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Novant Health rolls out MEDai’s patient-specific early warning system to nine hospitals in three states -

Health information provider MEDai, Inc., US, part of STM publisher Elsevier, has announced that Novant Health, a not-for-profit integrated group of hospitals and physician practices, will roll out MEDai’s Pinpoint Review to its nine hospitals in North Carolina, Virginia and South Carolina.

Pinpoint Review is a patient-specific early-warning system for hospitals that generates predictions for acute-care patients, focusing on the likelihood of a patient developing a complication, like decubitus ulcers, or at risk for being readmitted. Such events increase hospital mortality rates, boost healthcare costs, compromise patient care quality and safety, and result in lower government reimbursement to hospitals.

Pinpoint Review provides real-time predictions utilising MEDai’s superior modeling methodology to alert care providers to patients at risk for developing several of the conditions that the Centers for Medicare and Medicaid Services (CMS) no longer reimburse, called ‘never or non reimbursable events,’ which can cost hospitals millions of dollars. The Joint Commission on Accreditation of Healthcare Organizations consistently encourages hospitals to deliver a higher quality of care and fewer medical errors.

Pinpoint Review is the latest addition to a complete suite of MEDai-developed tools, which includes Pinpoint Quality and Pinpoint Compliance. Pinpoint Quality provides robust retrospective analysis that helps hospitals and physicians quickly identify areas for improvement, making it possible for clinicians to improve performance, outcomes, patient satisfaction, efficiency and cost savings. Pinpoint Compliance provides a simple, time-saving Web-based solution for organisations to collect, report and submit data to the Joint Commission and other regulatory agencies allowing hospitals to participate in initiatives related to accreditation, quality improvement and compliance. Pinpoint Review expands the suite by adding the element of real-time decision support, including the application of alerts and predictive models to patient data while patients are still in the hospital.

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