Filter By “Linking Real-World Data”

Medical research is beginning to make use of “real world data,” the data generated from everyday visits to doctors’ offices by normal patients. A new paper in The Oncologist provides a powerful demonstration of how this data can be applied to real studies. *** The gold standard of medical research has…

Slowly but surely, the pace of change in healthcare is starting to accelerate, and 2019 will be a dynamic year for the industry with major regulatory, technological, and business innovation. There will be false hype, breakthroughs, the failure of traditional business models, and plenty of cross-pollination both within healthcare (payers,…

An overview of data analytics in the patient, provider, payer, and life sciences segments, along with predictions for the future of healthcare data analytics.

Understand the difference between open and closed healthcare data ecosystems, and how to get to a more open data ecosystem.

A Survey of The Health Data Ecosystem At Datavant, we’re focused on the vision of connecting the world’s health data to improve patient care and speed the development of new treatments. As part of this, we put together an “ecosystem map,” outlining how data flows across healthcare today. *** Update (September 2019):…

Datavant’s vision is to organize the world’s health data. We believe that this is one of the most important data challenges of this era: if we are successful over the next 20 years, we are confident that our work will improve patient outcomes, bring medicines and medical solutions to market…

Lindsay P. Zimmerman, Satyender Goel, Shazia Sathar, Charon E. Gladfelter, Alejandra Onate, Lindsey L. Kane, Shelly Sital, Jasmin Phua, Paris Davis, Helen Margellos-Anast, David O. Meltzer, Tamar S. Polonsky, Raj C. Shah, William E. Trick, Faraz S. Ahmad, and Abel N. Kho
This paper outlined a novel workflow for recruiting potential trial patients. Members of the community were identified, surveyed, and then assigned an encrypted and hashed identifier. Concurrently, data from a variety of hospitals was linked together at the patient level. Via the encrypted and hashed identifier, the investigators could connect data from the hospital systems to understand whether someone was eligible for the study. The method of recruitment was significantly more efficient than the typical process for most clinical trials.

Theresa L. Walunas, Kathryn L. Jackson, Anh H. Chung, Karen A. Mancera-Cuevas, Daniel L. Erickson, Rosalind Ramsey-Goldman, and Abel Kho
By linking data across six different Chicago health institutions, the researchers were able to understand the extent to which patients with systemic lupus erythematosus (SLE) receive fragmented care and the impact of said care. In identifying 4,276 patients with SLE, 20 percent received care from more than 1 institution; those patients were more likely to have complications, including increased risk of infections, cardiovascular disease, and stroke.

Abel N. Kho, John P. Cashy, Kathryn L. Jackson, Adam R. Pah, Satyender Goel, Jörn Boehnke, John Eric Humphries, Scott Duke Kominers, Bala N. Hota, Shannon A. Sims, Bradley A. Malin, Dustin D. French, Theresa L. Walunas, David O. Meltzer, Erin O. Kaleba, Roderick C. Jones, and William L. Galanter
The study discusses the design and implementation of a tool that created a secure, privacy-preserving linkage of electronic health record (EHR) data across multiple sites in Chicago. There are a number of uses for this technology, including in clinical research and understanding the fragmentation of care. The software documented in the study was able to link data across the six institutions, resulting in five million unique patient records while de-duplicating seven million records. Today, the software is part of the Datavant offering.