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 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.
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.
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.
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.
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.