Guest post from David Mould, Ph.D., Predictive Analytics Scientist, MedeAnalytics, Inc.
The state of the economy is putting immense pressure on the healthcare industry.
Hospitals sit on the horns of a challenging dilemma—seeking to provide the best possible patient care while still trying to run a profitable business.
This dilemma exists, in part, because a growing number of patients being treated either lack health insurance or are underinsured, and turning “self-pay” patients away who lack the means to pay is not an option.
In fact, research has shown that only about 15 percent of these self-pay patients end up paying for their services, with the balance being written off by healthcare organizations as bad debt, forcing hospitals to contend with budget shortfalls. Working with already thin margins, being able to successfully capture funds from self-pay patients can often be the difference between achievement and failure with regard to financial goals.
The collection process itself can be time-consuming and expensive, requiring hours for phone calls and letter writing. Often, hospitals turn to outside collection agencies to help with the process, but that is a costly alternative.
Working with IBM, MedeAnalytics has helped more than 100 hospitals with large self-pay populations measure and predict patient payment behavior, reduce risk from bad debt, boost collection rates, and improve the morale of collections staff.
Based on IBM predictive analytics, our cloud-based Self-Pay Analytics solution maximizes the productivity of collectors by giving them a list of patients who are more likely to pay the hospital back and put the people who are unlikely to pay down at the bottom of the list, optimizing the work of collectors.
The solution analyzes 20 or more variables that typically influence payment behavior, such as the admission source (elective vs. an emergency), the type of treatment or procedure, and the income and demographics of the patient.
Instead of calling patients alphabetically by last name or who owes the most, hospitals can now easily identify which self-pay patients have a high probability of collections, which qualify for charity care, and which would be best to refer to financial counseling or directly to a collections agency. This also ensures that Medicaid eligibility is never missed.
For instance, one of our hospital clients in the Southeast United States, which treats more than two million people each year at its regional network of facilities, was struggling with bad debt write-offs and poor recovery rates with its self-pay patients.
Its 16 collectors spent much of their time trying to collect from patients who were unable or unwilling to pay. At the same time, patients legitimately eligible for charity care weren’t being identified, adding to the hospital’s bad debt.
By using our solution, the hospital achieved: 30 percent reduction in bad debt write-offs; 12 percent increase in self-pay collection rates; $270,000 per month savings on returned mail; $100,000 per month increase in Medicaid reimbursements; and $25,000 per month reduction in collection agency fees.
We attribute a lot of our client’s success to IBM’s business analytics software in the cloud. Hospitals don’t need to invest in technology infrastructure, and we can easily build predictive models faster with IBM SPSS Modeler software.
For our work using IBM predictive analytics, we are pleased to have been awarded a Business Technology Leadership Award by Ventana Research.
We are honored to share this award with IBM and all of our healthcare clients who are having a profound impact on improving patient care and quality of life, while controlling escalating healthcare costs.