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Banner Health to implement Regard’s AI tool across hospitals

In June 2022, the partnership between Regard and Banner Health began with a pilot at Banner Thunderbird Medical Center.

Srivani Venna March 19 2024

Banner Health is set to implement clinical platform Regard’s artificial intelligence (AI)-powered technology across all acute-care hospitals in the US.

Regard's clinical automation technology will be implemented in all 33 Banner Health's hospitals in six states by 2024.

This move is a strategic response to the challenges of clinician burnout and financial pressures within the healthcare sector.

The partnership between Regard and Banner Health began with a pilot at Banner Thunderbird Medical Center in June 2022.

Following the pilot's successful adoption and performance, Banner Health has decided to implement Regard's technology enterprise-wide.

The phased rollout is expected to enhance patient care by increasing the time clinicians spend with patients.

Regard chief operating officer and co-founder Nate Wilson said: “Banner is a leader in innovation, and we’re proud to partner with their teams to address care team burnout and ensure their patients receive the best care by surfacing critical information that exists within the EMR.”

Regard's platform utilises proprietary algorithms and AI to provide clinical insights, which are designed to improve workflows and patient outcomes.

The tool is capable of adding detailed information to a clinician's diagnosis quickly, streamlining the post-consultation process by enhancing clinical documentation.

This not only improves communication among clinicians but also optimises the process for eventual reimbursement.

The technology is set to transform the interaction between care teams and clinical documentation improvement (CDI) departments, significantly reducing the need for extensive reviews, queries, and denial appeals.

For busy physicians, Regard analyses the complete medical record and streamlines electronic health record (HER) navigation and enables them to diagnose patients and automate clinical workflows accurately.

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