Vanderbilt University Medical Center deployed predictive analytics to forecast the number and timing of expected surgeries so fewer staffers are on the clock during slow periods. Doing so allows the health system to schedule more efficiently, recouping costs that equate to the salaries of 2.8 anesthesiologists.
Similarly, a custom web-based app helps Envision Physician Services — a multispecialty medical group working with more than 900 facilities — identify patient traffic patterns and staff hours to better plan for unexpected ebbs and flows across locations. It’s a great example of working smarter, not harder.
There's Better Care Yet to Come
Although much progress has been made, we’re only at the starting line.
I’m encouraged by developments such as an artificial intelligence algorithm being trained by Stanford University researchers to screen chest X-rays in a matter of seconds and detect 14 different pathologies with an accuracy rivaling that of radiologists. The tool, developers say, could help triage patients based on need.
Patt, who co-authored research published last year in the ASCO Educational Book, cites “immense” potential for analytics to guide efforts in population health management, radiomics (computations of quantitative data from medical scans) and pathology.
Regardless of application or targeted ailment, predictive analytics will continue to positively shape healthcare delivery and patient outcomes. The movement doesn’t require a crystal ball to reveal big potential ahead.