The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index; none of the 7 sources its claims cite is checked for support yet.
Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers
Open accessAbstract
The vision of creating accessible, reliable clinical evidence by accessing the clincial experience of hundreds of millions of patients across the globe is a reality. Observational Health Data Sciences and Informatics (OHDSI) has built on learnings from the Observational Medical Outcomes Partnership to turn methods research and insights into a suite of applications and exploration tools that move the field closer to the ultimate goal of generating evidence about all aspects of healthcare to serve the needs of patients, clinicians and all other decision-makers around the world.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
OHDSI is an international open collaborative that builds on OMOP to create an open-source, federated network using the OMOP Common Data Model for large-scale observational health research. It reports that 58 existing OMOP databases had converted 682 million patient records and estimates over 200 million patients available in the OHDSI Research Forum.
Key findings
- OHDSI grew out of the five-year Observational Medical Outcomes Partnership (OMOP) and adopted/maintains the OMOP Common Data Model (CDM), requiring data holders to map their data to the CDM so that a query can be executed at any site without modification.
- A survey of current OMOP data model users found that 58 existing OMOP databases had collectively converted 682 million patient records to the OMOP CDM; this includes duplicated patients and sources and databases not currently participating in the OHDSI Research Forum. The paper estimates that the actual number of patients currently available in the OHDSI Research Forum is over 200 million.
- OHDSI’s first annual meeting at Columbia University Medical Center on 16–17 October 2014 had 58 participants; over 90 participants from around the world have joined the collaborative, with a vision to access a network of one billion patients.
- OHDSI tools include ACHILLES for data characterization and quality visualization, HERMES for vocabulary browsing, PLATO for patient-level prediction, HERACLES for cohort exploration, and HOMER for risk identification and comparative effectiveness studies. The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study.
Methods
- Descriptive program/vision paper, not an empirical study; it narrates OHDSI’s infrastructure, governance, software tools, and research network.
- Data/sample: a survey of current OMOP data model users, reporting 58 databases and 682 million converted patient records; the first annual meeting had 58 participants.
- Main analysis: narrative synthesis of a federated model in which data are retained locally, analyses are run locally, and de-identified aggregate results are transmitted centrally; standard terminologies include SNOMED for diagnoses, RxNorm for drug ingredients, and LOINC for laboratory results. Results may be reported without aggregation, summarized, or aggregated if sufficient homogeneity can be demonstrated.
Limitations
- The paper does not present a formal limitations section. It acknowledges that the 682 million records include duplicated patients and sources and databases not currently participating in the OHDSI Research Forum, so the count is not a unique-patient count.
- It notes that OMOP previously demonstrated databases from different sources can give vastly different answers, and that blind aggregation may increase variance due to heterogeneity instead of decreasing it due to sample size.
- Evident from the text: no formal evaluation or outcome data for OHDSI tools or network performance are reported; the paper is descriptive and vision-oriented.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
10 Own interpretation · 7 Sources not found · 6 No basis given · 1 Own result
In the paper
“The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study, assessing whether drugs with similar function or structure cause similar side effects [10]”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
What the sources say
- Not matchedcites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“They found that 58 existing OMOP databases have collectively converted 682 million patient records to the OMOP CDM.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
plainn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This enables multicenter, global analyses to be executed rapidly and efficiently using applications or programs developed at a single site.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A blind aggregation of the results may increase variance due to heterogeneity instead of decreasing it due to sample size.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
What the sources say
- Not matchedcites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The existence of multiple very large databases that use the OMOP Common Data Model demonstrates that a worldwide conversion of clinical data on all patients is in fact feasible.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Discussion and Conclusion
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, OHDSI vision is, in fact, feasible today.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
boostedn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Rests on an assumption: The existing OMOP databases and conversion experience are sufficient to make the OHDSI vision of accessing a network of one billion patients feasible today.
In the paper
“The team previously found that simply merging the databases is likely to give poor answers because of the heterogeneity [6].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
- Probably not a substantial claim. 8
- It may state two separate claims as one. 10
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“OHDSI held its first annual meeting at Columbia University Medical Center in New York City on 16 and 17 of October 2014.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Results
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, we estimate that the actual number of patients currently available in the OHDSI Research Forum is over 200 million.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Fifty eight participants reviewed the vision and goals giving rise to the creation of OHDSI and formed working groups to address the common data model, vocabulary, knowledge bases, estimation methods, phenotype generation, clinical characterization, and cohort definition.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Results
plaindirectn = 8- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Other investigators have used ACHILLES to do an initial assessment of whether the database is likely to hold a sufficient number of cases of interest to be worth investigating further.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data owners have used ACHILLES to assess the quality of their database, looking for gaps that may signify upload errors.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This large number includes both patients and sources that are duplicated across databases and also includes databases that are not currently participating in the OHDSI Research Forum.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
plainn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Nevertheless, the total count demonstrates the feasibility of imposing a strong information model and executing a CDM conversion on a number of records that would represent a significant fraction of the world's population.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > 2.
