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].”
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“In 2020, more than 100 databases from 20 countries (corresponding to more than 0.5 billion patients) have been integrated into the OMOP CDM [12].”
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Toward cross‐platform electronic health record ‐driven phenotyping using Clinical Quality Language
Brandt, P., Kiefer, R. C., Pacheco, J. A., Adekkanattu, P. M., Sholle, E., Ahmad, F. S., Xu, J., Xu, Z., Ancker, J. S., Wang, F., et al. (2020) · Learning Health Systems · doi:10.1002/lrh2.10233 · Cited by 24
“We investigate whether this approach does enable cross-platform phenotyping and demonstrate a newly built CQL evaluation engine that is able to execute CQL phenotype definitions against the Observational Health Data Sciences and Informatics (OHDSI) platform.(5) We used a clinically validated phenotype definition for p…”
MethodLee, S., Han, J., Park, R. W., Kim, G. J., Rim, J. H., Cho, J., Lee, K. H., Lee, J., Kim, S., & Kim, J. H. (2019) · Drug Safety · doi:10.1007/s40264-018-0767-7 · Cited by 20
Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes
Palmer, E. L., Hassanpour, S., Higgins, J. H., Doherty, J. A., & Onega, T. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0863-3 · Cited by 23
Khare, R., Kappelman, M. D., Samson, C. M., et al. (2020) · Learning Health Systems · doi:10.1002/lrh2.10243 · Cited by 23
Park, J., You, S. C., Jeong, E., Weng, C., Park, D., Roh, J., Lee, D. Y., Cheong, J. Y., Choi, J. W., Kang, M., et al. (2021) · JMIR Medical Informatics · doi:10.2196/23983 · Cited by 23
“In an international open science initiative, Observational Health Data Sciences and Informatics (OHDSI), the structured data of more than 200 hospitals worldwide were mapped into a standardized vocabulary and data structure referred to as the Observational Medical Outcomes Partnership (OMOP)-common data model (CDM) [4…”
Artificial Intelligence for Medical Diagnosis
Richens, J. G., & Buchard, A. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_29 · Cited by 25
Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities
Post, A. R., Burningham, Z. R., & Halwani, A. S. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00158 · Cited by 29
Schuemie, M. J., Arshad, F., Pratt, N. L., Nyberg, F., Alshammari, T. M., Hripcsak, G. M., Ryan, P. B., Prieto‐Alhambra, D., Lai, L. Y. H., Li, X., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.893484 · Cited by 23
“However, recent developments in large-scale analytics such as large-scale propensity scores (PS) (Tian et al., 2018), and opensource analytics software like that developed by the Observational Health Data Sciences and Informatics (OHDSI) (Hripcsak et al., 2015), mean the distinction between signal generation and confi…”
BackgroundInfluentialAyaz, M., Pasha, M. F., Alahmadi, T. J., Abdullah, N. N. B., & Alkahtani, H. K. (2023) · Healthcare · doi:10.3390/healthcare11121729 · Cited by 23
“For example, the Observational Health Data Sciences and Informatics (OHDSI) program has generated an enormous volume of work in the field of health data analytics, including the creation of the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) [30].”
BackgroundChen, W., Xie, F., McCarthy, D., Reynolds, K., Lee, M. S., Coleman, K. J., Getahun, D., Koebnick, C., & Jacobsen, S. J. (2023) · PubMed Central · doi:10.1093/jamiaopen/ooad039 · Cited by 27
Toh, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1754 · Cited by 23
“There has been an increase in the number of distributed data networks or multicenter collaboratives that employ a common data model, such as the Sentinel System,62 the Canadian Network for Observational Drug Effect Studies,63 and networks that leverage the infrastructure from the Observational Health Data Sciences and…”
BackgroundSpotnitz, H. M., Natarajan, K., Ryan, P. B., et al. (2020) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000003656 · Cited by 23
Kashyap, M., Seneviratne, M. G., Banda, J. M., Falconer, T., Ryu, B., Yoo, S., Hripcsak, G. M., & Shah, N. H. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa032 · Cited by 23
“17 This led to the development of the APH-RODITE R-package, an open-source implementation of the XPRESS framework with dynamic anchor learning built on the OHDSI common data model, which has shown comparable performance to rule-based definitions for 2 phenotypes (type 2 diabetes and myocardial infarction).”
