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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Bona, J. P. (2024) · Yearbook of Medical Informatics · doi:10.1055/s-0044-1800747 · Cited by 2
“N3C data uses the OMOP [38] common data model.”
Real-World Evidence and AI in Healthcare Analytics
Ozturk, N. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-6270-9.ch003 · Cited by 1
Cuk, P., Rosen, A. W., Mashkoor, M., Ellebæk, M. B., & Gögenür, I. (2025) · Techniques in Coloproctology · doi:10.1007/s10151-025-03146-y · Cited by 1
“Data were extracted from DCCG, Clinical Laboratory Register, and DNPR data and transformed to The Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM)[17].”
Joo, H. J., Hong, S. J., Yu, C. W., Shin, S. Y., & Kim, E. J. (2025) · The American Journal of Medicine · doi:10.1016/j.amjmed.2025.06.036 · Cited by 1
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The impact of the social exposome in cardiovascular health and disease
Mekkaoui, K. E., Mukherjee, C., Santana, C., et al. (2025) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2025.100015 · Cited by 1
Predictive Modeling for Clostridioides difficile Infection
Rao, K. K., & Wiens, J. (2025) · Infectious Disease Clinics of North America · doi:10.1016/j.idc.2025.07.015 · Cited by 1
Quantitative analysis of the comprehensiveness and granularity of biomedical terminology systems
Chang, E., & Sung, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-17737-0 · Cited by 1
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Kaplan, S., Ayele, H. T., Bennett, D., Grandi, S. M., Schachterle, S. E., Simeone, J. C., Sun, J. W., & Ukah, U. V. (2025) · Current Medical Research and Opinion · doi:10.1080/03007995.2025.2573652 · Cited by 1
“By leveraging electronic health records and claims data, RWD offers a valuable alternative for evaluating the safety and effectiveness of medical products during pregnancy and early childhood 19,20 .”
Koren, R., Elkan, M., Barouch, A., & Ziv‐Baran, T. (2025) · Internal and Emergency Medicine · doi:10.1007/s11739-025-04170-4 · Cited by 1
“The cohort was designed and characterized using ATLAS [24], an open-source tool developed by the Observational Health Data Sciences and Informatics (OHDSI) community [25].”
Ng, D. Q., Zheng, Z., Jemal, A., Chan, A., & Islami, F. (2026) · Supportive Care in Cancer · doi:10.1007/s00520-026-10880-y · Cited by 1
“In Bonferroni-corrected adjusted analyses of the full cohort, delayed healthcare was associated with age at survey (due to all 6 reasons), biological sex (all reasons except transportation), health insurance (all reasons except nervousness), income (affordability, transportation, and nervousness),…”
The Role of Data Analytics in Optimizing Hospital Resource Allocation and Decision-making
Edward, & Djaya, N. (2025) · Journal Of Tropical Pharmacy And Chemistry · doi:10.30872/jtpc.v9i2.313 · Cited by 2
Real-world data and evidence: pioneering frontiers in precision oncology
Jiang, J., Pan, W., Sun, L. P., Pang, L., Chen, H., Huang, J., & Chen, W. (2025) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b2400285 · Cited by 1
“Notable initiatives such as the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (Hripcsak et al., 2015) and the Fast Healthcare Interoperability Resources (FHIR) standard (Vorisek et al., 2022) have emerged to address these challenges by facilitating data harmonization…”
A Three Pillar Data-Centric Framework for Enabling AI-Driven Healthcare Collaboration
Choudhari, R. D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5906-9.ch002 · Cited by 1
Székely, O., Mészáros, Á., Kővári, E., Assabiny, A., Héja, T., Borbély, D., Kovács, A., Nemes, C., Sallai, A., Bali, O., et al. (2026) · Orvosi Hetilap · doi:10.1556/650.2026.33511 · Cited by 1
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“…Names and Codes (LOINC); structure is provided by common data models such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) [11]; and exchange is handled by messaging standards such as Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR) [12,13].”