boosteddirectn = 682- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: The 682 million patient records converted to the OMOP CDM represent a significant fraction of the world's population.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In return, this approach creates a unique opportunity of implementing a number of existing data exploration and evidence generation tools and participating in world-wide studies because any given query can be executed at any site without modification.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Any given query can be executed at any site without modification.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Previous success in algorithm development, software distribution, and evidence generation points to potential success in the overall evidencegenerating OHDSI project.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Discussion and Conclusion
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“The fiveyear project developed new methods in observational research and established an observational research laboratory.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ACHILLES enables the characterization, quality assessment, and visualization of observational data and provides users with an interactive, exploratory framework to assess patient demographics and the prevalence of all conditions, drugs, procedures, and observations stored in the dataset.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP) [4], which was a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“OHDSI's choice of standard terminologies does not limit who may participate because mappings and tools are supplied to translate from other terminologies.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Research Network
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Mappings and tools supplied by OHDSI are sufficient to translate from all other terminologies so that choice of standard terminologies does not limit participation.
In the paper
“A centerpiece of the OMOP project was the development of the OMOP Common Data Model (CDM) [4] which represents healthcare data from diverse sources in a consistent and standardized way (see Table 1).”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data are retained at the participant's site, simplifying patient and business privacy issues.”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“At the conclusion of this five-year project, the OMOP research investigators initiated the OHDSI effort, and the research laboratory moved to the Reagan-Udall Foundation for the FDA under the Innovation in Medical Evidence Development and Surveillance (IMEDS) Program [5].”
§ Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers > Methods
What the sources say
- Not matchedcites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
The paper relies on this without support.
The OMOP CDM is a 'strong' information model, in which the encoding and relationships among concepts are explicitly and formally specified.
“This CDM is a”
The paper relies on this without support.
The 682 million patient records converted to the OMOP CDM represent a significant fraction of the world's population.
“a number of records that would represent a significant fraction of the world's population”
The paper relies on this without support.
The existing OMOP databases and conversion experience are sufficient to make the OHDSI vision of accessing a network of one billion patients feasible today.
“In other words, OHDSI vision is, in fact, feasible today.”
The paper relies on this without support.
Mappings and tools supplied by OHDSI are sufficient to translate from all other terminologies so that choice of standard terminologies does not limit participation.
“mappings and tools are supplied to translate from other terminologies”
The paper relies on this without support.
Any given query can be executed at any site without modification.
“because any given query can be executed at any site without modification”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The OHDSI network and its open-source analytic tools have been previously used to generate reliable evidence in medicine, including ophthalmology.”
From Semaglutide and Nonarteritic Anterior Ischemic Optic Neuropathy (2025) · cites this paper as [12]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The Strengthening the Reporting of Observational Studies in Epidemiology guideline was followed.11”
From Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022. (2024) · cites this paper as [11]
Experimental — accuracy not yet measured
Citations
Cites
Launching PCORnet, a national patient-centered clinical research network
Fleurence, R., Curtis, L. H., Califf, R. M., Platt, R., Selby, J. V., & Brown, J. S. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002747 · Cited by 654
Validation of a common data model for active safety surveillance research
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000376 · Cited by 548 · cited 2×
“OHDSI grew out of the Observational Medical Outcomes Partnership (OMOP) [4], which was a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.”
BackgroundMethodEvaluating the Impact of Database Heterogeneity on Observational Study Results
Madigan, D., Ryan, P. B., Schuemie, M. J., Stang, P., Overhage, J. M., Hartzema, A. G., Suchard, M. A., DuMouchel, W., & Berlin, J. A. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt010 · Cited by 186 · cited 1×
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
BackgroundA ‘Green Button’ For Using Aggregate Patient Data At The Point Of Care
Longhurst, C., Harrington, R. A., & Shah, N. H. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0099 · Cited by 159 · cited 1×
“We envision a future where observational studies will inform clinical practice—by providing practice-based evidence— using the unprecedented amount of available patient data and the use of computerized systems to process the data [11].”
MethodMedication‐Wide Association Studies
Ryan, P. B., Madigan, D., Stang, P., Schuemie, M. J., & Hripcsak, G. M. (2013) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1038/psp.2013.52 · Cited by 59 · cited 1×
“The first published OHDSI study used databases maintained by one site to carry out a medication-wide association study, assessing whether drugs with similar function or structure cause similar side effects [10].”
