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
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“initiated the idea of using data on 678 lung cancer patients as an electronic ‘library of clinical experience’ to obtain personalized prognosis [12].”
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“23,30 To facilitate broader dissemination of ML technologies, published models should strive to provide their source code where possible.”
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Ong, E., Wang, L. L., Schaub, J. A., O’Toole, J. F., Steck, B., Rosenberg, A. Z., Dowd, F., Hansen, J., Barisoni, L. M. C., Jain, S. R., et al. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-00335-w · Cited by 66
“For example, the OPMI has been used as an ontology platform to model the metadata shown in the ClinicalTrials. gov database and other clinical trial repositories 49 .”
MethodLu, J., Boparai, M. S., Shi, C., Henninger, E. M., Rangareddy, M., Veeraraghavan, S., Mirhaji, P., Fisher, M. C., & Duong, T. Q. (2023) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfad020 · Cited by 64
Castro, V. M., Gainer, V. S., Wattanasin, N., Benoit, B., Cagan, A., Ghosh, B., Goryachev, S. D., Metta, R., Park, H., Wang, D., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab264 · Cited by 64
“i2b2 can also query data stored on the observational medical outcomes partnership (OMOP) common data model (CDM).(15) Information on OMOP on i2b2 is available at https://community.”
BackgroundMayo, K., Basford, M., Carroll, R. J., Dillon, M., Fullen, H., Leung, J., Master, H., Rura, S., Sulieman, L. M., Kennedy, N., et al. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-122120-104825 · Cited by 69
Lynch, K. E., Deppen, S. A., DuVall, S. L., Viernes, B., Cao, A., Park, D., Hanchrow, E. E., Hewa, K., Greaves, P., & Matheny, M. E. (2019) · Applied Clinical Informatics · doi:10.1055/s-0039-1697598 · Cited by 62
Vashisht, R., Jung, K., Schuler, A., Banda, J. M., Park, R. W., Jin, S., Li, L., Dudley, J. T., Johnson, K. W., Shervey, M. M., et al. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.1755 · Cited by 61
Li, X., Burn, E., Duarte‐Salles, T., Yin, C., Reich, C., Delmestri, A., Verhamme, K. M. C., Rijnbeek, P. R., Suchard, M. A., Li, K., et al. (2022) · BMJ · doi:10.1136/bmj-2022-071594 · Cited by 63
Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Datasets
Huser, V., DeFalco, F., Schuemie, M. J., Ryan, P. B., Shang, N., Velez, M., Park, R. W., Boyce, R. D., Duke, J. D., Khare, R., et al. (2016) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1239 · Cited by 69
Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources
Liang, L., Hu, J., Sun, G., Hong, N., Wu, G., He, Y., Li, Y., Hao, T., Liu, L., & Gong, M. (2022) · Drug Safety · doi:10.1007/s40264-022-01170-7 · Cited by 70
“Of these, OHDSI provides vocabularies and software for the full process of collaborative research, from data conversions to data analysis, and maintains an active community involving a large number of HCPs, researchers, engineers and enterprises.”
MethodRichesson, R., Smerek, M., & Cameron, B. (2016) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1232 · Cited by 62
A call to action to scale up research and clinical genomic data sharing
Stark, Z., Glazer, D., Hofmann, O., Rendon, A., Marshall, C. R., Ginsburg, G. S., Lunt, C., Allen, N. E., Effingham, M., Ward, J. H., et al. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-024-00776-0 · Cited by 60
“For example, the Observational Medical Outcomes Partnership (OMOP) common data model 60 , which is widely used to structure health data, comprises data schemas and data vocabularies.”
Representing Knowledge Consistently Across Health Systems
Rosenbloom, S. T., Carroll, R. J., Warner, J. L., Matheny, M. E., & Denny, J. C. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-018 · Cited by 64
Hiramatsu, K., Barrett, A., & Miyata, Y. (2021) · Drugs - Real World Outcomes · doi:10.1007/s40801-021-00266-3 · Cited by 61
A Learning Health Care System Using Computer-Aided Diagnosis
Cahan, A., & Cimino, J. J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6663 · Cited by 60
Tan, E. H., Sena, A. G., Prats‐Uribe, A., You, S. C., Ahmed, W., Kostka, K., Reich, C., DuVall, S. L., Lynch, K. E., Matheny, M. E., et al. (2021) · Rheumatology · doi:10.1093/rheumatology/keab250 · Cited by 55
“(24, 25) Results were extracted on 3 October 2020, and are constantly updated with new data in the web application.”
