Sift's Blake Sollenberger and Affinity Strategies' Claire Vincent talk about the impact of AI on healthcare payments. They cover the challenges health systems face as they increasingly act as financial lenders, the importance of a positive patient experience in billing, the need for compassionate collections, and the challenges of moving to AI in a risk-averse environment. It’s an enlightening listen and will give you a new perspective on AI and the revenue cycle.
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.
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.
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 to forecast payments and optimize outcomes.
Sift Healthcare is proud to be participating in AVIA's 25% challenge, aimed at helping US health systems adopt digital technologies that will reduce the notorious administrative waste in healthcare.
At Sift Healthcare we have seen time and time again that healthcare leaders are clear on care. When we talk to healthcare leaders, from CFOs and VPs of Revenue Cycle to CTOs and Chief Innovation Officers, they always start and end with care. Even the revenue cycle has to advance the mission around patients and care.
Hats off to the Chief Innovation Officers. 97% of health systems that have Chief Innovation Officers also had positive operating margins. For healthcare providers, a commitment to innovation and strategic change improves the quality of care, patient experience and financial performance.
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 utilization of advanced analytics to improve the revenue cycle.
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.
The term “automation” can refer to any number of automatic processes within the revenue cycle workflows. But, it doesn’t necessarily refer to the use of data science. Just because a process is “automated” doesn’t mean predictive analytics or any data science is applied. However, when you do truly use data science to automate your workflow (which you absolutely should do), you pick up a host of efficiencies and improvements. Our breakdown on automation vs rule-based segmentation vs true predictive analytics.
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.
Healthcare providers face an uphill battle when it comes to claim denials. Their denials management strategy should be to prioritize the denials that are most likely to be overturned (paid). These denials can be identified using data science -- advanced modeling on denials history, data pulled from 835’s and 837’s.
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.