Steven Posnack | September 30, 2022
On October 6, 2022, we reach the end of the more than two-year glide path laid out for the information blocking regulations. Moving forward, expect to see periodic, experience-driven regulatory updates as well as continued work on education, outreach, and oversight, including the establishment of disincentives for health care providers. There may also be the possibility of information blocking advisory opinions if Congress grants the Secretary such authority.
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Brenda Akinnagbe | September 26, 2022
Stephanie Garcia | September 19, 2022
Real world health data are critical for Patient-Centered Outcomes Research (PCOR). However, it’s often difficult, expensive, and time consuming for researchers to access real-world clinical health data because of privacy concerns, security restrictions, and usage issues. Although PCOR researchers, health information technology developers, and informaticists often depend on anonymized or de-identified clinical health data for testing theories, data models, algorithms, and prototype innovations, re-identification of anonymized data remains a possible security risk. Synthetic health data can provide a no-risk data source to complement research and support testing needs until real clinical health data are available.
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Ryan Argentieri | September 8, 2022
Since the beginning of the COVID-19 pandemic, public health staff have worked tirelessly to make sense of the extraordinary volumes of data coming at them at different times and in different ways. Because much of this information is unstructured or non-standardized, epidemiologists, scientists, and others must first bring these data into alignment before the real work can begin. The more differences there are in the data, the more painstaking the is work for people on the frontlines trying to put the pieces together fast.
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Kathryn Marchesini | September 7, 2022
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.
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