It’s 7 a.m., April 13, 2022, at Sarasota (Fla.) Memorial Healthcare System, and an overflow unit is opening as planned. Based on data reviewed the night before, Susan Grimwood, APRN-C, knew that the health system would not have enough beds to care for all the cardiac patients who were projected to be admitted.
Proactively, she had worked with other nursing leaders and made plans to open an overflow unit at 7 a.m. to accept the patients, preventing potential operational logjam and patient safety issues.
This is just one example of the kinds of bottlenecks that Grimwood addresses on an almost daily basis in her role as director of clinical logistics and patient throughput.
Grimwood and the logistics team continually track all patient movement across the public health system, which includes an 895-bed flagship hospital in Sarasota, a 110-bed hospital in Venice and a free-standing ED. Via computers, they continually monitor admissions, surgeries, ED visits, discharges and other data and work closely with unit leaders to determine staffing needs. The team is also charged with appropriately placing patients in the right unit based on acuity and specialty needs.
“Susan is like the air traffic controller of the health system,” says Bryan Dickerson, vice president of workforce, Hospital IQ. “She figures out what needs to move so planes don’t crash and the runways don’t become full of planes.”
Sarasota Memorial Healthcare uses a predictive analytics solution to help manage patient capacity and identify staffing needs. In addition to helping Grimwood and her clinical logistics team with patient throughput, the software is used by senior leaders for operational and strategic forecasting and by frontline leaders for staff scheduling.
The technology incorporates machine learning capabilities. It considers various data—including historical census, local COVID trends, scheduled surgeries, projected discharges and acuity levels of ED patients—and issues predictive forecasts of patient volumes by unit or service days or even weeks into the future.
“The better staffing models are not just predicting staffing needs but identifying possible bottlenecks to discharge,” Dickerson says.
For instance, on a recent Monday, 30 orthopedic surgeries were scheduled for a Wednesday at Sarasota Memorial Hospital. The predictive analytics tool projected that the orthopedic unit would be 10 to 15 beds short. “We worked with the orthopedic team manager to get some additional staff scheduled and to open an overflow area for short-stay orthopedic patients, Grimwood says. “This helped ensure we wouldn’t have any issues of patients backed up in the PACU [post-anesthesia care unit].”
Since COVID-19 struck two years ago, the work of Grimwood and her team has been vital to ensuring that the health system can care for large surges of patients with fewer staff. “The predictive analytics is like our magic behind the scenes that helps us know whether patients will need an ICU bed or a medical bed, what units are most burdened, how units are staffed and other key information,” Grimwood says.
Maggie Van Dyke is a freelance writer based in the Chicago area.
Proactively, she had worked with other nursing leaders and made plans to open an overflow unit at 7 a.m. to accept the patients, preventing potential operational logjam and patient safety issues.
This is just one example of the kinds of bottlenecks that Grimwood addresses on an almost daily basis in her role as director of clinical logistics and patient throughput.
Grimwood and the logistics team continually track all patient movement across the public health system, which includes an 895-bed flagship hospital in Sarasota, a 110-bed hospital in Venice and a free-standing ED. Via computers, they continually monitor admissions, surgeries, ED visits, discharges and other data and work closely with unit leaders to determine staffing needs. The team is also charged with appropriately placing patients in the right unit based on acuity and specialty needs.
“Susan is like the air traffic controller of the health system,” says Bryan Dickerson, vice president of workforce, Hospital IQ. “She figures out what needs to move so planes don’t crash and the runways don’t become full of planes.”
Sarasota Memorial Healthcare uses a predictive analytics solution to help manage patient capacity and identify staffing needs. In addition to helping Grimwood and her clinical logistics team with patient throughput, the software is used by senior leaders for operational and strategic forecasting and by frontline leaders for staff scheduling.
The technology incorporates machine learning capabilities. It considers various data—including historical census, local COVID trends, scheduled surgeries, projected discharges and acuity levels of ED patients—and issues predictive forecasts of patient volumes by unit or service days or even weeks into the future.
“The better staffing models are not just predicting staffing needs but identifying possible bottlenecks to discharge,” Dickerson says.
For instance, on a recent Monday, 30 orthopedic surgeries were scheduled for a Wednesday at Sarasota Memorial Hospital. The predictive analytics tool projected that the orthopedic unit would be 10 to 15 beds short. “We worked with the orthopedic team manager to get some additional staff scheduled and to open an overflow area for short-stay orthopedic patients, Grimwood says. “This helped ensure we wouldn’t have any issues of patients backed up in the PACU [post-anesthesia care unit].”
Since COVID-19 struck two years ago, the work of Grimwood and her team has been vital to ensuring that the health system can care for large surges of patients with fewer staff. “The predictive analytics is like our magic behind the scenes that helps us know whether patients will need an ICU bed or a medical bed, what units are most burdened, how units are staffed and other key information,” Grimwood says.
Maggie Van Dyke is a freelance writer based in the Chicago area.
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