MethodEstimating Prognosis with the Aid of a Conversational-Mode Computer Program
Feinstein, A. R., RUBINSTEIN, J. F., & Ramshaw, W. A. (1972) · Annals of Internal Medicine · doi:10.7326/0003-4819-76-6-911 · Cited by 42 · cited 1×
“initiated the idea of using data on 678 lung cancer patients as an electronic ‘library of clinical experience’ to obtain personalized prognosis [12].”
Method
Cited by
Sorted by citations
Items 401–500 of 1,105
Long-term outcomes of patients with a pre-existing neurological condition after SARS-CoV-2 infection
Hadidchi, R., Al‐Ani, Y., Choi, S., Renteria, S., Duong, K. S., Henry, S. S., Wang, S. H., & Duong, T. Q. (2025) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2025.123477 · Cited by 17
“EMR data was deidenti�ed to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) version 6 24 .”
Wang, L., Zhang, Y., Jiang, M., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy020 · Cited by 17
Tian, Y., Shang, Y., Tong, D., Chi, S., Li, J., Kong, X., Ding, K., & Li, J. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.008 · Cited by 18
Kim, H., Yoo, S., Jeon, Y., Yi, S., Kim, S., Choi, S. A., Hwang, H. Y., & Kim, K. J. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00409 · Cited by 17
Seong, Y., You, S. C., Ostropolets, A., et al. (2021) · Healthcare Informatics Research · doi:10.4258/hir.2021.27.1.29 · Cited by 17
“As a successor to the Observational Medical Outcomes Partnership (OMOP), OHDSI adopts the OMOP common data model (CDM) as its standard data structure and the OMOP vocabulary as its standard semantics [6].”
MethodReps, J., Williams, R. D., Schuemie, M. J., Ryan, P. B., & Rijnbeek, P. R. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01879-6 · Cited by 17
“The OHDSI network and collaboration could be used to scale up this study across more diverse databases in future work [24].”
MethodIdentifying Cases of Metastatic Prostate Cancer Using Machine Learning on Electronic Health Records.
Seneviratne, M. G., Banda, J. M., Brooks, J. D., Shah, N. H., & Hernandez‐Boussard, T. (2018) · Europe PMC (PubMed Central) · Cited by 17
Kim, M., Shin, S.-Y., Kang, M., Yi, B.-K., & Chang, D. K. (2019) · JMIR Medical Informatics · doi:10.2196/14083 · Cited by 17
Ostropolets, A., Reich, C., Ryan, P. B., et al. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103363 · Cited by 17
“OHDSI is an open-source, multi-stakeholder, interdisciplinary collaboration that leverages data standards to produce reliable evidence crucial to develop meaningful algorithms generalizable across databases and stakeholders [1].”
BackgroundUsing electronic health records for clinical pharmacology research: Challenges and considerations
Jafari, E. A., Blackman, M. H., Karnes, J. H., Driest, S. L. V., Crawford, D. C., Choi, L., & McDonough, C. W. (2024) · Clinical and Translational Science · doi:10.1111/cts.13871 · Cited by 16
Chung, G., Etter, K., & Yoo, A. C. (2020) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4980 · Cited by 16
Chandler, R. E. (2020) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5022 · Cited by 16
Campion, T. R., Sholle, E., Pathak, J., Johnson, S. B., Leonard, J. P., & Cole, C. L. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab266 · Cited by 20
“9 To support big data analytics , the Observational Health Data Science and Informatics consortium’s Observational Medical Outcomes Partnership (OMOP) CDM 10,11 enables access to almost all data from WCM and NYP EHR systems mapped to reference terminologies, such as ICD, CPT, LOINC, and RxNorm.”
MethodMariño, J., Kasbohm, E., Struckmann, S., Kapsner, L. A., & Schmidt, C. O. (2022) · Applied Sciences · doi:10.3390/app12094238 · Cited by 20
Characterizing variability of electronic health record-driven phenotype definitions
Brandt, P., Kho, A., Luo, Y., Pacheco, J. A., Walunas, T. L., Hakonarson, H. H., Hripcsak, G. M., Liu, C., Shang, N., Weng, C., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac235 · Cited by 15
Lee, D. Y., Choi, B., Kim, C., Fridgeirsson, E. A., Reps, J., Kim, M.-S., Kim, J., Jang, J., Rhee, S. Y., Seo, W., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/46165 · Cited by 16
“More than 2000 researchers worldwide participate in OHDSI, and 74 countries have converted or are converting data to OMOP CDM [19].”
