Kathryn Marchesini | December 12, 2022
As you design, market, and distribute a mobile health (mHealth) app that your customers will use to collect, share, use, or maintain individuals’ health information, it is likely you have questions about what U.S. federal laws apply. You may also wonder which federal agencies oversee various aspects of mHealth — including how this varies by how individuals, their health plan, or health care providers will use the app. Depending on who is expected to use an app and how they will get and use the app (e.g.,
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LaVerne Perlie | November 15, 2022
Hot off the presses, the Pediatric Health Information Technology: Neonatal Abstinence Syndrome Informational Resource (NAS IR) [PDF – 808 KB] is a new resource from ONC to support pediatric care and practice settings specific to neonatal abstinence syndrome. The NAS IR builds upon prior efforts included in the ONC Pediatric Health Information Technology Informational Resources (IR) for health IT developers and for health care providers, and includes information about the implementation of health IT and its use as part of delivering health care to infants experiencing withdrawal after maternal exposure to opioids and other substances during pregnancy.
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Micky Tripathi | October 31, 2022
This blog post is co-authored with Jennifer Roberts, Assistant Director for Health Technologies, White House Office of Science and Technology Policy, and Grail Sipes, Assistant Director for Biomedical Regulatory Policy, White House Office of Science and Technology Policy.
The COVID-19 pandemic demonstrated the need for a coordinated clinical trials enterprise, one that can swiftly characterize emerging viral threats and evaluate the effectiveness of vaccines, therapeutics, and other countermeasures across a diversity of trial participants.
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Kathryn Marchesini | October 19, 2022
As we’ve previously discussed, algorithms—step by step instructions (rules) to perform a task or solve a problem, especially by a computer—have been widely used in health care for decades. One clear use of these algorithms is through evidence-based, clinical decision support interventions (DSIs). Today, we see a rapid growth in data-based, predictive DSIs, which use models created using machine learning (ML) algorithms or other statistical approaches that analyze large volumes of real-world data (called “training data”) to find patterns and make recommendations.
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Brenda Akinnagbe | September 26, 2022
