Staying the course
The ‘one and done’ approach of big revenue cycle projects runs counter to healthcare’s mutability
In 2011, Jesse Ford, a finance leader at provider organizations for a quarter century, founded Salud Revenue Partners, realizing his vision of a technology-enabled service company that partners with providers to improve revenue cycle performance.
As COVID begins to loosen its grip, many in healthcare see a fundamental shift ahead toward value, quality and efficiency. In revenue cycle as in other departments, this is looking like a lot of spending on top-to-bottom reviews and/or expensive tech promising AI/machine learning. You see something amiss here. What is it?
JF: We are seeing a movement toward more outsourcing in which providers ask vendors and consulting firms to help them deliver on productivity, reduced accounts receivable and cleaner claims. Often, it involves new technology in the form of AI/machine learning. The hope is that we can automate most rote processes, reduce headcount and focus on long-term strategy. One big problem with this picture is that the outside firm is often looking ahead to the next install or deal, and isn’t around if reality doesn’t match up with promise. And the reality is that machine learning is not there for healthcare yet.
How does that square with you as a tech guy?
JF: Artificial intelligence will someday be a huge part of what we do, and we are actively planning for it, but it is still more dream than reality. One problem is data integrity. Healthcare is endlessly complex, and mutable. Government is constantly adding new payment models and insurance options. In the commercial market we are always uncovering new changes to coding, prior authorization and so on. Also, data isn’t always conformed; systems don’t communicate with one another, and there is a lack of transparency across the spectrum of healthcare. So the end result is you still need the human factor, the expertise of staff to sort it all out.
Can you give us a real world example of this problem?
JF: I’ll use denial management, a hot topic right now. When you put in a denial management system, you do a specific mapping of denials to categories to identify trends. With this data, and in the spirit of collaboration throughout the revenue cycle, denials are assigned to the department that caused them, such as health information management, utilization review, clinical service areas and registration. These departments are responsible for investigating the root cause of the denial and implementing improvements to ensure future claims won’t meet the same fate. The trouble is that denial data is inconsistent; many payers are using claim adjustment reason codes differently. If you spend all that time and money having a vendor set up inputs and routing and reporting, and then see them walk away, things are going to quickly get out of control, as circumstances change. When one commercial payer suddenly starts denying a claim that everyone else is paying, it means that payer either changed a policy or it is an error, but you need expertise and payer relationships to know how to interpret it.
How big a problem is ‘getting out of control?’
JF: For some payers, these inconsistencies could affect from 10% to 30% of denials that healthcare providers receive. When data are faulty, denial management teams make inaccurate assignments and conclusions.
So where does technology enter the picture?
JF: There are real-world solutions that enhance the accuracy of reporting denials and denial trends, accurately assign denials to responsible departments so they can identify and correct the root causes, and simplify and optimize denial workflow and training. At my company, we call this denial science, which means applying scientific method to identify and correct data anomalies. Scientific method consists of making observations, formulating hypotheses, testing hypotheses, drawing conclusions and refining hypotheses. It implies that there is potential to continuously evolve as new hypotheses lead to new conclusions.
Are many business office staff ready for that work?
JF: Probably not. Many revenue cycle staff have been laid off, so a lot of expertise has been lost. This is where having a true partnership with an outside firm that does have that level of expertise comes into play. It is why my company has invested so heavily in staff training and development — for coders, accounts receivable and other areas. It is also why we work in teams centered around common revenue cycle challenges, so staff can problem-solve and have an impact. People don’t just want rote work — they want a vocation.