by Jonathon Tyler LSSBB
A Clinical Site Administrator for a midsized oncology clinic contacted a lean healthcare consulting firm to get help for a patient wait time issue. During the preceding 6 weeks, patient wait times for exam visits rose sharply from an average of 13 minutes to 31 minutes. Many of the clinic’s patients are sensitive to excessive time spent away from work or personal activities and expressed their frustration. The administrator is troubled that they will leave and seek care elsewhere.
THE SITUATIONAL ANALYSIS
The black belt who accepted the challenge conducted a situational analysis. She wanted to wrap her mind around the issue so she can match everyone’s perception of the current state with the reality of the current state. In doing so, she established an operational definition for the performance she wanted to improve.
Operational Definition: Patient Wait Time
Patient wait time is defined as the time between the patients’ scheduled exam appointment time and the time at which the doctor enters the exam room to see the patient.
Process Flow and Scoping
Second, she wanted to scope the project and the major process activities. She drafted a SIPOC diagram (supplier, inputs, process, outputs and customers (see Figure 1)) outlining the 5 major activities in the process; and illustrating the activities mainly associated with patient wait times. Before the Oncologist can begin the exam, all patients must have blood drawn for testing at the on-site laboratory. The laboratory employs 3 full time phlebotomists who draw blood. Each blood draw takes approximately seven and a half minutes. Lab testing typically takes place when the patient arrives prior to the exam appointment with the doctor.
(Figure 1 – SIPOC)
Facts and Data Collection
The clinic is not an environment rich with performance data. A disadvantage, but represents the cards the black belt was dealt. She moved forward to understand the facts and circumstances. Clinic business hours are from 8:00 AM to 4:00 PM; and the clinic does not schedule appointments between 12:00 PM and 1:00 PM. Three new oncologists joined the practice six weeks ago increasing the number of oncologists from 5 to 8. The administrator also mentioned that wait times seem to be higher between 10:00 AM – 12:00 PM and 2:00 PM – 3:30 PM. An example of the data collected, for a typical day, is shown in the table below.
Analyses and Conclusions
Takt Time Analysis
Takt time is a concept used to determine a time element that equals demand rate and is defined as the time required to deliver quality services (to produce customer requirements). The word Takt is a German word for the baton that is used by an orchestra conductor. In lean thinking, Takt describes how a process can be engineered so work and handoffs can flow at a certain pace.
- Total Time Availability (Avail): The total applicable work time (scheduled time less breaks and lunches).
- Customer Requirements: work products to be produced (patients per day).
The black belt’s takt time analysis demonstrates that the organization has the capacity to handle the patient load for a typical day – no natural constraints.
Remember the clinic administrator mentioned that wait times seem to be higher between 10:00 AM and 12:00 PM and 2:00 PM and 3:30 PM.
The black belt’s additional analysis seemed to confirm the administrator’s assumption, but she wanted to know if the relationship between wait times and peak traffic times are statistically significant. In order to find out, she decided to use a six sigma analytical tool called the Chi-Square Test of Independence. Chi-Square analysis is an approach one uses to determine if there is in fact a relationship between two or more factors (variables); and if so, how significant is the relationship? In this case, the black belt used the method to determine if there is a relationship between slot status (filled/unfilled) and peak times (10:00 AM – 12:00 PM and 2:00 PM – 3:30 PM).
She designed her analysis to disprove the relationship. Her claimed assumption (null hypothesis) is slot status and peak times are not associated and her alternative hypothesis is slot status and peak times are associated. She used a pivot table to transform her data from qualitative to quantitative and performed her test of independence. The result of her analysis was very revealing. It concluded that there was a substantial relationship between slot status and peak times. In fact: if slot status and peak times were not related: you would expect 20 more filled/scheduled slots during non-peak times. If slot status and peak times were not related: you would expect 20 fewer filled/scheduled slots during peak times.
The same analysis was conducted for the new doctors and then for the original doctors. Those results were equally revealing. For the new doctors: if slot status and peak times were not related, you would expect 12 more filled/scheduled slots during non-peak times. If slot status and peak times were not related: you would expect 12 fewer filled/scheduled slots during peak times. For the original doctors: if slot status and peak times were not related then you would expect 7 more filled/scheduled slots during non-peak times. If slot status and peak times were not related then you would expect 7 fewer filled/scheduled slots during peak times.
SOLUTION AND NEXT STEPS
What our black belt concluded from the situational analysis is that the under-utilization of the clinic’s capacity, by approximately 20 patients, is responsible for the bottleneck in patient flow and the growth in patient wait times. Our black belt recommended the clinic’s business hours be changed from 8:00 AM – 4:00 PM to 10:00 AM – 6:00 PM. The clinic made the change and not only relieved the bottleneck but the solution also added additional capacity to serve 10 – 15 additional patients per day.
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