Improving Precision in Public Health through Innovative Data Sharing Approaches
This All In project showcase webinar features two public health departments, Chicago and Baltimore, that are creating a culture of innovation in their communities by using collaborative, data-driven approaches to examine health trends at the local level. They are forming new partnerships with universities, hospitals, and community organizations to create data sharing systems that use methods like predictive analytics and hotspotting to target resources and programs more efficiently for the greatest impact.
Chicago Department of Public Health, with its partners, created a predictive model to identify young children at risk of being lead poisoned. The model provides an opportunity to prevent lead paint exposure through proactive home lead inspections and blood testing at an earlier age.
Baltimore City Health Department, with its partners and the local Health Information Exchange, developed a real-time data surveillance system that tracks fall-related emergency department visits and hospitalizations. Data analyses are being used to target existing community programs and develop new falls prevention interventions.
This blog shares key takeaways from this webinar, which featured two All In projects, led by health departments in Chicago and Baltimore, that are using methods like predictive analytics and hotspotting to target resources more efficiently for greater impact.
This report from the de Beaumont Foundation and Johns Hopkins Bloomberg School of Public Health is a roadmap for how to overcome perceived barriers to using electronic health data for public health activities. HIPAA is often considered an impediment to sharing health data between health systems and public health departments, but this report outlines the legal underpinnings for data sharing and provides illustrative examples of constructive uses of data to advance patient and community health.
This webinar featured four community collaborations, led by health departments, working to integrate data from sectors that influence individual and community health, such as housing, education, social service, economic development, and safety. Each panelist discussed how they are accessing, analyzing and leveraging big data to address public health priorities.
The Center for Data Science and Public Policy (DSaPP) is using machine learning to create a real-time, actionable system for health professionals to predict and remediate lead poisoning hazards before children are poisoned and suffer lifelong health and development consequences. This integrated and innovative system will ensure that resources are used most efficiently and ultimately mean healthier children. This prevention stance will serve to better level the playing field by remediating a core determiner of health for low-income and at-risk children nationwide.
This video from the US Department of Health and Human Services’ Office of the National Coordinator for Health Information Technology provides a basic understanding of APIs and their potential to revolutionize health care data sharing. Using APIs as part of electronic health records systems, or EHRs, can make it easier for patients to get and share important health information. APIs can also help health care providers share patient information with other providers securely and efficiently.