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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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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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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Carmen Smiley | August 9, 2022
One of the single greatest social determinants of health is where a patient lives. It can determine their risk factor for a specific illness or chronic disease, such as asthma, and can also affect much broader measures of well-being and life expectancy. Thus, our ability as healthcare professionals to measure and act on such factors relies heavily on how we accurately capture and manage standardized patient addresses. The standardization of patient address data across healthcare strengthens our ability to measure the social,
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