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
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“The descriptive analysis was performed within the Observational Health Data Sciences and Informatics framework, allowing for replication across the three databases [11].”
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Comparative effectiveness and safety outcomes between denosumab and bisphosphonate in South Korea
Park, C., Kim, C., Park, R. W., & Jeon, J. Y. (2024) · Journal of Bone and Mineral Research · doi:10.1093/jbmr/zjae070 · Cited by 11
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Multicenter Privacy-Preserving Cox Analysis Based on Homomorphic Encryption
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Multi-Center Healthcare Data Quality Measurement Model and Assessment Using OMOP CDM
Kim, K., Choi, W., Ko, S., Chang, D. J., Chung, Y. W., Chang, S., Kim, J. K., Kim, D., & Choi, I. (2021) · Applied Sciences · doi:10.3390/app11199188 · Cited by 11
“The Observational Health Data Sciences and Informatics (OHDSI), a representative multicenter research network, is a network in which multiple stakeholders collaborate by analyzing large-scale medical data converted into the Common Data Model (CDM) format [12,16].”
BackgroundKim, J. E., Choi, Y., Oh, S. W., Kim, M.-G., Jo, S., Cho, W. Y., Ahn, S. Y., Kwon, Y., & Ko, G.-J. (2022) · Frontiers in Nephrology · doi:10.3389/fneph.2021.821585 · Cited by 12
Spotnitz, H. M., Ostropolets, A., Castano, V. G., Natarajan, K., Waldman, G. J., Argenziano, M., Ottman, R., Hripcsak, G. M., Choi, H., & Youngerman, B. E. (2022) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2022.108630 · Cited by 10
Lyu, T., Liang, C., Liu, J., Campbell, B. A., Hung, P., Shih, Y.-W., Ghumman, N., & Li, X. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276923 · Cited by 12
“Over decades, comprehensive clinical data for pregnancy care have not been widely available until recent advances in the use of normalized multi-system electronic health records (EHR), such as Observational Health Data Sciences and Informatics (OHDSI)’s Atlas [7], National Institutes of Health (NIH)’s All of Us, and N…”
BackgroundKreuzthaler, M., Brochhausen, M., Zayas, C. E., Blobel, B. G. M. E., & Schulz, S. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1073313 · Cited by 12
“In 2018, Brochhausen et al. pointed out, that there might be a disconnect regarding the use of the terms “semantic” or “semantics” in biomedical informatics: terminology systems, often in combination with Common Data Models (CDMs) are implemented to provide semantics and foster semantic integration and researchers usi…”
MethodInfluentialUse of Recommended Neurodiagnostic Evaluation Among Patients With Drug-Resistant Epilepsy
Spotnitz, M., Ekanayake, C. D., Ostropolets, A., et al. (2024) · JAMA Neurology · doi:10.1001/jamaneurol.2024.0551 · Cited by 11
Zeitler, E. P., Al‐Khatib, S. M., Drozda, J. P., Kessler, L. G., Kirtane, A. J., Kong, D. F., Laschinger, J. C., Marinac‐Dabic, D., Morice, M., Reed, T. L., et al. (2015) · American Heart Journal · doi:10.1016/j.ahj.2015.07.029 · Cited by 11
A FAIR Marketplace for Biomedical Data Custodians and Clinical Researchers
Trifan, A., & Oliveira, J. L. (2018) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2018.00040 · Cited by 11
Morrato, E. H., Hamer, M. K., Sills, M. R., Kwan, B. M., & Schilling, L. M. (2019) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.295 · Cited by 11
i2b2 implemented over SMART-on-FHIR.
Paris, N., Mendis, M., Daniel, C., Murphy, S. N., Tannier, X., & Zweigenbaum, P. (2018) · Europe PMC (PubMed Central) · Cited by 11
SCALPEL3: A scalable open-source library for healthcare claims databases
Bacry, E., Gaïffas, S., Leroy, F., Morel, M., Nguyen, D.-P., Sebiat, Y., & Sun, D. (2020) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2020.104203 · Cited by 11
Using the electronic health record for genomics research
Safarova, M. S., & Kullo, I. J. (2020) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000662 · Cited by 14
“To map medical terms to standardized vocabularies vocabularies, the OMOP-CDM makes use of the UMLS.”
