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Jordan Everson's Latest Blog Posts
New Study Shows Patients Prefer Immediate Access to Test Results and have Unmet Information Needs
Jordan Everson | March 20, 2023
A recent study of 8,000 patients that accessed their test results via an online patient portal found that more than 95% wanted to continue to immediately receive test results through their portal. That percentage stayed at 95% when focused on patients with non-normal results.
These findings come amid concerns that the immediate release of test results could lead to patient distress when patients access test results before their physicians could contact them and help to interpret those results.
Evidence on the Growing Use of Health IT to Address the Opioid Epidemic
Jordan Everson | January 19, 2023
In the midst of a growing drug overdose crisis exacerbated by the COVID-19 pandemic, clinicians’ use of prescription drug monitoring programs (PDMPs) and electronic prescribing of controlled substances (EPCS) technology is critical to improving opioid prescribing practices, informing treatment decisions, and supporting safe and effective patient care. Recent efforts, such as mandating use of EPCS technology and integrating PDMPs into electronic health record (EHR) systems, aim to improve prescribing practices by increasing the utility of information contained in PDMPs and decreasing prescription diversion and doctor shopping.Read Full Post.
Back to the Future: What Predictive Decision Support Can Learn from DeLoreans and The Big Short
Jordan Everson | December 13, 2022
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.Read Full Post.
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.