Eos and OMOCL: Towards a seamless integration of openEHR records into the OMOP Common Data Model
Kohler, S., Boscá, D., Kärcher, F., Haarbrandt, B., Prinz, M., Marschollek, M., & Eils, R. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104437 · Cited by 17
Second-Line Pharmaceutical Treatments for Patients with Type 2 Diabetes
Vashisht, R., Patel, A. R., Dahm, L., Han, C., Medders, K. E., Mowers, R. M., Byington, C. L., Koliwad, S. K., & Butte, A. J. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.36613 · Cited by 21
Ostropolets, A., Hripcsak, G. M., Husain, S. A., Richter, L. R., Spotnitz, H. M., Elhussein, A., & Ryan, P. B. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad202 · Cited by 16
“We used Columbia University Irving Medical (CUIMC) EHRs translated to OMOP CDM. 29 The database has electronic health records and records from administrative and ancillary systems for more than 6 million patients and currently holds information about the person (demographics), visits (inpatient and…”
BackgroundMethodWeaver, J. C., Hardin, J., Blacketer, C., Krumme, A. A., Jacobson, M. H., & Ryan, P. B. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02073-6 · Cited by 16
“The algorithm presented in this study offers a reproducible framework that can be implemented across different databases, particularly those transformed to the OMOP CDM.”
MethodInfluentialYi, Y., Kwon, E., Yun, G., Park, S., Jeong, J. C., Na, K. Y., Chin, H. J., Yoo, S., Kim, S., Oh, T. J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52078-4 · Cited by 17
An OMOP CDM-Based Relational Database of Clinical Research Eligibility Criteria
Si, Y., & Chunhua, W. (2017) · Studies in health technology and informatics · doi:10.3233/978-1-61499-830-3-950 · Cited by 16
Merging heterogeneous clinical data to enable knowledge discovery
Seneviratne, M. G., Kahn, M. G., & Hernandez‐Boussard, T. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0040 · Cited by 17
Hong, N., Zhang, N., Wu, H., Lu, S., Yu, Y., Hou, L., Lu, Y., Liu, H., & Jiang, G. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0686-7 · Cited by 17
Looten, V., Chang, L. K. W., Neuraz, A., Loriot, M., Vedie, B., Paul, J., Mauge, L., Rivet, N., Bonifati, A., Châtellier, G., et al. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.12.030 · Cited by 18
The Data Gap in the EHR for Clinical Research Eligibility Screening.
Butler, A. M., Wei, W., Yuan, C., Kang, T., Si, Y., & Weng, C. (2018) · Europe PMC (PubMed Central) · Cited by 16
Bergquist, T., Yan, Y., Schaffter, T., Yu, T. V., Pejaver, V. R., Hammarlund, N. E., Prosser, J., Guinney, J., & Mooney, S. D. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa083 · Cited by 19
Jin, S., Kostka, K., Posada, J., Kim, Y.-S., Seo, S. I., Lee, D. Y., Shah, N. H., Roh, S., Lim, Y., Chae, S. G., et al. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10040288 · Cited by 16
“Regional of institutional drug and diagnosis codes were converted to standardized OMOP vocabulary to provide interoperability between databases using different code systems, which has been developed and maintained by an international collaborative initiative, Observational Health Data Sciences and Informatics (OHDSI)…”
MethodInfluentialBig Data-Based Epidemiology of Uveitis and Related Intraocular Inflammation
Akhter, M., & Toy, B. C. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000364 · Cited by 16
“11 Furthermore, the application of artificial intelligence using machine learning to develop and implement natural language processing algorithms to more accurately and efficiently collect and analyze unstructured data directly from medical records holds great promise.”
BackgroundRogers, J. R., Hripcsak, G. M., Cheung, Y. K., & Weng, C. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103822 · Cited by 16
Song, M. J., Kim, S., Boo, D., Park, C., Yoo, S., Yoon, H. I., & Cho, Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-98069-7 · Cited by 21
You, S. C., Lee, S., Choi, B., & Park, R. W. (2022) · Korean Circulation Journal · doi:10.4070/kcj.2022.0294 · Cited by 16
Data Integration Strategies for Predictive Analytics in Precision Medicine
Frey, L. J. (2018) · Personalized Medicine · doi:10.2217/pme-2018-0035 · Cited by 17
A method for the graphical modeling of relative temporal constraints
Mate, S., Bürkle, T., Kapsner, L. A., Toddenroth, D., Kampf, M. O., Sedlmayr, M., Castellanos, I., Prokosch, H., & Kraus, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103314 · Cited by 16
Candore, G., Hedenmalm, K., Slattery, J., et al. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1785 · Cited by 16
A novel tool for standardizing clinical data in a semantically rich model
Freedman, H. T., Williams, H., Miller, M. A., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.yjbinx.2020.100086 · Cited by 16
Lee, S. M., Kim, K., Yoon, J. G., Park, S. K., Moon, S., Lee, S. E., Oh, J., Yoo, S., Kim, K., Yoon, H., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9092910 · Cited by 15
“Therefore, this study has proven that the acceleration and duplication of research at the national level may be possible [29], while minimizing inconsistent results caused by various methodological definitions and the risk of privacy leaks and data safety through a distributed research network [30].”