InfluentialThurin, N. H., Lassalle, R., Schuemie, M. J., et al. (2020) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4983 · Cited by 11
“The OMOP CDM was then improved and used for the development of updated analysis packages maintained by the Observational Health Data Sciences and Informatics (OHDSI) consortium.(13) France has a large nationwide longitudinal health care database—the Système National des données de Santé (SNDS)—which currently covers t…”
MethodCepeda, M. S., Kern, D. M., & Canuso, C. M. (2021) · Depression and Anxiety · doi:10.1002/da.23138 · Cited by 11
“A series of standardized analytic tools have been developed against the OMOP CDM as part of the Observational Health Data Sciences and Informatics collaborative (Hripcsak et al., 2015).”
MethodYoo, S., Choi, J., Ryu, B., & Kim, S. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1735166 · Cited by 11
A method for the early prediction of chronic diseases based on short sequential medical data
Wu, C., Zhou, T., Tian, Y., Wu, J., Li, J., & liu, Z. H. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102262 · Cited by 13
Distributed Networks of Databases Analyzed Using Common Protocols and/or Common Data Models
Toh, S., Pratt, N. L., Klungel, O. H., Gagne, J. J., & Platt, R. W. (2019) · Pharmacoepidemiology · doi:10.1002/9781119413431.ch25 · Cited by 11
Burn, E., Fernández‐Bertolín, S., Voss, E. A., Blacketer, C., Aragón, M., Recalde, M., Roel, E., Pistillo, A., Raventós, B., Reyes, C., et al. (2021) · medRxiv · doi:10.1101/2021.11.23.21266734 · Cited by 10
“To facilitate international collaboration, we set out to conform to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).(9) Our second aim was to summarise the occurrence of COVID-19 outcomes”
MethodGäbler, G., Lycett, D., & Gall, W. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19052491 · Cited by 10
Lamer, A., Fruchart, M., Paris, N., Popoff, B., Payen, A., Balcaen, T., Gacquer, W., Bouzillé, G., Marc Cuggia, A., Doutreligne, M., et al. (2022) · JMIR Medical Informatics · doi:10.2196/38936 · Cited by 12
“Observational Health Data Sciences and Informatics (OHDSI) is a community developed from the Observational Medical Outcomes Partnership (OMOP) [39-42].”
Frid, S. A., Cucó, G. B., Rojas, J. G., et al. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104505 · Cited by 10
Attention-based neural networks for clinical prediction modelling on electronic health records
Fridgeirsson, E. A., Sontag, D. A., & Rijnbeek, P. R. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-02112-2 · Cited by 19
“This has been addressed through the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) maintained by the Observational Health Data Sciences and Informatics (OHDSI) community [3].”
MethodMONTRA2: A web platform for profiling distributed databases in the health domain
Almeida, J. R., & Oliveira, J. L. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101447 · Cited by 10
A Systematic Review of NLP Applications in Clinical Healthcare: Advancement and Challenges
Garg, R., & Gupta, A. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-9521-9_3 · Cited by 17
Fruchart, M., Quindroit, P., Jacquemont, C., Beuscart, J., Calafiore, M., & Lamer, A. (2024) · JMIR Medical Informatics · doi:10.2196/49542 · Cited by 13
“Initiatives such as those proposed by the Observational Health Data Sciences and Informatics (OHDSI) consortium seek to (1) standardize data structure and vocabularies; (2) promote reproducible research and collaboration; and (3) share methods, tools, and results [29,30].”
