Healthcare Data Archives - Sift Healthcare
How Real-Time Clinical Data Is Revolutionizing Payer Reimbursement
How real-time clinical data is transforming insurance payer reimbursement, reducing claim denials, and streamlining healthcare revenue cycle management.
The $265 Billion Problem: How Unified Data Unlocks Revenue Intelligence in Healthcare
Discover how unified clinical and financial data can solve healthcare’s $265B revenue cycle inefficiency problem with actionable AI-driven insights.
5 Ways Machine Learning and RPA Work Together In The Revenue Cycle
Machine Learning makes RPA more effective in the revenue cycle. Being able to truly leverage payments data to drive decision-making makes automation efforts meaningful. ML enables RPA efforts to move from automating repetitive human processes to attacking the root causes...
Healthcare Providers Have Become Lending Organizations
Every day healthcare providers are extending credit in the form of care, and they have little idea whether, how much or when they will be paid. It’s time for healthcare providers to start deploying well-established data science and analytics tools...
6 Ways To Use Your Healthcare Payments Data To Improve Collections
AI, machine learning and predictive models are abstract terms in the revenue cycle. How do you actually move past the buzzwords and get value out of your healthcare payments data? Here are six ways healthcare providers and RCMs can truly...
Hospital CFO’s & The “Negligible” Use Of Data Analytics
93% of healthcare administrators say that data analytics are “crucial” to future healthcare operations. At the same time, 84% say the usage of advanced analytics at their organization is “negligible”. In healthcare payments, there are three key roadblocks to the...
Want To Control Your Cost To Collect & Utilization? You Need A Holistic View Of Your Healthcare Payments.
Every healthcare CFO and revenue cycle leader should be looking at their insurance payer payments in relation to patient payments -- identifying how they relate and influence one another. This is essential intelligence in an ever-complex revenue cycle.
Your Data Is Too Messy For Data Science
In healthcare payments, where data flows from multiple systems and standards are a moving target, data can be pretty filthy. This means mismatched formats, errors and inconsistencies. Having clean data is often the biggest roadblock to being able to reap...