Real World Data

The promise of learning from data collected for other purposes, such as clinical care, has been an aspiration of informatics for decades. However, with the widespread emergence of electronic health records (EHRs) throughout the world over the past decade, that latent promise is now a reality. These data resources are commonly called real-world data (RWD).

Many common data models (CDMs) have emerged, enabling transformation of entire EHR systems at scale into a comparable, consistent, predictable, and well-documented common format. The most mature and widely adopted CDM is the OHDSI OMOP model, which can function in a federated (where the data stays behind a firewall at the source institution) or centralized fashion; both have advantages.

Johns Hopkins faculty have deep experience with RWD in both federated and centralized applications. Our research teams have expertise in data transforms, including semantic as well as syntactic translation into many CDMs. Our research teams have leveraged CDMs to illustrate the Learning Health System across many domains. 

Recent Grant Awards

2023 Paul Nagy and Andreea Creanga Johns Hopkins Receives 14 Million NIH Award to Strengthen Maternal Health in the U.S.

2022 Paul Nagy and Khyzer Aziz Receive 8 Million CDC Contract to Modernize Maternal Infant Linking Surveillance.

Recent Publications

  1. Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership (OMOP) Concepts, Ophthalmology Science 3(4):100391.
  2. Value sets and the problem of redundancy in value set repositories. PLOS One, Dec 2024
  3. Hospital Length of Stay Prediction for Planned Admissions Using Observational Medical Outcomes Partnership Common Data Model: Retrospective Study. J Med Internet Res 2024;26:e59260
  4. Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM. JACC. 2024 Sep, 84 (10) 904–917.
  5.  A framework for understanding an open scientific community using automated harvesting of public artifacts, JAMIA Open, Volume 7, Issue 1, April 2024
  6.  Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study, Ophthalmology Retina, 2024
  7. Comparative Effectiveness of Second-line Antihyperglycemic Agents for Cardiovascular Outcomes: A Large-scale, Multinational, Federated Analysis of the LEGEND-T2DM Study medRxiv 2024.02.05.24302354