New tracking technologies and better data processing algorithms are looking to both better track patients and to better match those patients with hospital beds. In the process, their mission is to bring antiquated hospital operations finally, firmly, into the 21st Century.
Mount Sinai hospital located on New York’s Upper East Side is one of the busiest hospitals in the US. Typically, before it used to take seven phone calls to place a patient. Now it takes one” But, as with every thing else in Manhattan, the hospital has run up against the issue of space. Jeff Terry from General Electric says “the only way for them to continue to serve more patients is to become more efficient.” Mr Terry is part of a GE Healthcare team that worked with Mount Sinai on a pilot programme called AutoBed, which sought to solve this problem by better managing Mount Sinai’s beds.
When you put a male in bed, you need to put a male in a second bed, and it eventually becomes a game of Tetris,” says Dave Toledano, who co-wrote GE’s paper on bed matching.
The game of Tetris is what runs up against human limitations. A nurse might be able to match one patient to one bed relatively well. But in a place like Mount Sinai, where occupancy is upwards of 90%, figuring out how to match every patient to the appropriate bed while maximising overall hospital capacity is a task beyond any human brain.
This is where “industrial big data” comes in: AutoBed is an algorithm that uses the admitting nurse’s “triage” recommendation (in the form of the electronic medical records, which includes data on gender) and the real time data of which hospital beds are available (using real-time location awareness devices like radio-frequency identification tags, infrared, and computer vision) to figure out the best possible match.
It can process 80 bed requests, monitor up to 1,200 beds, and account for 15 different “attributes”, such as a patient’s need to be placed in a room near a nursing stand.
After a six-week trial, in which three separate algorithms were piloted, the programme was found to decrease wait times by one hour for more than 50% of incoming emergency room patients. In a place like Mount Sinai, the hospital could admit thousands more patients a year – and potentially save millions of dollars.
On the other hand not all medical professionals are convinced that better data processing technology will solve the problem of bed matching and patient monitoring on the whole.
Dr Jesse Pines, the director of the Center for Health Care Quality at George Washington University days ”Programmes that promote transparency can be beneficial but they still don’t solve a lot of the human inefficiency problems that cause delays. “Those inefficiencies include regulations surrounding patient privacy, and in the US, the complicated bureaucratic web of private health insurance.”
Moreover Dr Pines cautions that the technology might be expensive – not only for purchase, but to pay to train staff, maintain upgrades and having the right person to manage it.