One of the problems with block booking is accurately estimating the time blocks required for all of the “what-ifs”. An example is a patient that is referred to our practice for surgery. Using our EMR we know that 5 +/-3 patients are referred each day and that 80% of them follow-through with surgery. Of the patients who have surgery, 80% have an uneventful recovery. The remaining 20% need either 1, 2 or 3 post-operative appointments for complications. An insignificant number need greater than 3 post-op appointments.

While I enjoy statistics, I have no practical means to determine the number of appointments required accounting for the natural variation in the number of referred patients and all of the possible outcomes. The solution is to use a process model simulator. In this case I set-up the scenario as outlined above and ran the scenario to simulate a week (5 days).

While I enjoy statistics, I have no practical means to determine the number of appointments required accounting for the natural variation in the number of referred patients and all of the possible outcomes. The solution is to use a process model simulator. In this case I set-up the scenario as outlined above and ran the scenario to simulate a week (5 days).

I then instruct the process model simulator to repeat the scenario 25 times to estimate the average patients in each outcome and the variation associated with each. The result is a practical method to block book the number of appointment slots per week based on the outcomes calculated in the process simulator. The chart below calculates the average number of appointment slots per week required (consultation [21], surgeries [17] and post-op [8])

What makes this scenario especially difficult to estimate manually is that the arrival rate of patients (5 +/- 3) is highly variable. Therefore, it is also useful to look at the standard deviation of the number of appointments required.

For instance, the total number of surgeries per week is 17.2 +/- 8.7. In a scenario where wait times cannot be tolerated (e.g. cancer care or obstetrics) a clinic may choose to plan on the average + 2SD (95% of patients will receive care in the specified time) blocks. Where wait times are better tolerated the average may suffice. Using a simulator allows administrators to create multiple scenarios with repeated measures then block the day according to the health care wait time tolerances desired.

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