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FALL 2025 Innovators Summit
2025-12-02 - 2025-12-04    
10:45 am
NYC
What To Expect FALL 2025 Innovators Summit Panel discussions and keynote speeches from prominent digital health leaders Top-tier exhibitors showcasing cutting-edge digital health solutions, innovations, [...]
Events on 2025-12-02
White Papers

Using data analytics to identify revenue at risk

homeland security

Predictive and comparative analytics have the potential to drive improved value by pinpointing areas where proactive steps can better support optimal revenue cycle performance—as well as the organization’s mission.

The trend toward using predictive and comparative analytics to improve value in health care is on the rise, driven by advancements in technology, healthcare reform, regulatory mandates, and the emergence of value-based payment models.

Recently, hospital CIOs surveyed across 12 major health systems identified “creating an information driven health system using advanced analytics” as their No. 1 long-term priority (Health System Chief Information Officers: Juggling Responsibilities, Managing Expectations, Building the Future, Deloitte Center for Health Solutions, February 2013). Meanwhile, 60 percent of healthcare IT professionals responding to another survey indicated their organizations plan to increase investment in analytics this year to improve their limited ability to handle complex analytics (Miliard, M., “Big Data Driving Analytics Investments,” Healthcare IT News, Mar. 22, 2013).

The revenue cycle is one area where the power of predictive and comparative analytics has the potential to help healthcare leaders improve margins.

Insight-Driven Margin Improvement

The use of both predictive and comparative analytics is a key differentiator between organizations with strong revenue cycle performance and those that exhibit substantial leakage. Predictive analytics. In a function rich with patient and financial data, predictive analytics enables revenue cycle leaders to shift away from using retrospective data to make reactive decisions and move toward using real-time data to make prospective predictions that enhance an organization’s ability to respond to change. The question with predictive analytics is not a backward-looking “What happened?” but a forward-looking “What’s next?” and “What should we do about it?”

Download Complete Whitepaper Here