CRISPR is a gene-editing technology that enables scientists to chemically snip out and rearrange genetic sequences to alter DNA, often with the goal of eliminating hereditary diseases. The technology holds enormous implications for our ability to treat and manage various life-threatening diseases, including many types of cancer.
In many respects, CRISPR can be a helpful analogy for considering how the healthcare industry might collaborate to improve the revenue cycle management (RCM) process. For example, if we think of the revenue cycle as the genome of the health system’s administrative operations and carry that analogy further, it unlocks a lot of interesting thought work about how to apply “CRISPR-like” technology in the revenue cycle.
If we equate the RCM processes, policies, oral traditions, and systems to genes in a genome, and the combination of those different things as genetic expression, these RCM tools represent the DNA of how business gets done. Those tools and processes are used to interpret the “RCM genome” on a daily basis, within the context of all the environmental pressures that are exerted on them every day. For example, something as small as a phone call to a payer can change how a knowledge worker, akin to a cell, interprets what to do with a codified instruction like a process step.
These kinds of pressures are happening all the time on the RCM genome, but usually the amount of pressure exerted to change related RCM processes isn’t strong enough to cause immediate reaction. Similarly, genes in organisms evolve over time, building up immunities to diseases over generations. If RCM processes could be snipped and rearranged, much like DNA with CRISR technology, the impact on RCM could also be immense. With this new capability, we could automate the revenue cycle with a data-driven approach, using insights gleaned from advanced analytics to find automation opportunities to eliminate waste from RCM.
In practice, this would mean using technology to re-sequence and change RCM processes on the fly, in reaction to new environmental pressures, such as payer bulletins and new regulatory changes that would cause shifts in policy. Enter the concept of the “digital twin.”
Defining the ‘digital twin’ in RCM
A digital twin is a virtual representation of a machine, system, or other kind of complex organism that exists in the real world. Consider the example of a wind turbine; testing a turbine in the real world is challenging, time-consuming, and costly. Instead, scientists build computer programs that are perfect representations of wind turbines, and then present these models with different environmental factors, breakdowns, and other effects. These models are so sophisticated that scientists can use them to test design changes on wind turbines without ever having to build a real turbine until they are ready to launch the production-ready version.
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, traditional approaches like robotic process automation or computer vision technologies represent basic attempts at digital twins. However, these examples lack the contextual awareness of all the other data signals that are firing in the background.
That is where different techniques such as process mining come into play. Process mining is essentially just the discipline of figuring out what a process is and then, determining how it deviates from its expected model over time.
In RCM, process mining often involves sifting through all the data signals that are captured by health systems, including order systems, referral management systems, building platforms, electronic medical records, payer data, and EDI data. Next, gather all the data into one location to understand how all the data signals over the lifecycle of an encounter fit into telling RCM professionals information about the state of an account.
Once all that data has been catalogued, like the wind turbine, an organization has created a digital twin of the RCM process. After a digital twin has been created, organizations can simulate the effect that process changes may have on things like accounts receivable, payer relationships, and staffing. It is important to note that everything starts with a robust data strategy to create a virtual representation of an RCM “encounter,” whether it’s a claim or invoice, to fully account for an entire end-to-end business process.
To return to the CRISPR analogy, every human agent working within a digital twin becomes a CRISPR tool. Each person is working with an integrated workflow engine and is constantly snipping and rearranging pieces of the revenue cycle. By snipping out the wasteful, outdated RCM processes and replacing them with brand new sequences, the RCM digital twin enables health systems to achieve higher levels of business resilience and strength.