What Is AI Getting Right — and Wrong — in Healthcare’s Revenue Cycle?
AI hasn't solved all the problems plaguing healthcare's revenue cycle — but R1's Lee Kupferman says that it is getting closer, one gray area at a time.
AI hasn't solved all the problems plaguing healthcare's revenue cycle — but R1's Lee Kupferman says that it is getting closer, one gray area at a time.
The healthcare ecosystem is fixing how quickly decisions get made, without fixing how quickly money actually moves. This isn’t a failure of reform. It’s evidence that reform is working — and revealing where modernization needs to continue.
Financial assistance is moving from discretionary policy to regulated infrastructure. For health systems, that shift has significant operational and financial implications.
When revenue cycle partners wait to be asked, or rely on “tell us what you need” as a default, they miss critical opportunities to deliver meaningful performance, support their client organizations, and ultimately serve patients better. In today’s healthcare environment, partnership requires anticipation, not reaction.
Adonis raised a $40 million Series C funding round to help hospitals use AI to tackle denied and underpaid claims. Its platform automates manual revenue cycle tasks and provides real-time insights, which allows staff to focus on higher-value work. Customers include Mount Sinai Health System, Baptist Health South Florida, AdventHealth and ApolloMD.
As we see companies release more solutions targeting the revenue cycle, it’s time to distinguish excitement from impact.
We are taking a look at how health insurers are using AI, defining success, and managing cybersecurity risks. Give us your opinions by completing our brief, anonymous survey.
Data is not optional; it is essential to financial stability and strategic growth. The organizations that thrive will be those that use data to lead, not follow.
Some hospitals are turning to AI to help manage declining revenue and financial pressure, but one expert says success depends on more than just buying new tech. Prashant Karamchandani of Chartis urges health systems to pair AI adoption with foundational work like change management, continuous improvement and operating model redesign.
As healthcare leaders look for the low-hanging fruit for AI and automation investments in the revenue cycle, there are three things that should be considered to achieve maximum impact on labor effectiveness.
Whether trying to see more patients, retain more staff, expand the organization’s technology stack or bring more dollars in the door, it’s not uncommon for professionals to want to maximize their resources amidst a market influx.
As our industry continues its path toward value-based care, evolving payer models, and increasingly stringent regulations, revenue cycle processes will become even more complex. Providers can prepare by following these steps.
We know from everyday purchasing of goods and services that you have to combine what you’re getting with what you’re paying for that good or service. But how does this basic consumer supplier concept play out in US healthcare?
Hospitals are facing increasingly burdensome policies from commercial insurers, leading to problems with cash flow and patient safety. Hospital executives think federal regulators need to enact stricter policies that require payers to operate on faster timelines, as well as provide more transparency into their reasons for denying claims.
In an age where consumers have more options than ever about where to receive their care, improving the financial experience through embedded finance can give providers an advantage in an increasingly competitive marketplace.
While hospitals have contingency plans for events like power outages and extreme weather — backup generators, secondary chillers, and fiber for internet coverage — they can typically neglect vulnerabilities in the revenue cycle. Here are five proactive measures for hospitals and revenue cycle management/clearing house partners.