Carmela Couderc | December 1, 2022
December 1st is World AIDS Day, when we collectively remember those who died due to AIDS-related illnesses and renew our commitment to work together to end the HIV epidemic and support people with HIV. This year’s theme, “Putting Ourselves to the Test: Achieving Equity to End HIV,” emphasizes accountability and action. Collaborative, community-based, cross sector, and whole-of-government approaches that address clinical and social determinants of health (SDOH) are needed to support communities disproportionally affected by HIV and achieve national HIV/AIDS goals.
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Sonia Chambers | November 29, 2022
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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