REVENUE CYCLE MANAGEMENT Q&A Revenue Cycle Management
Today: Why RPM and AI Matter Revenue cycle management is rapidly evolving. How healthcare organizations and medical practices utilize these technologies will be vital for future success.
Revenue Cycle Management is evolving forward rapidly now in healthcare, as regulatory and payment pressures are compelling health system finance leaders to become more rigorous in working to optimize their revenue/claims/payment capture. What’s your perception of where finance leaders are
right now, broadly speaking? The top-of-mind trend in RCM manage- ment is the evaluation and implemen- tation of robotic process management (RPM) and artificial intelligence (AI). These technologies can reduce admin- istrative healthcare costs, improve the accuracy and efficiency in medical bill- ing, and enhance the patient experience. And that’s sorely needed to recharge medical practice revenues in view of rising regulatory and payment pressures and the lingering pandemic. However, finance leaders must go
beyond the hype to assess the practical- ity of these technologies in their specific healthcare setup. It is easier to introduce RPM in a large healthcare enterprise, where a finance leader is in control, and the bulk of the work happens as per SOP’s. In a single medical practice or group medical practice (where the decision-making is in the hands of physi- cians, nurses, or staff using the EMR), the absence of SOP’s makes it challenging to implement RPM or AI.
How do you see the evolution from the leveraging of business process automation and robotic process automation, towards the use of artificial intelligence (AI) and machine learning tools, progressing in the hospital/
health system finance arena? There is no dearth of AI-powered ideas in healthcare, and the innovations we see now are only the tip of the iceberg. As
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AI technology becomes more affordable, more healthcare entrepreneurs will be encouraged to incorporate AI in solving long-existing challenges in healthcare. AI based Chatbots are the best examples of solving patients queries about their bills or appointments.
Medical billing is a complex process.
Our medical coders currently handle over 14,000 ICD-10 codes for diagno- ses. And from January 2022, ICD-11 will come into effect with over 55,000 diag- nostic codes. That’s a mind-boggling number for even the most seasoned medical coder! You are going to need millions of billers and coders globally in the next decade if AI and RPM is not introduced in the RCM process. Coding is just one aspect of revenue cycle management;
it also includes
claims follow-up, denial resolu- tion, patient billing, and collections. Therefore, medical billing companies (and healthcare enterprises with in- house billing) must be agile in adopt- ing AI-powered software to enhance the accuracy of their medical billing staff and the efficacy of the entire RCM effort.
What are the main obstacles to the robust leveraging of AI/machine learning tools in revenue cycle management/ finance departments, right now, and how can healthcare finance
leaders overcome them? AI implementation works great in environments with standardized data capture and patient handling protocols. That is why AI software companies are primarily targeting large hospitals with standardized patient care and billing protocols. But in a small medical prac- tice or a group medical practice, physi- cians tend to follow their own protocol for medical billing, claims processing, and patient engagement. In such a sce- nario, there is still a lot of manual pro- cessing. Therefore, finance leaders must first ensure that their organization has
hcinnovationgroup.com | SEPTEMBER/OCTOBER 2021
Kunal Jain CEO/Owner Parul Garg RCM/Owner PracticeForces
the procedural framework for imple- menting AI.
Cost is another prohibitive in some medical practices as well as concerns around patient privacy, compliance, and practice data security. Finance leaders are well-aware of the advantages of implementing AI solutions in a high transaction environment, but must balance the benefits of AI adoption in RCM with their organizational goals and budgets.
What does the next couple of years look like
to you, in this area? AI is here to stay, but the uncertainty and pace of widespread adoption per- sists. Historically, healthcare has been a laggard on the uptake of technology. And over the past year and a half, the COVID-19 pandemic has been instru- mental in driving telemedicine accep- tance by payors, medical practitioners and patients. So perhaps we need a force majeure to make AI implementa- tion widespread in the healthcare sector. Overall, over the next two years, most large healthcare organizations will have adopted AI in RCM.
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