The dilemma is that if a clinic is already running near 100% capacity seeing someone too quickly has to have an effect on someone else. In other words, seeing a patient 10 days quicker than they really need (or expect) will leave someone else, more needy, waiting 10 days too long. Ideally, a clinic sets the goals for health care wait times for each group of patients and strictly enforces them – to the betterment of the population of patients.
The problem is in the practical application of this method. Setting the goals is easy enough. But to measure the actual wait time is difficult. Let me preface the next section by saying I am neither a mathematician nor politician just someone trying to meet the needs of patients. The easiest solution is for the clinic to have someone look ahead in the schedule and estimate the wait for each block of patients. It is time consuming and can be inaccurate. Alternatively the average wait for a group of patients in the preceding month or two can be measured directly but it will lag months behind and doesn’t help when trying to ramp up scheduled time during variation. For the more mathematically inclined, you can use standard queuing theory models such as M/M/1, M/D/n, etc… but all assume a queue that is not growing (where as we are trying to change the number of providers or servers to match the need) or Little’s Law (which breaks down when the queue empties).
All that is left is the brute force method of measuring the number of available appointments per day and the number of booked patients then dividing one by the other to come up with an average wait time. This is the most dynamic but requires data mining expertise. It is, however, the most accurate from what we have found.
The benefit for clinics in both private and public sectors comes from improvements in efficiency and better patient flow. By meeting the needs of the clients rather than exceeding some and leaving other short. In industrial terms, they call this leveling process and it refers to decreasing the variation that’s in the system. The less variation that is in the system, the more efficiently it will run.