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 601–700 of 1,105
Kweon, T., Kim, Y., Lee, K. J., Seo, W., Seo, S. I., Shin, W. G., & Shin, D. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-48430-9 · Cited by 8
Ahmadi, N., Nguyen, Q. V., Sedlmayr, M., & Wolfien, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-52723-y · Cited by 11
Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study
Chai, Y., Man, K. K. C., Luo, H., Torre, C. O., Wing, Y. K., Hayes, J., Osborn, D., Chang, W. C., Lin, X., Yin, C., et al. (2024) · Epidemiology and Psychiatric Sciences · doi:10.1017/s2045796024000088 · Cited by 8
Kim, K., Kim, J., Kim, J. H., Ahn, S. H., Jung, C. Y., Hwang, S. D., Lee, S. W., & Song, J. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-57382-7 · Cited by 8
“HIRA-NPS data from 2012 to 2017 were transformed into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM, version 5.0) developed by the Observational Health Data Sciences and Informatics (OHDSI) network with standard vocabulary and ontology 22 .”
MethodInfluentialHoffmann, K., Nesterow, I., Peng, Y., et al. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1377209 · Cited by 8
“To facilitate the data exchange of RWD for scientific purposes, the HL7 International (11) and the Observational Health Data Sciences and Informatics (OHDSI) (12, 13) community have announced collaboration in 2021 (14).”
MethodGuo, G. N., Jonnagaddala, J., Farshid, S., Huser, V., Reich, C., & Liaw, S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz143 · Cited by 8
Characterizing Basic and Complex Usage of i2b2 at an Academic Medical Center.
Sholle, E., Cusick, M. M., Davila, M. A., Kabariti, J., Flores, S., & Campion, T. R. (2020) · PubMed Central · Cited by 8
Lu, Y., Zhou, T., Tian, Y., Zhu, S., & Li, J. (2020) · Journal of Medical Internet Research · doi:10.2196/22555 · Cited by 9
“To allow researchers to obtain correct research data, all data providers must implement data standardization to transform the data into a common format, such as the Observational Medical Outcomes Partnership common data model from the Observational Health Data Sciences and Informatics collaborative [23].”
MethodPhenCards: a data resource linking human phenotype information to biomedical knowledge
Havrilla, J. M., Liu, C., Dong, X., Weng, C., & Wang, K. (2021) · Genome Medicine · doi:10.1186/s13073-021-00909-8 · Cited by 8
“ICD-10 [16], UMLS [17, 18], OHDSI ATHENA [15], MeSH [14] Elasticsearch on indexed database Yes, yearly Standardized phenotype and disease terms”
BackgroundYoon, J. Y., Kwak, M. S., Kim, H. I., et al. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15634 · Cited by 8
“As the OMOP-CDM version of HIRA-NPS database is provided as an open source in South Korea, it has advantages over original HIRA-NPS database, whichwas limited by a preapproval for use, a usage fee, and a limited use through a predetermined platform for on-line or off-line use.([24,25])”
MethodFrom clinical trials to clinical practice: How long are drugs tested and then used by patients?
Yuan, C., Ryan, P. B., Ta, C., Kim, J. H., Li, Z., & Weng, C. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab164 · Cited by 10
“1 standard concepts.(13) Follow-up times for each arm were extracted with a heuristic-based method and normalized by SUTime.”
MethodMorales, D. R., Ostropolets, A., Lai, L. Y. H., et al. (2022) · Journal of Asthma · doi:10.1080/02770903.2021.2025392 · Cited by 8
“All data were standardized to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) (16,17).”
Inteligência artificial: avanços e tendências
Cozman, F. G., Plonski, G. A., & Neri, H. (2021) · Universidade de São Paulo. Instituto de Estudos Avançados eBooks · doi:10.11606/9786587773131 · Cited by 54
Perioperative adverse cardiac events and mortality after non-cardiac surgery: a multicenter study
Choi, B., Oh, A. R., Park, J., et al. (2023) · Korean Journal of Anesthesiology · doi:10.4097/kja.23043 · Cited by 8
“The concept of a distributed research network is a recent concept that was developed to overcome privacy issues in a multicenter observational study [8].”
