Block Booking Example to Improve Patient Flow

Back on February 20th I wrote about the principals of block booking and why they control waiting. In a nutshell, blocking time for different patient groups ensures that everyone who needs an appointment wait’s the correct amount of time. As long as a clinic is using 80% of their time, you can only see one group of people quickly at the expense of others. So setting waiting goals is not only good for patient flow but it will improve patient satisfaction.

Today I’ll walk through an example. A clinic set guidelines for two sets of patients – those that require a skin lesion to be checked (Mole Group) and those that call in with sore joints (Joint Group). The clinic decides that the goal waits will be 10 days for the mole group and 20 days for the joint group.

Here are the steps to create the blocks:

Count how many people from each group are currently booked going forward
(appointment blocks are in bold)
Example: Joint 40 people, Mole 30 people
Count how many from each group have been seen in the last 30 days
Example: Joint 120 people, Mole 15 people
Calculate you completion rate (average number completed per day)
Example: Joint = 120/30=4 per day, Mole = 15/30=0.5 per day
Calculate Wait Time at Current Completion Rate = Booked/Completion Rate
Example: Joint = 40/4 = 10 days, Mole = 30/0.5 = 60 days
Calculated the number of appointment slots per day required to meet Wait Time standards
Example: Joint = 40/2 = 20 days, Mole = 30/3 = 10 days

In the schedule you need to block 2 appointments per day for people with joint pain and 3 per day for mole evaluation. Some of you may ask why I calculated the completion rate in Step 3. The reason is that there is going to be a change to the flow of patients in the clinic. The back staff may on be equipped to see one mole patient every 2 days. If they are going to be seeing 3 per day it may have an impact on what they need and/or how the set-up for this type of patient. Also, for these two groups of patients the number of reserved slots is increasing from an average of 4.5 per day to 5 per day. As long as 80% of the schedule is being booked these extra slots will have to come from another patient group.

If you’re clinic moves to block booking you also need to have a group of patients that could use more urgent appointments (short notice list). If a block has not been filled by ½ of the desired wait time (e.g. there is an open block for the mole group at 5 days and joint group at 10 days), fill it in with someone from the short notice list. The graph below is an example of how block booking dampened the amount of variation for a group of same day surgery patients (blocking started in early 2006).


For those clinics with more sophisticated technology (data mining techniques) other systems can be used to count the patients waiting or already treated and their mean wait times. In our clinic, the mean wait time is measured directly from the appointments (date of appointment – booked date) and the number of patients in the queue is also measured directly. Block booking will improve patient flow and satisfaction. It also let’s the clinic set and manage priorities in a very logical and planned fashion.

Meeting Wait Time Demands

The holy grail of wait time management is the merging of the need of the client and the availability of the provider. Imagine a system, where health care wait times of various patient groups are monitored then adjustments to the schedule are made (with block booking) to meet as many of the needs as possible.

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.