BackgroundInfluentialSeo, S. I., You, S. C., Park, C. H., Kim, T., Ko, Y. S., Kim, Y., Yoo, J. J., Kim, J., & Shin, W. G. (2020) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.14983 · Cited by 22
Edmondson, M. J., Luo, C., Duan, R., Maltenfort, M., Chen, Z., Locke, K., Justine Shults, A., Bian, J., Ryan, P. B., Forrest, C. B., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-99078-2 · Cited by 22
“Meta-analysis, which only requires effect size and variance estimates from each individual site, is easy to implement and widely accepted in medical literature; it is the primary analysis method used in comparative effectiveness studies conducted by the Observational Health Data Sciences and Informatics (OHDSI) collab…”
MethodThe use of real-world data in drug repurposing
Park, K. (2021) · Translational and Clinical Pharmacology · doi:10.12793/tcp.2021.29.e18 · Cited by 22
“One such example is found in Observational Health Data Sciences and Informatics (OHDSI) consortium [33].”
BackgroundKristoffersen, E., Bjorvatn, B., Halvorsen, P., Nilsen, S., Fossum, G., Fors, E., Jørgensen, P., Øxnevad-Gundersen, B., Gjelstad, S., Bellika, J. G., et al. (2022) · Scandinavian Journal of Primary Health Care · doi:10.1080/02813432.2022.2073966 · Cited by 19
Lin, V. A., Tsouchnika, A., Allakhverdiiev, E., Rosen, A. W., Gögenur, M., Clausen, J. S. R., Bräuner, K. B., Walbech, J. S., Rijnbeek, P. R., Drakos, I., et al. (2022) · Techniques in Coloproctology · doi:10.1007/s10151-022-02624-x · Cited by 27
“Data were standardized to the observational medical outcomes partnership (OMOP) common data model (CDM) and the standardized vocabularies [11, 12].”
MethodTong, J., Luo, C., Islam, M. N., Sheils, N. E., Buresh, J., Edmondson, M. J., Merkel, P. A., Lautenbach, E., Duan, R., & Chen, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00615-8 · Cited by 23
Incidence and risk factors for periprosthetic joint infection: A common data model analysis
Bae, K. J., Chae, Y. J., Jung, S. J., et al. (2022) · Joint Diseases and Related Surgery · doi:10.52312/jdrs.2022.671 · Cited by 22
“Additionally, the CDM is crucial for collaborative research involving various organizations.[20,21,37] Due to the fact that each hospital's patient information structure is unique, it is vital to standardize patient information when collaborating with numerous hospitals using the CDM tool.”
BackgroundMethodGrimes, J., Szul, P., Metke‐Jimenez, A., Lawley, M., & Loi, K. (2022) · Journal of Biomedical Semantics · doi:10.1186/s13326-022-00277-1 · Cited by 26
Good practices for clinical data warehouse implementation: A case study in France
Doutreligne, M., Degrémont, A., Jachiet, P.-A., Lamer, A., & Tannier, X. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000298 · Cited by 24
“The Paris hospitals, thanks to its regional coverage and the choice of OMOP, is also well advanced in multi-centric research.”
BackgroundInfluentialRanitidine Use and Incident Cancer in a Multinational Cohort
You, S. C., Seo, S. I., Falconer, T., Yanover, C., Duarte‐Salles, T., Seager, S. L., Posada, J., Shah, N. H., Nguyen, P., Kim, Y.-S., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.33495 · Cited by 24
The Emerging Role of the Chief Research Informatics Officer in Academic Health Centers
Mosa, A. S. M., Hollis, K. F., Tachinardi, U., et al. (2017) · Applied Clinical Informatics · doi:10.4338/aci-2017-04-ra-0062 · Cited by 22
Development and validation of the SickKids Enterprise-wide Data in Azure Repository (SEDAR)
Guo, L., Calligan, M., Vettese, E., Cook, S., Gagnidze, G., Han, O., Inoue, J., Lemmon, J., Li, J., Roshdi, M., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21586 · Cited by 22
Incidence, Prevalence, and Survival of Prostate Cancer in the UK
Tan, E. H., Burn, E., Barclay, N. L., Delmestri, A., Man, W. Y., Golozar, A., Serrano, À. R., Duarte‐Salles, T., Cornford, P. A., Prieto‐Alhambra, D., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.34622 · Cited by 25
Dernie, F., Corby, G., Robinson, A., Bezer, J. H., Mercadé‐Besora, N., Griffier, R., Verdy, G., Leis, Á., Ramírez‐Anguita, J. M., Mayer, M. Á., et al. (2024) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.70042 · Cited by 21
Delayed Hypertension Diagnosis and Its Association With Cardiovascular Treatment and Outcomes
Lu, Y. F., Brush, J. E., Kim, C., Liu, Y., Xin, X., Huang, C., Sawano, M., Young, P., McPadden, J., Anderson, M., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.20498 · Cited by 22
Hockett, C. W., Praveen, P. A., Ong, T. C., Amutha, A., Isom, S. P., Jensen, E. T., D'Agostino, R. B., Hamman, R. F., Mayer‐Davis, E. J., Lawrence, J. M., et al. (2020) · Pediatric Diabetes · doi:10.1111/pedi.12981 · Cited by 25
“OMOP is an internationally supported CDM for clinical data harmonization with rich support for standard terminologies and terminology mappings.(12,13) Using such a CDM allows each registry to maintain data securely without requiring data transfer to a common analytic system, thus ensuring confidentiality.”