BackgroundLee, D. Y., Cho, J., You, S. C., Park, R. W., Kim, C. S., Lee, E., Aizenstein, H. J., Andreescu, C., Karim, H. T., Hong, C. H., et al. (2020) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2020.09.016 · Cited by 15
Becker, T., & Ayton, P. (2023) · Safety Science · doi:10.1016/j.ssci.2023.106352 · Cited by 15
Choi, Y. J., Kim, T. J., Bang, C. S., et al. (2023) · World Journal of Gastroenterology · doi:10.3748/wjg.v29.i44.5882 · Cited by 15
Kalokyri, V., Kondylakis, H., Sfakianakis, S. G., Nikiforaki, K., Karatzanis, I., Mazzetti, S., Tachos, N. S., Regge, D., Fotiadis, D. I., Kostas Marias, A., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00101 · Cited by 15
An in-depth review of AI-powered advancements in cancer drug discovery
Le, M. H. N., Nguyen, P. K., Nguyen, T. P. T., Nguyen, H. Q., Tam, D. N. H., Huynh, H. H., Huynh, P. K., & Le, N. Q. K. (2025) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2025.167680 · Cited by 15
Psychiatric and neuropsychiatric sequelae of COVID-19 within 2 years: a multinational cohort study
Chai, Y., Lam, I. C. H., Man, K. K. C., Hayes, J., Wan, E. Y. F., Li, X., Chui, C. S. L., Lau, W. C. Y., Lin, X., Yin, C., et al. (2025) · BMC Medicine · doi:10.1186/s12916-025-03952-z · Cited by 14
“All data were routinely collected and converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), version 5, maintained by the Observational Health Data Sciences and Informatics (OHDSI) network [18].”
Zhang, H., He, Z., He, X., et al. (2018) · PubMed · Cited by 15
A Minimal Information Model for Potential Drug-Drug Interactions
Hochheiser, H., Jing, X., Garcia, E. A., Ayvaz, S., Sahay, R., Dumontier, M., Banda, J. M., Beyan, O., Brochhausen, M., Draper, E. W., et al. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.608068 · Cited by 16
“As large-scale observational efforts such as the Observational Health Data Sciences and Informatics (OHDSI) Hripcsak et al. (2015) effort continue to grow, retrospective extraction of such prevalence information from large cohorts is more of a practical possibility.”
BackgroundFrid, S. A., Pastor, X., Cucó, G. B., Pedrera‐Jiménez, M., Balazote, P. S., Carrero, A. M., & Lozano-Rubí, R. (2023) · JMIR Medical Informatics · doi:10.2196/44547 · Cited by 15
“The OMOP CDM is considered by several authors as the most adequate data model for sharing data in EHR-based longitudinal studies [11-13].”
MethodCui, L., Zeng, N., Kim, M. J., Mueller, R., Hankosky, E. R., Redline, S. S., & Zhang, G. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0682-y · Cited by 17
Lost in Translation: Diagnosis Records Show More Inaccuracies After Biopsy in Oncology Care EHRs.
Diaz‐Garelli, F., Strowd, R. E., III, Wells, B. J., Ahmed, T., Merrill, R., & Topaloĝlu, Ü. (2019) · PubMed Central · Cited by 15
Mehr, A. P., Sadeghi-Najafabadi, M., Chau, K., Messmer, J., Pai, R. N., Roy, N., Friedman, D. J., Pollak, M. R., Schlöndorff, J. S., Naljayan, M. V., et al. (2018) · Kidney International Reports · doi:10.1016/j.ekir.2018.09.012 · Cited by 14
Adverse drug reaction discovery from electronic health records with deep neural networks
Zhang, W., Kuang, Z., Peissig, P. L., & Page, D. (2020) · ACM Conference on Health, Inference, and Learning (CHIL) · doi:10.1145/3368555.3384459 · Cited by 15
Choi, H., Choi, B., Han, S., Lee, M. J., Shin, G., Kim, H., Son, M., Kim, K., Kwon, J., Park, R. W., et al. (2023) · Internal Medicine · doi:10.2169/internalmedicine.1459-22 · Cited by 14
“…AJIRB-MED-MDB-21-196 The database used in this study was standardized with the observational medical outcomes partnership common data model, which is an international clinical standardization sys-tem maintained by the Observational Health Data Sciences and Informatics community (22).”
