By - Cedars-Sinai recently developed a machine learning platform to forecast staffing needs, local hospitalization volumes, and the rate of confirmed COVID-19 cases.
The platform runs multiple forecasting models to help anticipate and prepare for increasing COVID-19 patient volumes with an 85 percent to 95 percent accuracy.
“Our goal is to have the capacity and the right care available every day to treat the patients who need us, which fluctuates on a daily basis,” Michael Thompson, executive director of enterprise data intelligence at Cedars-Sinai, said in the announcement.
“We need to match that daily demand with the necessary resources: beds, staff, PPE and other supplies,” he added.
The researchers’ estimates could inform managers on how to appropriately and effectively schedule employees and gauge how long current medical supplies will last, which will allow experts to prepare for increases and decreases in hospitalized patients., Thompson explained.
The platform was originally developed to optimize the way care is provided at the hospital, including data points regarding patients' vital signs and their length of stay, to predict the most effective treatments for patients.
The platform could also pinpoint the likelihood that a patient will be readmitted and the patients who will be most satisified with their hospital experience.
Because the medical center's platform uses machine learning, it can automatically learn from its own mistakes and become “smarter,” Thompson said.
“If it predicts that tomorrow we’ll have 100 COVID-19 patients, but that actual number turns out to be 90, then the platform automatically goes back and tries to relearn what changed to cause the outcome to be different,” Thompson said. “The platform hones its ability to recognize patterns and becomes smarter every day.”
When the COVID-19 pandemic began rapidly spreading around the world, hospitals had to uncover how to accurately forecast the number of patients who would need hospitalization, who would need treatment in the intensive care unit and require a ventilator, how long the patients would stay, and how much personal protective equipment would be needed.
Many organizations including HHS, Microsoft, and SADA, have launched platforms or portals to combat COVID-19.
A June study showed that technological advancements in healthcare, including service robots, artificial intelligence, and digital communications, have the ability to improve performances of the industry as a whole and can be used in response to COVID-19 outbreaks.
The study leveraged interviews from hospitals and home agency administrators, union representatives, healthcare IT experts and consultants, and technology developers from April 2018 to June 2019.
Researchers found that the drivers of the technological change include increasing access to healthcare, reducing cost of care, consolidating and coordinating healthcare delivery, facilitating chronic disease prevention and management, and responding to demographic trends.
Machine learning, a form of artificial intelligence, allows technology to develop its own rules and response by learning from already existing data and can essentially enhance existing digital technology, researchers explained.
How providers leverage this technology is dependent on what they specifically want to address.
For example, electronic visit verification monitors directs care workers using a smartphone, which has facilitated documentation, while boosting micro-management of workers..
On the other hand, the same workers could use similar technology, such as AI, in a very different manner:to decrease workforce, justify more limited activities, and pay for workers.
AI also has the potential to free up time for workers to focus on other activities, such as skills where humans excel when compared to robots, including empathy and communication.
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August 19, 2020 at 02:30AM
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