In this posting we will continue on with Russ Kershaw’s great case study of an oncology clinic and how the clinic used TOC, Lean and Six Sigma to create the capacity it needed to satisfy a growing need for chemotherapy. As I said in my last posting, in order to address the problems associated with this growing demand, the doctors, office manager, and staff for this oncology clinic examined various alternatives for increasing their ability to deliver chemotherapy to more patients. .
Because the clinic’s current financial status was tenuous at best, significant capital expenditures to expand the clinic’s physical facilities at least in the short term, were not possible. The office manager and staff were able to identify the constraint(s) in the treatment process by carefully examining the flow of patients at their clinic. They did so by creating a process map with time estimates for each step in the process. They compared their current patient volume to the calculated capacity at each step in the treatment process and determined that they had sufficient resources to handle the check-in process, perform lab tests, and perform the pretreatment process, but the unavailability of enough treatment chairs was found to be the primary cause for patient wait time. To complicate matters, an excessive amount of work-in-process inventory (preprocessed patients) was building up in front of the chemotherapy treatment chairs, which is symptomatic of a physical constraint.
Just to refresh your memory on Goldratt’s 5 Focusing Steps and his process of on-going improvement:
1. Identify the system constraint. This team used the aforementioned process map with time estimates to determine that the chemotherapy treatment chairs were the system constraint.
2. Decide how to exploit the system constraint. This team focused Lean and Six Sigma to identify ways to off-load time from the system constraint and reduce variation within it. Having a consistent and reliable process is absolutely necessary.
3. Subordinate everything else to the system constraint. There are two clear messages in this step: (1) No part of the process in front of the system constraint can run faster than the system constraint and (2) you must never let the constraint sit idle.
4. If necessary, elevate the system constraint. If the actions taken by this team did not free up the necessary capacity on the constraint, then they might have had to spend money (purchase more chairs, add more staff, etc.) to generate more constraint capacity. Fortunately, seldom does a team have to execute this step.
5. Return to Step 1, but don’t let inertia cause a new system constraint. Once the constraint is broken, the team had to review the policies and procedures that were in place to make sure they still apply and that they don’t create a new system constraint.
So the team completed Step 1 and now it was time to decide how to exploit the system constraint. Once the team had identified the treatment chairs as the clinic’s constraint, they were able to implement TOC in two phases. In the first phase, the office manager changed the patient scheduling process based on the capacity of the eight treatment chairs (This is the concept of subordination where processes upstream from the constraint can never out-pace it). The purpose of this action was to reduce the waiting queue and time in front of the constraint which they knew would improve customer satisfaction and it worked exactly as planned, as both patient waiting time and patient complaints both declined. The team knew that this change could only be considered as interim fix because the new schedule called for treatments outside the clinic’s normal work hours. And in order to cover this new schedule, RNs and other staff had to work extra hours to accommodate the new schedule. The team knew that if these extra hours continued, sooner or later there would be a morale problem with the RN’s, so it had to be a temporary fix.
Because the extra work hours were not sustainable, the team next focused their efforts on reducing time on the constraint. This is the essence of Step 2, decide how to exploit the system constraint. They examined the patient flow for opportunities to increase their capacity to administer chemotherapy by removing time from the constraint. Their analysis found that the average 2.5 hours of treatment time within the constraint were categorized as follows:
· Establish intravenous access 0.25 hours
· Administer prescribed drugs 2.00 hours
· Perform post-treatment education 0.25 hours
Total 2.50 hours
Since reducing any of these times in the treatment chair would immediately increase the capacity of the constraint, the team executed some of the “easier” fixes first. They found that the average treatment time could be reduced by about 15 minutes if their post-treatment education could be performed while the chemotherapy was being delivered rather than after it was completed. The RNs were adamant that this portion of the treatment process was critical and could not be compromised. The team consulted with the doctors and decided that this education piece could be completed during the final 15 minutes of chemotherapy so right away, they gained 15 minutes of new constraint capacity.