MethodEarly Detection of Pancreatic Cancer
Kenner, B. J., Abrams, N., Chari, S. T., Field, B. F., Goldberg, A. E., Hoos, W. A., Klimstra, D. S., Rothschild, L. J., Srivastava, S., Young, M. R., et al. (2021) · Pancreas · doi:10.1097/mpa.0000000000001882 · Cited by 63
How Confident Are We About Observational Findings in Health Care: A Benchmark Study
Schuemie, M. J., Cepede, M. S., Suchard, M. A., Yang, J., Schuler, Y. T. A., Ryan, P. B., Madigan, D., & Hripcsak, G. M. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.147cc28e · Cited by 57
“We subsequently apply this benchmark to a wide range of commonly used study designs and analysis approaches as implemented in the OHDSI Methods Library (https://ohdsi.github.io/ MethodsLibrary), an open source collection of R packages.”
MethodInfluentialOMOP CDM Can Facilitate Data-Driven Studies for Cancer Prediction: A Systematic Review
Ahmadi, N., Peng, Y., Wolfien, M., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms231911834 · Cited by 56
“and standardizing clinical data and to create scalable analytical tools [4,5].”
MethodOstropolets, A., Elias, P. A., Reyes, M. V., Wan, E. Y., Pajvani, U. B., Hripcsak, G. M., & Morrow, J. (2021) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009115 · Cited by 58
Iosifescu, A. L., Hoogenboom, W. S., Buczek, A., Fleysher, R., & Duong, T. Q. (2022) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.1914 · Cited by 55
The Science of Precision Prevention
Pearson, T. A., Vitalis, D., PRATT, C. A., Campo, R., Armoundas, A. A., Au, D. H., Beech, B. M., Brazhnik, O., Chute, C. G., Davidson, K. W., et al. (2023) · JACC Advances · doi:10.1016/j.jacadv.2023.100759 · Cited by 58
“21 The many applications include the linkage and layering of data and data tools to assess health and risk for disease within and between individuals and communities over time.”
BackgroundBuilding evidence and measuring clinical outcomes for genomic medicine
Peterson, J. F., Roden, D. M., Orlando, L. A., Ramirez, A. H., Mensah, G. A., & Williams, M. S. (2019) · The Lancet · doi:10.1016/s0140-6736(19)31278-4 · Cited by 59
Gruendner, J., Schwachhofer, T., Sippl, P., Wolf, N., Erpenbeck, M., Gulden, C., Kapsner, L. A., Zierk, J., Mate, S., Stürzl, M., et al. (2019) · PLOS ONE · doi:10.1371/journal.pone.0223010 · Cited by 54
Bernstam, E. V., Shireman, P. K., Meric‐Bernstam, F., Zozus, M. N., Jiang, X., Brimhall, B. B., Windham, A. K., Schmidt, S., Visweswaran, S., Ye, Y., et al. (2021) · Clinical and Translational Science · doi:10.1111/cts.13175 · Cited by 59
“…efforts demonstrate the value of AI, such as the Observational Health Data Sciences and Informatics Program (over half a billion patient records) 76 ; the Accrual to Clinical Trials Network (over 16 states) 77 ; the All of Us Program (over one million or more people from across the United…”
BackgroundEHR foundation models improve robustness in the presence of temporal distribution shift
Guo, L., Steinberg, E., Fleming, S. L., Posada, J., Lemmon, J., Pfohl, S. R., Shah, N. H., Fries, J., & Sung, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-30820-8 · Cited by 65
Walsh, C. G., Xia, W., Li, M., Denny, J. C., Harris, P. A., & Malin, B. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245917749652 · Cited by 55
Principles of Large-scale Evidence Generation and Evaluation across a Network of Databases (LEGEND)
Schuemie, M. J., Ryan, P. B., Pratt, N. L., Chen, R., You, S. C., Krumholz, H. M., Madigan, D., Hripcsak, G. M., & Suchard, M. A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa103 · Cited by 51
“(OHDSI) international collaborative [4] launched the Large-Scale Evidence Generation and Evaluation across a Network of Databases (LEGEND) research initiative.”