MethodKim, H. I., Yoon, J. Y., Kwak, M. S., et al. (2021) · Gut and Liver · doi:10.5009/gnl20222 · Cited by 21
“However, it is difficult to characterize or analyze the data from HIRA-NPS and each hospital’s data (especially international hospital data) in the same way or using the same tools because healthcare datasets are stored in databases that are built using a wide variety of data models and, often, local terminologies.(14…”
MethodAnalysis of Dual Combination Therapies Used in Treatment of Hypertension in a Multinational Cohort
Lu, Y., Zandt, M. V., Liu, Y., Li, J., Wang, X., Chen, Y., Chen, Z. J., Cho, J., Dorajoo, S. R., Feng, M., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.3877 · Cited by 22
A Unified Health Information System Framework for Connecting Data, People, Devices, and Systems
He, W., Zhang, Z., Wu, H., Li, W., & Shetty, S. S. (2022) · Journal of Global Information Management · doi:10.4018/jgim.305239 · Cited by 21
EasySMPC: a simple but powerful no-code tool for practical secure multiparty computation
Wirth, F. N., Kussel, T., Müller, A., Hamacher, K., & Praßer, F. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-05044-8 · Cited by 27
“One example are distributed data sharing networks, in which no individual-level data, but aggregated results, are being shared amongst the partners to perform various types of joint analyses [25–27].”
BackgroundCrisafulli, S., Ciccimarra, F., Bellitto, C., Carollo, M., Carrara, E., Stagi, L., Triola, R., Capuano, A., Chiamulera, C., Moretti, U., et al. (2024) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2024.1356405 · Cited by 29
“Examples of distributed data networks for pharmacovigilance and pharmacoepidemiological research include the European Health Data and Evidence Network (EHDEN) (European Health Data Evidence Network, 2024) and the Observational Health Data Sciences and Informatics (OHDSI) Collaborative (Hripcsak et al., 2015) in Europe…”
Ye, Y., D, M. M. W. M., Cooper, G. F., Ferraro, J. P., Su, H., Gesteland, P. H., Haug, P. J., Millett, N. E., Aronis, J. M., Nowalk, A. J., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0174970 · Cited by 25
Considerations for Improving the Portability of Electronic Health Record-Based Phenotype Algorithms.
Rasmussen, L. V., Brandt, P., Jiang, G., Kiefer, R. C., Pacheco, J. A., Adekkanattu, P. M., Ancker, J. S., Wang, F., Xu, Z., Pathak, J., et al. (2019) · PubMed Central · Cited by 22
An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal InferencePreprint
Shimoni, Y., Karavani, E., Ravid, S., Bak, P., Ng, T. H., Alford, S. H., Meade, D., & Goldschmidt, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00442 · Cited by 49
Can artificial intelligence help identify elder abuse and neglect?
