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.
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
Claims the paper makes
0 of 7 cited sources checked · 0 supported · 0 flagged
- Findingcitation
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.
“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]”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [10]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Findingown data
The OHDSI team surveyed current users of the OMOP data model and found that 58 existing OMOP databases have collectively converted 682 million patient records to the OMOP CDM.
“They found that 58 existing OMOP databases have collectively converted 682 million patient records to the OMOP CDM.”
Quote in the paperSubstantial claim - Interpretationreasoning
This enables multicenter, global analyses to be executed rapidly and efficiently using applications or programs developed at a single site.
“This enables multicenter, global analyses to be executed rapidly and efficiently using applications or programs developed at a single site.”
Quote in the paperSubstantial claim - Interpretationreasoning
A blind aggregation of results may increase variance due to heterogeneity instead of decreasing it due to sample size.
“A blind aggregation of the results may increase variance due to heterogeneity instead of decreasing it due to sample size.”
Quote in the paperSubstantial claim - Premisecitation
OMOP previously demonstrated that databases from different sources can give vastly different answers.
“OMOP previously demonstrated that databases from different sources can give vastly different answers [9].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [9]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationown data
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.
“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.”
Quote in the paperSubstantial claim - Interpretationown data
OHDSI vision is, in fact, feasible today.
“In other words, OHDSI vision is, in fact, feasible today.”
Quote in the paperSubstantial claim- 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.
- Premisecitation
The team previously found that simply merging databases is likely to give poor answers because of heterogeneity.
“The team previously found that simply merging the databases is likely to give poor answers because of the heterogeneity [6].”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [6]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Classified as probably not a substantial claim. 8
- May combine two claims. 10
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenOHDSI held its first annual meeting at Columbia University Medical Center in New York City on 16 and 17 October 2014.
“OHDSI held its first annual meeting at Columbia University Medical Center in New York City on 16 and 17 of October 2014.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenThe actual number of patients currently available in the OHDSI Research Forum is estimated to be over 200 million.
“Furthermore, we estimate that the actual number of patients currently available in the OHDSI Research Forum is over 200 million.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenFifty-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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenOther 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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenData owners have used ACHILLES to assess the quality of their database, looking for gaps that may signify upload errors.
“Data owners have used ACHILLES to assess the quality of their database, looking for gaps that may signify upload errors.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenThe 682 million patient record count includes both patients and sources that are duplicated across databases and also includes databases that are not currently participating in the OHDSI Research Forum.
“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.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationown dataThe total count of 682 million patient records 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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: The 682 million patient records converted to the OMOP CDM represent a significant fraction of the world's population.
May combine two claims.
InterpretationreasoningThis approach creates a unique opportunity of implementing 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.
“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.”
Quote in the paperSubstantial claim- Rests on an assumption: Any given query can be executed at any site without modification.
Probably not a substantial claim: may not pass S2 (checkable), S4 (based).
InterpretationreasoningPrevious success in algorithm development, software distribution, and evidence generation points to potential success in the overall evidence-generating OHDSI project.
“Previous success in algorithm development, software distribution, and evidence generation points to potential success in the overall evidencegenerating OHDSI project.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationThe five-year OMOP project developed new methods in observational research and established an observational research laboratory.
“The fiveyear project developed new methods in observational research and established an observational research laboratory.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationnone givenACHILLES 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.
“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.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationOHDSI grew out of the Observational Medical Outcomes Partnership (OMOP), a public-private partnership established in the US to inform the appropriate use of observational healthcare databases for studying the effects of medical products.
“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.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
InterpretationreasoningOHDSI's choice of standard terminologies does not limit who may participate because mappings and tools are supplied to translate from other terminologies.
“OHDSI's choice of standard terminologies does not limit who may participate because mappings and tools are supplied to translate from other terminologies.”
Quote in the paperNot a substantial claim- 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.
May combine two claims.
PremisecitationA centerpiece of the OMOP project was the development of the OMOP Common Data Model (CDM), which represents healthcare data from diverse sources in a consistent and standardized way.
“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).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [4]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenData are retained at the participant's site, simplifying patient and business privacy issues.
“Data are retained at the participant's site, simplifying patient and business privacy issues.”
