Every day, hospital staff do the best they can to navigate the daily chaos of bed management by making educated guesses as to what is going to happen over the course of the day. Relying on team huddles throughout the day, staff pore over Excel or paper spreadsheets to predict how many beds will open up and when. They try to estimate demand for those beds by the time of day, unsure when to deploy "surge capacity." On some days, this method works out well. However, more often than not, the staff's best efforts result in long patient waits, unwanted staff overtime, and ultimately lower access to care.

Long wait times for patients can be the undoing of a large clinic or a hospital system. According to a recent survey, 84% of patients consider a reasonable wait somewhat or very important to a quality experience, and 30% say they've left a medical appointment because of an inordinately long wait. And a whopping 20% of patients say they'd consider changing providers due to wait times alone. That's why Mohan Giridharadas, a Stanford graduate and former McKinsey and Company partner, founded Charlotte, North Carolina- and Santa Clara-based LeanTaaS. It's a predictive analytics company that taps data science to "radically" improve health care provider performance, using cloud-based solutions to solve problems like resource utilization.