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Data-Driven Healthcare Decisioning: Unleashing the Power of External Data Sets and Predictive Analytics

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U.S. hospitals and physician practices are struggling with the rise of self-pay patients, which comprise both the uninsured and underinsured. An October 2009 study by research firm McKinsey & Company stated that healthcare providers can expect to retrieve only half of a patient’s balance after discharge. According to the Hospital Accounts Receivable Analysis Report on First Quarter 2011, patients were the third largest debtors behind managed care and Medicare, comprising 19.27 percent of outstanding accounts receivables (A/R) owed to the central business office (CBO). However, self-pay represents only 6.42 percent of CBOs’ gross revenue. All indicators point to self-pay continuing its upward trajectory, with no signs of slowing down. McKinsey & Company note that if current trends persist, increased patient liabilities and poor collection rates could reduce a hospital’s net revenue yield by 4 to 5 percent within five years. In this constrained environment, healthcare providers are scrambling for strategies and robust solutions that drive efficiencies and as a result increase self-pay collections.

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