Though all patient encounters are unique, each begins with an appointment. And because there is a finite number of appointments within a given day, there is a set limit of revenue to be gained. And for all practices, filling the available slots are key to maximizing practice revenue. After all, operating expenses don’t go away when an appointment doesn’t take place. Staff and rent both need to be paid, regardless if a patient is seen. If you’re wondering how to optimize patient scheduling with RPA, you’ve come to the right place. Let’s explore this popular bot that maximizes practice throughput and revenue. This particular bot addresses PA-1 of the HFMA Map Keys: Percent of Patient Schedule Occupied.
Should you be concerned about your percent of Patient Schedule Occupied (PA-1)?
National data from the HFMA in 2015 reflected offices in the 75th percentile filling from 84% to 87%
Though many practices also track cancellations and no-shows, HFMA recommends that this metric lump no-shows and cancellations as “occupied.”
Given that the average family physician sees twenty patients per day, elevating a practice from the 3rd to 4th quartile would yield more than $30,000 per practicing physician, assuming an average reimbursement of $140 per visit, working 240 days per year. Said, differently, with thirty practicing physicians, it’s a one million dollar opportunity! Many practices turn to RPA when looking to close the gap. If you’re not hitting the benchmarks established by the HFMA, keep reading to learn how to optimize patient scheduling with RPA.
The Reality of Patient Scheduling
Anyone who has ever scheduled an appointment has a basic understanding of the process schedulers go through when booking an appointment with a physician.
The scheduler first learns the patient’s insurance information so that the physician list can be winnowed down to those that accept the patient’s coverage. At this point, patients are typically asked for their preferences as well. These two filters serve as the constraints introduced into the system by the patient and the scheduler attempts to find an available slot. While some scheduling systems allow for this type of filtering, others do not, resulting in multiple windows having to be opened for the scheduler to properly view the available times.
While theoretically simple, the practicality of such scheduling can be daunting. As even poor-performing practices will have the majority of their slots taken, the scheduler is already working with a relatively small set of available appointments. Filtering for insurance coverage and patient availability trims the potential slots even more.
Meanwhile, the telephone is ringing with someone else seeking an appointment, other schedulers are actively booking available time and appointments are being canceled. In situations where the system is less efficient, less savvy schedulers might have a tendency to simply look further into the future, where openings are more likely to be found.
This decision, while hardly questionable given the parameters, contributes to more slots going unfilled in the near term. This delay of service is one of the three commonly identified in healthcare.
The Reality of Scheduling Systems
To be clear, the most sophisticated scheduling software can ease some of the burden described above. And many of the practices that achieve 90% booking (or better) are likely utilizing these newer systems. But replacing the wrong system with the right system is far easier said than done. Aside from logistical and financial concerns, even if a platform’s scheduling capabilities are subpar, the software might be proving to be effective at other processes within the practice.
RPA is a clear alternative for situations where the existing platform is not going away and its limitations have been clearly identified and the practice is seeking to increase its Percent of Available Appointments Booked (PA-1).
Nothing New. Everything Faster.
One of the reasons robotic process automation is swiftly becoming a go-to choice for hospitals and physicians is that, rather than reinventing processes, RPA streamlines those that are already in place but haven’t been optimized. While replatforming a physician’s office can take months (or longer) of preparation and retraining, not to mention a substantial budget, RPA can be deployed for a fraction of both the time and expense.
UDig’s Patient Scheduling Bot automates the multi-click process of finding available physician slots according to both insurance coverage and patient availability. It runs the same multi-faceted searches a human would, but does so in a fraction of the time. And because it operates so quickly, there’s no need for the scheduler to “look further ahead” to complete the task, sooner. If there is an available time slot, sooner, the bot will find it.
Customized for Every Platform
Though all scheduling platforms have similar intent, each have their own nuances, which also means that the bots must be customized for the use case. The best practices will deploy bots that swiftly recognize cancellations or changes in insurance coverage. Others may simply wish to automate reports of scheduling performance. The simplest bots are those that overcome discrete challenges. UDig’s Scheduling Bot maximizes your practice’s Percentage of Available Appointments booked without impacting other systems within the practice.
Optimize Patient Scheduling With RPA: The Last Word
All businesses have constraints, the guardrails that prevent us from providing more services and, in turn, collecting more revenue. For physician practices, the most immovable constraint may be service slots. These represent the inventory that a practice has to sell.
Moreover, the operating costs of a practice do not decrease in line with unbooked appointments. Staff still receive their salaries and rent on buildings still need to be paid. Optimizing the Percentage of Patient Schedule Occupied is an activity that goes directly to the bottom line of the practice while boosting the efficiency for patients. As such, this bot should be at the top of consideration for any practice seeking to maximize their potential.