Characterizing atrial fibrillation symptom improvement following de novo catheter ablation
Turchioe, M. R., Volodarskiy, A., Guo, W. L., Taylor, B., Hobensack, M., Pathak, J., & Slotwiner, D. J. (2023) · European Journal of Cardiovascular Nursing · doi:10.1093/eurjcn/zvad068 · Cited by 8
A data-driven concept schema for defining clinical research data needs
Hruby, G. W., Hoxha, J., Ravichandran, P. C., et al. (2016) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2016.03.008 · Cited by 8
Leveraging Terminology Services for Extract-Transform-Load Processes: A User-Centered Approach.
Peterson, K. J., Jiang, G., Brue, S. M., et al. (2016) · PubMed · Cited by 8
Chakrabarti, S., Sen, A. K., Huser, V., Hruby, G. W., Rusanov, A., Albers, D. J., & Weng, C. (2017) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-017-0005-6 · Cited by 8
Gamba, F. L., Corrao, G., Romio, S., Sturkenboom, M., Trifirò, G., Schink, T., & de Ridder, M. A. J. (2017) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4280 · Cited by 8
Roth, J. A., Goebel, N., Sakoparnig, T., et al. (2018) · JAMIA Open · doi:10.1093/jamiaopen/ooy039 · Cited by 8
A Data Element-Function Conceptual Model for Data Quality Checks
Rogers, J. R., Callahan, T. J., Kang, T., Bauck, A. E., Khare, R., Brown, J. S., Kahn, M. G., & Weng, C. (2019) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.289 · Cited by 9
Mapping Local Biospecimen Records to the OMOP Common Data Model.
Michael, C., Sholle, E., Wulff, R. T., et al. (2020) · PubMed · Cited by 8
Cossin, S., Thiébaut, R., & Informatics, S. E. F. T. I. Y. S. O. P. H. A. E. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1702020 · Cited by 8
Lee, K. A., Jin, H. Y., Kim, Y. J., Kim, S. S., Cho, E.-H., & Park, T. S. (2022) · Medicine · doi:10.1097/md.0000000000028823 · Cited by 9
Golozar, A., Lai, L. Y. H., Sena, A. G., et al. (2020) · medRxiv · doi:10.1101/2020.10.25.20218875 · Cited by 8
Seol, S., Choi, J. R., Choi, B., Kim, S., Jeon, J. Y., Park, K. N., Park, J. H., Park, M. W., Eun, Y., Park, J. J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-45654-7 · Cited by 7
“The Observational Health Data Science and Informatics (OHDSI) program is an international attempt to optimize analytic services for a large network of health databases 34 .”
Backgroundvan Nassau, S. C. M. W., Bol, G. M., van der Baan, F. H., Roodhart, J. M. L., Vink, G. R., Punt, C. J. A., May, A. M., Koopman, M., & Derksen, J. W. G. (2024) · Current Treatment Options in Oncology · doi:10.1007/s11864-024-01186-4 · Cited by 8
“The CDM developed by the Observational Medical Outcomes Partnership (OMOP) is recognized as most promising and is maintained and deployed by the international Observational Health Data Sciences and Informatics (OHDSI) collaboration [102, 103].”
