“How long do I have to wait for an appointment?” Our office uses two measures of health care wait time. The wait to get an appointment and the wait once the patient arrives. I have already described the technique we use to measure waiting in the office and the application of six sigma techniques (e.g. a goal that 95% of patients are in the office less than 50min). Today I will discuss measuring wait times for an appointment.
Comparing the wait to get an appointment between offices is difficult because no one seems to agree on how to define it. Time to next available appointment? The mean or median time waited? Some other metric?
Previous posts have described Korner Wait Time, 3rd to Next Available and Mean Time to Wait (MTW). Mean Time to Wait is the difference in days between when the appointment was created and when it occurred. The advantages of MTW are that it’s easily programmed into an Excel spreadsheet to download the data from an EMR (ApptDate – CreateDate) and it can measure an endless supply of appointment types for those who use block booking. The down side is that MTW is a retrospective analysis so changes can lag behind reality by the length of the wait. Because of that of that lag our office also directly measures 3rd to next. The advantage of 3rd to next is that you can see wait time problems in real time. The down side is that without detailed schedule templates and appointment types it has to be measured manually. It is of greater utility in open access booking where there are only a few types of appointments.
Another disadvantage of MTW is that it requires a normal distribution. Mean time to wait can be skewed with a bimodal patient population. Consider a patient class that has both an urgent and non-urgent patient pool (e.g. asthma). Together, the mean is in the trough of a bimodal population which would be an inaccurate reflection of health care wait time:
But separated, the two populations each have their own mean which is more reflective of the average time waited by patients for an appointment.
Interpreting wait time measures is tougher than it looks. Consider office or ER wait times with two different types of appointments each ‘competing’ for the same appointment blocks.
In the first graph the wait is balanced with the two types of appointments increasing and decreasing in proportion to one another.
In the second graph type A is decreasing while type B increases.
This is a common problem in block booking practices where over-booking type A appointments blocks out type B appointments. Typically, appointment type A is easier for a patient to book (less morbidity, less recovery, less time off work, less cost, etc…) and shorter duration. Since type A is easier to book it fills up the appointment slots faster than type B appointments. The more that short, type A appointments are booked the less time will remain for longer type B appointments. The effect is a widening in the wait time between the two appointment types and a lack of access for type B patients.
Having watched this scenario play out several times over the years it tends to occur with a) poor management of a block booking schedule, b) inexperience in the administrative centre c) moving from a slow season to a busy one. Our office also uses the 3rd to next technique to catch these problems as soon as they happen.
Our office is procedural based and a specialist office so complete open access would not be effective. Because we combine open access with block booking I've found that monitoring MTW and a real time monitor allows us to control the blocks of time. Monitoring wait times within a practice is a simple metric that maintains wait time equity between patient pools it also lets you better control standards of care for wait times.
Comparing the wait to get an appointment between offices is difficult because no one seems to agree on how to define it. Time to next available appointment? The mean or median time waited? Some other metric?
Previous posts have described Korner Wait Time, 3rd to Next Available and Mean Time to Wait (MTW). Mean Time to Wait is the difference in days between when the appointment was created and when it occurred. The advantages of MTW are that it’s easily programmed into an Excel spreadsheet to download the data from an EMR (ApptDate – CreateDate) and it can measure an endless supply of appointment types for those who use block booking. The down side is that MTW is a retrospective analysis so changes can lag behind reality by the length of the wait. Because of that of that lag our office also directly measures 3rd to next. The advantage of 3rd to next is that you can see wait time problems in real time. The down side is that without detailed schedule templates and appointment types it has to be measured manually. It is of greater utility in open access booking where there are only a few types of appointments.
Another disadvantage of MTW is that it requires a normal distribution. Mean time to wait can be skewed with a bimodal patient population. Consider a patient class that has both an urgent and non-urgent patient pool (e.g. asthma). Together, the mean is in the trough of a bimodal population which would be an inaccurate reflection of health care wait time:
But separated, the two populations each have their own mean which is more reflective of the average time waited by patients for an appointment.
Interpreting wait time measures is tougher than it looks. Consider office or ER wait times with two different types of appointments each ‘competing’ for the same appointment blocks.
In the first graph the wait is balanced with the two types of appointments increasing and decreasing in proportion to one another.
In the second graph type A is decreasing while type B increases.
This is a common problem in block booking practices where over-booking type A appointments blocks out type B appointments. Typically, appointment type A is easier for a patient to book (less morbidity, less recovery, less time off work, less cost, etc…) and shorter duration. Since type A is easier to book it fills up the appointment slots faster than type B appointments. The more that short, type A appointments are booked the less time will remain for longer type B appointments. The effect is a widening in the wait time between the two appointment types and a lack of access for type B patients.
Having watched this scenario play out several times over the years it tends to occur with a) poor management of a block booking schedule, b) inexperience in the administrative centre c) moving from a slow season to a busy one. Our office also uses the 3rd to next technique to catch these problems as soon as they happen.
Another pattern frequently seen is when two different blocks of appointments of different duration become equal or invert. This usually means that there are open slots in the schedule which can be filled with other blocks of appointments. In the graph below, the soonest that patients choose to book an appointment is 6-8 days.
Our office is procedural based and a specialist office so complete open access would not be effective. Because we combine open access with block booking I've found that monitoring MTW and a real time monitor allows us to control the blocks of time. Monitoring wait times within a practice is a simple metric that maintains wait time equity between patient pools it also lets you better control standards of care for wait times.
Posts
No comments:
Post a Comment