Evidence map›Paper›PMID 33882595›Full record

ArticleYearbook of medical informatics2021

Drawing Reproducible Conclusions from Observational Clinical Data with OHDSI.

George Hripcsak, Martijn J Schuemie, David Madigan, Patrick B Ryan, Marc A Suchard

Abstract read
In one paragraph

Article in Yearbook of medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Federated learning's uncomfortable truth: why human networks matter more than neural networks.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  8. PhenoFit: a framework for determining computable phenotyping algorithm fitness for purpose and reuse.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  9. Review
  10. Article
  11. Assessing Covariate Balance with Small Sample Sizes.medRxiv : the preprint server for health sciences · 2025
    Article
  12. Article
  13. Observational
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

George HripcsakDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Martijn J SchuemieObservational Health Data Sciences and Informatics, New York, New York, USA.
David MadiganObservational Health Data Sciences and Informatics, New York, New York, USA.
Patrick B RyanDepartment of Biomedical Informatics, Columbia University, New York, New York, USA.
Marc A SuchardObservational Health Data Sciences and Informatics, New York, New York, USA.

Funding

DISCOVERING AND APPLYING KNOWLEDGE IN CLINICAL DATABASESR01LM006910 · NLM · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HRIPCSAK, GEORGE M · 2000 to 2023
$10.6M
NLM NIH HHS R01 LM006910
6 · The paper itself

Abstract

objectiveThe current observational research literature shows extensive publication bias and contradiction. The Observational Health Data Sciences and Informatics (OHDSI) initiative seeks to improve research reproducibility through open science.

methodsOHDSI has created an international federated data source of electronic health records and administrative claims that covers nearly 10% of the world's population. Using a common data model with a practical schema and extensive vocabulary mappings, data from around the world follow the identical format. OHDSI's research methods emphasize reproducibility, with a large-scale approach to addressing confounding using propensity score adjustment with extensive diagnostics; negative and positive control hypotheses to test for residual systematic error; a variety of data sources to assess consistency and generalizability; a completely open approach including protocol, software, models, parameters, and raw results so that studies can be externally verified; and the study of many hypotheses in parallel so that the operating characteristics of the methods can be assessed.

resultsOHDSI has already produced findings in areas like hypertension treatment that are being incorporated into practice, and it has produced rigorous studies of COVID-19 that have aided government agencies in their treatment decisions, that have characterized the disease extensively, that have estimated the comparative effects of treatments, and that the predict likelihood of advancing to serious complications.

conclusionsOHDSI practices open science and incorporates a series of methods to address reproducibility. It has produced important results in several areas, including hypertension therapy and COVID-19 research.

Indexed as

Information DisseminationObservational Studies as TopicPublication BiasCOVID-19HumansReproducibility of Results

Identifiers

PMID33882595
PMCPMC8416226

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.