BackgroundMarkov modeling for cost-effectiveness using federated health data network
Haug, M., Oja, M., Pajusalu, M., Mooses, K., Reisberg, S., Vilo, J., Giménez, A. F., Falconer, T., Danilović, A., Maljković, F., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae044 · Cited by 7
“13 The packages remove the need for ad-hoc approaches between OHDSI software infrastructure and downstream modeling tools, such as an R-package for health economic simulation modeling and decision analysis called hesim 17 and an R-package for multistate models named msm. 18 We stand-ardize the…”
MethodGülden, C., Macho, P., Reinecke, I., Strantz, C., Prokosch, H., & Blasini, R. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108411 · Cited by 8
Park, G., Lee, Y., & Cho, M. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104682 · Cited by 8
A practical guide to apply AI in childhood cancer: Data collection and AI model implementation
Wen, S., Theobald, S., Gangas, P., Jiménez, K. C. B., Merks, J. H. M., Schoot, R. A., Meyerheim, M., & Graf, N. M. (2024) · EJC Paediatric Oncology · doi:10.1016/j.ejcped.2024.100197 · Cited by 7
Hou, D., Liu, Z., Li, X., Shen, P., Li, W., Zhang, M., Cheang, I., Lin, H., Zhan, S., Sun, F., et al. (2025) · Respiratory Research · doi:10.1186/s12931-025-03316-4 · Cited by 7
“The Yinzhou database was standardized to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) version 5, which is maintained by the Observational Health Data Sciences and Informatics (OHDSI) Network [13, 14].”
How to compete with Google and Co.
Schoenthaler, M., Boeker, M., & Horki, P. (2018) · Current Opinion in Urology · doi:10.1097/mou.0000000000000578 · Cited by 6
ReviewR: a light-weight and extensible tool for manual review of clinical records
Mayer, D. A., Rasmussen, L. V., Roark, C. D., Kahn, M. G., Schilling, L. M., & Wiley, L. K. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooac071 · Cited by 7
“We provide development functions for extending ReviewR to support any custom data model and offer out of the box support for multiple versions of the OMOP common data model 42 and the MIMIC3 database.”
MethodKim, M., Park, D. H., Choi, H., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12093145 · Cited by 7
“The OHDSI network is an international collaboration that aims to develop data-sharing systems [17,18] by applying open-source data analytics to a large number of health databases [19].”
BackgroundWabo, G. K., Prasser, F., Gierend, K., Siegel, F., & Ganslandt, T. (2023) · JMIR Research Protocols · doi:10.2196/46471 · Cited by 6
“…(i2b2) TranSMART CDM [20], the Observational Medical Outcomes Partnership’s Observational Health Data Sciences and Informatics (OMOP OHDSI) CDM [21], the Patient-Centered Outcomes Research network (PCORNet) CDM [22], and the Clinical Data Interchange Standards Consortium’s (CDISC’s)…”
MethodYu, J. Y., Kim, D., Yoon, S., Kim, T., Heo, S., Chang, H., Han, G. S., Jeong, K. W., Park, R. W., Gwon, J. M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-54364-7 · Cited by 8
Lee, M., & Cha, J. M. (2024) · Gut and Liver · doi:10.5009/gnl230541 · Cited by 7
“9,10 In CDM, the meaning of each content is represented using standard concepts, and concepts associated with content are stored with their concept-ids as foreign keys to the concept table in a standardized vocabulary.”
Ji, H., Kim, S., Sunwoo, L., et al. (2024) · JMIR Medical Informatics · doi:10.2196/59187 · Cited by 7
“Among these, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is noteworthy for its advantages in converting diverse sources of data into a consistent format [5,6].”
BackgroundImproving A Large Healthcare System Research Data Warehouse Using OHDSI's Data Quality Dashboard
Marteau, B. L., Hornback, A. M., Zhong, Y., Lowson, C., Woloff, J., Smith, B. M., Hilton, C., & Wang, M. D. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913573 · Cited by 7
Pharmacovigilance via Baseline Regularization with Large-Scale Longitudinal Observational Data
Kuang, Z., Peissig, P. L., Costa, V. S., et al. (2017) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3097998 · Cited by 7
Lin, F.-C., Wang, C., Shang, R.-J., Hsiao, F., Lin, M., Hung, K., Wang, J., Lin, Z., Lai, F., Shen, L., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9477 · Cited by 7
Zaccaria, G. M., Ferrero, S., Rosati, S., Ghislieri, M., Genuardi, E., Evangelista, A., Sandrone, R., Castagneri, C., Barbero, D., Schirico, M. L., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00049 · Cited by 7
Mun, Y., Park, C., Lee, D. Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-14386-5 · Cited by 7
Kim, T., Seo, S. I., Lee, K. J., Park, C. H., Kim, T. J., Kim, J., & Shin, W. G. (2022) · Antibiotics · doi:10.3390/antibiotics11081052 · Cited by 7
“Since our database was converted into the OMOP-CDM, all medications, diagnoses, and disease severity (Charlson Comorbidity Index) were included as covariates.”