Quote in the paperNot a substantial claimMay combine two claims.
PremisecitationAt the conclusion of the five-year OMOP project, OMOP research investigators initiated the OHDSI effort, and the research laboratory moved to the Reagan-Udall Foundation for the FDA under the IMEDS Program.
“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].”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [5]
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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 OHDSI network and its open-source analytic tools have been previously used to generate reliable evidence in medicine, including ophthalmology.
“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]
The Introduction describes OHDSI as a network developing open-source analytics to create reliable medical evidence, and the Discussion and Conclusion mentions previous success in evidence generation. Neither passage documents a prior OHDSI study in ophthalmology; the paper also frames much of the network’s evidence-generating work as a goal rather than an established use. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe study followed the STROBE guideline.
“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]
The Methods and Research Network sections describe OHDSI’s research tools and study-development process, but do not say that this paper or a study followed STROBE. Searches for “STROBE,” the guideline’s full name, and reporting-guideline adherence found no supporting passage in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measured
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Yuan, C., Ryan, P. B., Ta, C., Guo, Y., Li, Z., Hardin, J., Makadia, R., Jin, P., Shang, N., Kang, T., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy178 · Cited by 170 · see citing works
Self-citationArtificial Intelligence in Health Care: Current Applications and Issues
Park, C.-W., Seo, S. W., Kang, N., Ko, B., Choi, B. W., Park, C. M., Chang, D. K., Kim, H., Kim, H., Lee, H., et al. (2020) · Journal of Korean Medical Science · doi:10.3346/jkms.2020.35.e379 · Cited by 171 · see citing works
“40 42 Further, IBM has developed Watson for Oncology, which provides optimal personalized treatments for cancer patients, and its clinical trials are underway.”
BackgroundElectronic Health Record Data Quality Assessment and Tools: A Systematic Review
Lewis, A. E., Weiskopf, N., Abrams, Z. B., Foraker, R., Lai, A. M., Payne, P. R. O., & Gupta, A. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad120 · Cited by 171 · see citing works
“13 Although not considered a universal tool, this tool can be openly accessed and used to assess the quality of data conforming to the OMOP structure.”
Distributed learning on 20 000+ lung cancer patients – The Personal Health Train
Deist, T. M., Dankers, F. J. W. M., Ojha, P., Marshall, M. S., Janssen, T. M., Finn, C. F., Masciocchi, C., Valentini, V., Wang, J., Chen, J., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.11.019 · Cited by 160 · see citing works
“An infrastructure to bring research questions to the data has been demonstrated to work recently in projects such as euroCAT [4,5], DataSHIELD [6] and OHDSI [7].”
MethodGoing Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare
Tresp, V., Overhage, J. M., Bundschus, M., Rabizadeh, S., Fasching, P. A., & Yu, S. (2016) · Proceedings of the IEEE · doi:10.1109/jproc.2016.2615052 · Cited by 154 · see citing works
Machine learning approaches for electronic health records phenotyping: a methodical review
Yang, S., Varghese, P., Stephenson, E., Tu, K., & Gronsbell, J. L. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac216 · Cited by 142 · see citing works
WebDISCO: a web service for distributed cox model learning without patient-level data sharing
Lu, C.-L., Wang, S., Ji, Z., Wu, Y. S., Xiong, L., Jiang, X., & Lucila Ohno-machado, A. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv083 · Cited by 143 · see citing works
Hripcsak, G. M., Suchard, M. A., Shea, S., Chen, R., You, S. C., Pratt, N. L., Madigan, D., Krumholz, H. M., Ryan, P. B., & Schuemie, M. J. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2019.7454 · Cited by 139 · see citing works
