Predictive modeling and the elusive 1–2 percent

Amidst the multiple major changes occurring in healthcare today, there is a persistent theme across conversations in healthcare boardrooms and executive offices:

“We know we’re leaving money on the table.”

To say there are dozens of instances when hospitals are uncompensated or underpaid for the care they provide is laughable. There are thousands.

These opportunities are scattered across millions of claims and tens of millions of potentially relevant patient-, procedure-, and provider-level data points. The only way to uncover them is through data-mining and predictive analytics – and it is increasingly vital that they be uncovered.

For hospitals, these hidden opportunities translate to millions of dollars a year – in the realm of charge-capture alone, hospitals on average leave 1–2 percent of net revenue on the table. Yet even the traditional processes for preventing and capturing more of this net revenue can drive up costs – and are almost impossible to optimize without machine-learning and predictive modeling.

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CDM, charge description master, predictive modeling, revenue cycle integrity, ZirMed


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