BackgroundGagalova, K., Elizalde, M. A. L., Portales-Casamar, É., & Görges, M. (2020) · JMIR Formative Research · doi:10.2196/17687 · Cited by 53
“Another popular CDM that has been used more frequently in recent years is the Observational Medical Outcomes Partnership (OMOP) [279], adopted by 3 IDRs, namely MIDH [27], OpenFurther [46], and STARR [20].”
MethodKlann, J. G., Estiri, H., Weber, G. M., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab018 · Cited by 52
“The approach relies on shared analytics scripts supporting 2 common research analytics formats in which analysis is local and aggregation is central.(7,8) By leveraging investments in standard analytic models while respecting data governance and patient privacy, we completed the initial phase of the study within 2 mon…”
BackgroundHow data science and AI-based technologies impact genomics
Jing, L., & Ngiam, K. Y. (2023) · Singapore Medical Journal · doi:10.4103/singaporemedj.smj-2021-438 · Cited by 53
“To standardise the format and content of observational data, several common data models (CDMs) have been proposed by collaborative research networks such as Observational Health Data Sciences and Informatics (OHDSI), Informatics for Integrating Biology and the Bedside (i2b2) and Patient‐ Centered Clinical Research Net…”
MethodReps, J. M., Williams, R. D., You, S. C., Falconer, T., Minty, E., Callahan, A., Ryan, P. B., Park, R. W., Lim, H.-S., & Rijnbeek, P. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-00991-3 · Cited by 40
Pharmacovigilance and Biomedical Informatics: A Model for Future Development
Beninger, P. R., & Ibara, M. A. (2016) · Clinical Therapeutics · doi:10.1016/j.clinthera.2016.11.006 · Cited by 51
Leveraging electronic health records for clinical research
Raman, S. R., Curtis, L. H., Temple, R., Andersson, T., Ezekowitz, J. A., Ford, I., James, S. K., Marsolo, K. A., Mirhaji, P., Rocca, M., et al. (2018) · American Heart Journal · doi:10.1016/j.ahj.2018.04.015 · Cited by 56
Warner, J. L., Dymshyts, D., Reich, C., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103239 · Cited by 51
Kakoti, B. B., Bezbaruah, R., & Ahmed, N. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1007315 · Cited by 57
Cepeda, M. S., Reps, J., Fife, D., Blacketer, C., Stang, P., & Ryan, P. (2018) · Depression and Anxiety · doi:10.1002/da.22705 · Cited by 50
Self-citationUsing big data to promote precision oral health in the context of a learning healthcare system
Finkelstein, J., Zhang, F., Levitin, S. A., & Cappelli, D. P. (2020) · Journal of Public Health Dentistry · doi:10.1111/jphd.12354 · Cited by 53
“77 In the educational domain , research on utilizing multiple data streams to better monitor student performance and identify areas for personalized improvement will promote personalized education and individualized student support tailored to individualized performance pro fi le.”
BackgroundFu, S., Leung, L. Y., Raulli, A.-O., Kallmes, D. F., Kinsman, K. A., Nelson, K. B., Clark, M. S., Luetmer, P. H., Kingsbury, P., Kent, D. M., et al. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1072-9 · Cited by 51
Tsai, M., Chen, K., & Chen, P. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.036946 · Cited by 56
“61,62 The Observational Medical Outcomes Partnership common data model enables researchers to systematically analyze data from diverse sources while maintaining data quality and consistency, with its adoption growing significantly across health care institutions worldwide.”
A survey on adverse drug reaction studies: data, tasks and machine learning methods
Nguyễn, Đ. A., Nguyen, C. H., & Mamitsuka, H. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz140 · Cited by 48
Predicting hospital length of stay using machine learning on a large open health dataset
Jain, R., Singh, M., Rao, A., & Garg, R. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11238-y · Cited by 53
“One such effort is the organization for Observational Health Data Sciences and Informatics (OHDSI) as described in [80].”