Sharafeldin, N., Bates, B. A., Song, Q., Madhira, V., Yan, Y., Dong, S., Lee, E., Kuhrt, N., Shao, Y. R., Liu, F., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.21.01074 · Cited by 139 · see citing works
Morales, D. R., Conover, M. M., You, S. C., Pratt, N. L., Kostka, K., Duarte‐Salles, T., Fernández‐Bertolín, S., Aragón, M., DuVall, S. L., Lynch, K. E., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30289-2 · Cited by 136 · see citing works
Volpi, S., Bult, C. J., Chisholm, R. L., Deverka, P. A., Ginsburg, G. S., Jacob, H. J., Kasapi, M., McLeod, H. L., Roden, D. M., Williams, M. S., et al. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1048 · Cited by 135 · see citing works
Smart Medical Information Technology for Healthcare (SMITH)
Winter, A., Stäubert, S., Ammon, D., Aiche, S., Beyan, O., Bischoff, V., Daumke, P., Decker, S., Funkat, G., Gewehr, J. E., et al. (2018) · Methods of Information in Medicine · doi:10.3414/me18-02-0004 · Cited by 135 · see citing works
MIRACUM: Medical Informatics in Research and Care in University Medicine
Prokosch, H., Acker, T., Bernarding, J., Binder, H., Boeker, M., Boerries, M., Daumke, P., Ganslandt, T., Hesser, J., Höning, G., et al. (2018) · Methods of Information in Medicine · doi:10.3414/me17-02-0025 · Cited by 132 · see citing works
Long COVID risk and pre-COVID vaccination in an EHR-based cohort study from the RECOVER program
Brannock, M. D., Chew, R., Preiss, A., Hadley, E., Redfield, S., McMurry, J. A., Leese, P., Girvin, A. T., Crosskey, M., Zhou, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38388-7 · Cited by 138 · see citing works
“Demographics were de fi ned through standard concepts available in the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) 37,38 .”
Clinical Decision-Support Systems
Musen, M. A., Blackford Middleton, A., & Greenes, R. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_24 · Cited by 125 · see citing works
Finding missed cases of familial hypercholesterolemia in health systems using machine learning
Banda, J. M., Sarraju, A., Abbasi, F., Parizo, J., Pariani, M., Ison, H., Briskin, E., Wand, H., Dubois, S., Jung, K., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0101-5 · Cited by 123 · see citing works
“28 The community has developed both a common data model (CDM), as well as a standard vocabulary for consistent representation of EHR data across sites.”
MethodSelf-citationBurn, E., You, S. C., Sena, A. G., Kostka, K., Abedtash, H., Abrahão, M. T. F., Alberga, A., Alghoul, H., Alser, O. H., Alshammari, T. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18849-z · Cited by 123 · see citing works
Kumar, S., Oh, I. Y., Schindler, S. E., Lai, A. M., Payne, P., & Gupta, A. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooab052 · Cited by 125 · see citing works
“OHDSI produces tools like the OMOP (Observational Medical Outcomes Partnership) Common Data Model, which transforms data within disparate observational databases into a common representation (terminologies, vocabularies, and coding schemes) so that they can be analyzed using standardized analytics tools.(96) Despite t…”
MethodSong, X., Yu, A. S. L., Kellum, J. A., Waitman, L. R., Matheny, M. E., Simpson, S. Q., Hu, Y., & Liu, M. (2020) · Nature Communications · doi:10.1038/s41467-020-19551-w · Cited by 120 · see citing works
“To ensure data harmonization and consistency across health systems, many healthcare institutions have transformed data contained in their EHR into a common representation (terminologies, vocabularies, coding schemes) as well as a common format (common data model or CDM) 15,16 .”
BackgroundWhy Is the Electronic Health Record So Challenging for Research and Clinical Care?
Holmes, J. H., Beinlich, J., Boland, M. R., Bowles, K. H., Chen, Y., Cook, T. S., Demiris, G., Draugelis, M. E., Fluharty, L., Gabriel, P. E., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1731784 · Cited by 125 · see citing works
“31 Researchers interested in patient-centered outcomes research may fi nd the PCOR-Net CDM useful in part through research opportunities facilitated by the Patient-Centered Outcomes Research Institute (PCORI).”