MethodInfluentialSecure Access Control Realization Based on Self-Sovereign Identity for Cloud CDM
Kang, Y., & Park, Y. B. (2022) · Applied Sciences · doi:10.3390/app12199833 · Cited by 7
Reinecke, I., Siebel, J., Fuhrmann, S., Fischer, A., Sedlmayr, M., Weidner, J., Wilhelm, M., & Bathelt, F. (2022) · JMIR Medical Informatics · doi:10.2196/40312 · Cited by 8
“Currently, the method is limited to determining ATC codes for unstructured drug prescriptions and does not consider other terminologies such as RxNorm.”
Querying semantic catalogues of biomedical databases
Pereira, A., Almeida, J. R., Lopes, R. P., & Oliveira, J. L. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104272 · Cited by 8
Li, R., Tian, Y., Shen, Z., et al. (2023) · JMIR Medical Informatics · doi:10.2196/47862 · Cited by 7
“The recent rise of observational biomedical research, driven by greatly expanding electronic health records (EHRs) and the prevalence of machine learning methods, has drawn great attention [1-4].”
BackgroundFeasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model.
Kim, H., Choi, J., Jang, I., Quach, J., & Lucila Ohno-machado, A. (2016) · PubMed Central · Cited by 7
Rees, S., Akbari, A., Collins, H., Lee, S. C., Marchant, A. L., Rees, A., Thayer, D. S., Wang, T., Wood, S., & John, A. M. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0953-2 · Cited by 7
First Steps to Evaluate an NLP Tool’s Medication Extraction Accuracy from Discharge Letters
Caliskan, D., Zierk, J., Kraska, D., et al. (2021) · Studies in health technology and informatics · doi:10.3233/shti210073 · Cited by 7
“The MIRACUM consortium [5], one of four consortia funded within MII, aims at tapping hospital routine data and integrating them into research data repositories, such as i2b2 [6] and OMOP [7,8], in order to make them available for scientific purposes.”
BackgroundRogers, J. R., Pavisic, J., Ta, C., Liu, C., Soroush, A., Cheung, Y. K., Hripcsak, G. M., & Weng, C. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104032 · Cited by 7
Improving the 'Fitness for Purpose' of Common Data Models through Realism Based Ontology.
Blaisure, J. C., & Ceusters, W. M. (2017) · PubMed · Cited by 7
Multi-language Concept Normalisation of Clinical Cohorts
Almeida, J. R., & Oliveira, J. L. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00056 · Cited by 8
“Currently, this organisation supports an ecosystem with several open-source solutions to perform medical product safety surveillance using observational databases [5].”
BackgroundUnraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDISPreprint
Prieto‐Alhambra, D., Kostka, K., Duarte‐Salles, T., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-279400/v1 · Cited by 7
“Figures Page 19/21 COVID-19 cases across the OHDSI COVID-19 network.”
BackgroundMethodInfluentialBridging borders: Current trends and future directions in comparative health systems research
Bowden, N., Figueroa, J. F., & Papanicolas, I. N. (2024) · Health Services Research · doi:10.1111/1475-6773.14385 · Cited by 7
Zahid, J. A., Gögenur, M., Ekeloef, S., & Gögenür, I. (2025) · Annals of Surgery Open · doi:10.1097/as9.0000000000000560 · Cited by 6
“27,28”
A Synergistic Bridge Between Human–Computer Interaction and Data Management Within CDSS
Azadi, A., & García‐Peñalvo, F. J. (2025) · Data · doi:10.3390/data10050060 · Cited by 6
SQL on FHIR - Tabular views of FHIR data using FHIRPath
Grimes, J., Brush, R., Rhyzhikov, N., Szul, P., Mandel, J. C., Gottlieb, D., Grieve, G., Sadjad, B. S., & Sanyal, A. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01708-w · Cited by 7
“WhileotherwidelyusedstandardslikeOMOPCDM(Observational Medical Outcomes Partnership Common Data Model) focus on standardising data for research and analytics 1 , FHIR ’ s primary focus is on real-time data exchange and interoperability.”
