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Jordan Everson's Latest Blog Posts
Two Sides of the AI/ML Coin in Health Care
Jordan Everson | 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.
Read Full Post.Minimizing Risks and Maximizing Rewards from Machine Learning
Jordan Everson | 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.
Read Full Post.Getting the Best out of Algorithms in Health Care
Jordan Everson | June 15, 2022
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.
Read Full Post.Information Blocking Through the Eyes of Health Information Exchanges
Jordan Everson | May 10, 2021
The information blocking regulations at 45 CFR Part 171 began to apply to health care providers, health IT developers of certified health IT, health information exchanges, and health information networks on April 5, 2021, per ONC’s recent interim final rule. That makes now a good time to consider stakeholders’ views about practices that may constitute information blocking, including the extent to which they exist. Our recent study in the Journal of the American Medical Informatics Association reports on a survey of health information exchanges’ (HIEs) perceptions of other stakeholders’ practices related to information blocking.
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