MethodKlann, J. G., Joss, M. A., Embree, K., & Murphy, S. N. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212463 · Cited by 117 · see citing works
Semaglutide and Nonarteritic Anterior Ischemic Optic Neuropathy
Cai, C. X., Hribar, M., Baxter, S., Goetz, K., Swaminathan, S. S., Flowers, A., Brown, E. N., Toy, B., Xu, B., Chen, J., et al. (2025) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2024.6555 · Cited by 114 · see citing works
EliIE: An open-source information extraction system for clinical trial eligibility criteria
Kang, T., Zhang, S., Tang, Y., Hruby, G. W., Rusanov, A., Elhadad, N., & Weng, C. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx019 · Cited by 110 · see citing works
Venous or arterial thrombosis and deaths among COVID-19 cases: a European network cohort study
Burn, E., Duarte‐Salles, T., Fernández‐Bertolín, S., Reyes, C., Kostka, K., Delmestri, A., Rijnbeek, P. R., Verhamme, K. M. C., & Prieto‐Alhambra, D. (2022) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(22)00223-7 · Cited by 111 · see citing works
Accuracy of an automated knowledge base for identifying drug adverse reactions
Voss, E. A., Boyce, R. D., Ryan, P. B., van der Lei, J., Rijnbeek, P. R., & Schuemie, M. J. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.12.005 · Cited by 108 · see citing works
Richesson, R., Sun, J., Pathak, J., Kho, A., & Denny, J. C. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.05.005 · Cited by 106 · see citing works
Electronic health records and polygenic risk scores for predicting disease risk
Li, R., Chen, Y., Ritchie, M. D., & Moore, J. H. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0224-1 · Cited by 103 · see citing works
“For example, the Observational Health Data Sciences and Informatics (OHDSI) initiative 80 includes dozens of sites from around the world that have agreed to adopt the OMOP standard for EHR data.”
BackgroundFederated learning: Applications, challenges and future directions
Bharati, S., Mondal, M. R. H., Podder, P., & Prasath, V. B. S. (2022) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-220006 · Cited by 117 · see citing works
“Even while typical data models, i.e., OMOP [107], enable more widespread replication analysis, shared data access remains a major restriction.”
Does health informatics have a replication crisis?
Coiera, E. W., Ammenwerth, E., Georgiou, A., & Magrabi, F. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy028 · Cited by 105 · see citing works
Real-World Evidence for Regulatory Decision-Making: Guidance From Around the World
Burns, L. C., Roux, N. L., Kalesnik-Orszulak, R., Christian, J. B., Hukkelhoven, M., Rockhold, F. W., & O’Donnell, J. C. (2022) · Clinical Therapeutics · doi:10.1016/j.clinthera.2022.01.012 · Cited by 106 · see citing works
Applications of artificial intelligence in drug development using real-world data
Chen, Z., Liu, X., Hogan, W. R., Shenkman, E. A., & Bian, J. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.013 · Cited by 108 · see citing works
“Finally, the establishment of large research networks such as the national Patient-Centered Clinical Research Network (PCORnet) [51], Observational Health Data Sciences and Informatics (OHDSI) consortium [52], and the Clinical and Translational Service Award Accrual to Clinical Trails (CTSA ACT) network [53] facilitat…”
BackgroundThe inclusion of augmented intelligence in medicine: A framework for successful implementation
Bazoukis, G., Hall, J. L., Loscalzo, J., Antman, E. M., Fuster, V., & Armoundas, A. A. (2022) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100485 · Cited by 104 · see citing works
“27 Finally, in order that semantic meaning to be accurately added to data structure representations across countries, common data models such as the Logical Observation Identifiers Names and Codes, the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), should…”
BackgroundElectronic health records and clinician burnout: A story of three eras
Johnson, K. B., Neuss, M. J., & Detmer, D. E. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa274 · Cited by 97 · see citing works
“As EHR technology became widespread and computational power increased, biomedical informatics researchers could demonstrate the power of EHR data and realize the secondary uses such as education, 35 research, 36–38 and public health 39 outlined in Table 1.”
