For medical specialists, referrals are a critical source of revenue. In a world where more consumers turn to Google first, referrals still loom large as a key reason a patient reaches out to book an appointment. Increasingly, medical practices seeking to maximize patient revenue are turning to robotic process automation (RPA). In this Use Case, we’ll examine how to drive referrals with RPA.
For most practices, referrals represent a predictable source of revenue. Referrals inherently imply trust and therefore tend to lead to service, assuming correct handling. And yet, research shows that despite the increased number of referrals, medical offices have shown themselves to be remarkably inadept, failing to convert many referrals to revenue.
This article will examine the referral process breakdown and how Robotic Process Automation (RPA) can ensure that referrals translate to revenue.
Part One: Ghosts in the Machine
One of the ways that the referral process frequently breaks down is through inappropriate referrals. For example, specialty practitioners may receive referrals when they are not accepting patients or may get referrals from patients who have ineligible insurance plans. These referrals should never have gone through in the first place.
One often-cited report suggests that upwards of twenty million referrals per year might be “inappropriate” for several reasons. To be fair, the study’s methodology (and thus the results) are rife for discussion; still, a system with shortcomings must eventually slow down to correct its deficiencies.
When providers receive multiple inappropriate referrals, it introduces friction into the system. Employees of specialty practices spend excessive time combing through all referrals, including the millions estimated to be unfit.
Part Two: Time Isn’t On Your Side
In healthcare, we often refer to the Three Delays Model, a framework that explores the three common hindrances to care. Often unconsidered is the fact that – by definition – a referral to a specialist introduces an additional fourth delay to the model.
The first delay in healthcare refers to the time interval that passes as a patient considers care. Marketing and proactive communication from healthcare systems encourage patients to seek care; this delay is mainly out of practitioners’ hands.
The second and most relevant delay is the one that occurs when the patient is attempting to secure care by reaching their healthcare facility. However, the referral model may include two steps if the patient’s PCP refers care to a specialist. The referral process is a barrier to care, as seeking specialist care doubles patients’ efforts to receive care. Additionally, specialists often address time-sensitive issues; as such, the referral handoff is a direct pinch-point in effectively delivering care.
Furthermore, the decision to seek care is not always easy, and delays in the process can lessen patient motivation. If a patient seeking care spends three weeks waiting to see their PCP, learning that a specialist can’t see them for an additional six weeks may cause them to abandon healthcare efforts.
Part Three: Insufficient Data
There are thousands of medical specialists and primary care providers in the US. While healthcare strives for standardization, different providers have different requirements. The information required to accept a referral varies, and referral partners rarely communicate these requirements.
These “insufficient” referrals often end up in a nebulous black hole: without enough information to reject them or accept them. Insufficient referrals frequently come to light when proactive patients phone the office, and a representative looks up the account to find missing data. At that point, the office staff member can question the patient to receive the information required to move care forward.
Not all patients are proactive, and not all representatives can determine why a referral is stuck. For the specialist, this is money left on the table; for the patient, it’s a care delay.
Given the multitude of specialists in the United States, it’s not reasonable to assume that all the required data would be available at the time of referral. However, it’s fair to presume the system should swiftly recognize what data is missing and when.
In systems engineering, a lack of sufficient data would be considered an unavoidable failure. Unavoidable failures are best addressed by clear communication of failure points, often neglected in standard ERP integrations. This brings us to the final failure point of referral systems.
Part Four: Outdated Technology
Understanding what a referral looks like from a data perspective is essential. The referring physician has a patient record consisting of multiple fields designed to meet the business requirements of the PCP. While all PCPs understand that referrals are critical to their business, it’s impossible to plan for each specialty provider’s needs. Thus, PCP processes and data fields exist to facilitate referrals, not to gather all of the data that specialists require.
And the challenge looks identical for the specialist as well! Though efforts are made to receive referrals as seamlessly as possible, it’s simply not practical for their systems to be able to “speak” with those of their PCPs.
Consider also that medicine and technology continuously evolve. In the end, a referral attempts to connect ever-changing systems to meet the needs of their respective marketplaces. That’s why more practices seek to drive referrals with RPA.
A Better Solution: Drive Referrals With RPA
Recognizing the inherent friction in the referral process, how can robotic process automation improve the passing of the care baton from one provider to another? Let’s look closely at how technology can facilitate the referral process.
Standardization of the Queue
As mentioned, patient information type and quality when providing a referral can vary significantly. Phone calls, faxes, and electronics are each used abundantly today, yet specialty practices often prioritize one over the other.
For example, a practice that checks its voicemail for referrals at the close of business inadvertently deprioritizes those referrals and favors electronically submitted requests. In contrast, a process that seeks to route all referrals to a standard queue ensures a consistent flow of information, regardless of the source material. The specialty queue can be standardized by synthesizing the data from all referral sources.
Notifications for Incorrect/Incomplete Data
Though many systems fail to seamlessly share information, most readily acknowledge when the correct data is missing, even if the acknowledgment is simply an error code. Commonly, such reports are often gathered by the office and then prioritized for outreach and cleanup.
Yet, in many cases, the missing information is easily obtained. Thus, it’s not the information that creates the delay but the fact that the office must contact the referrer to provide it. Yet, RPA technology could determine which data was missing and request the data when the disconnect was recognized, thereby reducing the delay in requesting the information.
Acceleration for Complete Patient Files
Inefficient systems tend to gravitate towards more inefficiency. As incomplete referrals back up, there is a tendency for the entire system to be impacted. Here, again, RPA can help.
Just as a bot can recognize when a file is not ready for scheduling, the same bot could determine when all materials are in place, prioritizing these “complete” files for scheduling.
Drive Referrals With RPA: The Final Word
Robotic Process Automation thrives in environments with processes optimized to theoretical but not practical efficiency. While referrals provide all parties with a roadmap for effective healthcare, inherent disconnects yearn for greater optimization to enhance the time required to complete the journey.
Specialty practices that drive referrals with RPA provide swifter healthcare to patients and reduce the administrative burden on staff.
Looking to see what it might look like in action? Download our Referral Bot and see how we’ve trimmed out 100% of the human touches in one practice!