MethodReal-World Effectiveness of Statin Therapy in Adult Asthma
Park, C., Jang, J.-H., Kim, C., et al. (2023) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2023.10.029 · Cited by 14
Bebbington, E., Miles, J., Young, A. E., van Baar, M. E., Bernal, N. P., Brekke, R. L., van Dammen, L., Elmasry, M., Inoue, Y., McMullen, K. A., et al. (2024) · Burns · doi:10.1016/j.burns.2024.01.004 · Cited by 14
“Pooling data not only allows clinical benchmarking, but also increases sample size to achieve higher power for embedded trials, study of rare outcomes, and tracking of emerging trends [ 8–10 ] .”
Cha, J. M., Kim, M.-S., Jo, H. H., et al. (2024) · Gut and Liver · doi:10.5009/gnl230406 · Cited by 14
“10,11 When the data of patients are loaded into the CDM, the original “patient_ ids” are deleted, and the random “person_ids” are generated for CDM data to maintain personal security, privacy and confidentiality.”
MethodYan, D., Bao, S., Zhang, Z., Sun, J., & Zhou, M. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00748-8 · Cited by 16
“Observational Health Data Sciences and Informatics (OHDSI; https://ohdsi.org/) 46 , dedicated to facilitating data exploration and evidence generation, provides a set of standard vocabularies to represent multi-source healthcare data in a standardized and consistent manner 1,2 .”
Hogan, W. R., Hanna, J., Hicks, A., et al. (2017) · Journal of Biomedical Semantics · doi:10.1186/s13326-017-0121-5 · Cited by 14
Jeon, H., You, S. C., Kang, S. Y., Seo, S. I., Warner, J. L., Belenkaya, R., & Park, R. W. (2021) · JMIR Medical Informatics · doi:10.2196/25035 · Cited by 14
“The Observational Health Data Sciences and Informatics (OHDSI) collaboration, which is a multistakeholder group organized for global collaboration studies, provides the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to contribute to medical research on harmonized observational databases [9].”
BackgroundComparative effectiveness of medical concept embedding for feature engineering in phenotyping
Lee, J., Liu, C., Kim, J. H., Butler, A. M., Shang, N., Pang, C., Natarajan, K., Ryan, P. B., Ta, C., & Weng, C. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab028 · Cited by 16
“OHDSI is a multi-stakeholder, interdisciplinary collaborative that aims to bring out the value of health data through large-scale analytics.(22) The OMOP CDM harmonizes several different medical coding systems, including but not limited to ICD-9-CM, ICD-10-CM, SNOMED CT, and LOINC, to achieve standardized vocabularies…”
BackgroundAssigning diagnosis codes using medication history
Hansen, E. R., Sagi, T., Hose, K., et al. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102307 · Cited by 14
Park, J., Lee, D. Y., Kim, C., Lee, Y. H., Yang, S., Lee, S., Kim, S.-J., Lee, J., Park, R. W., & Shin, Y. (2022) · Child and Adolescent Psychiatry and Mental Health · doi:10.1186/s13034-022-00515-5 · Cited by 17
“The cohort was converted into the Observational Medical Outcomes Partnership–Common Data Model (OMOP–CDM) version 5 format, an anonymised and standardised medical database format proposed by the Observational Health Data Sciences and Informatics (OHDSI) consortium [21].”
MethodLuo, G., Stone, B. L., Koebnick, C., He, S., Au, D. H., Sheng, X., Murtaugh, M. A., Sward, K. A., Schatz, M. X., Zeiger, R. S., et al. (2019) · JMIR Research Protocols · doi:10.2196/13783 · Cited by 14
Singh, H., Kaur, R., Saluja, S., Cho, S. J., Kaur, A., Pandey, A. K., Gupta, S., Das, R., Kumar, P. M., Palma, J. P., et al. (2019) · JAMIA Open · doi:10.1093/jamiaopen/ooz064 · Cited by 14
Deep Contextual Clinical Prediction with Reverse Distillation
Kodialam, R., Boiarsky, R. P., Lim, J., Sai, A., Dixit, N., & Sontag, D. A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i1.16099 · Cited by 16
“This data is mapped into the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) version 6 (Hripcsak et al. 2015).”
