How to reduce healthcare finance risk through data analytics
By using analytics to forecast cash flow and optimize collections, CFOs can begin reversing declining profitability. A survey from Black Book Market Research, featured in Healthcare Finance, found that 87 percent of small and community hospitals are facing serious healthcare finance risk, with declining-to-negative profitability through 2015, and that 61 percent of the CFOs in struggling hospitals anticipate losing their jobs.
Despite the challenges healthcare organizations are having with lower reimbursements, unpaid patient bills and underused billing and recordkeeping technology, hospitals already have the information they need to create a more accurate financial picture of cash flow and collection statuses. This comes in the form of data.
Collecting debt can be time intensive, and no matter the amount of effort, some debt is simply not going to be collected. This is a major issue in the shift to population health management, in which care providers are adopting performance-based reimbursements. Hospitals & Health Networks reported the biggest financial challenge in moving from a fee-for-service to performance contract model is that it takes longer to assess patient outcomes and calculate payment. John Harris, principal with DGA Partners, says that this often means waiting six to 18 months to be compensated.
Hospitals can improve cash flow by using predictive analytics to determine which outstanding accounts are most likely to be paid, and then focus collection efforts on these patients. By analyzing characteristics of previous paid and unpaid accounts, CFOs can predict which accounts are more likely to be collected successfully. The collections department can then use these criteria to determine which accounts will have the highest ROI for collection time.
Using data analytics to increase value-based care reimbursement
One Alabama hospital is using data and predictive analytics to reduce readmissions, which has increased the performance of the hospital and helped alleviate expenses. The hospital found through data analytics that the self-pay population visiting the emergency room were typically between the age of 18 and 26, and they could not afford to fill their prescriptions. Further, they would often disregard doctors' orders, resulting in additional emergency-room visits with worsening symptoms and conditions. This trend was decreasing cash flow. After analyzing the issue, the hospital implemented a new prescription subsidization program, available to patients who agreed to comply with medical orders.
Real-time data allows more accurate prediction of cash flow
It's not a new practice for hospitals to analyze their cash flow. But with the new changes toward performance-based payment, the traditional accounting and prediction system is no longer as effective. One of the biggest challenges is that organizations lack significant insight into historical changes. How can the healthcare system and hospitals determine best practices without this information? One of the major benefits of big data analytics is being able to examine real-time data, which provides immediate information that can be used to make predictions and decisions to optimize cash flow quicker.
There's significant lag time between organizations' awareness of cash issues and their data collection and analysis. This has been a bottleneck for hospitals as they try to remain solvent. However, real-time analytics makes detection and response seamless, and CFOs who support this technology can help their organizations avoid healthcare finance risk. Hospitals that successfully overcome their cash flow obstacles will still be standing when the dust in the performance-based payment storm settles.