Over the course of the past couple of months I have been doing a lot of work in the healthcare field, helping hospital improve the throughput of patient. I’ve done work in various hospital departments including Emergency Departments, Radiology Departments, Out Patient Clinics, etc. The common denominator in all of these departments was the length of time it was taking to process and treat incoming patients. All of these departments were experiencing excessively long patient waiting times to receive their needed treatment. In my posting today I want to address these long wait times and discuss why I believe they occur with such regularity.
The best way to demonstrate this phenomena is to look at two identical processes such as an eye clinic. In both clinics, as depicted in the graphic below, patients enter into Department 1, are processed and treated, then sent to Department 2 where they receive more treatment and finally on to Department 3 where they receive the final part of their treatment before being released. Both clinics have the same resources and capabilities and both clinics have the same arrival and departure rates of 30 patients per hour.
Obviously what is drastically different is the total amount of patients within each clinic’s system. Clinic 1 has a total WIP level of 60 patients within all three departments while Clinic 2 has a total WIP level of 20 patients and includes the total number of patients being seen or treated as well as the patients waiting in front of each department. We’ll come back to some of the reasons why the WIP levels might be different in the two clinics, but for now let’s explore the impact of these WIP differences between Clinic 1 and Clinic 2.
The bottom line here is that high levels of WIP (in this case, patients) will always lead to long lead times. Actually the average response time of the system is proportional to the level of WIP within the system. So automatically we know that Clinic 1’s response time will be longer than Clinic 2’s. In fact, John Little, in his work on queuing theory, gave as a way to calculate the average response time. Little explained that the average response time of a process is equal to the amount of WIP divided by the system throughput rate or RT = WIP/T. The response time for Clinic 1 is therefore 60 patients divided by 30 patients per hour or 2 hours. Clinic 2’s response time is 20 patients divided by 30 patients per hour or 0.666 hours or 40 minutes. Two hours versus forty minutes….in which clinic would you like to receive treatment?
It should be clear to everyone that most of the time spent through these clinics, but more especially for Clinic 1, is waiting. There have been many studies that have demonstrated that the actual work times or value-added times are typically less than 10 percent of the total response time. So in Clinic 1 the actual work time is probably on the order of 12 minutes (i.e. 120 minutes x 10 % = 12 minutes).
Earlier I said we’d explore some of the causes of excess WIP in Clinic 1 versus Clinic 2. One of the major causes of excess WIP is not controlling the input rate. In other words, a lack of synchronization into, through and out of any process will create excessive amounts of WIP. In comparing Clinic 1 to Clinic 2, one of the major reasons for Clinic 1’s WIP problem is that there was no scheduling system in place. That is, Clinic 1 did not have a scheduling process in place so waves of patients entered the clinic. Clinic 2, on the other hand, required patients to call ahead and schedule a time for their treatment. In doing this, Clinic 2 avoided the “large batches” of patients coming to their clinic. Clinic 2 did take “walk-ins,” but the walk-ins were told that they would have to be worked-in when time slots became available.
Another potential reason for Clinic 1’s excessive WIP was that they did not “pull” patients into their process. Clinic 2 employed either a Kanban System or Drum Buffer Rope to synchronize the flow of patients. Clinic 2 recognized the existence of a constraint and never let the constraint sit idle. Clinic 2 did things like staggering lunches and breaks so that the constraint was always working. In our diagram, Department 2 was the constraint, so patients would experience excessive wait times in front of this department.
The most negative effect of the excessive WIP in this healthcare scenario was unhappy patients which caused the patient satisfaction metric to decrease significantly. Controlling the amount of WIP within any process is paramount to keeping patient lead times to a minimum.