Rosen, T., Zhang, Y., Bao, Y., Clark, S., Elman, A., Wen, K. J., Jeng, P. J., & Lachs, M. S. (2019) · Journal of Elder Abuse & Neglect · doi:10.1080/08946566.2019.1682099 · Cited by 22
Assessing Covariate Balance With Small Sample Sizes
Hripcsak, G. M., Zhang, L., Chen, Y., Li, K., Suchard, M. A., Ryan, P. B., & Schuemie, M. J. (2025) · Statistics in Medicine · doi:10.1002/sim.70212 · Cited by 24
Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis
Levitin, S. A., Grbic, J. T., & Finkelstein, J. (2019) · JMIR Medical Informatics · doi:10.2196/13008 · Cited by 22
Metformin is associated with reduced COVID-19 severity in patients with prediabetes
Chan, L., Casiraghi, E., Laraway, B. J., Coleman, B., Blau, H., Zaman, A., Harris, N. L., Wilkins, K. J., Antony, B., Gargano, M. A., et al. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.110157 · Cited by 18
“We defined metformin users as those patients who had recorded use [30] of metformin beginning on or before the start date of the visit”
MethodLombardo, G., Couvert, C., Kose, M., Begum, A., Spiertz, C., Worrell, C., Hasselbaink, D. M., Didden, E., Sforzini, L., Todorovic, M., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104553 · Cited by 20
Cai, C. X., Nishimura, A., Bowring, M. G., Westlund, E., Tran, D., Ng, J. H., Nagy, P., Cook, M., McLeggon, J.-A., DuVall, S. L., et al. (2024) · Ophthalmology Retina · doi:10.1016/j.oret.2024.03.014 · Cited by 20
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data
Levine, M. E., Albers, D. J., & Hripcsak, G. M. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.014 · Cited by 20
MONTRA: An agile architecture for data publishing and discovery
Silva, L. A. B., Trifan, A., & Oliveira, J. L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.03.024 · Cited by 21
Park, R. W. (2019) · Korean Journal of Medicine · doi:10.3904/kjm.2019.94.4.309 · Cited by 20
You, S. C., Jung, S.-J., Swerdel, J. N., Ryan, P. B., Schuemie, M. J., Suchard, M. A., Lee, S., Cho, J., Hripcsak, G. M., Park, R. W., et al. (2019) · Korean Circulation Journal · doi:10.4070/kcj.2019.0173 · Cited by 20
Detecting Systemic Data Quality Issues in Electronic Health Records
Ta, C., & Chunhua, W. (2019) · Studies in health technology and informatics · doi:10.3233/shti190248 · Cited by 22
Chandran, U., Reps, J., Stang, P., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0226255 · Cited by 20
Ryu, B., Yoo, S., Kim, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-02395-9 · Cited by 20
Cummings, B. C., Blackmer, J. M., Motyka, J. R., Farzaneh, N., Cao, L., Bisco, E. L., Glassbrook, J. D., Roebuck, M. D., Gillies, C. E., Admon, A. J., et al. (2023) · Critical Care Medicine · doi:10.1097/ccm.0000000000005837 · Cited by 19
“Using standardized clinical ontologies such as Logical Observation Identifiers Names and Codes (LOINC) codes or the Observational Medical Outcomes Partnership common data model could significantly expedite this process (27, 28).”
MethodBhatia, A., Preiss, A., Xiao, X., Brannock, D., Alexander, G. C., Chew, R., Davis, H., Fitzgerald, M., Hill, E., Kelly, E., et al. (2025) · PLoS Medicine · doi:10.1371/journal.pmed.1004493 · Cited by 33
Advancing Real-World Evidence Through a Federated Health Data Network (EHDEN): Descriptive Study
Blacketer, C., Schuemie, M. J., Moinat, M., Voss, E. A., Camprubi, M., Rijnbeek, P. R., & Ryan, P. B. (2025) · Journal of Medical Internet Research · doi:10.2196/74119 · Cited by 19
“The OMOP CDM is maintained by the Observational Health Data Science and Informatics (OHDSI) community, an open science effort that aims to improve health by empowering a community to collaboratively generate the evidence that promotes better health decisions and better care [25].”
Impending Challenges for the Use of Big Data
Huser, V., & Cimino, J. J. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2015.10.060 · Cited by 21
Recalde, M., Roel, E., Pistillo, A., Sena, A. G., Prats‐Uribe, A., Ahmed, W., Alghoul, H., Alshammari, T. M., Alser, O. H., Areia, C. M., et al. (2021) · International Journal of Obesity · doi:10.1038/s41366-021-00893-4 · Cited by 19
“The OHDSI network maintains the OMOP-CDM, along with a wide range of tools developed by its members to facilitate analyses of mapped data [10].”
MethodTemporal relationship of computed and structured diagnoses in electronic health record data
Schulz, W. L., Young, H. P., Coppi, A., Mortazavi, B. J., Lin, Z., Jean, R. A., & Krumholz, H. M. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01416-x · Cited by 20
Yoo, S., Yoon, E., Boo, D., et al. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1748144 · Cited by 19
Yan, Z., Zachrison, K. S., Schwamm, L. H., Estrada, J. J., & Duan, R. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280192 · Cited by 19
“Thus, this practice is widely adopted among international research networks [6, 14, 15].”
