In the third blog in our series on artificial intelligence (AI) and machine learning (ML)-driven predictive models (data analytics tool or software) in health care, we discussed some potential risks (sometimes referred to as model harms) related to these emerging technologies and how these risks could lead to adverse impacts or negative outcomes. Given these potential risks, some have questioned whether they can trust the use of these technologies in health care.

We are encouraged to see that some stakeholders are demonstrating that a predictive model is fair,

The post Back to the Future: What Predictive Decision Support Can Learn from DeLoreans and The Big Short appeared first on Health IT Buzz.

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.

The post Addressing Evolving Health Information Technology Needs in Pediatric Care: Neonatal Abstinence Syndrome (NAS) Informational Resource (IR) appeared first on Health IT Buzz.

With a heightened focus on health equity throughout our work, ONC has adopted the concept of “health equity by design.” Along those lines, health IT can, and should, be used to better identify and mitigate disparities while enhancing opportunities for underrepresented populations. In 2019, under the Leading Edge Acceleration Projects (LEAP) for Health IT program, ONC funded the University of Texas at Austin’s Dell Medical School (Dell Med) to design,

The post Where APIs meet Health Equity by Design: Introducing the FHIRedApp Health Innovation appeared first on Health IT Buzz.

When talking about artificial intelligence (AI) today, people are usually referring to predictive models—often driven by machine learning (ML) techniques—that “learn” from historic data and make predictions, recommendations, or classifications (outputs) which inform or drive decision making. The power of ML is in its enormous flexibility. You can build a model to predict or recommend just about anything, and we have seen it transform many sectors.
The potential for ML and related technologies in health care is exciting.

The post Minimizing Risks and Maximizing Rewards from Machine Learning appeared first on Health IT Buzz.

The same basic technology that can predict what movie you might want to watch, what song you might want to listen to, or what item you might want to buy online, can also predict the onset of diseases, forecast costs of care, and recommend treatment options for your doctors, nurses, and pharmacists.
Algorithms are not new to health care. Indeed, an important and long-standing part of our Health Information Technology (Health IT) Certification Program has included Clinical Decision Support (CDS) certification criterion covering “rules-based” algorithms and software systems that use rules derived by experts,

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