BackgroundRisk of Contrast-Induced Acute Kidney Injury in Computed Tomography
Choi, B., Heo, S., McDonald, J. S., Choi, S. H., Choi, W., Lee, J. B., Lee, E., Park, S. H., Seol, S., Gan, S., et al. (2024) · Investigative Radiology · doi:10.1097/rli.0000000000001141 · Cited by 10
Pant, D., Øystein Nytrø, A., LEVENTHAL, B. L., Clausen, C. E., Koochakpour, K., Stien, L., Westbye, O. S., Koposov, R. A., Røst, T. B., Frodl, T., et al. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03021-8 · Cited by 14
Eddin, L. E., Omrani, M. A., Chalabianloo, N., et al. (2025) · Journal of Psychopharmacology · doi:10.1177/02698811251349190 · Cited by 10
Standardized Health data and Research Exchange (SHaRE): promoting a learning health system
Davis, S., Ehwerhemuepha, L., Feaster, W. W., et al. (2022) · JAMIA Open · doi:10.1093/jamiaopen/ooab120 · Cited by 10
“Cerner makes CRWD available in the Observational Medical Outcomes Partnership (OMOP) format.(19) Figure 2.”
MethodDQAgui: a graphical user interface for the MIRACUM data quality assessment tool
Mang, J. M., Seuchter, S. A., Gülden, C., Schild, S., Kraska, D., Prokosch, H., & Kapsner, L. A. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01961-z · Cited by 12
“Opposed to other solutions, like the data quality assessment tools for the OMOP CDM [39, 40], the DQA tool can be used for DQ checks of any (tabular) database, as long as the meta-data is defined properly and correct SQL statements are provided for each data element.”
MethodA secure architecture for exploring patient-level databases from distributed institutions
Almeida, J. R., Barraca, J. P., & Oliveira, J. L. (2022) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms55023.2022.00086 · Cited by 11
“One of the main strategies used to increase the dataset dimension consists in the adoption of data sharing techniques between distinct health institutions [3], [4].”
BackgroundMethodSyntactic interoperability and the role of syntactic standards in health information exchange
Umberfield, E. E., Staes, C. J., Morgan, T. P., Grout, R. W., Mamlin, B. W., & Dixon, B. E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90802-3.00004-6 · Cited by 11
allofus: an R package to facilitate use of the All of Us Researcher Workbench
Smith, L. H., & Cavanaugh, R. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae198 · Cited by 10
A Modified Skip-Gram Algorithm for Extracting Drug-Drug Interactions from AERS Reports
Wang, L., Pan, W., Wang, Q., Bai, H., Liu, W., Jiang, L., & Zhang, Y. (2020) · Computational and Mathematical Methods in Medicine · doi:10.1155/2020/1747413 · Cited by 10
“Our study refers to the framework of OHDSI (Observational Health Data Sciences and Informatics) and puts Banda et al.’s research into practice, completing the cleaning and standardization of AERS reports [14, 15]; all the structured tables are stored in a PostgreSQL database.”
BackgroundMethodInfluentialKnowledge-aware multi-center clinical dataset adaptation: Problem, method, and application
Chu, J., Chen, J., Chen, X., Dong, W., Shi, J., & Huang, Z. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103710 · Cited by 10
Network-based Active Defense for Securing Cloud-based Healthcare Data Processing Pipelines
Akashe, V., Neupane, R. L., Alarcon, M. L., Wang, S., & Calyam, P. P. (2021) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn52240.2021.9522267 · Cited by 12
“To facilitate the orchestration of such data pipelines, exemplar technologies, such as OHDSI [29] have been adopted for use in cloud environments by healthcare organizations.”
MethodDevelopment of a repository of computable phenotype definitions using the clinical quality language
Brandt, P., Pacheco, J. A., & Rasmussen, L. V. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab094 · Cited by 10
Accelerating Multi-site Health Informatics with Streamlined Data Infrastructure using OMOP-on-FHIR
Marteau, B. L., Zhu, Y., Giuste, F., et al. (2022) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871865 · Cited by 10
“The OMOP CDM facilitates collaboration between several entities and organizations and shared analysis tools or validation tools [1].”
