Technologies under the general category of “automation” include artificial intelligence (AI), machine learning, robotic process automation (RPA), and self-service tools which can improve efficiency or replace entirely the work human administrators do today. The revenue cycle traditionally is a complex operational process managed by a large staff of administrative personnel. Staffing costs are typically a significant chunk of the operating expense of a health system and any strategy to increase margin and improve the balance sheet includes lowering these costs. This has driven investments in technologies that automate aspects of the revenue cycle and improve staff efficiency.
The challenge that many providers find however, is that they made investments in this technology without first establishing a full end-to-end automation strategy. When you only target tactical pain points as you adopt new expensive technology, it is very difficult to lower costs at a level that justifies the cost of investment.
So how can leaders generate real ROI when it comes to automation? Simply put, provider organizations need to be ready to make big decisions. Automation needs to be applied to large-scale processes that span the entire revenue cycle and implemented in a way that enables work to be done better, faster and at scale.
Given the learning curve and expense, it is not feasible for a health system to achieve true ROI in their investment in automation by going alone. Organizations need a trusted partner with a proven end-to-end approach to automating the revenue cycle.
Before embracing a partner, it’s crucial to take a step back and understand why automation is an ideal solution for optimizing the revenue cycle. AI works particularity well for the revenue cycle because it can handle large volumes of transactions with high levels of complexity by evaluating complex rules and working through long lists of variables. When AI is applied to the revenue cycle, it takes your data, evaluates each variable and “tags” it so that it’s codified for future use. AI then determines the most effective way to complete transactions—whether it be how best to code a claim, match a patient to a provider or check insurance eligibility.
These multifaceted transactions – with variables that are generated at all points along the revenue cycle – are what drive trends and patterns that can lead to the accuracy and efficiency of automation.
A key area of financial opportunity in AI and machine learning is the optimization of the adjudication process with payers, reducing denials that can be costly for providers. The system learns over time the conditions where a prior authorization or additional clinical information will be needed to avoid a denial – and ensures this information is correctly captured on the front-end. It learns the edge cases where a given denial is highly unlikely to be overturned and removes these from various points of workflow to optimize staff efficiency. It identifies transactions that may be missing appropriate charges and promotes them into revenue integrity workflows. In these examples, the variables that drive the AI are captured throughout various points of the end-to-end revenue cycle processes.
RPA automates rote tasks so humans can focus on higher level activity. Instead of an administrator looking up information on a health plan portal and copy-and-pasting it into a claim, a bot performs this action. RPA focuses on granular, menial tasks that are deployed within a larger operational choreography of workflow tools and specialized human operations.
Like the traditional assembly line of manufacturing, effective revenue cycle work is completed with high quality and at scale by breaking the effort up into granular atomic steps that can be performed in parallel by highly efficient and specialized resources. RPA is essentially a continuation of this effort. As individual steps become more rote and explicitly defined, they are prime candidates for automation through RPA. This automation process is analogous to the robotics that have revolutionized manufacturing and dramatically lowered the cost of consumer goods.
Most health systems that run their own revenue cycle operations have not developed the level of specialization needed to receive value from the expensive cost of developing and deploying RPA. An end-to-end partner with a mature standard operating procedure has the visibility and control to identify the optimal hooks for RPA. By scaling their process across a large set of customers, an end-to-end partner can also finance the high cost of development.
Given the shift in consumer expectations, its critical to incorporate automation into a more patient-centric revenue cycle. Once again, rather than purchasing point solutions or automating pieces of the puzzle, it’s better to look at how automating the entire revenue cycle can deliver the type of financial patient experience consumers expect.
From the point of scheduling all the way through the billing process, patients complete multiple transactions and interact with many different points of contact. One automation strategy is to improve these interactions through self-service tools which are both user-friendly and easy-to-use. Instead of having your staff play phone tag with patients during scheduling, a patient chooses his or her preferred appointment time slot in a mobile app. Instead of patients completing registration forms with pen and clipboard (which administrative staff then rekey into another system), a patient enters information directly on their computer or a kiosk in the hospital. Patients set-up online payment plans before a scheduled service, saving back-end administrative costs for collections. A primary care physician codifies a referral directly versus faxing and re-keying, with workflow guardrails that ensure the patient stays in the network.
These are all examples where you need an end-to-end scope to optimally solve. The information entered by the patient online or at a kiosk needs to carry through all phases of the revenue process. An accurate estimate up-front (which can drive payment plans for example) requires the back-end modeling of health plan contracts plans and real-time transactions. Scheduling and referral functionality require understanding of eligibility across the network (which can be quite dynamic). The patient on-line experience potentially touches all phases of the revenue cycle yet needs to bring it together into a consistent experience, look and feel, and branding to broaden adoption and the intended efficiencies.
Automation capabilities are expensive to develop, manage and maintain. Because of this, it’s not generally viable for providers to invest money in developing this technology in-house.
Instead, the best approach is to work with an end-to-end revenue cycle partner that can implement an automated and integrated solution to connect to your existing systems and scale to your needs. Hooking up a bot or applying AI to part of the revenue cycle is not enough to stay competitive. Having a partner who will take on the initial risk and knows how to holistically apply automation to your financials in a tailored, meaningful way is how your health system will see results and save money.
So, to answer our earlier question, “How can leaders generate real return when it comes to automation?” It requires a well-planned strategy from a trusted partner who can demonstrate revenue cycle transformation as the first step toward improving the overall health and profit margins of your health system.