Beauchemin, M., Khurana, R., DeStephano, D. M., Valera, K., Walker, D. A. H., Algave, M., Kahn, J. M., Mei, B., Cherng, H.-J. J., Tolu, S. S., et al. (2025) · JCO Oncology Practice · doi:10.1200/op-24-00868 · Cited by 6
Lau, R., Sadeghi, H., Ahmadi, F., et al. (2025) · Journal of Antimicrobial Chemotherapy · doi:10.1093/jac/dkaf231 · Cited by 6
“16 We integrated RxNorm Concept Unique Identifiers (RxCUIs), generic drug names and codes from the Anatomical Therapeutic Chemical (ATC) classification system to further augment our data-set.”
Wei, Z., Xu, W., Wang, Y., Tian, Y., Wang, Z., Jing, S., Liu, W., Shen, S., Qin, C., Zhang, X., et al. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02892-5 · Cited by 7
“…a multicenter cohort analysis across observational databases from the Observational Health Data Science and Informatics (OHDSI) distributed data network, which utilizes data from multiple centers and performs integrated comparisons via analytical techniques to mitigate confounding factors [13].”
Yan, D., Lyu, B., Yu, J. Z., Bao, S., Zhang, Z., Zhou, M., & Sun, J. (2025) · British Journal of Pharmacology · doi:10.1111/bph.70195 · Cited by 6
“0 09-SEP-22) (Hripcsak et al., 2015).”
Use of real‐world evidence in translational pharmacology research
Charvériat, M., Darmoni, S. J., Lafon, V., Moore, N. D., Bordet, R., Veys, J., & Mouthon, F. (2021) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.12734 · Cited by 6
“In France, the Health Data Hub (HDH) is one of the most significant initiatives among 180 others dedicated to RWE/D defragmentation, sharing, interoperability protection and access regulation inventoried in EU [17, 18].”
BackgroundSeo, S. I., Kim, T. J., Park, C. H., Bang, C. S., Lee, K. J., Kim, J., Kim, H. H., & Shin, W. G. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12040584 · Cited by 7
“Therefore, the study can be extended to other databases with common analytic R code [32].”
BackgroundKnowledge Representation and Ontologies
Fung, K. W., & Bodenreider, O. (2023) · Health informatics · doi:10.1007/978-3-031-27173-1_18 · Cited by 6
Bayesian safety surveillance with adaptive bias correction
Bu, F., Schuemie, M. J., Nishimura, A., Smith, L. H., Kostka, K., Falconer, T., McLeggon, J., Ryan, P. B., Hripcsak, G. M., & Suchard, M. A. (2023) · Statistics in Medicine · doi:10.1002/sim.9968 · Cited by 6
“We use six historical vaccines with known side effects, a large set of experimental control outcomes for which the true RR values are known, two commonly used analysis designs (the historical comparator and SCCS), and run the analyses on a network of observational healthcare databases mapped to Observational Health Da…”
MethodOse, I., Rosen, A. W., Bräuner, K. B., Colov, E. P., Christensen, M. G., Mashkoor, M., Vogelsang, R. P., Gögenür, I., & Bojesen, R. D. (2024) · Colorectal Disease · doi:10.1111/codi.16937 · Cited by 6
Calculating daily dose in the Observational Medical Outcomes Partnership Common Data Model
Burkard, T., López‐Güell, K., Gorbachev, A., et al. (2024) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5809 · Cited by 6
“3 However, conducting drug utilization studies in the context of the European Union's Data Analysis and Real-World Interrogation Network (DARWIN EU) 4 initiative revealed that a standardized way to calculate drug daily dose was still missing.”
