Your no-showed appointments might be giving you a lot of valuable information about your practice. All you have to do is look a little bit deeper.
There are very few things that upset a medical provider more than a getting stood up. We aren’t talking about dating here, we are talking about a patient no showing a visit. In a time where we are struggling to see all of the patients who need care, having providers doing nothing when they could have been treating a patient is wrong. These no shows also prompt emotional responses from the providers to change their appointment templates or switch up their patient engagement tools. They are on the right track, in that both of these actions might impact some change, but letting emotion be the guide can be a mistake.
It is possible that no shows might be a result of a medical practice being too busy. How so? If a new patient is told that they have to wait longer than they are willing for an appointment, they may seek care elsewhere. Often times though, they accept the appointment that is too far in the future and then start seeking a more favorable appointment time with a different organization. And guess what, it is highly likely that they will not circle back around to cancel the original appointment which ends up as a no show.
Based upon data findings (not emotion) tweaks can be made to schedule templates and patient engagement tools.
- In busy practices there is often a disconnect between how long we think new patients are waiting to receive an appointment and how long they are REALLY waiting. Availability can be held for patients who need the care most. This type of automation ensures that the patient never feels the need to seek care elsewhere!
- Sometimes the data uncovers that providers have been holding too many slots for new patients based upon actual demand, but they are hesitant to change something that could negatively impact practice growth. Using predictive analytics to fill your schedule enables providers to adjust to the true demand of their patient population.
- We also see trends around having too many complex patients at the same time. Bettering the distribution of those visits across the span of a day improves provider satisfaction and improves patient care.
- Analyzing how and when patients change their appointments (we call this schedule churn) can give valuable insight into how patient engagement tools should be configured. If visits are churning too close to the date of service you may not have enough time to backfill those appointments with your normal demand.
These complex problems have been plaguing the healthcare industry for decades and we know we can do better. Utilizing data to understand the trends and impacts followed by using predictive analytics tools to schedule is critical to improve patient’s access to care and the health of your organization.
