by Saemica Wilkins
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.
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.
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.
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.
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.
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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