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Clinical decision support is one of digital health’s great promises. Faced with a surplus of information about a patient’s history and symptoms, algorithms built into electronic health records can provide important alerts and reminders, automated prescription suggestions, and even diagnostic support — hopefully, helping patients receive the right care.

But those systems don’t always hold up after their initial testing. Most recently, work pointed to flaws in an algorithm to predict the risk of sepsis, integrated into Epic’s electronic health record platform. A recent STAT investigation found those shortcomings extend to other Epic algorithms, including those used by hospitals to predict how long patients will be hospitalized or who will miss appointments.

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