No Show Project

Many patients miss their appointments or give late cancellation notice, leading to unused booking slots and missed opportunities to receive quality healthcare. In this study, we found the characteristics and factors that drove patient No Shows for Radiology exams. We then created a predictive algorithm for those at-risk patients that allowed for tailored interventions.

The client did not initially track any metrics for no shows (patients that missed their appointments without any prior notice). Anecdotally, they knew that there was a problem from interviews with their front office staff and radiology technologists but did not have a handle on extent of the issue.

We designed the engagement in two phases. The first was to establish a measurement methodology to assess the extent of the problem which then allowed for a ROI calculation to see if the second phase (intervention) was cost effective. The intervention phase examined the patient demographics, appointment characteristics, and practice management to see if No Show patients could be predicted and make it possible to apply an intervention.

During the first phase, it was important to adopt metrics that were trusted by all stakeholders. In practice, this meant that the data needed to “cleaned” by understanding the practice patterns that led to specific entries being made by front office and scheduling staff. After this phase, we found that the percentage of No Shows ranged from 5 to 30%. For scheduled slots (MRI’s and CT’s), it is often difficult to fill at short notice, so a missed appointment leads to idle equipment and staff. A simple ROI calculation using industry standard $800/hour operating costs for MRI’s showed that No Shows led to ~$1M in non-reimbursed costs and even more in lost opportunity.

In the second phase, a predictive model was constructed using knowledge of the patient demographics (based on zip code) and appointment characteristics to predict the likelihood of the patient missing the appointment. The model then allowed the healthcare provider to intervene earlier with additional communication and alter strategies for appointment scheduling.

While the No Show problem is universal, the reasons for patient behavior is specific to your patient population.

Snowstone can help you quantify and understand your No Show challenges.