MethodBroderick, C., Christian, N., Apfelbacher, C., et al. (2021) · British Journal of Dermatology · doi:10.1111/bjd.20587 · Cited by 14
Almeida, J. R., Silva, J., Matos, S., & Oliveira, J. L. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103849 · Cited by 15
Pan, W., Hathi, D., Xu, Z., Zhang, Q., Li, Y., & Wang, F. (2025) · Nature Communications · doi:10.1038/s41467-025-59092-8 · Cited by 15
Chung, A. E., Evans, C. S., White, P. J., & Lomotan, E. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-93765-2_5 · Cited by 13
Design and validation of a FHIR-based EHR-driven phenotyping toolbox
Brandt, P., Pacheco, J. A., Adekkanattu, P. M., Sholle, E., Abedian, S., Stone, D. J., Knaack, D., Xu, J., Xu, Z., Peng, Y., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac063 · Cited by 13
A REVIEW OF ARTIFICIAL INTELLIGENCE IN TREATMENT OF COVID-19
Gandla, K., Teja, K., Babu, P. V., Sagapola, R., & Sudhakar, P. (2022) · Journal of Pharmaceutical Negative Results · doi:10.47750/pnr.2022.13.s01.31 · Cited by 16
Implementation of inclusion and exclusion criteria in clinical studies in OHDSI ATLAS software
Blasini, R., Buchowicz, K. M., Schneider, H., Samans, B., & Sohrabi, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-49560-w · Cited by 14
“OHDSI offers a wide range of open-source tools to support various data analytics use cases on observational patient-level data, all of which interact with databases using the Common Data Model 8,12 .”
BackgroundNew Tools for Data Harmonization and Their Potential Applications in Organ Transplantation
Hosseini, S. A. T., Kazemzadeh, R., Foster, B. J., Arpalı, E., & Süsal, C. (2024) · Transplantation · doi:10.1097/tp.0000000000005048 · Cited by 19
Lee, K. A., Kim, J. S., Kim, Y. J., Goak, I. S., Jin, H. Y., Park, S. Y., Kang, H., & Park, T. S. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14062065 · Cited by 15
“All EHR data were transformed into the Observational Medical Outcomes Partnership Common Data Model, a standardized database model that consolidates and integrates various real-world data sources, including EHRs [22].”
Bate, A., Chuang‐Stein, C., Roddam, A., & Jones, B. C. (2018) · Pharmaceutical Statistics · doi:10.1002/pst.1908 · Cited by 13
Visweswaran, S., Samayamuthu, M. J., Morris, M. M., Weber, G. M., Macfadden, D. K., Trevvett, P., Klann, J. G., Gainer, V. S., Benoit, B., Murphy, S. N., et al. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab036 · Cited by 13
Privacy-protecting, reliable response data discovery using COVID-19 patient observations
Kim, J., Neumann, L., Paul, P., Day, M. E., Aratow, M., Bell, D. S., Doctor, J. N., Hinske, L. C., Jiang, X., Kim, K. K., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab054 · Cited by 13
“This involves rewriting and updating existing extract-transform-load (ETL) scripts to map source EHR data to target the common data model (CDM, which in our case is the Observational Medical Outcomes Partnership, OMOP).(16) The institutions with the same EHR system or database management system share common experience…”
MethodKnowledge Representation and Ontologies
Fung, K. W., & Bodenreider, O. (2019) · Health informatics · doi:10.1007/978-3-319-98779-8_15 · Cited by 13
ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection
Morel, M., Bacry, E., Gaïffas, S., Guilloux, A., & Leroy, F. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz003 · Cited by 1
Naqishbandi, T. A., & Ayyanathan, N. (2019) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-24318-0_64 · Cited by 14
Mun, Y., Kim, J., Noh, K. J., et al. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01370-0 · Cited by 13
“In addition, OMOP CDM that allows systematic analysis by transforming and integrating clinical data into standardized formats are emerging as one of the most important approaches in medical research [19, 20].”
MethodKim, J., Kim, S., Ryu, B., Song, W., Lee, H., & Yoo, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-86564-w · Cited by 13
Schüttler, C., Prokosch, H., Sedlmayr, M., & Sedlmayr, B. (2021) · JMIR Medical Informatics · doi:10.2196/25531 · Cited by 13
“As the implementation of such a platform was intended to take place as quickly as possible, a preselection of three implementations already used in the consortium (also freely accessible for researchers in Germany) was made for the usability evaluation: the MIRACUM i2b2 (Informatics for Integrating Biology and the Bed…”
BackgroundMethodDobbins, N. J., Mullen, T., Uzuner, Ö., & Yetişgen, M. (2022) · Scientific Data · doi:10.1038/s41597-022-01521-0 · Cited by 15
“Chia OMOP-derived entities generally follow the naming convention of OMOP domains and SQL database tables, such as Person , Condition , Device and so on.”
