First, not every clinic is going to have a lot of wait in the office. A clinic needs to be greater than 80% utilized before it will see significant wait times (this is just an approximatation but I’ll get to that in a minute). Remember, that wait increases exponentially as utilization increases.
But as clinic utilization increases so do the chances of excessive wait. How do you judge an excessive wait? Assume that patients arrive at a health care clinic expecting a certain amount of wait. It will be different for every specialty but for a basic clinic we’ve found that from the time of the booked appointment to the time they leave a wait of greater than 50 minutes has an effect on the patients’ follow-through.
The way we determined this, was not by using the CSAT but by treatment follow-through using a group of homogenous patients that presented to the clinic for consultation prior to minor surgery (in this case removal of wisdom teeth). The group was then divided into 10 minute segments and their follow-up rate compared. For those patient that left within 20 minutes of their appointment start time just over 80% followed through, 30min 75% and so on. When the group that was less than 50 minutes was compared to the group greater than 50 minutes there was a significant difference in follow-through. In a quick-and-dirty study such as this I make no attempt to establish the cause of their unhappiness, only that it is related to the time spent in the clinic. I can say this data was found when comparing multiple variables including location of clinic, doctor, age and gender. Time waiting seems to be an independent variable. The biggest assumption in this is that the length of consultation, x-rays and administration is approximately the same.
Our clinic, therefore defines a failure in service to be keeping a patient waiting for consultation greater than 50 minutes. In industries, they measure failures as defects per million units. A high failure rate is 3 sigma which is 66,000 defects per million (where most industry operates) and a low failure rate is 3.4 defects per million. You do not, however, have to wait for 1 million patients to come through the door, an adequate sample can be taken (in our case we data-mined approximately a thousand but several hundred is usually more than enough) and created this curve of waiting.
In the curve you can see that our sigma value has actually gone up! Or in other words, as the years have progressed we’ve kept more people waiting longer than we like. Why? Our office increased utilization and moved to a bigger space. One would assume that a bigger office and more capacity would help with in-office wait but it seems to disrupt the patient flow (we’re working hard on decreasing the in-office wait with process analysis but that’s another blog).
I would suggest that each of you examine your different patient pools and make an estimation of how long a wait is too long for each group. Then track how many patients fall outside of that range and work to six-sigma levels of not keeping patients waiting.