Payne, P. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-800681-8.00006-2 · Cited by 1
Common Data Model for Healthcare Data
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Billing and Review Perspectives in Healthcare
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Scaling and Querying a Semantically Rich, Electronic Healthcare Graph.
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“The OMOP common data model [147] and its linked standardized terminologies [148] standardize administrative and clinical variables from ≥10 major American health care systems [183,184].”
MethodOh, S., Sung, M., Rhee, Y., Hong, N., & Park, Y. R. (2021) · JMIR Medical Informatics · doi:10.2196/24940 · Cited by 1
“However, the results of this study are reliable because the SynPUF5PCT data set is an officially published data set by the OHDSI [50].”
BackgroundInfluentialArtificial Intelligence for Medical Diagnosis
Richens, J. G., & Buchard, A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_29-1 · Cited by 1
BIG DATA ANALYTICS IN PHARMACOVIGILANCE - A GLOBAL TREND
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“Networks such as Observational Medical Outcomes Partnership and Observational Health Data Sciences and Informatics, primarily focus on method testing and informatics tool development for data networks [16].”
BackgroundA Roadmap for Boosting Model Generalizability for Predicting Hospital Encounters for Asthma
Luo, G. (2022) · JMIR Medical Informatics · doi:10.2196/33044 · Cited by 1
“Our proposed predictive models are based on the OMOP common data model [63] and its linked standardized terminologies [64], which standardize administrative and clinical variables from at least 10 large healthcare systems in the United States [47,70].”
MethodInfluentialBiomedical Literature Mining for Repurposing Laboratory Tests
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Building a Clinically-Focused Problem List From Medical Notes
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“Despite strides in interoperability through Common Data Elements (CDEs) and Common Data Models (CDMs), there are still hurdles, notably the lack of unified governance and the emergence of isolated data silos[2,3,4,5,6,7,8].”
BackgroundWong, C., van Oostrom, J. M., Pittet, V. E. H., Bossuyt, P. J., Hanžel, J., Samaan, M., Tripathi, M., Czuber‐Dochan, W., Burisch, J. M., Leone, S., et al. (2024) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjae004 · Cited by 1
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Transformative AI in Biomedicine Analysis
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“OMOP/OHDSI CDM exemplifies a unified approach to data terminology [5], [6], [7].”
Park, C., Hwang, G., Choi, W., et al. (2025) · Journal of Viral Hepatitis · doi:10.1111/jvh.70006 · Cited by 1
“All databases were anonymised and converted to the international healthcare data standard, Observational Medical Outcomes Partnership (OMOP) – Common Data Model (CDM) version 5.3.4 [12].”
InfluentialLi, W., Yang, Q., Chen, Y., et al. (2025) · Respiratory Research · doi:10.1186/s12931-025-03248-z · Cited by 1
“The Yinzhou database was standardized to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5, which is maintained by the Observational Health Data Sciences and Informatics (OHDSI) Network [16, 17].”
Leveraging Profiling to Bridge Healthcare Silos for Federated Analyses
Barret, N., Bernasconi, A., Cappiello, C., Palu, G., & Pinoli, P. (2025) · Lecture notes in business information processing · doi:10.1007/978-3-031-94590-8_3 · Cited by 1
Preface - Access to Knowledge Revisited
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Li, F., Rao, G., Du, J., Xiang, Y., Zhang, Y., Selek, S., Hamilton, J. E., Xu, H., & Tao, C. (2019) · Health Informatics Journal · doi:10.1177/1460458219832059 · Cited by 1
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Exploring the Value of Electronic Health Records from Multiple Datasets
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Risk Stratification and Prognosis Using Predictive Modelling and Big Data Approaches
Visweswaran, S., & Cooper, G. F. (2019) · Health informatics · doi:10.1007/978-3-030-18626-5_7 · Cited by 1
Pragmatic Trials and New Informatics Methods to Supplement or Replace Phase IV Trials
Mendonça, E. A., & Tachinardi, U. (2019) · Health informatics · doi:10.1007/978-3-030-18626-5_14 · Cited by 1
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MORPHER - A Platform to Support Modeling of Outcome and Risk Prediction in Health Research
da Cruz, H. F., Bergner, B., Konak, O., Schneider, F., Bode, P., Lempert, C., & Schapranow, M.-P. (2019) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2019.00090 · Cited by 1
Common Data Model for Healthcare data
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Real World Data and Artificial Intelligence in Diabetology
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A Multi-User Terminology Mapping Toolbox
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“” In recent years, many initiatives and research networks have emerged for sharing data for secondary use, such as PCORnet [1], OHDSI [2], or SHRINE [3].”
