The Health Analytics Ecosystem
This is part of a series of posts on how the health data ecosystem is organized. Datavant’s vision is to connect the world’s health data to improve patient outcomes and bring new treatments to patients faster. In our business, we see a wide range of health data sources and uses;…
Applying the “Do No Harm” Principle to Health Data
At Datavant, we build software that makes it possible to share patient data securely, but our mission is bigger than that: to make it easier for players in the healthcare system to connect and use their health data for the good of patients. This mission requires a broad conception of…
Job offer letters as invitations to employment
As a candidate, receiving an offer letter for a job you want is a terrific feeling. So is giving offer letters! For many companies, an offer letter is more of a contract written by lawyers than a letter or invitation. Despite the glad tidings offer letters bring, they often feel prickly, generic,…
Open vs. Closed Data Ecosystems in Healthcare
Earlier this week, Google, Amazon, IBM, Microsoft, Oracle and Salesforce announced their joint commitment to improving healthcare data interoperability. In other words, the biggest players in cloud computing have taken a small — yet critical — step towards an open data ecosystem. The technology world frequently oscillates between “open” and “closed” systems. Closed systems…
The Fragmentation of Health Data
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):…
The Datavant Vision: Organizing the World’s Health Data
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…
A Novel Patient Recruitment Strategy: Patient Selection Directly From the Community Through Linkage to Clinical Data
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.
Disease Outcomes and Care Fragmentation among Patients With Systemic Lupus Erythematosus
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.
Validity of Cardiovascular Data From Electronic Sources
Faraz S. Ahmad, Cheeling Chan, Marc B. Rosenman, Wendy S. Post, Daniel G. Fort, Philip Greenland, Kiang J. Liu, Abel N. Kho, and Norrina B. Allen
The authors sought to understand the degree of agreement of electronic data research networks as compared to data collected by standardized research approaches in a cohort study. The comparisons were made by linking data from MESA (Multi-Ethnic Study of Atherosclerosis), a community-based cohort, with EHR’s from six Chicago-area hospitals. Ultimately, nearly 70 percent had data in both systems and demonstrated mixed results. For some measurements, such as BMI, the correlations between the MESA and EHR data were quite high. For others, such as systolic blood pressure, the correlation coefficient was only 0.39.