Applying FJA, ACCs job are classified as clinical, administrative, and staff-to-staff communications as shown in Fig. 2. Each task category is further divided into a list of tasks that can be assigned to either CPs or CPTs. For instance, the clinical tasks are further classified into incoming calls, outgoing calls, F2F meeting, and other clinical tasks such as calculating patient dose. The tasks are then prepared as task statements following the FJA methodology (Best et al. 2007; Moore 1999).
The appropriate assignment of each task to either the CPs or CPTs is determined in two ways. First, from the clinic visits and observations, the tasks that are currently being performed by CPTs are deemed as appropriate for CPTs to perform them. Second, for those tasks whose appropriate assignments are not clear, the results of a meeting with subject-matter experts which is reported in Kuhn et al. (2016) were used. In the meeting, ACC coordinators from eight ACC sites were asked to rate whether it is appropriate for a given task to be performed by a CPT or not. They identified that CPTs can perform ACC tasks such as triaging incoming calls, providing in-range patient advices (both F2F and telephone) with the support of CPs, call no-show patients, order labs, and do other administrative tasks.
The CPTs were classified based on their experience and training levels into regular CPTs and advanced practice CPTs (AP-CPTs). Regular CPTs are recent graduates with no experience in the ACC delivery care processes. On the other hand, AP-CPTs have experience and extra training in the ACC services. Based on this distinction, if a regular CPT is employed to work with the CP, the appropriate workload assignment is to assign 31% of the ACC workload to the CPT and the remaining 69% to the CP, as shown in Fig. 3a. On the other hand, an AP-CPT can perform around 59% of ACCs workload, as shown in Fig. 3b.
Determination of standard times
The standard time for each of the major tasks identified by the FJA is determined by following the steps presented in Ozcan (2005), which provides a method to estimate standard times, namely: determining the observed times, normal times, and standard times, respectively. The observed time is defined as the average of the recorded times obtained from the time study. The normal time is defined as the amount of time it takes an average or “normal” worker to perform the job without interruption or delay. It is obtained by multiplying the observed time and the performance rating (PR) of the observed worker. PR would be greater than 1 if the observed worker is a faster-than-average worker, and vice versa. Finally, the standard time is obtained by multiplying the normal time by an allowance factor (AF). AF accounts for the natural inefficiencies or variations in the performance of the worker. The summary of the standard time determination is provided in Table 3. After discussion with subject-matter experts, the values of PR and AF are set to 1.3 and 1.2, respectively.
Semi-automated FTE calculator
A semi-automated FTE calculator (shown in Fig. 4) is developed to facilitate the FTE determination. In this tool, users can enter the annual number of scheduled patient visits, the different task categories and their proportions, and other parameters such as the target staff utilization level of the clinic and the holidays and leave allowances.
The output of the semi-automated FTE calculator is the right FTEs for each type of staff that would be hired to operate an ACC. In Fig. 4, three types of outputs are shown based on the types of staff mix hired. For instance, a clinic with 17,933 annual patient visits would need around 4.33 CP FTEs and 2.62 AP-CPT FTEs.
The FTEs determined by the step-by-step procedure are used for developing a baseline staff mix model. Then, various staff mix scenarios were compared using a simulation model. The simulation model is built using Simio simulation software, Version 7. In building the simulation model, three types of task flow cases were considered.
With no task transfer The tasks that are originally assigned to CPTs cannot be transferred to CPs. This task flow is represented by Fig. 5.
With task transfer The tasks that are originally assigned to CPTs can be transferred to CPs when all the CPTs are busy. This task flow is designed to make maximum use of the time of the CPs. This is shown in Fig. 6. In this task flow, two types of queuing systems are considered:
Threshold-based transfer Tasks are transferred to CPs when the number of a tasks waiting in a queue for the CPTs exceeds a threshold.
Common-waiting queue Tasks wait in a single queue and will be handled by whichever staff is available first.
System description and assumptions
It is assumed that the simulated clinic is opened at 8:00 am and closed at 4:30 pm with the ACC staff taking a one-hour break. Hence, the system is modeled as a terminating simulation where the systems start and end when the clinic is opened and closed, respectively. The sequential procedure presented in Kelton and Law (2007) was applied to determine the number of replications for the simulation model. The number of replications is determined in such a way that the average utilization levels for the CPs and CPTs have 95% confidence intervals. For α = 0.05 and r = 0.05, the number of replications was selected when the relative error reaches r/(1 + r) = 0.0476 as summarized in Table 4. For the analysis, 20 replications were used and the simulation was run for 20 weeks.
