AI and ML can help providers manage workforce cycle challenges
The healthcare revenue cycle is facing a workforce shortage. But as hospitals and health systems deal with workforce challenges and the associated financial impact of claims submission and tracking bottlenecks, more and more hospitals are finding help with automation.
The use of machine learning and robotic process automation has the potential to give healthcare providers the ability to streamline key RCM workflows while filling critical workforce gaps.
The shortage of talent places healthcare providers in a difficult economic position. They are under pressure to keep costs in check, but labor shortages are leading to high wages. And without enough revenue cycle staff, it’s difficult for providers to collect what patients and payers are owed.
Providers cannot provide patients with the financial guidance they need to understand their responsibilities and options to meet them. They cannot deal effectively with refusals. This hurts the provider’s revenue stream and cash flow.
These issues can also make RCM work more difficult and less satisfying for the professionals who remain in the provider settings. This dissatisfaction leads to retention problems, forcing providers to once again tap into an expensive labor market.
We recently spoke with Noel Felipe, Senior Vice President and Revenue Cycle Practice Leader at Firstsource, a business process outsourcing company serving several industries, including healthcare. He focuses on the role that artificial intelligence and machine learning can play in helping service providers address RCM workforce challenges.
Q. How can AI and machine learning help with the workforce shortage? What can it do? What shouldn’t it do?
A. The short answer is that AI and machine learning are force multipliers for providers and their revenue cycle professionals. These tools can address an increasingly broad range of activities, both inside and outside the revenue cycle.
At the lowest level of intelligence, robotic process automation software bots can automate tasks such as suitability verification. Machine learning algorithms are becoming increasingly sophisticated and can find the patterns in denied claims and help identify root causes. Even further, generative AI can summarize doctors’ notes and recommend correct medical codes.
These are just a few application examples. Combining AI tools will deliver even more powerful solutions. A generative AI tool can monitor—essentially read—health insurers’ claims filing rules and regulations and then inform a machine learning algorithm of rule changes so it can review claims before they’re filed and alert RCM professionals.
Generative AI tools can scan a medical record in seconds to retrieve any additional data.
That said, these tools cannot make complex decisions. People will always need to stay informed to validate the output of machine learning and AI tools used in more advanced processes. Additionally, AI/ML solutions are often more expensive and take longer to implement than a software bot.
Q. What are the results of applying AI to RCM? You propose to avoid high labor costs, increase financial flexibility, improve job satisfaction and provide better patient experiences. How come?
A. As is the case with any good automation, an AI solution can simply do more work, faster and more accurately, than a human. Providers can achieve more with fewer staff. They may still pay higher rates for revenue professionals, but they won’t need as many.
Moreover, with AI doing the routine and boring work, the professionals will have time for patient financial guidance, complicated claims and managing appeals. AI tools can assist with these tasks, essentially acting as co-pilots so RCM professionals can focus on the most critical work.
The financial experience is an integral part of the overall patient experience and their perception of its quality. Healthcare payment processes are complex, confusing and difficult for patients to navigate.
Healthcare providers that streamline their revenue cycles with AI that augments human talent can provide patients with a much better financial experience. They can use AI to guide patients through the pre-registration process, presenting different payment options based on patient demographics.
They can make better estimates of what patients will owe RCM professionals will have time to explain these estimates. Prior authorization questions and requests can be made in near real time. Cleaner claims created with automation and AI reduce claims denials. All of these AI-enabled actions create a smoother financial experience for patients while improving revenue streams for healthcare providers.
Q. What are some tips you can offer healthcare providers looking to implement AI tools in RCM?
A. Start small. Make sure the task really requires AI. If a task requires very little intelligence, experience, or insight, it may be a better candidate for RPA. RPA software bots mimic the keystrokes of human operators and can perform these repetitive tasks endlessly, without tiring or making mistakes. This includes claim status checks, benefit verification and eligibility checks.
Understand the complexity of the decisions involved in a process. Software bots can follow simple, set rules for making if/then decisions. AI and ML can make more sophisticated decisions based on data models that contain more variables but have observable patterns. The simpler the decision, the faster the technology implementation.
Go after opportunities like that Improve RCM experiences of employees and patients. RCM employees almost certainly know what their pain points are and what patients complain about. Bots and algorithms can quickly address something like a backlog of checking claim status and summarizing the reasons for denied claims. Build on smaller successes: These also improve data accuracy, and good data is the key to good results with AI and machine learning.
Stay up to date on how AI is becoming a reality within the revenue cycle. The technology’s capabilities are developing rapidly, and major vendors like Microsoft and Google are working to make AI simple and intuitive for business people. Providers should ensure that their RCM software and service providers are already integrating AI capabilities into their solutions.
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