MethodOntology-based categorization of clinical studies by their conditions
Liu, H., Carini, S., Chen, Z., Hey, S. P., Sim, I., & Weng, C. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104235 · Cited by 10
Homayouni, H., Ghosh, S., & Ray, I. (2018) · Advances in computers · doi:10.1016/bs.adcom.2017.12.005 · Cited by 10
Duan, R., Chen, Z., Tong, J., Luo, C., Lyu, T., Tao, C., Maraganore, D. M., Bian, J., & Chen, Y. (2020) · medRxiv · doi:10.1101/2020.08.03.20167619 · Cited by 10
Nishimura, A., & Suchard, M. A. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2057859 · Cited by 35
Introducing the BlendedICU dataset, the first harmonized, international intensive care dataset
Oliver, M., Allyn, J., Carencotte, R., Allou, N., & Ferdynus, C. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104502 · Cited by 13
Kim, J., Kim, J. S., Kim, S., Yoo, S., Lee, J. K., & Kim, K. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01215-4 · Cited by 9
“X-2308-846-906) granted a waiver of approval and informed consent, considering that the data used in this study were de-identi fi ed and based on observational electronic medical records from the OMOP CDM.”
MethodInfluentialKimura, E., Kawakami, Y., Inoue, S., & Okajima, A. (2024) · Healthcare Informatics Research · doi:10.4258/hir.2024.30.4.355 · Cited by 11
Assessing acute kidney injury risk after COVID vaccination and infection in a large cohort study
Pan, Y., Han, Y., Zhou, C., et al. (2024) · npj Vaccines · doi:10.1038/s41541-024-00964-3 · Cited by 9
Balise, R. R., Hu, M., Calderon, A. R., Odom, G., Brandt, L., Luo, S. X., & Feaster, D. J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312695 · Cited by 9
Janetzki, J. L., Kim, J. H., Minty, E. P., et al. (2025) · EClinicalMedicine · doi:10.1016/j.eclinm.2025.103096 · Cited by 9
Aziz, M., Beaton, M., Aziz, M. A., Opoku-Anane, J., & Elhadad, N. (2025) · npj Women s Health · doi:10.1038/s44294-025-00086-8 · Cited by 11
“This study leverages the Observational Health Data Sciences and Informatics (OHDSI) framework 47 to conduct a retrospective cohort study at scale.”
Hou, G., Dong, B.-Q., Yu, B., Dai, J.-Y., Lin, X., & Cheng, Z. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1633416 · Cited by 12
“Other global initiatives, such as the Observational Health Data Sciences and Informatics (OHDSI) program and the Global Alliance for Genomics and Health (GA4GH), provide scalable models for harmonizing diverse datasets and promoting interoperability in biomedical research (40, 41).”
Ye, Y., Barapatre, S., Davis, M. K., et al. (2021) · Journal of Medical Internet Research · doi:10.2196/20028 · Cited by 9
Drug-drug interactions in atrial fibrillation patients receiving direct oral anticoagulants
Lee, J. Y., Oh, I., Lee, J.-H., Kim, S., Cho, J.-H., Park, C. H., Yoo, S., & Bang, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-01786-2 · Cited by 9
Martin, E. A., D’Souza, A. G., Lee, S. “., Doktorchik, C. T. A., Eastwood, C. A., & Quan, H. (2023) · CMAJ Open · doi:10.9778/cmajo.20210170 · Cited by 9
Maturity in enterprise data warehouses for research operations: Analysis of a pilot study
Knosp, B. M., Dorr, D. A., & Campion, T. R. (2023) · Journal of Clinical and Translational Science · doi:10.1017/cts.2023.23 · Cited by 9
Lim, J. H., Sang, H., & Kim, H. I. (2023) · JHEP Reports · doi:10.1016/j.jhepr.2023.100721 · Cited by 10
Phenotyping in distributed data networks: selecting the right codes for the right patients.
Ostropolets, A., Ryan, P. B., & Hripcsak, G. M. (2022) · PubMed · Cited by 9
Goyal, J., Ng, D. Q., Zhang, K., Chan, A., Lee, J., Zheng, K., Hurley‐Kim, K., Nguyen, L., He, L., Nguyen, M., et al. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02206-3 · Cited by 10
“2 [24].”
