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,

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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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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.

The post This World AIDS Day – Reflecting on Health IT as a Tool for Advancing Health Equity and Ending the HIV Epidemic appeared first on Health IT Buzz.