BackgroundPrivacy-preserving data sharing infrastructures for medical research: systematization and comparison
Wirth, F. N., Meurers, T., Johns, M., & Praßer, F. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01602-x · Cited by 98 · see citing works
Electronic health records for the diagnosis of rare diseases
Garcelon, N., Burgun, A., Salomon, R., & Neuraz, A. (2020) · Kidney International · doi:10.1016/j.kint.2019.11.037 · Cited by 99 · see citing works
Seo, S. I., Park, C. H., You, S. C., Kim, J., Lee, K. J., Kim, J., Kim, Y., Yoo, J. J., Seo, W., Lee, H., et al. (2021) · Gut · doi:10.1136/gutjnl-2020-323845 · Cited by 96 · see citing works
Quality assessment of real-world data repositories across the data life cycle: A literature review
Liaw, S., Guo, J., Ansari, S., Jonnagaddala, J., Godinho, M. A., Borelli, A. J., de Lusignan, S., Daniel Capurro, N., Liyanage, H., Bhattal, N., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa340 · Cited by 95 · see citing works
“25 The Achilles DQ rules have been well tested, including a published application to 24 large healthcare datasets across 7 different organizations in America, Europe, and Asia.”
MethodLi, J., Tian, Y., Zhu, Y., Zhou, T., Li, J., Ding, K., & Li, J. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101814 · Cited by 91 · see citing works
Belenkaya, R., Gurley, M. J., Golozar, A., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00079 · Cited by 94 · see citing works
“The Observational Health Data Sciences and Informatics (OHDSI) community conducts research on a global scale using standardized representations of healthcare data and reproducible standardized open source analytics.(1)”
BackgroundResearch and Reporting Considerations for Observational Studies Using Electronic Health Record Data
Callahan, A., Shah, N. H., & Chen, J. H. (2020) · Annals of Internal Medicine · doi:10.7326/m19-0873 · Cited by 92 · see citing works
“Issues encountered in our own prior research (6–8) and discussed by collaborative groups, such as the Observational Health Data Sciences and Informatics (OHDSI) (6) initiative, inform our recommendations.”
BackgroundInfluentialA Survey of Visual Analytics for Public Health
Preim, B., & Lawonn, K. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13891 · Cited by 93 · see citing works
Czernichow, S., Beeker, N., Rives‐Lange, C., Guérot, E., Diehl, J., Katsahian, S., Hulot, J., Poghosyan, T., Carette, C., & Jannot, A. (2020) · Obesity · doi:10.1002/oby.23014 · Cited by 89 · see citing works
“The later database retrieves electronic health records from all AP-HP facilities and aggregates them into a clinical data warehouse following the Observational Medical Outcomes Partnership common data model (11).”
MethodThe impact of commercial health datasets on medical research and health-care algorithms
Alberto, I. R. I., Alberto, N. R. I., Ghosh, A. K., Jain, B., Jayakumar, S., Martinez-Martin, N., McCague, N., Moukheiber, D., Moukheiber, L., Moukheiber, M., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00025-0 · Cited by 86 · see citing works
“To facilitate these multi-institutional data pipelines, initiatives such as the Observational Health Data Sciences and Informatics programme 57 and the US Food and Drug Administration’s Sentinel system 58 have developed guidelines for data quality and standardisation, and software that enables…”
MethodBiomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review
Yang, X., Huang, K., Yang, D., Zhao, W., & Zhou, X. (2023) · Global Challenges · doi:10.1002/gch2.202300163 · Cited by 91 · see citing works
Glicksberg, B. S., Johnson, K. W., & Dudley, J. T. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy114 · Cited by 84 · see citing works
Hoogenboom, W. S., Pham, A., Anand, H., Fleysher, R., Buczek, A., Soby, S., Mirhaji, P., Yee, J., & Duong, T. Q. (2021) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2021.100041 · Cited by 83 · see citing works
Hogan, W. R., Shenkman, E. A., Robinson, T., Carrasquillo, O., Robinson, P. S., Essner, R. Z., Bian, J., Lipori, G., Harle, C. A., Magoč, T., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab221 · Cited by 83 · see citing works
Finding factors that predict treatment-resistant depression: results of a cohort study
Cepeda, M. S., Reps, J., & Ryan, P. (2018) · Depression and Anxiety · doi:10.1002/da.22774 · Cited by 80 · see citing works
Artificial Intelligence and Mapping a New Direction in Laboratory Medicine: A Review
Herman, D. S., Rhoads, D. D., Schulz, W. L., & Durant, T. J. S. (2021) · Clinical Chemistry · doi:10.1093/clinchem/hvab165 · Cited by 84 · see citing works
