Filter By “EHR/Clinical Data”

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Evolution of Electronic Health Record (EHR) Data

An interview with Stacey Long (Chief Strategy Officer, OMNY Health) by Su Huang (Head of Data Strategy, Datavant)

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Health Data Trends Part II: New Data Types in the Datavant Ecosystem

In my first post, I reviewed the most common questions clients attempt to answer with health data. This time, I’ll review new data available through Datavant’s ecosystem partners and its value in answering specific questions. As partners continue to join our ecosystem, there are more opportunities for health systems, insurers and biopharma companies to connect data that completes the picture of patient health.

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How to Conduct More Effective HEOR Analyses with Linked Data
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Case Study: Linking EHR, Genomic & Claims Data to Find Ultra-Rare Disease Patients
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Research Using Connected Health Data
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Case study: Profiling and Diagnosing Rare Disease Patients
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Using Fit-for-Purpose Real-World Data to Enhance Our Understanding of COVID-19

In this webinar we will provide an overview of how linking real-world data across institutions is necessary to accelerate COVID-19 research,etc....

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Google Acquiring Fitbit: The Battle for the Personal Health Record starts with a Skirmish for…

Google Acquiring Fitbit: The Battle for the Personal Health Record starts with a Skirmish for Consumer Wearables Google’s acquisition of FitBit has resulted in many great stories recounting the ins and outs of the fitness tracker and smartphone wars, Google’s troubled attempts to get a toehold in the wearables space,…

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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.

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