BackgroundA Recommender System to Help Refining Clinical Research Studies
Almeida, J. R., Silva, J., Matos, S., et al. (2021) · Studies in health technology and informatics · doi:10.3233/shti210174 · Cited by 1
“This process allows researchers to reuse stored information to perform observational studies without having to collect patient data [2].”
BackgroundAn Architecture to Define Cohorts over Medical Imaging Datasets
Almeida, J. R., Monteiro, E., & Oliveira, J. L. (2021) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms52027.2021.00088 · Cited by 1
Pediatric data from the All of Us research program: demonstration of pediatric obesity over time
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“2 maintained by the Observational Health and Data Sciences Initiative collaborative.(3) To protect participant privacy, a series of de-identification algorithms were applied.”
BackgroundInformation Technology Systems
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Visualising Time-evolving Semantic Biomedical Data
Pereira, A., Almeida, J. R., Lopes, R. P., & Oliveira, J. L. (2022) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms55023.2022.00053 · Cited by 1
“However, as shown in other work [2], [19]–[21], it is essential to adopt distributed data to conduct more impactful studies.”
BackgroundDescription of a French Population of Diabetics Treated Followed up by General Practitioners
Fruchart, M., Lamer, A., Lemaître, M., et al. (2023) · Studies in health technology and informatics · doi:10.3233/shti230289 · Cited by 1
Opportunities and Challenges in Data-Driven Healthcare ResearchPreprint
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Exploration Of The Timeliness Of ICD Codes In Administrative Databases: A Nationwide StudyPreprint
Simon, M., Rance, B., Katsahian, S., Bounebache, S. K., Rey, G., Châtellier, G., Neuraz, A., Burgun, A., & Looten, V. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/491951 · Cited by 1
Machine learning based predictive model of early mortality in stage III and IV prostate cancerPreprint
Chen, R. F. (2020) · medRxiv · doi:10.1101/2020.04.15.20065938 · Cited by 1
Commentary on Professor Austin Bradford Hill's Alfred Watson Memorial Lecture
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Disease Progression Modeling Workbench 360Preprint
Suryanarayanan, P., Chakraborty, P., Madan, P., Bore, K., Ogallo, W., Chandra, R., Ghalwash, M. F., Buleje, I., Remy, S. L., Mahatma, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.13265 · Cited by 4
Lu, Y., Li, J., Wang, X., Cho, J., Dorajoo, S. R., Feng, M., Hsu, M., Hsu, J. C., Hwang, J., Iqbal, U., et al. (2021) · medRxiv · doi:10.1101/2021.06.28.21258167 · Cited by 1
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AI-Driven Longitudinal Characterization of Neonatal Health and MorbidityPreprint
Francesco, D. D., Reiss, J. D., Roger, J. M., Tang, A., Chang, A. L., Becker, M., Phongpreecha, T., Espinosa, C. A., Morin, S., Berson, E., et al. (2022) · medRxiv · doi:10.1101/2022.03.31.22273233 · Cited by 1
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Validation of an electronic frailty index with electronic health records: eFRAGICAP indexPreprint
Orfila, F., Carrasco-Ribelles, L. A., Abellana, R. M., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-1368757/v1 · Cited by 1
Trusted resource allocation in volunteer edge-cloud computing for scientific applications
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Deep learning and data warehousing techniques applied to real data in the medical domain
de Lima, D. M. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2023.tde-01092023-164636 · Cited by 1
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Incidence and Survival of Head and Neck Cancers in the United Kingdom 2000-2021Preprint
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Método de extração de coortes em bases de dados assistenciais para estudos da doença cardiovascular
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Método de normalización de datos y abstracción de consultas basado en estándares médicos
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A Software Solution for Clinical Protocol ManagementPreprint
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Clinical data science in radiotherapy
van Soest, J. (2018) · Research Publications (Maastricht University) · doi:10.26481/dis.20181128js
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Scalable Electronic Phenotyping For Studying Patient Comorbidities.
