Readers Write: Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin

Reversing RCM Brain Drain and Creating Revenue Cycle’s Digital Twin
By Jim Dumond

Jim Dumond, MS is senior product manager at VisiQuate of Santa Rosa, CA,

Across all industries, the need to retain knowledge of key processes and details has gained new emphasis as the labor supply has tightened and grown more expensive. In the revenue cycle space, health systems are competing against not only each other, but other industries to retain talent and ensure that their organizations run smoothly. The loss of seasoned RCM professionals is creating a knowledge gap or “brain drain,” which makes it harder for systems to keep their businesses moving, let alone do so efficiently.

As result, the question these organizations must answer is: how do we guard against this loss of RCM knowledge by having robust, prescriptive workflow systems in place that direct employees what to do, when to do it, and how to do it based on predictive analytics that mine data to suggest actions that successfully have solved the same issues in the past?

Health systems today are primarily reliant on their human “tribes” of users to pass key knowledge about specific payer processes, required details, and thousands of other minutiae. This has created a system where users inefficiently share that knowledge via occasional Zoom calls, PowerPoints or job aids, and often emails or hallway conversations (if they are back in the office) that don’t get recorded except in a single brain at a time. That verbal tradition of the health system is what is creating the impact that sites are seeing today as users leave for other systems or careers.

Why not create a centralized database of knowledge for all the activities that move an account through the revenue cycle from scheduling to a zero balance? We live in a proactive world. Amazon and Netflix use a recommendation engine to identify what we should buy or watch next. Why not utilize that same approach for the revenue cycle? Use all the available data and user history to provide specific next best steps help the user efficiently work the account.

Just like Waze takes real-time data from drivers, the recommendation engine could be further enhanced by crowdsourcing, gathering data from revenue cycle shops across the country and getting smarter every day.

A digital twin is a virtual representation of a machine, system, or other complex organism that exists in real life. Think of it like a simulated wind turbine in a computer program. You can run it through different kinds of environmental or mechanical break downs and make real-time design changes without costly real-world experiments.

In other words, digital twins are complete, virtual representations of all the actions and sequences of actions taken by a human agent performing a job. In the revenue cycle world, this means curating and combing through all the data signals that are created by a human worker, as well as signals that are coming from third-party systems like payer remits, to create a perfect representation of what the human is doing to a given encounter record.

Some might say that creating such system is unnecessary. After all, most systems have some form of bot automation. That should solve the problem just as well, right?

Automation and bots can be great for productivity, as once online they work endlessly and never skip a step. But bots have to be methodically crafted to perform specific sets of tasks in a specific order, and they require continual maintenance. Turnover contributes to the problem, when the employees who depart are the ones who developed the business rules for the bot.

The next step then is to start to combine intelligent process automation with the centralized, ever-learning, ever-adapting recommendation engine. That recommendation engine should continuously breadcrumb what a worker is doing and even allow workers to add new recommendations to a knowledge repository. That knowledge repository should be connected to incoming data signals so the engine can show the right knowledge to the right person at the right time for a given piece of work the staff member is doing.

Using the recommendation engine enables the system to visualize the end-to-end revenue cycle process, allowing organizations to see where those recommendations and changes lead to better performance or not. The digital twin provides the data and analytics to help revenue cycle leaders prioritize the right work for their users, determine process inefficiencies, help define where best to apply bots, and help those bots change over time. More efficient revenue cycle operations benefit the organization overall because its focus can be placed on the core mission of delivering exceptional patient care.