Input data modeling
The input parameters for the simulation model include task times and annual patient volume. One of the important steps in developing and analyzing a simulation model is to use appropriate distributions for the input parameters. ExpertFit software, version 7.0 is used to choose the best fitted task-time distribution for each task. The summary of the fitted distributions for each task are and the result of Goodness of Fit Test given in Table 5. A 1-year patient volume data (from July, 2014 to June, 2015) was pulled from databases of VA Boston Healthcare System. For those tasks whose sample size is small, a triangular distribution is used.
Majority of the patient visits occur in the morning. The arrival rate of patients is assumed to vary by hour of the day with more patient arrivals in the morning as shown in Table 6.
Model verification and validation
The model is verified and validated for accuracy following the methods presented in Sargent (2005). To verify the correctness of the simulation logic, model animation is used to count the flow of tasks at the different components of the simulation model as the tasks are routed through the system, and compare them with expected values. The simulation logic is compared against the process map of the ACC care delivery processes. In addition, the task assignments used in developing the FTE calculator are also compared with the task assignments used in the simulation logic.
The simulation model is validated by the subject-matter experts for its representation of the actual staffing mixes of the anticoagulation clinics. The result of the simulation model for a given ACC (represented by a given patient population and staffing mix) was compared and approved by the pharmacists. In addition, the result of the simulation was compared with the FTEs calculated from VBA tool. Table 7 shows a comparison of the result of the simulation model and the FTEs calculated using the VBA tool. For example, based on the VBA tool, a clinic with annual patient visit volume of 10,000 patients and 80% staff utilization target would require 2.44 CPs and 2.61 AP-CPTs. Based on the simulation model, the clinic’s utilization target is met with 2.5 CPs and 2.3 AP-CPTs.
Simulation study results
The objective of the simulation study is to compare different staff mix scenarios and evaluate them against the performance measures. As stated in the objective, the goal of the staffing model is to determine the right staff mix that is cost effective, productive, and improves quality of care. Staffing cost and staff utilization levels are used as performance measures to compare the different staff mix scenarios. Considering staffing cost is important because staffing cost directly affects financial performance and sustainability. The staff utilization level also affects the quality of care. As reported in Rose et al. (2012), adequate staff and staff support improves the performance of ACCs.
The OptQuest tool in Simio in which the users specifies the objective function(s), the search space defined by the independent input parameters. The tool generates various scenarios using the input parameters and computes the objective function(s) for the given inputs, from the user chooses the best scenario(s). The results of the simulation study are shown in Figs. 7 and 8. Utilization levels of an ACC for the various numbers of CP and CPT FTEs, respectively, were presented. The three types of task flow scenarios were also compared. While the utilization levels decrease with the increase of the number of FTEs, the staffing cost increases with the increase of FTEs.
Figure 7 provides the utilization levels and staffing cost and CP and AP-CPT when the staffing levels are varied. Comparing the task flow scenario, the “no transfer” task flow resulted in lower pharmacist utilization levels as compared to the scenarios where CPT tasks are transferred to the CPs. When task transfer is allowed, the scenario with a “common waiting queue” resulted in higher utilization level of CPs, which implies more CPT tasks are completed by CPs.
Figure 8 provides the utilization levels and staffing costs of CPTs. The utilization levels of CPTs are higher when CPT tasks are not transferred to CPs. When a common queue task flow system is used, more tasks are transferred to CPs. This lowered the utilization levels of CPTs.
Effects of changes in system parameters on staff utilization levels
It is important to analyze the effects of changes in system parameters on the performance of ACCs. Here, the effect of changes in the volume of patients visiting an ACC and task transfer policies on the utilization levels of CPs and CPTs.
Figures 9 and 10 depict the effects of the changes in patient volume on the utilization levels of CPs and CPTs, respectively. The task flow with common waiting queue is used to illustrate the effect of these changes. The annual volume of patients visiting an ACC is decreased by 10% and increased by 10 and 20%. Figure 9 provides CP utilization levels when the patient volume is varied. The figure shows that while a decrease in the volume of patient visits would lower CP utilization level, an increase in the volume of patient arrivals would also increase CP utilization level.
Figure 10 shows the effect of changes in volume of patient arrivals on CPT utilization level. The figure shows that while a decrease in the volume of patient visits would lower CPT utilization level, an increase in the volume of patient arrivals would also increase CPT utilization level. However, compared to that of CP’s, the effect of change in patient volume on CPT utilization level is not that significant.
One important decision with respect to transferring CPTs’ tasks to be performed by CPs is to decide when to trigger the task transfer. The value of the threshold value represents the number of tasks waiting for a CPT before starting to transfer tasks to be completed by CPs. Figure 11 and 12 provide that changes in the threshold value for task transfer on the utilization levels of CPs and CPTs. In this illustration, the threshold value is set to 5, 10, and 20 tasks. The figures shows that an increase in the task transfer threshold value would increase the utilization level of CPTs and lower the utilization level of CPTs.