Patient Transportation Staffing Simulation Final Report To: LaKita Pogue B.S., Manager – Patient Transportation/Lift Team Sam Clark, Industrial Engineer Lead - Program and Operations Analysis Supervising Instructor: Mark P. Van Oyen, Professor - IOE 481 Mary Duck, Industrial Engineer Lead - Program and Operations Analysis From: University of Michigan IOE 481 Project Team 8 Mr. Ethan Dennis Mr. Connor McKee Ms. Jenna Sparling Ms. Yiwei Zhu Date: April 22, 2014 Table of Contents EXECUTIVE SUMMARY ............................................................................................................ 1 Background ................................................................................................................................. 1 Methodology ............................................................................................................................... 1 Findings ....................................................................................................................................... 2 Conclusions ................................................................................................................................. 2 Recommendations ....................................................................................................................... 2 INTRODUCTION .......................................................................................................................... 3 BACKGROUND ............................................................................................................................ 3 Current Process ........................................................................................................................... 3 Current Staffing Schedules.......................................................................................................... 3 Midnight Shift.......................................................................................................................... 4 Afternoon Shift ........................................................................................................................ 4 Day Shift .................................................................................................................................. 4 Previous Staffing Analysis .......................................................................................................... 4 Patient Transportation Performance Goals ................................................................................. 4 Transport Request Acceptance Time ....................................................................................... 5 Transport Cancellation Rate .................................................................................................... 5 Transporter Production Rate .................................................................................................... 5 KEY ISSUES .................................................................................................................................. 5 GOALS AND OBJECTIVES ......................................................................................................... 5 PROJECT SCOPE .......................................................................................................................... 5 METHODOLOGY ......................................................................................................................... 6 Performed Literature Search ....................................................................................................... 6 Collected Data ............................................................................................................................. 6 Reorganized Data ........................................................................................................................ 6 Analyzed Data ............................................................................................................................. 7 Developed Simulation Base Model ............................................................................................. 7 Locations ................................................................................................................................. 7 i Entities ..................................................................................................................................... 7 The team created entities in the simulation named Patients. ................................................... 7 Arrivals .................................................................................................................................... 7 Shifts ........................................................................................................................................ 8 Resources ................................................................................................................................. 8 Processing ................................................................................................................................ 8 Variables .................................................................................................................................. 8 Macros ..................................................................................................................................... 8 Validated Simulation Baseline Model......................................................................................... 9 Adjusted Validated Simulation Baseline Model to Determine Impacts ..................................... 9 FINDINGS .................................................................................................................................... 11 Transport Tracking Data ........................................................................................................... 11 From Proposed Staffing Model ................................................................................................. 11 From Modified Shift Schedules ................................................................................................ 13 CONCLUSIONS........................................................................................................................... 15 Standard Shift Times ................................................................................................................. 15 UMHS Transport Request Acceptance Time ........................................................................ 