How AI can facilitate greater objectivity & interoperability in hospitals


Healthcare providers and payers are embracing shared goals of automation and interoperability. Artificial intelligence can ensure these elements work together and translate into operational improvements.

During an April 22 webinar hosted by Becker's Hospital Review and sponsored by XSOLIS, healthcare technology leaders discussed how AI can improve the accuracy and efficiency of clinical decisions, as well as greatly reduce the burden of administrative tasks.

The speakers were:

  • Joan Butters, founder and CEO at XSOLIS

  • Paul Cummings, chief product officer at XSOLIS

  • Heather Bassett, MD, chief medical officer at XSOLIS

Five key points:

  1. The healthcare industry has an abundance of data, but it lacks a collaborative exchange of insights, according to Ms. Butters. It's often difficult for providers to derive actionable insights from their data, and even when they can, it's difficult to communicate these insights within the hospital or to payers.

  2. Utilization management had largely been ignored by healthcare technology vendors, specifically for operations optimization and cognitive computing, even though UM is linked to revenue cycle performance, Dr. Bassett said. It's critical to deploy an AI platform designed to make data usable.

  3. Real-time data needs to be brought to life. It can be used to create projections and predictions, allowing clinicians to make better decisions for patients while they are in the hospital. Hospitals should use AI to access a complete and objective view of a patient's clinical story.

  4. AI can realign work hospital staff is already doing, using a much more data-driven process: specifically administrative tasks can be automated while patient-centered care can be prioritized. Such an approach improves the consistency and efficiency of care delivery.

  5. AI tools should be focused on objectivity and extracting meaningful elements of a patient's story, according to Mr. Cummings. These tools should be trained to spot patterns, recognize important data elements and make predictions. AI can do this consistently and neutrally in repetition, whereas humans cannot.

To learn more about XSOLIS and its approach to artificial intelligence, click here.

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