The team then investigated ways of reducing the time required to establish intravenous access to the patient’s arm or hand which required the insertion of a small plastic tube needed to deliver the medication. They reasoned that if this could be done prior to the patient arriving at the treatment chair, then another 15 minutes of constraint time could be eliminated. Logically they thought that this part of the process could be done while the patient was at the lab getting blood tests or during the pretreatment process. The advantage was that the chemotherapy treatment could begin just as soon as the patient arrived at the chemotherapy chair. The team decided that the lab technicians should be able to insert the intravenous access when the blood samples were being collected. They tried it and it worked, so another 15 minutes of constraint time was eliminated by off-loading work from the constraint to another resource that was not capacity constrained.
Part of the 2 hours of chemotherapy treatment involved waiting for the pharmacist to mix the needed chemotherapy medications. The process being used was patients would bring their doctor issued treatment orders with them when they moved to the chemotherapy treatment chairs. The RNs reviewed the orders and then physically delivered them to the group’s pharmacist so that the specific chemotherapy medication could be provided. While the chemotherapy medication was being prepared in the pharmacy, the patient had to wait. The team simply changed to a multipart form for doctor’s orders with one copy accompanying the patient to the treatment chairs and the other part going directly to the pharmacist. In taking this simple action, another 5 to 10 minutes of “dead time” was removed from the constraint.
The next activity the team changed was to reduce the time wasted looking for equipment and supplies, if and when the patients experienced negative side effects during their receipt of treatment. In order to reduce this time, the team developed a simple mobile supply cart that contained all of the equipment and supplies that might be needed if treatment problems occurred during the chemotherapy treatment. The cart was designed so that it could be moved easily from chair to chair and it practically eliminated the time wasted looking for any needed equipment and supplies (another 5-10 minutes).
The last improvements that the team developed were other opportunities for decreasing the treatment time while the patient was in the chemotherapy chair and, as it turned out, these improvements resulted in significant amounts of capacity. The RNs indicated that patients receiving chemotherapy treatment for the first time (3 to 4 patients per week) required substantial pretreatment education. This education often took an hour or more to deliver and was done so by the RNs while the patient sat in a treatment chair awaiting treatment. The team concluded that if this training could be completed at a different time and/or location that an additional 5 to 6 hours a week could be gained for additional treatments.
Another opportunity evolved when the office manager explained that very often the treatment chairs were used for other activities like blood transfusions. This accounted for an estimated 9 to 10 patients per week with each transfusion taking 3 to 4 hours of chair time to complete. The team concluded that if the transfusions could be performed at a different location, another estimated 30 hours of chemotherapy treatment time per week could be added to the clinic’s capacity. These issues (i.e. pretreatment education location and transfusions in different treatment chairs) are classic examples of policy constraints or the “way we’ve always done it” syndrome. Goldratt told us that policy constraints typically account for over 90% of all constraints and this team’s actions demonstrated this truth. By simply changing the existing paradigm, huge gains in treatment chair capacity were realized.
Finally, the RNs were required to review the lab results prior to beginning the chemotherapy treatment. Since the nurses had to physically walk to the lab to get the results, the team reasoned that if they simply installed an inexpensive printer in the treatment room to print the lab results, the results would be available much sooner to the nurses and eliminate the nurse’s travel time to and from the lab. Not a lot of time saved, but every minute saved is a minute gained in constraint capacity.
So with all of these “simple” changes and improvements to the chemotherapy process that the team made to the system constraint, what kind of improvements did they see in the clinic’s capacity? Prior to the changes in the clinic’s chemotherapy process, realistically the clinic could handle, on average, 24 to 25 patients per day or roughly 125 patients per week. After implementing their improvements to their treatment process, the clinic’s capacity increased to an average of 35 to 40 patients per day (~200 per week) or a 40% to 67% increase in the clinic’s treatment capacity. Do you think patient satisfaction increased proportionally? Do you think the clinic’s revenue increased? You bet they did! By using the basic TOC based process of on-going improvement (POOGI) in combination with Lean Six Sigma, without spending enormous sums of money, this clinic not only survived, it thrived! Revenue jettisoned upward along with patient satisfaction!
While this case study was a great example of using this integrated approach, there are other areas within a typical hospital environment that can benefit enormously using the same techniques. In my next posting we’ll take a look at some of these other areas within a typical hospital that could result in similar gains in throughput and patient satisfaction.