A FAIR Approach to Real-World Health Data Management and Analysis
Almeida, J. R., Silva, J. M., & Oliveira, J. L. (2023) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms58004.2023.00338 · Cited by 10
“By adopting standardized data models like the OMOP Common Data Model, it is ensured that data from different sources can be seamlessly integrated, compared, and analyzed, enabling more efficient and effective research and decision-making [10].”
BackgroundMethodDoes the “Obesity Paradox” have an expiration date? A retrospective cohort study
Elkan, M., Kofman, N., Minha, S., Rappoport, N., Zaidenstein, R., & Koren, R. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216765 · Cited by 9
“We obtained the cohort design and description using ATLAS [27], an open-source web-based tool developed by the Observational Health Data Sciences and Informatics (OHDSI) community [28].”
MethodLearning competing risks across multiple hospitals: one-shot distributed algorithms
Zhang, D., Tong, J., Jing, N., Yang, Y., Luo, C., Lu, Y., Christakis, D., Güthe, D., Hornig, M., Kelleher, K. J., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae027 · Cited by 12
Causal fairness assessment of treatment allocation with electronic health records
Zhang, L., Richter, L. R., Wang, Z., Ostropolets, A., Elhadad, N., Blei, D. M., & Hripcsak, G. M. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104656 · Cited by 10
Lee, S., Choi, Y. H., Kim, H. M., et al. (2024) · Cancer Research and Treatment · doi:10.4143/crt.2024.218 · Cited by 9
“For example, the Observational Medical Outcomes Partnership (OMOP) CDM is widely adopted to standardize and facilitate efficient large-scale analysis of medical data [2].”
Post, A. R., Ai, M., Pai, A. K., et al. (2017) · PubMed · Cited by 9
Miao, S., Dong, X., Zhang, X., Jing, S., Zhang, X., Xu, T., Wang, L., Du, X. L., Xu, H., & Liu, Y. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12894 · Cited by 9
Cho, J., You, S. C., Lee, S., Park, D., Park, B., Hripcsak, G. M., & Park, R. W. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17217824 · Cited by 9
Lee, Y. J., Yoo, S., Yi, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94194-5 · Cited by 9
An Empirical Study of a Trustworthy Cloud Common Data Model Using Decentralized Identifiers
Kang, Y., Cho, J., & Park, Y. B. (2021) · Applied Sciences · doi:10.3390/app11198984 · Cited by 12
Transfer of Clinical Drug Data to a Research Infrastructure on OMOP – A FAIR Concept
Reinecke, I., Zoch, M., Wilhelm, M., Sedlmayr, M., & Bathelt, F. (2021) · Studies in health technology and informatics · doi:10.3233/shti210815 · Cited by 9
“The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) developed by the Observational Health Data Sciences and Informatics (OHDSI) is one option to foster reliability of retrospective, observational studies on real-world data [3] and compared to other CDMs, e.”
MethodKnowledge graph analytics platform with LINCS and IDG for Parkinson's disease target illumination
Yang, J. J., Gessner, C. R., Duerksen, J. L., Biber, D., Binder, J., Ozturk, M., Foote, B., McEntire, R., Stirling, K., Ding, Y., et al. (2022) · BMC Bioinformatics · doi:10.1186/s12859-021-04530-9 · Cited by 9
Tan, H. X., Teo, D. C. H., Lee, D. Y., Kim, C., Neo, J. W., Sung, C., Chahed, H., Ang, P. S., Tan, D. S., Park, R. W., et al. (2022) · Healthcare Informatics Research · doi:10.4258/hir.2022.28.2.112 · Cited by 9
“The sections are coloured to facilitate comparisons within and between agents [17] (Figure 6).”
BackgroundTan, X., Chang, C. H., Zhou, L., & Tang, L. (2021) · PubMed · doi:10.48550/arxiv.2103.06261 · Cited by 13
“Distributed research networks have been established in many large scale studies (Fleurence et al., 2014; Hripcsak et al., 2015; Platt et al., 2018; Donohue et al., 2021).”