BackgroundLelong, R., Soualmia, L. F., Julien Grosjean, A., Taalba, M., & Darmoni, S. J. (2019) · JMIR Medical Informatics · doi:10.2196/13917 · Cited by 19
“Another example of a distributed solution is the Observational Medical Outcomes Partnership Common Data Model [27].”
BackgroundSeasonal variation of peptic ulcer disease, peptic ulcer bleeding, and acute pancreatitis
Yoon, J. Y., Cha, J. M., Kim, H. I., et al. (2021) · Medicine · doi:10.1097/md.0000000000025820 · Cited by 19
Jannot, A., Countouris, H., Straaten, A. V., Burgun, A., Katsahian, S., & Rance, B. (2021) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2020-216068 · Cited by 19
Large-scale characterization of gender differences in diagnosis prevalence and time to diagnosisPreprint
Sun, T., Hardin, J., Nieva, H. R., Natarajan, K., Cheng, R.-F. J., Ryan, P. B., & Elhadad, N. (2023) · medRxiv · doi:10.1101/2023.10.12.23296976 · Cited by 19
Choi, S. A., Kim, H., Kim, S., et al. (2020) · Epilepsia · doi:10.1111/epi.16472 · Cited by 18
Chung, Y. G., Jeon, Y., Yoo, S., Kim, H., & Hwang, H. Y. (2021) · Korean Journal of Pediatrics · doi:10.3345/cep.2021.00766 · Cited by 18
A Privacy-Preserving and Standard-Based Architecture for Secondary Use of Clinical Data
Ciampi, M., Sicuranza, M., & Silvestri, S. (2022) · Information · doi:10.3390/info13020087 · Cited by 19
Lee, D. Y., Kim, C., Lee, S., Son, S. J., Cho, S.-M., Cho, Y. H., Lim, J., & Park, R. W. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.844442 · Cited by 18
“We used the AUMC database formatted according to the Observational Medical Outcomes Partnership–Common Data Model (OMOP-CDM) version 5.3.1, maintained by the Observational Health Data Sciences and Informatics (OHDSI) and de-identified (27).”
MethodInfluentialCastano, V. G., Spotnitz, H. M., Waldman, G. J., et al. (2022) · Epilepsia · doi:10.1111/epi.17409 · Cited by 18
Pinto, E. P., Normando, P., Flores-Ortiz, R., Afzal, M. U., Jamil, M. A., Fernández‐Bertolín, S., Oliveira, V. D. A., Martufi, V., de Sousa, F., Bashir, A., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac180 · Cited by 18
“9) 50–59 167 933 (13) 105 102 (14) 6653 (19) 3118 (18) 2075 (15) 60–69 94 587 (7.”
Park, K.-Y., Kim, M. H., Choi, S., & Pang, E. (2022) · Journal of Periodontal & Implant Science · doi:10.5051/jpis.2202480124 · Cited by 18
“However, additional costs and time are required to convert the data into a standardized format [15-19].”
BackgroundRekkas, A., Klaveren, D. V., Ryan, P. B., Steyerberg, E. W., Kent, D. M., & Rijnbeek, P. R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00794-y · Cited by 17
Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways
Lin, H., Ni, L., Phuong, C., & Hong, J. C. (2024) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s396971 · Cited by 22
“61 Furthermore, challenges related to language discrepancies, variations in healthcare delivery systems, and inconsistent data formatting across institutions can hamper this ambitious goal.”
BackgroundA conceptual framework for evaluating data suitability for observational studies
Shang, N., Weng, C., & Hripcsak, G. M. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx095 · Cited by 23
Chen, R., Ryan, P. B., Natarajan, K., Falconer, T., Crew, K. D., Reich, C., Vashisht, R., Randhawa, G. S., Shah, N. H., & Hripcsak, G. M. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00107 · Cited by 19
“METHODS For this study, we used the OHDSI network, a multistakeholder, interdisciplinary, international collaborative implementing the OMOP CDM to standardize observational data from diverse sources.(25-27) OMOP CDM is a deep information model (Fig 1) that specifies how to encode and store clinical data at a fine-grai…”
MethodDiaz‐Garelli, F., Strowd, R. E., III, Lawson, V., Mayorga, M. E., Wells, B. J., Lycan, T. W., & Topaloĝlu, Ü. (2020) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00114 · Cited by 18
“Current clinical data reuse research seeks to develop data quality assessment methods separately from clinical practice.(6,53-64) However, our work shows that clinical data and clinical workflows are closely related and should be viewed as a product and production process.”
