Life-Saving Equipment Delivered to Patients in Record Time, Lower Cost at Hospitals Using Award-Winning GoUSME Connect™
(HOUSTON) March 18, 2024—A new AI system providing clinicians on-demand access to medical equipment while saving hospitals time and money earned US Med-Equip (USME) Foundry’s CIO 100 Award today.
GoUSME Connect returns valuable time to clinicians caring for critical patients, allowing them to place an urgent order for medical equipment, such as a life-saving ventilator or bariatric bed, within the electronic medical record (EMR) portal they already use to help manage their patients’ care.
“The system does all the magic creating a much more streamlined process. It has been amazing and is 1,000% saving us time,” a nurse manager for South Carolina’s largest private, nonprofit healthcare system shared about the artificial intelligence-driven GoUSME Connect, which requires no complex programming and is easily configured in hours.
Operating 24/7, USME leads the nation in medical equipment distribution and technology with an average delivery time of three hours. Hospitals using GoUSME Connect receive equipment even faster.
As clinicians record patients’ therapy needs in their EMR as usual, GoUSME Connect automatically matches those needs with equipment. This saves hospitals from paying equipment costs that may accrue after a patient no longer needs it, eliminating the need for clinicians to place equipment transfer or pickup orders while caring for patients.
“Getting medical equipment when they need it, where they need it to help care for patients should be hassle-free for clinicians,” US Med-Equip Chief Information Officer Antonio Marin said. “GoUSME Connect was designed to help these healthcare heroes focus on helping their patients heal rather than on processes that take up time and money.”
USME partners with top hospitals across the nation in the rental, sales, service and asset management of medical equipment ranging from respirators and infusion pumps to patient beds and therapeutic surfaces that a hospital may need based on the number of patients expected or admitted at the time.