Ling, A. Y., Alsentzer, E., Chen, J., Banda, J. M., Tamang, S. R., & Minty, E. P. (2018) · Europe PMC (PubMed Central)
A Survey of Automatic Methods for Nutritional AssessmentPreprint
Brenna, L., Johansen, H. D., & Johansen, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.07245 · Cited by 2
Johnson, A. M., Bikak, M., Griffin, P. M., & Adibuzzaman, M. (2019) · medRxiv · doi:10.1101/19006023
Scalable Algorithms for Large Competing Risks Data
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A novel tool for standardizing clinical data in a realism-based common data modelPreprint
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Sentiment Analysis to Quantify Healthcare Data
Britto, J., Desai, K., Kothari, H., et al. (2020) · Springer eBooks · doi:10.1007/978-3-030-41862-5_126
Tong, J., Luo, C., Islam, M. N., Sheils, N. E., Buresh, J., Edmondson, M. J., Merkel, P. A., Lautenbach, E., Duan, R., & Chen, Y. (2020) · medRxiv · doi:10.1101/2020.11.17.20220681
Personalisierte Medizin live erleben
Bathelt, F., Kümmel, M., Helfer, S., et al. (2020) · Springer eBooks · doi:10.1007/978-3-662-62193-6_2
Knowledge graph analytics platform with LINCS and IDG for Parkinson’s disease target illuminationPreprint
Yang, J. J., Gessner, C. R., Duerksen, J. L., Biber, D., Binder, J., Ozturk, M., Foote, B., McEntire, R., Stirling, K., Ding, Y., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.30.424881
Predicting Stroke Risk in Patients with Atrial Fibrillation Using Machine LearningPreprint
Jung, S., Lee, E.-J., Lee, M., Bae, S., Kim, Y.-Y., Lee, D., Song, M., & Yoo, S. (2021) · Research Square · doi:10.21203/rs.3.rs-209356/v1
Distributed research networks and applications to pragmatic randomized trials
Panozzo, C. A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-817663-4.00012-x
Generating Enriched Synthetic German Hospital Claims Data – A Use Case Driven Approach
Helfer, S., Kümmel, M., Bathelt, F., & Sedlmayr, M. (2021) · Studies in health technology and informatics · doi:10.3233/shti210051 · Cited by 2
“We were able to reverse the ETL process, which we had already designed and implemented to load German claims data into the OMOP CDM [19] using semantic mappings of German patient data to standardized OMOP Concepts.”
MethodInfluentialインタビュー 生ごみ対策はまず家庭から--明治大学農学部客員教授 藤原俊六郎氏に聞く (特集 首都圏における家庭生ごみ対策)
藤原, 俊. (2010) · 月刊廃棄物
Harmonising Alzheimer's Disease Cohorts using a Common ETL ToolPreprint
Almeida, J. R., Pazos, A., & Oliveira, J. L. (2022) · Research Square · doi:10.21203/rs.3.rs-1145054/v1
“This leads to establishing organisations and methodologies to explore clinical databases by reusing existent data [6].”
BackgroundResultInfluential
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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