Published on November 11, 2009 at 7:54 AM
Yesterday, at the SPSS Directions 2009 North American Conference in Las Vegas, Nev., David Mould, Ph.D., predictive analytics scientist at MedeAnalytics, delivered a presentation to business executives, IT professionals, and researchers on ways MedeAnalytics uses cloud computing, also known as SaaS (Software-as-a-Service), to provide clients with advanced analytic tools and solutions.
Mould discussed how, using SPSS Clementine servers and other predictive analytics technologies, the company helped a hospital client optimize collections from a rapidly growing population of self-pay patients. In another case study, he showed how MedeAnalytics employed cloud computing to provide clients with forecasting tools for budgeting and monthly updates.
“In our current economy, there is a growing pool of uninsured and under-insured patients — in some states more than 17 percent of the patients are self-paying,” said Mould. “Not only does this issue impact access to healthcare and a hospital’s ability to fulfill its mission, it affects its ability to protect margins and keep the doors open.” Segmenting patients into different groups with their own collection strategies, the MedeAnalytics self-pay model used a variety of independent variables to rank-order patients by their propensity to pay their bill, thereby maximizing the hospital’s revenue collection. “The predictive model is clearly superior to randomly selecting patients for collection efforts,” said Mould, noting that because hospital clients can access their work lists on MedeAnalytics servers, they are able to avoid sizable capital expenditures.
Mould shared a second example in which a company with multiple overseas offices needed to consolidate a number of revenue, expense and headcount forecasts. Cloud computing enabled the sharing of a set of advanced forecasting tools that all of the client’s budget analysts could access on a secured server. “Our cloud computing model adapts to customer business goals to provide real-time insight into disparate healthcare data sources, and it requires limited IT support resources,” he said. “This capability helps budget coordinators develop more accurate forecasts and budgets.”
Source: MedeAnalytics