MethodInfluentialClinical Databases for Breast Cancer Research
Hwang, K.-T. (2021) · Advances in Experimental Medicine and Biology · doi:10.1007/978-981-32-9620-6_26 · Cited by 12
OARD: Open annotations for rare diseases and their phenotypes based on real-world data
Liu, C., Ta, C., Havrilla, J. M., Nestor, J. G., Spotnitz, H. M., Geneslaw, A. S., Hu, Y., Chung, W. K., Wang, K., & Weng, C. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.08.002 · Cited by 12
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Dobbins, N. J., Han, B., Zhou, W., Lan, K. F., Kim, H. N., Harrington, R., Uzuner, Ö., & Yetişgen, M. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad149 · Cited by 14
“Yuan et al developed Criteria2Query [10], a hybrid information extraction (IE) pipeline and application which uses both rules and machine learning to generate database queries on an Observation Medical Outcomes Partnership (OMOP) [19] database, later expanded by Fang et al [12].”
MethodInfluentialUse of Metadata-Driven Approaches for Data Harmonization in the Medical Domain: Scoping Review
Peng, Y., Bathelt, F., Gebler, R., et al. (2023) · JMIR Medical Informatics · doi:10.2196/52967 · Cited by 12
“DARWIN EU uses the Observational Medical Outcomes Partnership (OMOP) CDM [5] as the base model, which is provided by the Observational Health Data Sciences and Informatics [6] community.”
MethodRao, G., Shoaibi, A., Makadia, R., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0310634 · Cited by 12
“CDM admini strati ve cl aims s ubmi tted for payment by providers and pharmacies are veri fied, adjudi cated and de-identified pri or to i nclus i on.”
MethodInfluentialPalmer, E. L., Higgins, J. H., Hassanpour, S., Sargent, J. D., Robinson, C. M., Doherty, J. A., & Onega, T. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0864-2 · Cited by 12
Characterizing physicians’ information needs related to a gap in knowledge unmet by current evidence
Ostropolets, A., Chen, R., Zhang, L., et al. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa012 · Cited by 12
The Use of Inter-terminology Maps for the Creation and Maintenance of Value Sets.
Fung, K. W., Xu, J. Z., & Gold, S. (2019) · PubMed · Cited by 12
Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation
Spengler, H., Lang, C., Mahapatra, T., Gatz, I., Kuhn, K. A., & Praßer, F. (2020) · JMIR Medical Informatics · doi:10.2196/15918 · Cited by 12
Scalable Algorithms for Large Competing Risks Data
Kawaguchi, E. S., Shen, J. I., Suchard, M. A., & Li, G. (2020) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2020.1841650 · Cited by 12
Severe hypertension in China: results from the China PEACE million persons project
China PEACE MPP Collaborative Group (2020) · Journal of Hypertension · doi:10.1097/hjh.0000000000002675 · Cited by 12
Im, Y.-G., Han, M. Y., & Baek, H.-S. (2023) · Nutrients · doi:10.3390/nu15132860 · Cited by 13
“A diagnosis record in the OMOP-CDM includes a patient identifier, diagnosis date, diagnosis code, and the mapping and coding system, such as the International Classification of Diseases, Tenth Revision (ICD-10), linked to the OMOP-CDM Standardized Vocabularies [31].”
MethodA Roadmap for Optimizing Asthma Care Management via Computational Approaches
Luo, G., & Sward, K. A. (2017) · JMIR Medical Informatics · doi:10.2196/medinform.8076 · Cited by 12
Time to really share real-world data?
Graham, S., McDonald, L., Wasiak, R., Lees, M., & Ramagopalan, S. V. (2018) · F1000Research · doi:10.12688/f1000research.15517.1 · Cited by 12
Zou, K. H., Li, J. Z., Salem, L. A., Imperato, J., Edwards, J., & Ray, A. (2020) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-020-00223-7 · Cited by 12
“For example, the Observational Health Data Sciences and Informatics (OHDSI) group is at the forefront of harmonizing and standardizing the analysis of observational data and has created a common-data model (Hripcsak et al. 2015).”
BackgroundMazzotti, D. R. (2021) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2021.101529 · Cited by 12
Bhatia, A., Preiss, A., Xiao, X., et al. (2023) · medRxiv · doi:10.1101/2023.05.03.23289084 · Cited by 12
“Within the N3C enclave, the “Paxlovid or nirmatrelvir” concept set was used to identify drug exposures that correspond to Paxlovid (10 Observational Medical Outcomes Partnership [OMOP] concepts).(40) We followed patients for a 28-day period following their COVID-19 index date.”
Schoenthaler, M., Fichtner, U. A., Boeker, M., et al. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-08375-7 · Cited by 11
“Notable examples of software platforms and data models for CDWH are i2b2, TranSMART, and OHDSI (observational medical outcomes partnership/observational health data sciences and informatics) [31].”
Method
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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