalez, A., Astle, D. E., et al. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf625 · Cited by 2
“…Observational Health Data Sciences and Informatics (OHDSI), the National Patient-Centered Clinical Research Network (PCORnet), the Sentinel Initiative, and the NIH-funded Health Care Systems Research Collaboratory (Brown et al. 2013, Collins et al. 2014, Hripcsak et al. 2015, Platt et al. 2018).”
EC2Seq2Sql: Patient-trial matching with LLM agents
Yang, L., Han, Y., Liu, L., Jiang, X., Li, Y., Huang, J., & Su, Q. (2026) · PLoS ONE · doi:10.1371/journal.pone.0341827 · Cited by 5
“It maps free-text eligibility criteria to a standardized semantic model and generates SQL queries compatible with clinical databases such as the OMOP Common Data Model (CDM) [19].”
Raguram, K. H., Sidhu, M., Omrani, M. A., Baskaran, B. S., Chalabianloo, N., Chhabra, M., Sampasa‐Kanyinga, H., & Muanda, F. T. (2026) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkag083 · Cited by 2
MEDS — An Emerging Data Standard and Ecosystem for Health AI Research
McDermott, M. B. A., Steinberg, E., Fries, J., van de Water, R. P., Pang, C., Rockenschaub, P., Renc, P., Oh, J. H., Stankevičiūtė, K., Xu, J., et al. (2026) · NEJM AI · doi:10.1056/aira2501253 · Cited by 3
Digital health ecosystems: Use of informatics, connectivity and system interoperability
Hovenga, E. J. S., & Lowe, C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-816977-3.00009-5 · Cited by 2
Ha, J. H., Lee, E., Lee, D. Y., Cho, Y. H., Lee, H., Park, B., & Son, S. J. (2020) · 신경정신의학 · doi:10.4306/jknpa.2020.59.3.243 · Cited by 2
Datta, S., Sachs, J. P., Cruz, H. F., et al. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab048 · Cited by 2
Hersh, W. (2020) · Health informatics · doi:10.1007/978-3-030-47686-1_2 · Cited by 2
Semantic Data Visualisation for Biomedical Database Catalogues
Pereira, A., Almeida, J. R., Lopes, R. P., & Oliveira, J. L. (2022) · Healthcare · doi:10.3390/healthcare10112287 · Cited by 2
Visual analytics in public health
Preim, B., Raidou, R. G., Smit, N. N., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-822962-0.00019-5 · Cited by 2
Cazzolato, M. T., Gutierrez, M. A., Traina, C., et al. (2023) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms58004.2023.00202 · Cited by 2
“As future work, GraF-EDA can potentially benefit from re-modeling the data into a Common Data Model (CDM) from the medical domain ( e.g. OMOP-CDM [17]).”
MethodLi, M., Itzel, T., Montagut, N. E., et al. (2023) · Scandinavian Journal of Gastroenterology · doi:10.1080/00365521.2023.2239974 · Cited by 2
“Local health data from diverse sources within OHDSI network were translated in a consistent and standardized way, namely to the Observational Medical Outcomes Partnership (OMOP) Common Data Model with defined vocabularies and coding system [17].”
BackgroundMethodArinze, J. T., de Ridder, M. A. J., Vojinović, D., et al. (2023) · Drug Safety · doi:10.1007/s40264-023-01354-9 · Cited by 2
“All databases were standardised and mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which was developed by the Observational Health Data Sciences and Informatics (OHDSI) initiative to facilitate data harmonisation and standardised data analytics [7].”
MethodA High-Fidelity Combined ATC-Rxnorm Drug Hierarchy for Large-Scale Observational Research
Ostropolets, A., Talapova, P., de Wilde, M., et al. (2024) · Studies in health technology and informatics · doi:10.3233/shti230926 · Cited by 2
Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes
Ranallo, P. A., Southwell, B. J., Tignanelli, C. J., Johnson, S., Krueger, R. A., Sevareid-Groth, T., Carvel, A., & Melton, G. B. (2024) · Studies in health technology and informatics · doi:10.3233/shti230929 · Cited by 2
Kim, H. (2024) · Journal of Yeungnam Medical Science · doi:10.12701/jyms.2023.01256 · Cited by 2
Boeker, M., Zöller, D., Blasini, R., Macho, P., Helfer, S., Behrens, M., Prokosch, H., & Gülden, C. (2024) · Trials · doi:10.1186/s13063-024-07918-z · Cited by 4
“To accomplish this, we developed software which leverages the OMOP Common Data Model (CDM) [18] and HL7 FHIR to provide an interoperable and open technical infrastructure for supporting the patient recruitment process [19].”
