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
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A curated and standardized adverse drug event resource to accelerate drug safety research
Banda, J. M., Evans, L., Vanguri, R. S., Tatonetti, N. P., Ryan, P. B., & Shah, N. H. (2016) · Scientific Data · doi:10.1038/sdata.2016.26 · Cited by 246
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German Medical Informatics Initiative
Semler, S. C., Wissing, F., & Heyder, R. (2018) · Methods of Information in Medicine · doi:10.3414/me18-03-0003 · Cited by 230
Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models
Banda, J. M., Seneviratne, M. G., Hernandez‐Boussard, T., & Shah, N. H. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013315 · Cited by 228
Li, X., Ostropolets, A., Makadia, R., Shoaibi, A., Rao, G., Sena, A. G., Martinez-Hernandez, E., Delmestri, A., Verhamme, K., Rijnbeek, P. R., et al. (2021) · BMJ · doi:10.1136/bmj.n1435 · Cited by 219
Body Mass Index and Risk for Intubation or Death in SARS-CoV-2 Infection
Anderson, M. R., Geleris, J. D., Anderson, D. R., Zucker, J. E., Nobel, Y. R., Freedberg, D. E., Small-Saunders, J. L., Rajagopalan, K. N., Greendyk, R. A., Chae, S., et al. (2020) · Annals of Internal Medicine · doi:10.7326/m20-3214 · Cited by 216
Trends in glucagon-like peptide 1 receptor agonist use, 2014 to 2022.
Watanabe, J. H., Kwon, J., Nan, B., & Reikes, A. (2024) · Journal of the American Pharmacists Association · doi:10.1016/j.japh.2023.10.002 · Cited by 216
Hemingway, H., Asselbergs, F. W., Danesh, J., Dobson, R., Maniadakis, N., Maggioni, A. P., van Thiel, G. J. M. W., Cronin, M., Brobert, G., Vardas, P. E., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx487 · Cited by 218
Privacy-first health research with federated learning
Sadilek, A., Liu, L., Nguyen, D., Kamruzzaman, M., Serghiou, S., Rader, B., Ingerman, A., Mellem, S., Kairouz, P., Nsoesie, E. O., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00489-2 · Cited by 226
“…recent years, with a speci fi c focus on smartphone-based analytics and learning 2 and learning across data silos of various healthcare systems 3 – 7 , its applications to clinical and epidemiological studies over individuals ’ data are only beginning to emerge — for example in a new study on…”
BackgroundClinical Data Reuse or Secondary Use: Current Status and Potential Future Progress
Meystre, S. M., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., & Lehmann, C. U. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-007 · Cited by 204
Big data hurdles in precision medicine and precision public health
Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0719-2 · Cited by 200
Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis
Lee, J., Sun, J., Wang, F., Wang, S., Jun, C.-H., & Jiang, X. (2018) · JMIR Medical Informatics · doi:10.2196/medinform.7744 · Cited by 196
Recent Developments in Clinical Terminologies — SNOMED CT, LOINC, and RxNorm
Bodenreider, O., Cornet, R., & Vreeman, D. J. (2018) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1667077 · Cited by 193
Federated benchmarking of medical artificial intelligence with MedPerf
Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00652-2 · Cited by 188
“• Data model standardization efforts to enable collaboration between institutions, such as the OMOP Common Data Model 80,81 , possibly leveraging the highly multimodal nature of biomedical data 82 .”
Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives
Romano, J. D., & Tatonetti, N. P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00368 · Cited by 183
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164
“[80] George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, Johan Van Der Lei, Nicole Pratt, G Niklas Norén, Yu-Chuan Chuan Li, Paul E Stang, David Madigan, and Patrick B Ryan.”
BackgroundYou, S. C., Rho, Y., Bikdeli, B., Kim, J., Siapos, A., Weaver, J. C., Londhe, A. A., Cho, J., Park, J., Schuemie, M. J., et al. (2020) · JAMA · doi:10.1001/jama.2020.16167 · Cited by 167
A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse
Garcelon, N., Neuraz, A., Salomon, R., Faour, H., Benoît, V., Delapalme, A., Münnich, A., Burgun, A., & Rance, B. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.02.019 · Cited by 182
Tian, Y., Schuemie, M. J., & Suchard, M. A. (2018) · International Journal of Epidemiology · doi:10.1093/ije/dyy120 · Cited by 171
Criteria2Query: a natural language interface to clinical databases for cohort definition
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
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 187
“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
“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
“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
Volker Tresp, C., 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 162
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 149
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 140
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 138
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
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 138
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
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 143
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 129
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
“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., Middleton, B., & Greenes, R. A. (2021) · Springer eBooks · doi:10.1007/978-3-030-58721-5_24 · Cited by 166
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
“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
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
“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
“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., Beinlich, J., Boland, M., Bowles, K., Chen, Y., Cook, T., Demiris, G., Draugelis, M., Fluharty, L., Gabriel, P., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1731784 · Cited by 115
“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
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 129
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
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
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
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 110
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 106
“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 126
“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 107
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
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 109
“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 111
“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 102
“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 114
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
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
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 100
“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
Belenkaya, R., Gurley, M. J., Golozar, A., et al. (2021) · JCO Clinical Cancer Informatics · doi:10.1200/cci.20.00079 · Cited by 94
“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 99
“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 96
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 96
“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
“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 99
Glicksberg, B. S., Johnson, K. W., & Dudley, J. T. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy114 · Cited by 84
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
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 86
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
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 98
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 86
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 83
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
Ross, E., Jung, K., Dudley, J. T., Li, L., Leeper, N. J., & Shah, N. H. (2019) · Circulation Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.118.004741 · Cited by 83
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
Heterogeneity-aware and communication-efficient distributed statistical inference
Duan, R., Ning, Y., & Chen, Y. (2021) · Biometrika · doi:10.1093/biomet/asab007 · Cited by 82
“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 80
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 81
“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
Weeks, J. M., & Pardee, R. E. (2019) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.279 · Cited by 78
Johnston, S. S., Morton, J. M., Kalsekar, I. D., Ammann, E. M., Hsiao, C., & Reps, J. (2019) · Value in Health · doi:10.1016/j.jval.2019.01.011 · Cited by 78
Lu, J. Q., Lu, J., Wang, W., Liu, Y., Buczek, A., Fleysher, R., Hoogenboom, W. S., Zhu, W., Hou, W., Rodríguez, C. J., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.103821 · Cited by 79
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 85
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 80
Federated Learning in Health care Using Structured Medical Data
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Liu, W., Laranjo, L., Klimis, H., Chiang, J., Yue, J., Marschner, S. L., Quiroz, J. C., Jorm, L. R., & Chow, C. K. (2023) · European Heart Journal - Quality of Care and Clinical Outcomes · doi:10.1093/ehjqcco/qcad017 · Cited by 91
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., Bissell, M. M., Bradwell, K., et al. (2021) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocab217 · Cited by 65
“14 In a federated data network, each participating site’s data stay behind its institutional firewall, but are structured according to a CDM.”
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.
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