Intelligent automation (IA) is not a panacea for all that’s wrong in healthcare revenue cycle management (RCM). It should not be applied to every task, nor does it result in instant ROI. Plus, automating in healthcare is different than automating in other verticals. Let’s explore common false claims to better understand how to implement intelligent automation (IA) in your revenue cycle operations.
The first step in automation isn’t about technology, but rather knowing what tasks should be automated and which should remain people driven. Understanding the subtleties takes deep understanding of the revenue cycle process as a whole, for example, task dependencies and impacts.
The reality is 100% automation in any business process isn’t practical and should never be the goal given the complexity and nuance of revenue cycle operations and the relationships among payers and providers to facilitate the outcome. However, once you start to apply technologies like natural language processing (NLP), optical character recognition (OCR), machine learning (ML) and robotic process automation (RPA), more processes to automate are revealed.
While complexity is a universal truth for healthcare organizations, systems, processes and goals are all different, so IA must be customized to a particular revenue cycle environment. Automation should address your distinctive IT workflows and integrate with key internal systems like your EMR or patient accounting system. Just because a solution has a specific capability does not mean it will address your pain points.
Any scenario modeling should demonstrate how it will work for your intended revenue cycle outcomes. This usually means that standalone or bolt-on technology, which often lacks integration and process optimization, isn’t the answer.
For example, if you speed up a flawed process or deploy a couple of bots, you might see short-term efficiency. But there’s no value in so-called automation if you are still managing patient data in multiple systems with minimal interoperability. Your staff will need workarounds, and operating expenses will keep increasing.
The promise of a quick and easy implementation is more realistically translated to standard operating procedure. And we’ve already noted that standard implementations do not work for complex healthcare systems.
The same can be said of instant results with claims of high-powered technology capabilities. These types of off-the-shelf solutions or piecemeal approaches cannot produce consequential improvement.
And, as difficult as developing the right solution is, implementing and maintaining automation are just as hard. An in-house deployment would require substantial funds and resources. Instead, health systems need purpose-built applications with the efficacy to work at scale because automating large-scale processes is the game-changer. Success in IA depends on prioritizing strategy, scalability and holistic solutioning.
Organizations need to move away from standalone technology, instant win advertisements and one-sided engagements, and, instead, work with proven experts who can deploy and manage automation to generate more operational capacity.
A platform approach that combines powerful technology levers like ML, NLP, OCR or RPA and workflow orchestration in the right way will expand the number of revenue cycle challenges your health system can tackle through automation to improve your margins.
Learn more about what happens when you combine automation, deep expertise and scale.
Ron has served as a Director in R1’s Digital Transformation Office since 2019 where he oversees the automation product portfolio. Prior to joining R1, Ron spent nearly ten years in management consulting, first with Navigant (now Guidehouse) and then with Deloitte Consulting. While in consulting, he advised the nation’s largest acute, specialty, and outpatient provider networks on operational transformations and business strategy.