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revenue cycle
Introducing the Solventum Revenue Integrity System
Introducing the Solventum Revenue Integrity System from Sift and Solventum. This AI-driven solution unifies clinical and payments data, predicts and prevents clinical denials and optimizes revenue cycle management.
It's Time To Move Past These 3 Common Revenue Cycle Management Mistakes
It's time for health systems and hospitals to move past these three all-too-common business tactics that have remained constant, despite the ever-changing payer landscape and the mounting pressure for providers to lower costs and provide a more seamless payments experience.
The Revenue Cycle Is Ready For AI... Are You?
AI can drive payments and cut waste, but where do you start? Download our roadmap for healthcare executives, AI for the Healthcare Revenue Cycle, a free and unbiased implementation guide.
Rules Are Too Rigid For The Revenue Cycle
Automation (RPA) makes the revenue cycle more efficient — saving time and decreasing errors. While this is an improvement for health systems and hospitals RPA efficiencies are one-dimensional. Fully optimizing the revenue cycle and getting the most out of AI (including RPA) requires a clear and holistic view of payments, an understanding of the full lifecycle flow of claims.
Want To Add AI to Your Revenue Cycle? Data Intelligence is the First Step.
So you want to add AI to your revenue cycle? You have to start by establishing a solid foundation of data intelligence. This comes from normalizing and organizing payments data in a way that provides actionable insights. This works will establish the where/why/how of your AI goals.
3 Ways Data Can Fix Denials
The impact of insurance claim denials for healthcare providers in 2021, and how providers' own historical payments data and machine learning provide solutions for both denials prevention and appeals prioritization.
Machine Learning In Action - 5 Revenue Cycle Examples
Integrating Machine Learned adds a decision engine to the revenue cycle, enabling you to determine the next best action that should be taken with a claim, denial, patient account, payer contract or vendor. Can't envision it? Here are five examples of what ML in the revenue cycle looks like, in action...
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 of inefficiencies and implementing data-driven strategies around revenue cycle work efforts, vendor outsourcing, patient financial engagement and payer reimbursement.
3 Reasons Why The Future Of Healthcare Payments Is Not Automation.
RPA Follows Rules. Machine Learning Generates Intelligence. Your revenue cycle will benefit from both. Learn about the limitations of RPA and how machine learning provides can have a more meaningful impact on the healthcare revenue cycle.
Fintech Meets Medtech To Improve Healthcare Payments
Wonder how Sift impacts healthcare? Watch Sift Healthcare's Founder and CEO, Justin Nicols talk with StartUp Health's Logan Plaster about the impact of Sift's AI and analytics on healthcare payments.
How a 120-Day Machine Learning Experiment Led to a 6.5% Increase in Patient Collections
In late 2019, Sift Healthcare and State Collection joined forces to test AI in the revenue cycle. Together, we conducted a rigorous 120-day live, scientific test (with a control group) for Wake Forest Baptist Health, to determine if machine learning impacted patient financial engagement outcomes. The results were impressive — a 6.5% increase in patient collections.
Introducing The Sift Quality Score
Introducing The Sift Quality Score. For revenue cycle managers, understanding account quality and its impact on patient collections is essential for forecasting revenue, optimizing rcm workflows and ensuring the best strategy is in place for managing resources. Sift’s Quality Score is derived from Sift’s predictive model scores — it tracks account quality over the early-out period, enabling RCMs to more intelligently forecast revenue throughout each billing cycle.
How Do You Optimize Patient Collections During COVID-19?
While triage around COVID-19 continues, patient bills are going to pile up, cash on hand is going to dwindle and a more significant number of patients will struggle to pay their medical bills. Now is the time to be strategic and truly commit to being a flexible payment partner for patients. The starting point? Data analytics. Which easily dovetails into machine learning, both providing a powerful advantage for patient collections.
What Dirt Do You Move First?
Unless your hospital or health system's denial rate is 0% you're missing earned revenue. Denials management is more than clean claim rate and prevention. How you prioritize insurance denials has a powerful impact on cash flow and revenue. Predictive analytics can tell you which denials your teams should work, in what order, to get the best payment outcomes.
There Aren't Enough Bodies To Save Healthcare
We're at a tipping point where healthcare providers will *have* to implement automation and AI into the revenue cycle. Not only to recover more dollars but also to keep up with the growing (& massive) administrative burden that is persistent in healthcare payments.
Rural & Community Hospitals Need AI The Most
Unpaid patient medical bills are a growing problem for community hospitals and a downright crippling problem for rural hospitals. These healthcare providers can’t afford not to optimize their revenue cycle, improve their understanding of patient payment behavior and implement collection strategies based on their unique patient populations.
3 Healthcare Payments Processes That Are Still Manual
The healthcare payments market is in desperate need of approaches and innovations that reduce manual intensive processes — and save money. Even in today's day and age of big data, artificial intelligence and automation, many healthcare payments processes are painfully manual. Manual typically means cumbersome, prone to error, complicated and expensive.
