Say the words “healthcare” and “automation” together. What typically springs to mind are...
By Joe Polaris
Say the words “healthcare” and “automation” together. What typically springs to mind are visions of exciting clinical applications that leverage machine learning, artificial intelligence (AI) and robotic process automation (RPA). Who hasn’t been intrigued, for example, by reports about various new ways clinicians are starting to use AI to better predict disease risk or treatment efficacy?
This clinical emphasis isn’t surprising given the deep clinical investments of the past decade or so. In 2019, however, it’s time to apply similar digital transformation to business processes in healthcare.
As hospitals and health systems look to reinvent their businesses, key focal points over the next year will include the patient financial experience and overall revenue cycle management (RCM). Recent technology breakthroughs promise to help hospitals transform antiquated financial strategies and work smarter to solve perennial challenges with denials, underpayments, cycle times and more — and to significantly improve patient interactions in the process.
Recent automation breakthroughs
If we reflect on developments during 2018 and before, we see a striking increase in the technical capabilities of RCM automation. While many legacy platforms were limited to extremely basic user emulation, today’s RPA tools are far more robust. They’re more easily wired to handle greater variation, which means hospitals can more fully automate the revenue cycle.
In fact, RPA advances make the total digitization of entire RCM processes possible. When paired with visualization and performance management tools, this presents game-changing opportunities for hospitals to operationalize RCM automation at scale. This can lead to step-change reductions in cost and errors, as well as the ability to do more value-added work than was done previously. It’s possible to enhance both the velocity and the quality of RCM work.
Similarly, improvements in basic automation tools like real-time system integration, and smart, intuitive self-service can create significant value in RCM. These tools take on significantly expanded importance as patient “consumerism” continues its rise. While there’s no question that automation can — and will — help speed insurance workflows, catch underpayments and reduce denials, new approaches are also required to support the patient financial experience. The intersection of RPA, AI, and digital self-service tools are starting to make it easier for hospitals to help patients find the financial information and tools they need, when they need them.
By this time next year…
If recent RCM technology advances center on automation capabilities, what can we hope to expect going forward? How might RPA, machine learning and AI differ by this time next year — and how might those changes impact both hospital RCM and the patient financial experience?
With the increasing ability of bots to handle workflow variation, hospitals should expect automation to be able to complete far more tasks per process. Moreover, the availability of performance management tools designed for a digital workforce should enable more interaction and collaboration between bots and people.
It’s not unrealistic to anticipate that hospital RCM staff soon will drive bots, visualize work in process and actually do only the small percentage of work bots can’t complete autonomously. From a patient experience perspective, digital transformation will ensure faster and easier access to the tools and information patients need to understand and pay their healthcare bills — much like in financial and retail transactions.
Going a step further: Machine learning and AI can help catalyze continuous improvements so that bots are capable of automating more and more of these internal and patient-facing processes over time. Indeed, this may well be how machine learning and AI applications begin to add value for RCM.
When it comes to RCM, hospitals need practical, meaningful applications. The buzz over machine learning and AI is gone; while a comfort level with these tools exists, there’s also frustration that they’re not yet solving the big problems.
That means the environment is ripe for mature organizations to come in and resolve more modest problems through automation support. For example: Applying natural language processing (NLP) and machine learning to the decision trees that bots already use could enhance scalability, as well as make the continuous improvement of RPA tools more automatic.
We’re also likely to see substantial leaps in predictive analytics capabilities in the near future, just as we’ve experienced with clinical applications. While there’s no denying that initial machine learning and AI applications have not materially changed many major key performance indicators (KPIs), progress is possible with the right use cases. Flashy use cases don’t necessarily create real value. Rather, it’s in the deeper evaluations; using AI to forecast what a hospital’s KPIs will be tomorrow, for instance, so that it can act to better them today.
RCM automation: Here now, and here to stay
When it comes to digital transformation, the future is already here. Hospitals are racing to reinvent themselves, bold decisions are being made to expand RCM strategies with focused partnerships and patients now have consumer-like expectations of their healthcare financial experience.
Through the use of advanced RPA, machine learning and AI technologies, new collaborations are supporting efforts to reduce denial rates and operating costs, create operational efficiencies and improve financial experience. In 2019, it is critical that hospitals take advantage of this automation technology if they want to achieve long-term success and significantly change revenue, costs and business results in their RCM processes. The shortest path for hospitals to leverage automation is to seek out the right revenue cycle management partner that learned automation and is deploying it effectively in their solutions. Choosing that partner is a key strategic decision.