BackgroundDevelopment and usage of an anesthesia data warehouse: lessons learnt from a 10-year project
Lamer, A., Moussa, M., Marcilly, R., Logier, R., Vallet, B., & Tavernier, B. (2022) · Journal of clinical monitoring and computing · doi:10.1007/s10877-022-00898-y · Cited by 7
“The OMOP-CDM provides standardized table and column names, as well as a unified vocabulary [29].”
MethodImproving precision medicine using individual patient data from trials
Cahan, A., & Cimino, J. J. (2016) · Canadian Medical Association Journal · doi:10.1503/cmaj.160267 · Cited by 9
Hinderer, M., Boeker, M., Wagner, S., et al. (2017) · Pharmacogenomics · doi:10.2217/pgs-2017-0027 · Cited by 9
Digital networks for laboratory data: potentials, barriers and current initiatives
Ganslandt, T., & Neumaier, M. (2018) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2018-1131 · Cited by 9
Choi, Y. I., Kim, Y. J., Chung, J., Kim, K. O., Kim, H., Park, R. W., & Park, D. K. (2020) · JMIR Medical Informatics · doi:10.2196/15124 · Cited by 9
“Comparison With Prior Work The K-CDM uses the OHDSI database system, which aims to facilitate global, large-scale observational research that is reproducible, because it is based on CDMs and queries [18,36-39].”
BackgroundMethodInfluentialRoel, E., Pistillo, A., Recalde, M., Sena, A. G., Fernández‐Bertolín, S., Aragón, M., Puente, D., Ahmed, W., Alghoul, H., Alser, O. H., et al. (2021) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-21-0266 · Cited by 9
“The OHDSI network maintains the OMOP-CDM, and its members have developed a wide range of tools to facilitate analyses of such mapped data (11).”
Payne, P., Embí, P. J., & Cimino, J. J. (2013) · Springer eBooks · doi:10.1007/978-1-4471-4474-8_26 · Cited by 9
Bergquist, T., Schaffter, T., Yan, Y., Yu, T., Prosser, J., Gao, J., Chen, G., Charzewski, Ł., Nawalany, Z., Brugere, I., et al. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad159 · Cited by 8
The Development and Initial Validation of the PROMIS®+HF-27 and PROMIS+HF-10 Profiles
Ahmad, F. S., Jackson, K., Yount, S., Rothrock, N. E., Kallen, M. A., Lacson, L., Bilimoria, K. Y., Kho, A., Mutharasan, R. K., McCullough, P. A., et al. (2022) · ESC Heart Failure · doi:10.1002/ehf2.14061 · Cited by 8
“5) 22 (29) Categories, N (%) New diagnosis or first hospitalization — 11 (15) Cardiac rehabilitation — 5 (7) Cardiac resynchronization therapy — 5 (7) Left ventricular assist device — 11 (15) Hospitalization primarily for HF — 43 (57) Clinical characteristics, N (%) Diabetes 80 (13) 28 (37) Chronic obstructive lung di…”
BackgroundComputer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence
Thaxton, C. T., & Dardik, A. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.05.003 · Cited by 8
National COVID Cohort Collaborative data enhancements: a path for expanding common data models
Walters, K. M., Clark, M., Dard, S., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae299 · Cited by 8
Hybrid natural language processing tool for semantic annotation of medical texts in Spanish
Campillos-Llanos, L., Valverde-Mateos, A., & Capllonch-Carrión, A. (2025) · BMC Bioinformatics · doi:10.1186/s12859-024-05949-6 · Cited by 11
Schuemie, M. J., Ostropolets, A., Zhuk, O., Korsik, U., Seo, S. I., Suchard, M. A., Hripcsak, G. M., & Ryan, P. B. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01433-4 · Cited by 11
“For example, this standard concept encompasses 223 ICD-10 codes, including M81.”
Levine, M. E., Albers, D. J., & Hripcsak, G. M. (2016) · PubMed Central · Cited by 8
Liu, X., Duan, R., Luo, C., Ogdie, A., Moore, J. H., Kranzler, H. R., Bian, J., & Chen, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-14029-9 · Cited by 8
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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