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AI, Technology & Compassion. Give it a listen.
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.
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.
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.
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 operationalize healthcare payments data to improve patient collections and revenue cycle operations.
Rev/Track Leverages AI To Improve Revenue Cycle Operations
Rev/Track leverages Sift Healthcare's AI and machine learning to help healthcare providers and RCMs to optimize revenue cycle operations. Rev/Track delivers detailed intelligence around payments behavior, insurance denials, collection trends, patient segments and revenue cycle work efforts.
Automation Does Not Mean Data Science
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.
4 Innovative Strategies For Patient Payments
When healthcare providers leverage their patient payments data to drive their collections strategies, they create new and powerful opportunities to increase the revenue they collect, building patient relationships and using payments data to inform business operations. Here are four new and intelligent ways that healthcare providers are using their patient payments data to be more strategic and to drive better payment outcomes. Here are four innovative ways that healthcare providers are leveraging their patient payments data
High Dollar Denials Are Often The Least Likely To Be Paid.
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.
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 the benefits of data science.