BackgroundAhn, S. H., Seo, S., Jung, C. Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-68356-0 · Cited by 6
“It enables integration of heterogenous data sources into a common format (concept IDs) 18,19 .”
BackgroundPredicting ICU Admission Risk in Children with Respiratory Syncytial Virus
Lee, Y. H., Choe, Y. J., Yoon, Y. S., Park, J. Y., Kim, Y. K., Joo, H. J., Choi, S., Kim, H. J., & Bertizzolo, L. (2025) · Infectious Diseases and Therapy · doi:10.1007/s40121-025-01155-w · Cited by 6
“In 2014, Obser‑ vational Health Data Sciences and Informatics (OHDSI) introduced the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to transform data—including patient demographics, diagnoses, and labora‑ tory results—into a uniform format for stream‑ lined analysis [9].”
Sun, Y., Butler, A. M., Diallo, I., Kim, J. H., Ta, C., Rogers, J. R., Liu, H., & Weng, C. (2021) · Applied Clinical Informatics · doi:10.1055/s-0041-1733846 · Cited by 6
Murphy, S. N., Visweswaran, S., Becich, M. J., Campion, T. R., Knosp, B. M., Melton, G. B., & Lenert, L. A. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac024 · Cited by 6
Yu, J., Pacheco, J. A., Ghosh, A. S., Luo, Y., Weng, C., Shang, N., Benoit, B., Carrell, D., Carroll, R. J., Dikilitas, O., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01759-z · Cited by 6
“Lastly, the potential to introduce ambiguity and vague-ness through under-specification is mitigated in part with the use of common data models (CDMs) and har-monized terminologies [7, 20–23].”
Jang, J. M., Park, S. B., Yoon, J. Y., et al. (2022) · Medicine · doi:10.1097/md.0000000000029266 · Cited by 6
“EHRs are stored in databases that are built using a wide variety of data models and, often, local terminologies, therefore, an analysis across multiple disparate databases either tailors the analysis to accommodate each of the underlying data models and terminologies or converts the databases to a CDM.([16,17]) As CDM…”
BackgroundMethodInfluentialNishimura, A., Xie, J., Kostka, K., Duarte‐Salles, T., Fernández‐Bertolín, S., Aragón, M., Blacketer, C., Shoaibi, A., DuVall, S. L., Lynch, K. E., et al. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.945592 · Cited by 8
“All data sources had been previously mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDMv5) (Hripcsak et al. 2015).”
MethodTransformEHRs: a flexible methodology for building transparent ETL processes for EHR reuse
Pedrera‐Jiménez, M., Barrio, N. G., Rubio-Mayo, P., et al. (2022) · Methods of Information in Medicine · doi:10.1055/s-0042-1757763 · Cited by 6
Surian, D., Wang, Y., Coiera, E. W., & Magrabi, F. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac220 · Cited by 5
Henke, E., Peng, Y., Reinecke, I., Zoch, M., Sedlmayr, M., & Bathelt, F. (2023) · JMIR Medical Informatics · doi:10.2196/47310 · Cited by 9
Clinical Data Integration Strategies for Multicenter Studies
Almeida, J. R., Pazos, A., & Oliveira, J. L. (2023) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-36007-7_13 · Cited by 7
Agapito, I., Ng, D. Q., Milam, J. E., Ziogas, A., Anton‐Culver, H., & Chan, A. (2023) · Cancer Medicine · doi:10.1002/cam4.6641 · Cited by 6
Zhang, D., Tong, J., Stein, R. E., Lu, Y., Jing, N., Yang, Y., Boland, M. R., Luo, C., Baldassano, R. N., Carroll, R. J., et al. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104595 · Cited by 6
Kim, C., Yu, D. H., Baek, H., et al. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae062 · Cited by 6
Löffler, L., Gögenür, I., & Gögenur, M. (2024) · International Journal of Colorectal Disease · doi:10.1007/s00384-024-04631-w · Cited by 6
Coutinho‐Almeida, J., Sáez, C., Cruz-Correia, R. J., & Rodrigues, P. P. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae062 · Cited by 7
“The work from Liaw et al 17 already reviewed some data quality assessment tools, like tools from OHDSI 18 or TAQIH.”
