Population and Public Health
Leveraging data with key technology partners pays off for St. Joseph Healthcare, UnityPoint and Children’s Hospital of Pittsburgh to improve clinical effectiveness.
Case Study: UnityPoint Health connects providers and hospitals with predictive analytics to improve…
Matching the right patients with the right data, in real-time, is how UnityPoint Health is helping providers and hospitals in Iowa, Western Illinois and Southern Wisconsin develop effective, quality care outcomes.
Case study: Children's Hospital of Pittsburgh develops customized prediction indicator for children…
Children's Hospital of Pittsburgh of UPMC was the first pediatric hospital in the U.
Case Study: St. Joseph Healthcare sees dramatic improvement serving high-risk population with Healt…
Harnessing analytics has led to big gains for Maine-based health system to dramatically reduce readmissions, identify high-risk patients and utilize real-time data.
There are two major roadblocks for analytics in healthcare, says Sriram Vishwanath, professor of engineering and data science at University of Texas, Austin.
The confluence of new care models and technology are enabling data scientists to pinpoint gaps in access to care, address social determinants of health, and map data that informs tactics to improve outcomes at the patient and population level.
The pediatric endocrinologist at Stanford University's Lucile Packard Children’s Hospital is known for his HealthKit pilot study on Type 1 diabetes patients.
Vice President Joe Biden unveiled the precision medicine database on Monday, speaking at its operations center at the University of Chicago. Genomic Data Commons is a National Cancer Institute initiative and is central to the National Cancer Moonshot and Precision Medicine Initiative.
Research firm’s latest report also reveals that electronic health records software vendors have made substantial clinical workflow enhancements since 2014 but lacking interoperability continues having an impact on users.
Nicholas Marko, MD breaks down the differences between big data analytics and business intelligence, and explains how Geisinger chooses between centralizing and federating data.