Biedermann, P., Ong, R. C.-Y., Davydov, A., Orlova, A., Solovyev, P., Sun, H., Wetherill, G., Brand, M., & Didden, E. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01434-3 · Cited by 85 · see citing works
Sitapati, A. M., Kim, H., Berkovich, B., Marmor, R. A., Singh, S., El-Kareh, R. E., Clay, B. J., & Lucila Ohno-machado, A. (2017) · WIREs Systems Biology and Medicine · doi:10.1002/wsbm.1378 · Cited by 81 · see citing works
Towards Implementation of OMOP in a German University Hospital Consortium
Maier, C., Lang, L., Storf, H., Vormstein, P., Bieber, R., Bernarding, J., Herrmann, T., Haverkamp, C., Horki, P., Laufer, J., et al. (2018) · Applied Clinical Informatics · doi:10.1055/s-0037-1617452 · Cited by 81 · see citing works
Ross, E., Jung, K., Dudley, J., Li, L., Leeper, N., & Shah, N. (2019) · Circulation. Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.004741 · Cited by 54 · see citing works
The natural history of symptomatic COVID-19 during the first wave in Catalonia
Burn, E., Tebé, C., Fernández‐Bertolín, S., Aragón, M., Recalde, M., Roel, E., Prats‐Uribe, A., Prieto‐Alhambra, D., & Duarte‐Salles, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21100-y · Cited by 78 · see citing works
Heterogeneity-aware and communication-efficient distributed statistical inference
Duan, R., Ning, Y., & Chen, Y. (2021) · Biometrika · doi:10.1093/biomet/asab007 · Cited by 81 · see citing works
“One concrete example is the Observational Health Data Sciences and Informatics consortium, which contains over 82 clinical databases from over 20 countries around the world (Hripcsak et al., 2015).”
BackgroundMamykina, L., Heitkemper, E., Smaldone, A. M., Kukafka, R., Cole-Lewis, H. J., Davidson, P. G., Mynatt, E. D., Cassells, A. N., Tobin, J. N., & Hripcsak, G. M. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.09.013 · Cited by 79 · see citing works
Use of unstructured text in prognostic clinical prediction models: a systematic review
Seinen, T. M., Fridgeirsson, E. A., Ioannou, S., Jeannetot, D. Y. B., John, L. H., Kors, J. A., Markus, A. F., Pera, V., Rekkas, A., Williams, R. D., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac058 · Cited by 80 · see citing works
“However, assessing generalizability and external validity remains important in model development.32 Frameworks exist, such as the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM),33 that deal with the lack of syntactic and semantic interoperability in health data.”
BackgroundInfluentialHealthcare predictive analytics: An overview with a focus on Saudi Arabia
Alharthi, H. (2018) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2018.02.005 · Cited by 79 · see citing works
Weeks, J. M., & Pardee, R. E. (2019) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.279 · Cited by 78 · see citing works
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Deep learning for rare disease: A scoping review
Lee, J. H., Liu, C., Kim, J., Chen, Z., Sun, Y., Rogers, J. R., Chung, W. K., & Weng, C. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104227 · Cited by 78 · see citing works
Lorberbaum, T., Sampson, K. J., Chang, J. B., Iyer, V., Woosley, R. L., Kass, R. S., & Tatonetti, N. P. (2016) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2016.07.761 · Cited by 79 · see citing works
Federated Learning in Health care Using Structured Medical Data
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The Georges Pompidou University Hospital Clinical Data Warehouse: A 8-years follow-up experience
Jannot, A., Zapletal, É., Avillach, P., Mamzer, M., Burgun, A., & Patrice Degoulet, A. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.02.006 · Cited by 76 · see citing works
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Automatic trial eligibility surveillance based on unstructured clinical data
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Pfaff, E., Girvin, A., Gabriel, D., Kostka, K., Morris, M., Palchuk, M., Lehmann, H., Amor, B. R. C., Bissell, M. M., Bradwell, K., et al. (2021) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocab217 · Cited by 64 · see citing works
“14 In a federated data network, each participating site’s data stay behind its institutional firewall, but are structured according to a CDM.”
- 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 · 2013: 1
- 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.
Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-15.
Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-15.
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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