Florida Hospital Implements AI Tool to Improve Patient Throughput

Tampa General Hospital (TGH) announced the implementation of an artificial intelligence (AI) tool designed to enhance the processes of admitting, moving, and discharging patients to improve patient experience and efficiency.

Operational flow is a major challenge for many hospitals, as it relies on coordinating staff and patients across the hospital, according to the press release. Most hospital processes depend on people being in the right place at the right time, and improving efficiencies in this area can boost care quality.

To improve operational flow, TGH teamed up with start-up Enroute to develop and refine an AI platform to streamline intra-hospital patient transportation. Traditionally, a dispatcher handles patient transport manually and coordinates all runs throughout the hospital. For example, to schedule transport from a hospital room to the operating room, a dispatcher assigns the run to a transporter via a message on a hospital-issued device.

Using this method, runs can only be scheduled one at a time, and coordinating transportation in real time can be challenging. Enroute’s Software-as-a-Service solution, however, utilizes existing, real-time data communicated over the internet through TGH’s EMR system.

“With Enroute, we can see the transporters’ availability, location, and if they have a wheelchair or stretcher with them in real time,” said Donna Tope, senior director of support services at TGH, in the press release. “The system can then automatically assign the closest transporter with the right equipment to transport that patient. It’s critical to our world-class care that the patient transport department be as efficient as possible in moving patients to the services they need to recover.’’

The tool can also create multiple assignments simultaneously, increasing efficiency at the 1,041-bed hospital.

In addition, the system organizes and reports the information it gathers while coordinating intra-hospital transport to enhance response times.

By implementing this tool and improving patient flow, TGH hopes to avoid conflicts when scheduling patient trips, coordinate multiple trips to minimize patient transports, speed up discharge notification, decrease waiting time and improve patient satisfaction, increase bed utilization, and more effectively move equipment, materials, linens, specimens, and other items, according to the press release.

Other hospitals have taken similar approaches to enhance patient flow.

Last February, the University of Colorado Hospital shared how data analytics allowed the health system to manage patient capacity and flow in intensive care units during COVID-19 surges.

The organization had previously developed several of its own tools to utilize hospital- and patient-generated data, but staff found that using such tools increased manual data collection and administrative work for frontline clinicians, which can contribute to burnout.

To combat this, the hospital’s capacity management team developed an analytics tool designed to detect when the ICU is nearing maximum capacity, enabling staff to transport patients to less-strained areas of the hospital.