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Generate Revenue using Scheduling Data and Creativity
by Saemica Wilkins
Improve Revenue with Operational Data
Traditionally, data has been used to streamline operations, making them more efficient. This can also translate to cutting expenses, and sometimes cutting headcount too. While it is important to keep things running smoothly, with the correct staffing levels, there are oftentimes more creative ways to leverage operational data that generate revenue and add to the bottom line. This blog will detail how I was able to gain insight from scheduling data and apply a bit of creativity to improve our scheduling process, increase provider utilization – and grow billings!
The Back Story
For more than 5 years, I worked at Duke Health System “Duke Primary Care” division. Each day, the leadership team did a data review to control finances. These types of meetings shaped my career and provided a robust understanding of the importance of data and how to use it for driving improvement. While rigor is generally good, this process rigidity can quelch creativity. With such a tight operational framework, we found ourselves not fully using data to find areas of opportunity. It was a classic case of not seeing the forest through the trees.
Pulling a Thread
I noticed that the rate of patient no-shows was higher than it should be. As part of our PDSA (Plan-Do-Study-Act) we resulted the reasons why this might be occurring. Data analysis yielded transportation was a factor. Additional reasons included readily available access to care, managing personal finances, among others. All of those reasons negatively impacted our daily encounters and ability to manage patient access to healthcare which led to negative variances. As a result of patients not keeping appointments appointment, slots went unused. It is a shame to waste an appointment slot, particularly when there is a waiting list of folks who want to be seen. Since regular screening and care is important to good health and managing chronic conditions, it is also frustrating to the providers when a block of time goes unused. And, from a business perspective, nobody wants an unbilled appointment slot since it impacts financial performance.
Using Data to Noodle Through the Problem
To address the no-show problem, I wanted to do a bit of digging into a couple of patients who seemed to be very unreliable. The primary care leadership entrusted me to research the reason and propose a resolution. We formed a committee and set out to test out my scheduling theory. We controlled the number of physician templates that were used to isolate the daily impact, clinic improvements and operational improvements.
Like many larger health systems, our electronic health record system was Epic. While it is great to have a single source of information, it can be difficult to get data that has been put into it back out. In primary care, we needed to secure the time of a technical reporting writing resource to build reports and help with data analysis. I worked with our report writing team to pull data spanning the past 24 months to ensure statistical significance. Through subsequent data analysis, I was able to identify a propensity for no-shows due to the transportation reason code.
The findings were vetted with other practice managers in our region, and we all shared similar concerns. They corroborated my analysis and reported an increase in patient no-shows due to local staffing shortages in roles such as government transportation, commuter rail services, etc. Staff shortages resulted in bus routes being cut and combined. These changes were necessary to preserve some level of basic transportation during the staffing shortage. However, it created longer commutes and often unpredictable service levels that negatively affected the patients who depended on these modes of transportation for their appointments.
Solving the Problem
Since our practice cannot solve for government or public transportation issues, we were forced to address the issue through our scheduling system. We began using data to identify patients who were at high risk of no-show. IT resources then added a custom pop-up box to the scheduling system to ensure the scheduler was aware of the patient’s status. The UX change also recommended double booking high-risk appointments due to the likelihood of patient no-shows.
The Results
Now there are far less vacant appointment slots. The high utilization rates generate more billings and higher collections. With less open slots, more patients can be seen which is good for both chronic care and timely screening visits. Occasionally, both the high-risk and the duplicate booking do show up for the appointment. However, nobody on the team seems to mind working them both in.
Conclusion
This example is proof that scheduling data and collaboration with IT can effectively identify opportunities and inform solutions that have positive financial and clinical impact. With data analysis, insight into patient no-shows can be gleaned and adjustments to the scheduling template made. Double bookings for patients at high risk for no-show appointments has led to a significant improvement in provider utilization. Because more appointment slots are being used, particularly by patients on the waiting list, they are also pleased to be seen faster. Since shorter wait times are a huge patient satisfier, this project had the unintended consequence of driving up customer satisfaction scores and generating more revenue, creating a win for all stakeholders.
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