Luo, G., Stone, B. L., Johnson, M. D., Tarczy-Hornoch, P. Z., Wilcox, A. B., Mooney, S. D., Sheng, X., Haug, P. J., & Nkoy, F. L. (2017) · JMIR Research Protocols · doi:10.2196/resprot.7757 · Cited by 45
Poucke, S. V., Thomeer, M., Heath, J., & Vukičević, M. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5549 · Cited by 49
Levenson, M. S., He, W., Chen, J., Fang, Y., Faries, D. E., Goldstein, B. A., Ho, M., Lee, K., Mishra-Kalyani, P. S., Rockhold, F. W., et al. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1883473 · Cited by 46
“Common data models such as the Sentinel and Observational Medical Outcomes Partnership (OMOP) data models (Hripcsak et al. 2015) aim to standardize data from different sources into a comment format.”
MethodTouré, V., Krauss, P., Gnodtke, K., Buchhorn, J., Unni, D. R., Horki, P., Raisaro, J. L., Kalt, K., Teixeira, D., Crameri, K., et al. (2023) · Scientific Data · doi:10.1038/s41597-023-02028-y · Cited by 46
“Several “common” data models for clinical data representation have been developed 3–5 to standardise recorded information, but there are still problems with sharing and reusing clinical resources 6 .”
BackgroundDrawing Reproducible Conclusions from Observational Clinical Data with OHDSI
Hripcsak, G. M., Schuemie, M. J., Madigan, D., Ryan, P. B., & Suchard, M. A. (2021) · Yearbook of Medical Informatics · doi:10.1055/s-0041-1726481 · Cited by 45
“The Observational Health Data Sciences and Informatics (OHDSI) initiative [1] is a multi-stakeholder, interdisciplinary, inter-”
An atomic approach to the design and implementation of a research data warehouse
Visweswaran, S., McLay, B., Cappella, N., Morris, M. M., Milnes, J. T., Reís, S. E., Silverstein, J. C., & Becich, M. J. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab204 · Cited by 45
Outcomes of Hospitalized Patients With COVID-19 With Acute Kidney Injury and Acute Cardiac Injury
Lu, J., Buczek, A., Fleysher, R., Hoogenboom, W. S., Hou, W., Rodríguez, C. J., Fisher, M. C., & Duong, T. Q. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.798897 · Cited by 45
A Comparison of Data Quality Assessment Checks in Six Data Sharing Networks
Callahan, T. J., Bauck, A. E., Bertoch, D., Brown, J. S., Khare, R., Ryan, P. B., Staab, J., Zozus, M. N., & Kahn, M. G. (2017) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.223 · Cited by 50
Barriers to Achieving Economies of Scale in Analysis of EHR Data: A Cautionary Tale
Sendak, M. P., Balu, S., & Schulman, K. A. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-03-cr-0046 · Cited by 45
Glicksberg, B. S., Oskotsky, B., Thangaraj, P. M., Giangreco, N., Badgeley, M. A., Johnson, K. W., Datta, D., Rudrapatna, V. A., Rappoport, N., Shervey, M. M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz409 · Cited by 45
Ta, C., Dumontier, M., Hripcsak, G. M., Tatonetti, N. P., & Weng, C. (2018) · Scientific Data · doi:10.1038/sdata.2018.273 · Cited by 45
Rogers, J. R., Liu, C., Hripcsak, G. M., Cheung, Y. K., & Weng, C. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.4732 · Cited by 44
Health Informatics: Engaging Modern Healthcare Units: A Brief Overview
Yogesh, M., & Karthikeyan, J. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.854688 · Cited by 61
“…Biology and the Bedside (i2b2) and Observational Health Data Sciences and Informatics (OHDSI), have developed informatics tools and approaches that allow researchers to query organizational participants and support transformation or analytics of relevant data to facilitate research (53, 54).”