15 Radiology Transport Request Acceptance Time ................................................................... 15 Overall Transport Time ......................................................................................................... 16 Modified Shift Times ................................................................................................................ 16 UMHS Transport Request Acceptance Time ........................................................................ 16 Radiology Transport Request Acceptance Time ................................................................... 16 Overall Transport Time ......................................................................................................... 16 RECOMMENDATIONS .............................................................................................................. 17 Increase in Patient Transportation Staffing Levels ................................................................... 17 Modification in Patient Transportation Shift Schedules ........................................................... 17 EXPECTED IMPACT .................................................................................................................. 17 APPENDIX A: PREVIOUS ANALYSIS .................................................................................... 19 APPENDIX B: ACCESS DATABASE ANALYSIS RESULTS ................................................ 20 APPENDIX C: SCREENSHOT OF SIMULATION ................................................................... 23 ii List of Tables and Figures Table 1: Current Staffing Level - Number of Transporters per day and shift………………... 8 Table 2: Comparison of Simulation Output Metrics to Real Life Scenario…………………... 9 Table 3: Proposed Staffing Level - Transporters per day and shift…………………………... 10 Table 4: Different Staffing Levels Considered……………………………………………….. 10 Table 5: Modified Shift Times………………………………………………………………... 10 Figure 1: Completed Transports by Origin…………………………………………………… 11 Figure 2: Average Transport Request Acceptance Times for 10 Staffing Levels……………. 12 Figure 3: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels… 12 Figure 4: Average Patient Transport Times for 10 Staffing Levels………………………….. 13 Figure 5: Average Transport Request Acceptance Times for 10 Staffing Levels at Modified Shift………………………………………………………………………………… 13 Figure 6: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels at Modified Shift Times……………………………………………………………….. 14 Figure 7: Average Patient Transport Times for 10 Staffing Levels and Modified Shift Times 14 Table 6: Expected Impact on Patient Transportation with Regards to Performance Goals…... 18 Figure B1: Completed Transports by Origin…………………………………………………. 20 Figure B2: Completed Transports by Destination……………………………………………. 20 Figure B3: Completed Transports by Day of Week………………………………………….. 21 Figure B4: Completed Transports by Hour of Day (12am through 11am)…………………… 21 Figure B5: Completed Transports by Hour of Day (12pm through 11pm)…………………... 22 Table B1: Mean and Standard Deviation of Transport Times by Origin……………………... 22 Figure C1: Screenshot of Simulation Model…………………………………………………. 23 iii EXECUTIVE SUMMARY The Patient Transportation department within the University of Michigan Health System (UMHS) is responsible for moving patients and medical equipment, such as stretchers, between different units of the hospital. Current Patient Transportation staffing levels are believed to be too small to cover the UMHS demand of the Patient Transportation department. The Manager of Patient Transportation asked an IOE 481 student team from the University of Michigan to develop a simulation containing various units of UMHS that will record impacts associated with increasing staffing levels and provide recommendations for future staffing levels. These impacts include, but are not limited to, increased patient throughput, decreased turnaround time, and increased ancillary test machine utilization. The primary goal of this project is to simulate Patient Transportation’s performance with current and proposed staffing levels to determine potential impacts associated with increasing Patient Transportation staffing levels. Background The Patient Transportation process begins by a transport request from the area where the patient is currently located. Once a patient transporter becomes available to fulfill a transport request, the transporter accepts the request. The transporter will then locate the patient to be moved and will take the patient to their destination. Once the transporter completes the request, the transporter will confirm the transport was successful and call in to receive directions for the next transport. The Patient Transportation department was started to reduce workload of patient care providers, by taking the responsibility of transporting patients from the patient care providers. Currently, Patient Transportation fulfils an average of 536 orders per day, serving every area of UMHS with the exception of the OR. Patient Transportation currently has a total of 70 full-time employees. Methodology The team performed seven types of tasks to determine the impacts (increased patient throughput, decreased turnaround time, and increased ancillary test machine utilization) associated with increasing Patient Transportation staffing levels and to provide recommendations on how to move forward with staffing levels. ● Collected Transport Tracking Data. The team obtained data in Microsoft Excel format from a Programs and Operations Analysis employee for all patient transport entries from January 2013 to December 2013. This data contained 132,618 completed transports and 24,611 cancelled transports ● Reorganized Transport Tracking Data. The Team reorganized the Transport Tracking System data by removing all room numbers from the data and grouping together specific operations. The following operations were grouped with the Radiology unit: EMG, EEG, General Imaging, MRI, Nuclear Medicine, Interventional Ultrasound, and Ultrasound. 