NewYork-Presbyterian, the nation’s biggest public health system, considers itself smart at implementing smart technologies such as artificial intelligence and telehealth that, once implemented, get results.
For example, the health system’s NYP OnDemand telehealth program has helped to reduce emergency department times to 30-45 minutes compared with a previous time of about two hours.
Artificial intelligence is used in NewYork-Presbyterian’s clinical operations center, a remote monitoring system and command center that connects clinical care programs throughout the healthcare system and provides support for the entire system. At the center, registered nurses can track physiologic data of patients in the emergency department in real-time, as well as monitor the temperature of refrigerators that store lifesaving medicine, the health system says.
But implementing new technology across a complex healthcare enterprise is a journey with deliverables and a timeline. It’s not a quick trip, former NewYork-Presbyterian vice president care coordination and now chief nursing officer at Stony Brook University Hospital Julie Mirkin and assistant professor of medicine Michael Gao told attendees Thursday at World Congress 2019 Care Coordination & Technology Conference in Atlanta.
NewYork-Presbyterian had several challenges to identify and solve before a successful roll-out of AI, including issues such as teams working in silos, missing patient length-of-stay targets and care plans that weren’t always defined, Mirkin told attendees. “Does this sound familiar?” she said.
For other healthcare organizations looking to launch or expand new technology, Mirkin and Gao offered these best practices:
- Rally the staff, structure communication, predict barriers and team increase accountability
- Eliminate silos
- Develop performance accountability
- Build relationship and collaboration among staffers
- Standardization is critical
- Staffers support what they help create
Following its own best practices, NewYork-Presbyterian saw a nice initial return on its first investment in AI, including improving the accuracy of estimated date of discharge from 41% to 93%.
“Technology helped to create a care traffic control board,” the health system says.
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