Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers

By George Hripcsak, Jon D. Duke, Nigam H. Shah, C. Reich, V. Huser, M. Schuemie, Marc A. Suchard, R. W. Park, I. Wong, Peter R. Rijnbeek and 7 more. Medinfo, 2015

1,262 citations recorded by the source corpus.

The vision of creating accessible, reliable clinical evidence by accessing the clinical experience of hundreds of millions of patients across the globe is a reality. The 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.

Subfields: Health Information Management, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/d5424a5f93f9d410cf17c5393eb5e6a1821dc135

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health Information Management

1. Anita M. Preininger
2. Fei Wang
3. Yuan Luo

In Public Health, Environmental and Occupational Health

1. Eran Segal
2. Hagai Rossman
3. Fei Wang
4. S. Shilo
5. Anita M. Preininger

Citations in the corpus

• Federated Learning for Healthcare Informatics (2019, methodology): “The creation and utility of standardized common data models, such as OMOP [36], allow for more wide-spread replication analyses but it does not overcome the limitations of joint data access.”
• A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data (2016, background): “72 The ultimate value of this work will be seen when data owners develop and report DQ measures based on the DQ categories in Table 1.”
• Harnessing multimodal data integration to advance precision oncology (2021, background): “…information from unstructured text, and ontologies such as those of the Observational Health Data Sciences and Informatics (OHDSI) programme 112 and the American Association for Cancer Research project Genomics Evidence Neoplasia Information Exchange (AACR project GENIE) 1 structure…”
• TabLLM: Few-shot Classification of Tabular Data with Large Language Models (2022, methodology): “The data is stored in the Observational Medical Outcomes Partnership (OMOP) Common Data Model version 6.0 (Hripcsak et al., 2015).”
• Axes of a revolution: challenges and promises of big data in healthcare (2020, background): “Standardizing medical data into a universal format will enable collaborations across multiple countries and resources 34,35 .”
• Multimodal biomedical AI (2022, background): “There are large-scale efforts to provide meaningful harmonization to biomedical datasets, such as the Observational Medical Outcomes Partnership Common Data Model developed by the Observational Health Data Sciences and Informatics collaboration 140 .”
• AI in Health: State of the Art, Challenges, and Future Directions (2019, background): “Examples of such efforts include the OHDSI project [131] we introduced in Section 2.”
• GraphRAG on Consumer Hardware: Benchmarking Local LLMs for Healthcare EHR Schema Retrieval (2026, unknown): “Electronic health record (EHR) systems rely on large, highly structured schemas to organize clinical and operational data, where accurate interpretation of inter-table relationships is essential for analytics, reporting, and decision-support workflows [7].”