1 ● Analyzed Transport Tracking Data. The team imported the reorganized data in Microsoft Access and designed queries to summarize the completed and cancelled transportation data by origin, destination, origin and destination (entire trip), day of week, and hour of day. The average and standard deviation of transporter dispatch-to-completion times were calculated. ● Developed Simulation Base Model. The team built the simulation base model with ProModel based on the data analyzed. Variables were created to track the performance of current and proposed staffing level. ● Validated Simulation Baseline Model. The baseline ProModel simulation was validated by comparing the model’s pending-to-dispatch time, dispatch-to-completion time, and weekly transport volume to the current metrics. ● Adjusted Simulation Baseline Model to determine impacts. Changes were made to the model’s staffing levels and staffing schedule to determine how these changes would impact the key metrics listed above ● Performed literature search. A literature search resulted in gaining valuable data and information from a previous UMHS employee’s work. Findings The team simulated ten different scenarios of staffing levels for 26 weeks using Patient Transportation’s current shift schedules and modified schedules. Increasing staffing levels was seen to exponentially decrease Patient Transportation performance metrics, which include decreased patient transport request acceptance time, decreasing room turnaround time, and increasing ancillary machine utilization. In addition to this, the team also saw the same relationship with increasing staffing levels in addition to modifying the shift schedules. Conclusions The results of the simulation showed that when the percent increase in staffing levels for Patient Transportation was over 10%; modified shift times helped Patient Transportation meet their performance goal best. One of the Patient Transportation performance goals is to have 90% of transport request acceptance times to be less than five minutes, and the simulation showed that an increase in staffing of over 15% will allow Patient Transportation to meet this goal. Recommendations Based on the results of the simulation and previous analysis done, the team recommends the following changes to Patient Transportation staffing levels: (1) Increase Patient Transportation staffing levels by 13.4 FTE and (2) modify the shift schedules to fit high demand times of the day. 2 INTRODUCTION The Patient Transportation department within the University of Michigan Health System (UMHS) is responsible for moving mostly patients, but also medical equipment, such as stretchers, between different units of the hospital. Patient Transportation has been unable to meet their performance goals, which is believed to be attributed to staffing shortages, leading to longer patient waiting times and low ancillary test machine utilization. The Manager of Patient Transportation asked an IOE 481 student team from the University of Michigan to develop a simulation containing various units of UMHS that will record the impacts of increasing staffing levels and modifying the shift schedules. Furthermore, the team was asked to provide recommendations as to how to move forward with staffing levels. The team analyzed existing Patient Transportation data. From this data the team developed a ProModel simulation that models the current staffing levels within Patient Transportation. The team used this simulation to provide recommendations for how Patient Transportation should proceed with its staffing levels. This report will outline how the team analyzed the Patient Transportation data to develop the ProModel simulation, and how the team used the output from the ProModel simulation to make staffing recommendations to Patient Transportation. BACKGROUND The Patient Transportation department was created to reduce the workload of patient care providers by taking the responsibility of transporting patients from the patient care providers. Currently, Patient Transportation fulfils an average of 536 orders per day, serving every area of UMHS with the exception of the Operating Rooms (OR). This section contains information regarding the current process, the current staffing schedules, a previous staffing analysis completed by a Junior Management Engineer at Program and Operations Analysis within UMHS, and the indicators of staffing shortages. Current Process The Patient Transportation process begins when a transport request is made from the area where the patient is currently located. Once a patient transporter becomes available to fulfill a transport request, the transporter accepts the request. The transporter will then locate the patient to be moved and will take the patient to the required destination. Once the transporter completes the request, the transporter confirms the transport was successful and calls in to receive directions for the next transport. Current Staffing Schedules Patient Transportation has six part time employees and 70 full time employees. These employees work in three shifts. These shifts include: the midnight shift, the afternoon shift, and the day shift. 3 Midnight Shift The midnight shift takes place from 11:00 pm to 7:30 am. Five patient transporters work on Sundays, Mondays, Fridays and Saturdays during the midnight shift. Out the five transporters, four transport and one manages transportation equipment (such stretchers, wheelchairs, etc.). Six patient transporters work Tuesdays through Thursdays. Out of the six transporters, five transport and one manages transportation equipment. Afternoon Shift The afternoon shift takes place from 3:00 pm to 11:30pm. Seven transporters work on Sundays during the afternoon shift. Out of the seven transporters, six transport and one manages transportation equipment. On Mondays there are 14 transporters. Out of the 14 transporters, 11.5 transport, two manage the transportation equipment and one works in Physical Therapy (PT) for half a shift (0.5). On Tuesdays through Fridays, 16 patient transporters work. Out of the 16 transporters, 13.5 transport, two manage transportation equipment, and one works in PT for half a shift. Nine transporters work on Saturdays. Out of the nine transporters, eight transport and one manages transportation equipment. Day Shift The day shift takes place from 7:00 am to 3:30 pm. On Sundays, 11 patient transporters work. Out of the 11 transporters, 10 transport and one completes mini-tasks. On Mondays, Tuesdays, Wednesdays, Thursdays and Fridays there are respectively 29, 34, 33, 31, and 34 patient transporters. Out of the transporters that work Mondays through Fridays, three manage transportation equipment, five work in PT, one completes mini-tasks and the rest transport. On Saturdays, 12 patient transporters work. Out of the 12 transporters, 10.5 transport, and 1.5 completes mini-tasks. Previous Staffing Analysis In fall 2013, a Junior Management Engineer at Program and Operations Analysis within UMHS, proposed a staffing model for Patient Transportation. The staffing model uses a linear program in Microsoft Excel to calculate the staffing levels that would be required to meet the department’s performance goals. The final result of the previous staffing model was a request for an additional 17 full-time employees for Patient Transportation. To determine the impacts of increasing staffing levels, the simulation project was requested. Patient Transportation Performance Goals According to the previous staffing analysis, Patient Transportation has been unable to meet their performance goals (Appendix A). These performance goals include: Transport Request Acceptance Time, Transport Cancellation Rate, and Transporter Production Rate. The Manager of Patient Transportation believes they can attribute staffing shortages to their inability to meet these performance goals. 