BackgroundLongitudinal Outcomes of Severe Asthma: Real-World Evidence of Multidimensional Analyses
Lee, Y., Park, Y., Kim, C., Lee, E., Lee, H. Y., Woo, S., You, S. C., Park, R. W., & Park, H. (2020) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2020.09.055 · Cited by 18
Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model
Maier, C., Kapsner, L. A., Mate, S., Prokosch, H., & Kraus, S. (2021) · Applied Clinical Informatics · doi:10.1055/s-0040-1721481 · Cited by 18
Mun, Y., You, S. C., Lee, D. Y., Kim, S., Chung, Y.-R., Lee, K., Song, J. H., Park, Y. G., Park, Y., Roh, Y., et al. (2021) · Epidemiology and Health · doi:10.4178/epih.e2021097 · Cited by 20
“The OMOP CDM, currently maintained by the OHDSI, enables the integration of RWD from multiple and heterogeneous forms of clinical data using common conventions and structures to represent the data [27].”
MethodGeldof, T., Huys, I., & Dyck, W. V. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00043 · Cited by 18
DQueST: dynamic questionnaire for search of clinical trials
Liu, C., Yuan, C., Butler, A. M., Carvajal, R. D., Li, Z. R., Ta, C., & Weng, C. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz121 · Cited by 20
NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort studyPreprint
Reese, J., Coleman, B. D., Chan, L., Blau, H., Callahan, T. J., Cappelletti, L., Fontana, T., Bradwell, K. R., Harris, N. L., Casiraghi, E., et al. (2021) · medRxiv · doi:10.1101/2021.04.13.21255438 · Cited by 18
FedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Fu, S., Jia, H., Vassilaki, M., Keloth, V. K., Dang, Y., Zhou, Y., Garg, M., Petersen, R. C., St Sauver, J., Moon, S., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104623 · Cited by 17
Converge or Collide? Making Sense of a Plethora of Open Data Standards in Health Care
Tsafnat, G., Dunscombe, R., Gabriel, D. L., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/55779 · Cited by 17
“Observational Health Data Sciences and Informatics’(OHDSI) Observational Medical Outcomes Partnership Common Data Model (OMOP) [11] is an open standard designed for longitudinal interrogation of clinical records.”
Long-term outcomes of patients with pre-existing coronary artery disease after SARS-CoV-2 infection
Hadidchi, R., Lee, P., Qiu, S., Changela, S., Henry, S. S., & Duong, T. Q. (2025) · EBioMedicine · doi:10.1016/j.ebiom.2025.105778 · Cited by 17
Li, J., Tian, Y., Li, R., Zhou, T., Li, J., Ding, K., & Li, J. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102024 · Cited by 18
Lu, J., Zhu, J., Zhu, J., & Duong, T. Q. (2022) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2022.07.034 · Cited by 21
Yoo, J., Lee, J.-H., Min, J. Y., Choi, S. W., Kwon, J., Cho, I., Lim, C., Choi, M. Y., & Cha, W. C. (2022) · Journal of Medical Internet Research · doi:10.2196/37928 · Cited by 19
“The Observational Medical Outcomes Partnership (OMOP) common data model (CDM), the Sentinel CDM, and the National Patient-Centered Clinical Outcomes Research Network (PCORnet) CDM for standardized data structures and types [14,15] are other major standards developments.”
MethodRepresenting and utilizing clinical textual data for real world studies: An OHDSI approach
Keloth, V. K., Banda, J. M., Gurley, M., Heider, P. M., Kennedy, G., Liu, H., Liu, F., Miller, T., Natarajan, K., V Patterson, O., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104343 · Cited by 16
Lee, Y., Kim, C., Lee, E., Lee, H. Y., Woo, S., You, S. C., Park, R. W., & Park, H. (2023) · Clinical & Experimental Allergy · doi:10.1111/cea.14362 · Cited by 17
Raventós, B., Fernández‐Bertolín, S., Aragón, M., Voss, E. A., Blacketer, C., Méndez-Boo, L., Recalde, M., Roel, E., Pistillo, A., Reyes, C., et al. (2023) · Clinical Epidemiology · doi:10.2147/clep.s419481 · Cited by 16
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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