{"id":16856,"date":"2020-12-10T15:15:35","date_gmt":"2020-12-10T20:15:35","guid":{"rendered":"https:\/\/healthit-gov.go-vip.net\/buzz-blog\/?p=16856"},"modified":"2026-01-05T20:27:11","modified_gmt":"2026-01-05T20:27:11","slug":"exploring-a-new-computing-model-to-enhance-health-care-analysis","status":"publish","type":"post","link":"https:\/\/healthit.gov\/blog\/insights-updates\/exploring-a-new-computing-model-to-enhance-health-care-analysis\/","title":{"rendered":"Exploring a New Computing Model to Enhance Health Care Analysis"},"content":{"rendered":"

This post was written by guest contributors.<\/em><\/strong><\/p>\n

To battle the COVID-19 pandemic, health professionals need immediate access to relevant health data in order to inform clinical decision making, conduct timely research, understand trends, and inform response efforts.<\/p>\n

The pandemic, though, highlighted a known shortcoming of our nation\u2019s health data infrastructure. Decision makers, like health care providers, must rely on information reporting services outside the health care system to obtain real-time epidemiological data that, even then, only showed a limited picture.<\/p>\n

Before the pandemic started, researchers at the National Institutes of Health (NIH) began to study how our nation\u2019s health information exchanges (HIEs) could solve this problem. HIEs already perform the challenging task of aggregating, harmonizing, and reporting comprehensive data. The challenge for NIH was to find an approach where this data could be shared while adhering to existing privacy laws and protections.<\/p>\n

If unlocked, the potential insight derived from these data could provide researchers and data analysts with new discoveries that could improve the care, treatment, and prevention of everything from cancer and heart disease to COVID-19.<\/p>\n

Our efforts also aligned with the larger goals of the Office of the National Coordinator for Health IT, to discover improved ways to facilitate the electronic exchange of health information.<\/p>\n

Pilot Project Serves as Proof of Concept<\/strong><\/h2>\n

In cooperation with the Nebraska Health Information Initiative (NEHII) and the National Cancer Institute (NCI), NIH launched a pilot program earlier this year that uses a machine learning concept known as federated learning<\/em> that could leverage existing HIE data while ensuring privacy. Federated learning is a machine learning technique that shares an analytical model between different groups, allowing them to analyze their own data \u2013 and share those results \u2013 without needing to share the actual raw data.<\/p>\n

The pilot aimed to expand clinical insights regarding COVID-19 using the existing ways health data are stored and protected in the US. In our pilot, each organization connected to a central cloud ecosystem known as the Multi-state Federated Architecture for Shared Analytics, better known as MuFASA.<\/p>\n

MuFASA serves as the central hub for the federated learning model. It can send analytic models to HIEs who can then analyze their own data. The HIEs then send the results back to MuFASA. When MuFASA has collected these results, it can create a deeper analysis using this information.<\/p>\n

By using this back-and-forth process, HIEs do not give unfettered control of their data to an outside party. This ensures that patient privacy remains intact, while researchers can still leverage data to study public health. NCI and NEHII tested the federated learning model to create three predictive models centered on COVID-19:<\/p>\n