MethodFrench, M. A., Hartman, P., Hayes, H., Ling, L., Magel, J. S., & Thackeray, A. (2024) · Applied Clinical Informatics · doi:10.1055/a-2401-3688 · Cited by 2
Mapping OMOP-CDM to RDF: Bringing Real-World-Data to the Semantic Web Realm
Chytas, A., Bassiliades, N., & Natsiavas, P. (2024) · Studies in health technology and informatics · doi:10.3233/shti240674 · Cited by 2
Identifying pathways to the prevention of dementia: the Netherlands consortium of dementia cohorts
Oomens, J. E., Moonen, J. E. F., Vos, S. J. B., Beran, M., Mateus, P., Deyn, P. P. D., van der Flier, W. M., Geerlings, M. I., Huisman, M. A., Ikram, M. A., et al. (2025) · BMC Neurology · doi:10.1186/s12883-024-03995-4 · Cited by 2
Park, D., Seo, S. I., Kang, J. G., Shin, W. G., Kim, J., Lee, K. J., & Park, S. W. (2025) · Pancreatology · doi:10.1016/j.pan.2025.09.032 · Cited by 2
Molecularly Targeted Cancer Medications and Kidney Health
Ziolkowski, S., Long, J., Zhong, Y., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.41221 · Cited by 2
Deformable phrase level attention: A flexible approach for improving AI based medical coding
Metzner, C., Gao, S. B., Herrmannová, D., Gounley, J. P., & Hanson, H. A. (2025) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2025.103299 · Cited by 2
Kim, H. I., Yoon, J. Y., Kwak, M. S., et al. (2020) · Digestive Diseases and Sciences · doi:10.1007/s10620-020-06494-x · Cited by 2
“However, it is difficult to characterize or analyze data from HIRA-NPS and each hospital’s data (especially international hospital data) in the same way or using the same tools because healthcare data sets are stored in databases that are built using a wide variety of data models and, often, local terminologies [14, 1…”
MethodAutoMap: Automatic Medical Code Mapping for Clinical Prediction Model Deployment
Wu, Z., Xiao, C., Glass, L. M., Liebovitz, D., & Sun, J. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-26390-3_29 · Cited by 2
Data Governance and Strategies for Data Integration
Marsolo, K. A., & Kirkendall, E. S. (2016) · Translational bioinformatics · doi:10.1007/978-981-10-1104-7_6 · Cited by 2
Other Sources of Information for Monitoring Drug Safety: Now and in the Future
Tuccori, M., & Wallberg, M. (2016) · Springer eBooks · doi:10.1007/978-3-319-40400-4_17 · Cited by 2
Diaz‐Garelli, F., Johnson, T. R., Rahbar, M. H., & Bernstam, E. V. (2021) · PubMed Central · Cited by 2
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.17403.2 · Cited by 2
Duraku, L. S., Hoogendam, L., Hundepool, C. A., Power, D., Rajaratnam, V., Slijper, H. P., Feitz, R., Zuidam, J. M., & Selles, R. W. (2022) · Journal of Plastic Reconstructive & Aesthetic Surgery · doi:10.1016/j.bjps.2022.02.065 · Cited by 2
Evaluation of Criteria2Query: Towards Augmented Intelligence for Cohort Identification
Liu, C., Liu, H., Ta, C., Roger, J., Butler, A. M., Lee, J., Kim, J., Shang, N., & Weng, C. (2022) · Studies in health technology and informatics · doi:10.3233/shti220082 · Cited by 2
Pratt, N. L., Camacho, X., Vajdic, C. M., Degenhardt, L. J., Laba, T., Hillen, J. B., Etherton‐Beer, C., Preen, D. B., Jorm, L. R., Donnolley, N. R., et al. (2022) · International Journal for Population Data Science · doi:10.23889/ijpds.v6i3.1726 · Cited by 5
Perceived Risk of Re-Identification in OMOP-CDM Database: A Cross-Sectional Survey
Tak, Y. W., You, S. C., Han, J. H., Kim, S. S., Kim, G.-T., & Lee, Y. (2022) · Journal of Korean Medical Science · doi:10.3346/jkms.2022.37.e205 · Cited by 2
Characterizing and Coding Psychiatric Diagnoses Using Electronic Health Record Data
Kuplicki, R., & Thompson, W. K. (2022) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.2736 · Cited by 2
Milne, R., Sheehan, M., Barnes, B., Kapper, J., Lea, N., N'Dow, J. M., Singh, G., Martín-Uranga, A., & Hughes, N. (2022) · Frontiers in Big Data · doi:10.3389/fdata.2022.945739 · Cited by 2
“Some of these challenges can be considered primarily “technical”–for example related to the ability to manage the security of health data, or deal with multiple and potentially incompatible data formats or models (Curtis et al., 2012; Hripcsak et al., 2015).”