BackgroundA Systematic Review of Crohn’s Disease Case Definitions in Administrative or Claims Databases
Hutfless, S. M., Jasper, R. A., Tilak, A., et al. (2022) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izac131 · Cited by 44
Park, J., Fang, Y., Ta, C., Zhang, G., Idnay, B., Chen, F., Feng, D. D., Shyu, R., Gordon, E. R., Spotnitz, M., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104649 · Cited by 42
Kim, C., You, S. C., Reps, J., Cheong, J. Y., & Park, R. W. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa277 · Cited by 46
“24,25 Considering that the current PLP framework can only predict a binary outcome, we developed an ensemble method to predict multiclass outcomes.”
MethodKim, H., Kim, D.-H., Kim, D. M., et al. (2021) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.20.01663 · Cited by 44
Burn, E., Li, X., Kostka, K., Stewart, H. M., Reich, C., Seager, S., Duarte‐Salles, T., Fernandez‐Bertolin, S., Aragón, M., Reyes, C., et al. (2022) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5419 · Cited by 43
Self-citationRambla, J., Baudis, M., Ariosa, R., et al. (2022) · Human Mutation · doi:10.1002/humu.24369 · Cited by 43
Sulieman, L. M., Cronin, R. M., Carroll, R. J., Natarajan, K., Marginean, K., Mapes, B. M., Roden, D. M., Harris, P. A., & Ramirez, A. H. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac046 · Cited by 44
Data-driven longitudinal characterization of neonatal health and morbidity
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. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adc9854 · Cited by 49
“All EHRs from inpatient and outpatient data were mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) version 5.3.1 ( 28 , 29 ).”
MethodInfluentialGenetic Variation and Sickle Cell Disease Severity
Kirkham, J. K., Estepp, J. H., Weiss, M. J., & Rashkin, S. R. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.37484 · Cited by 48
Leading causes of cardiovascular hospitalization in 8.45 million US veterans
Krishnamurthi, N., Francis, J., Fihn, S. D., Meyer, C. S., & Whooley, M. A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0193996 · Cited by 43
Big Data in Laboratory Medicine—FAIR Quality for AI?
Blatter, T. U., Witte, H., Nakas, C. T., & Leichtle, A. B. (2022) · Diagnostics · doi:10.3390/diagnostics12081923 · Cited by 43
Effect of vocabulary mapping for conditions on phenotype cohorts
Hripcsak, G. M., Levine, M. E., Shang, N., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy124 · Cited by 42
Papež, V., Moinat, M., Voss, E. A., Bazakou, S., van Winzum, A., Peviani, A., Payralbe, S., Lara, E. G., Kallfelz, M., Asselbergs, F. W., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac203 · Cited by 47
“During the COVID-19 pandemic, OHDSI used the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) [9] to transform disparate data into a standardized format and perform federated analyses to produce high quality evidence about COVID-19 for policy makers and healthcare providers [4,6–8].”
The hope, hype and reality of Big Data for pharmacovigilance
Bate, A., Reynolds, R. F., & Caubel, P. (2017) · Therapeutic Advances in Drug Safety · doi:10.1177/2042098617736422 · Cited by 42
Clinical Research Informatics for Big Data and Precision Medicine
Kahn, M. G., & Weng, C. (2016) · Yearbook of Medical Informatics · doi:10.15265/iy-2016-019 · Cited by 42
Lamer, A., Depas, N., Doutreligne, M., Parrot, A., Verloop, D., Defebvre, M.-M., Ficheur, G., Chazard, E., & Beuscart, J. (2020) · Applied Clinical Informatics · doi:10.1055/s-0039-3402754 · Cited by 41
You, S. C., Krumholz, H. M., Suchard, M. A., Schuemie, M. J., Hripcsak, G. M., Chen, R., Shea, S., Duke, J. D., Pratt, N. L., Reich, C., et al. (2021) · Hypertension · doi:10.1161/hypertensionaha.120.16402 · Cited by 41
Paris, N., Lamer, A., & Parrot, A. (2021) · JMIR Medical Informatics · doi:10.2196/30970 · Cited by 42
Peng, Y., Henke, E., Reinecke, I., Zoch, M., Sedlmayr, M., & Bathelt, F. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104925 · Cited by 43
Challenges and recommendations for high quality research using electronic health records
Honeyford, K., Expert, P., Mendelsohn, E., Post, B., Faisal, A. A., Glampson, B., Mayer, E. K., & Costelloe, C. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.940330 · Cited by 49
“One of the end goals is data integration across platforms, trust and countries, an international standard for data representation, such as the one developed by Observational Health Data Science and Informatics needs to be developed and widely adopted (45).”