MethodPatchwork of contrasting medication cultures across the USA
Melamed, R., & Rzhetsky, A. (2018) · Nature Communications · doi:10.1038/s41467-018-06205-1 · Cited by 6
Eight practices for data management to enable team data science
McDavid, A., Corbett, A. M., Dutra, J., et al. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.501 · Cited by 6
“In our view, the greatest value in adopting common data models comes from the potential to harmonize multiple studies after completion [31].”
BackgroundOpportunities for the use of health information technology to support research
Zayas‐Cabán, T., & Wald, J. S. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa037 · Cited by 7
“and internationally, have developed informatics tools and approaches that allow researchers to query organizational participants and support transformation or analytics of relevant data to facilitate research.(25,26) Specifically, i2b2 has standardized data models and distributed computational tools to allow instituti…”
BackgroundThe Critical Care Learning Healthcare System: Time to Walk the Walk*
Surma, V. J., Kudchadkar, S. R., Bembea, M. M., et al. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004700 · Cited by 6
“A number of large-scale efforts are now a decade old including, i2b2 (12), Observational Health Data Sciences and Informatics (13), and National PatientCentered Clinical Research Network (14).”
BackgroundDevelopment of Food Allergy Data Dictionary: Toward a Food Allergy Data Commons
Sehgal, S., Gupta, R. S., Wlodarski, M., Bilaver, L., Wehbe, F., Spergel, J. M., Wang, J., Ciaccio, C. E., Makhija, M., & Starren, J. B. (2022) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2022.02.024 · Cited by 7
Building an i2b2-Based Population Repository for COVID-19 Research
Pedrera‐Jiménez, M., Barrio, N. G., Hernández, G., et al. (2022) · Studies in health technology and informatics · doi:10.3233/shti220460 · Cited by 6
Hartwig, M. F. S., Bräuner, K. B., Vogelsang, R. P., et al. (2022) · International Journal of Colorectal Disease · doi:10.1007/s00384-022-04284-7 · Cited by 6
Physics of the Medical Record: Handling Time in Health Record Studies
Hripcsak, G. M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19551-3_1 · Cited by 6
A Multiagent System for Integrated Detection of Pharmacovigilance Signals
Koutkias, V. G., & Jaulent, M. (2015) · Journal of Medical Systems · doi:10.1007/s10916-015-0378-0 · Cited by 6
Malenfant, J., Hochstadt, J., Nolan, B., et al. (2019) · Learning Health Systems · doi:10.1002/lrh2.10187 · Cited by 6
Accurate and reproducible prediction of ICU readmissionsPreprint
Nguyen, D.-P. (2019) · medRxiv · doi:10.1101/2019.12.26.19015909 · Cited by 8
Lai, L. Y. H., Golozar, A., Sena, A. G., Margulis, A. V., de Haro, N., Casajust, P., Valveny, N., Prats‐Uribe, A., Minty, E. P., Ahmed, W., et al. (2020) · medRxiv · doi:10.1101/2020.10.13.20211821 · Cited by 6
Recalde, M., Pistillo, A., Fernández‐Bertolín, S., Roel, E., Aragón, M., Freisling, H., Prieto‐Alhambra, D., Burn, E., & Duarte‐Salles, T. (2020) · medRxiv · doi:10.1101/2020.11.25.20237776 · Cited by 6
BIcenter: A collaborative Web ETL solution based on a reflective software approach
Almeida, J. R., Coelho, L. G. S., & Oliveira, J. L. (2021) · SoftwareX · doi:10.1016/j.softx.2021.100892 · Cited by 6
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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