Keith Carlson | August 7, 2023
With Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) application programming interfaces (APIs) now widely available across the United States, health IT developers and application developers should keep up-to-date on API security work and practice good API security hygiene when implementing applications and tools that leverage FHIR APIs.
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Jordan Everson | June 14, 2023
In a recent study in the Journal of the American Medical Informatics Association (JAMIA), we leveraged data from the 2020 American Hospital Association (AHA) Information Technology Supplement gathered from April-June 2021, shortly after the initial applicability date of the information blocking regulations (April 5, 2021). We found that 42% of hospitals perceived that at least one type of information blocking “actor” (health care provider, health information network/health information exchange, or health IT developer of certified health IT) engaged in practices that may constitute information blocking.
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Kathryn Marchesini | 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.
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Kathryn Marchesini | 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.
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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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