BackgroundBIcenter-AD: Harmonising Alzheimer’s Disease cohorts using a common ETL tool
Almeida, J. R., Pazos, A., & Oliveira, J. L. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.101133 · Cited by 2
Mehrotra‐Varma, J., Kumthekar, A. A., Henry, S. S., Fleysher, R., Hou, W., & Duong, T. Q. (2023) · ACR Open Rheumatology · doi:10.1002/acr2.11580 · Cited by 2
“are stored in standard vocabulary concepts (19).”
Anand, T. V., Bu, F., Schuemie, M. J., et al. (2024) · Journal of Clinical Hypertension · doi:10.1111/jch.14793 · Cited by 2
Vora, B., Velasquez, E. F., Zhang, R., et al. (2024) · Pharmacology Research & Perspectives · doi:10.1002/prp2.1239 · Cited by 2
Mining Data in Electronic Health Record Systems: Opportunities for Librarians
Hamasu, C., Burroughs, C. M., Glenn, E. J., & Ball, A. L. (2017) · Journal of Hospital Librarianship · doi:10.1080/15323269.2017.1366776 · Cited by 2
Construction of Artificial Intelligence Training Platform for Multi-Center Clinical Research
Lee, C., Ji-Eon, K., No, S.-H., et al. (2020) · KIPS Transactions on Computer and Communication Systems · doi:10.3745/ktccs.2020.9.10.239 · Cited by 2
Essay, P., Mosier, J. M., & Subbian, V. (2021) · Studies in health technology and informatics · doi:10.3233/shti210148 · Cited by 2
“Mapping to Common Data Models (CDM) such as the Observational Medical Outcomes Partnership (OMOP) CDM [6] may offer a basic approach for data harmonization, provided there are standardized concepts for various ventilation events.”
MethodInfluentialYu, J., Pacheco, J. A., Ghosh, A., Luo, Y., Weng, C., Shang, N., Benoit, B., Carrell, D., Carroll, R., Dikilitas, O., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-723873/v1 · Cited by 2
Reps, J., Williams, R. D., Schuemie, M. J., Ryan, P. B., & Rijnbeek, P. R. (2022) · Research Square · doi:10.21203/rs.3.rs-1217376/v1 · Cited by 2
Khera, R., Dhingra, L. S., Aminorroaya, A., Li, K., Zhou, J. J., Arshad, F., Blacketer, C., Bowring, M. G., Bu, F., Cook, M., et al. (2022) · medRxiv · doi:10.1101/2022.12.27.22283968 · Cited by 2
Examination of the coverage of functional assessments in the OMOP common data modelPreprint
French, M. A., Hartman, P., Hayes, H., et al. (2024) · medRxiv · doi:10.1101/2024.05.03.24306841 · Cited by 2
“[8-10 8-10] Of these CDMs, the OMOP CDM[11, 12] has gained popularity due to its broad coverage of standard terminologies, flexibility and simplicity, and robust open-source tools.”
BackgroundBhattacharya, N., & Kumar, P. (2024) · Research Square · doi:10.21203/rs.3.rs-4310669/v1 · Cited by 2
Hwang, S., Soung, Y., Kang, S. U., Yu, D. H., Baek, H., & Jang, J. (2023) · Dementia and Neurocognitive Disorders · doi:10.12779/dnd.2023.22.4.121 · Cited by 1
Daumke, P., Haverkamp, C., Heckmann, S., Kuper, M., Müller, A., Oemig, F., Ripperger, U., Sabutsch, S., Sander, A., & Schulz, S. (2024) · Springer eBooks · doi:10.1007/978-3-658-43236-2_41 · Cited by 1
Wirth, F. N., Attieh, H. A., & Praßer, F. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1378866 · Cited by 1
Risk of dementia in patients with age‐related medical conditions: a retrospective cohort study
Kim, H. (2024) · Psychogeriatrics · doi:10.1111/psyg.13222 · Cited by 1
Park, C., Lee, S. H., Lee, D. Y., et al. (2025) · JMIR Medical Informatics · doi:10.2196/64422 · Cited by 1
“The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) is an internationally standardized model designed to harmonize medical data across various health care institutions [1].”
Interpretable Prediction and Feature Selection for Survival Analysis
Ness, M. V., & Udell, M. (2024) · Knowledge Discovery and Data Mining · doi:10.1145/3690624.3709245 · Cited by 4
“The EHR data is structured in the OMOP Common Data Model (CDM) [25].”
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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