BackgroundTrends and opportunities in computable clinical phenotyping: A scoping review
He, T., Belouali, A., Patricoski, J., Lehmann, H. P., Ball, R., Anagnostou, V., Kreimeyer, K., & Botsis, T. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104335 · Cited by 38
“Moreover, the Observational Health Data Sciences and Informatics (OHDSI) program [14], the National Patient-Centered Clinical Research Network (PCORnet) [15,16], and the US Food and Drug Administration Sentinel System [17,18] have promoted the reusability of phenotype definitions across…”
Initializing a hospital-wide data quality program. The AP-HP experience.
Daniel, C., Serre, P., Orlova, N., Bréant, S., Paris, N., & Griffon, N. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.10.016 · Cited by 49
Pacheco, J. A., Rasmussen, L. V., Kiefer, R. C., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy101 · Cited by 39
Impact of Use of Antibiotics on Response to Immune Checkpoint Inhibitors and Tumor Microenvironment
Khan, U., Ho, K., Hwang, E. K., Peña, C., Brouwer, J., Hoffman, K., Betel, D., Sonnenberg, G. F., Faltas, B. M., Saxena, A., et al. (2021) · American Journal of Clinical Oncology · doi:10.1097/coc.0000000000000813 · Cited by 40
Duke, J. D., Ryan, P. B., Suchard, M. A., Hripcsak, G. M., Jin, P., Reich, C., Schwalm, M.-S., Khoma, Y., Wu, Y., Xu, H., et al. (2017) · Epilepsia · doi:10.1111/epi.13828 · Cited by 39
Understanding enterprise data warehouses to support clinical and translational research
Campion, T. R., Craven, C. K., Dorr, D. A., & Knosp, B. M. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa089 · Cited by 42
“For retrospective observational studies, the Observational Medical Outcomes Partnership common data model (CDM) has gained traction among data scientists who use standardized queries and advanced statistical techniques such as machine learning to analyze EHR and claims data.(26) The Observational Medical Outcomes Part…”
MethodVora, P., Stewart, H. M., Russell, B. A., Asiimwe, A., & Brobert, G. (2022) · International Journal of Clinical Practice · doi:10.1155/2022/6707985 · Cited by 37
“&e common data model is updated by the Observational Health Data Sciences and Informatics (OHDSI) community—an interdisciplinary collaboration based on the principle of open-source data analytics [19].”
Predicting Ischemic Stroke in Patients with Atrial Fibrillation Using Machine Learning
Jung, S., Lee, E., Lee, E.-J., Bae, S., Kim, Y.-Y., Lee, D., Song, M.-K., & Yoo, S. (2022) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2703080 · Cited by 39
“In recent years, the Observational Health Data Sciences and Informatics (OHDSI) project has been attempting to standardize and expand the EMR information of hospitals; however, it is currently being conducted only for a few hospitals, and technical and institutional improvements are needed [43].”
BackgroundLuo, H., Lau, W. C. Y., Chai, Y., Torre, C. O., Howard, R. J., Liu, K. Y., Lin, X., Yin, C., Fortin, S., Kern, D. M., et al. (2023) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2022.4448 · Cited by 37
Cardiovascular Care Innovation through Data-Driven Discoveries in the Electronic Health Record
Dhingra, L. S., Shen, M., Mangla, A., & Khera, R. (2023) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2023.06.104 · Cited by 42
Lai, E. C., Ryan, P. B., Zhang, Y., Schuemie, M. J., Hardy, N. C., Kamijima, Y., Kimura, S. H., Kubota, K., Man, K. K. C., Cho, S. Y., et al. (2018) · Clinical Epidemiology · doi:10.2147/clep.s149961 · Cited by 37
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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