4 Transport Request Acceptance Time The transport request acceptance time represents the time from when a transport request is being submitted to when a transporter becomes available to accept the request. The goal of Patient Transportation is to have 90% of transport request acceptance times less than five minutes. Currently, Patient Transportation has 51% of transport request acceptance times less than five minutes (Appendix A). Transport Cancellation Rate A transport can be cancelled for a number of reasons. Some of these cancellation reasons include a patient being transported by a different transporter or a patient’s wait time being too long. The goal of the Patient Transportation department is to have a cancellation rate of 5%. Currently, Patient Transportation has a cancellation rate of 16%. Transporter Production Rate Patient Transportation has a performance goal for each transporter to complete 2.5 transports per hour. Currently patient transporters average 1.53 transports per hour per hour. In order to complete this goal, the average overall transport time must be less than 24 minutes. KEY ISSUES The following key issues are driving the need for this project: ● Long transportation order acceptance times ● High transport order cancellation rates ● High workload and utilization required of patient transporters GOALS AND OBJECTIVES The primary goal of this project is to simulate Patient Transportation’s performance with current and proposed staffing levels to provide potential impacts associated with increasing Patient Transportation staffing levels. To achieve this goal, the team addressed the following objectives: ● ● ● ● Increase patient throughput Decrease transportation turnaround time Increase ancillary test equipment utilization Provide recommendations for Patient Transportation staffing levels PROJECT SCOPE This project included patient transportation that originates within UMHS. The units that were focused on in the simulation include: inpatient nursing units grouped by floor (Floors 4 through 9), Radiology, Adult Emergency Department, Cardiovascular Center (CVC), and Children and Women’s (CW). The project simulated the time between when a transport request was made to when the patient transporter confirmed the transportation was completed. 5 This project did not include any hospital operations besides patient transportation, nor did it include patient transports that originate outside of the areas that Patient Transportation currently provides service. The following units were not included in the simulation: Mott Hospital, Taubman Center, Cancer Center, and Med Inn. METHODOLOGY The team performed seven types of tasks to determine the impacts (increased patient throughput, decreased turnaround time, and increased ancillary test machine utilization) of increasing Patient Transportation staffing levels and to provide recommendations on how to move forward with staffing levels. These tasks include performing a literature search, collecting data, reorganizing data, analyzing data, developing a simulation base model, validating the simulation base model, and adjusting the validated simulation base model to determine impacts. Performed Literature Search The team looked at a previous staffing model analysis developed by a UMHS Program and Operations Analysis employee in fall 2013, which is summarized in Appendix A below. The team used the proposed staffing levels from this analysis as input for the proposed staffing levels in developing the simulation. Collected Data An employee in the Program and Operations Analysis department at UMHS provided the team with data from the Transport Tracking system in Excel format on January 31, 2014. This data included all patient transport entries from January 2013 to December 2013, which contained 132,618 completed transports and 24,611 cancelled transports. The origin, destination, and monitored timestamps of the transports were included as fields in this data. All patient identifiers were removed from the data before the data was handed to the team. Reorganized Data With the Transport Tracking system data, the team used Microsoft Excel to reorganize the data. The team reorganized the data by removing all room numbers from the data and grouping together specific units. The following smaller units were grouped with the Radiology unit: EMG, EEG, General Imaging, MRI, Nuclear Medicine, Interventional Ultrasound, and Ultrasound. Once the data was organized by unit, it was adjusted to represent a 95% transport completion rate, which meets Patient Transportation’s goal of a 5% cancellation rate. This was done by combining the number of completed transports and cancelled transports for each unit by day of week and hour of day, and then considering 95% of the resulting total of transport orders. 6 Analyzed Data Once the data was organized by unit and adjusted to represent a 95% transport completion rate, the team used Microsoft Access to analyze the data. Queries were designed to compile all of the data into a single database table as well as to calculate the transporter dispatch-to-completion times, which represent the time a patient transporter is busy transporting a patient. Queries were then written that summarized the completed and cancelled transportation data by: Origin Destination Origin and destination (entire trip) Day of week Hour of day In addition, queries were designed to analyze the average and standard deviation for the transporter dispatch-to-completion times and transport order volume by unit. Developed Simulation Base Model Using the results from the Transport Tracking System data analysis, the team developed a simulation model with ProModel with the purpose of determining the impacts of increasing staffing levels. The following sections will discuss the locations, entities, arrivals, processing, shifts, resources and variables used in the simulation model. A screenshot of this ProModel simulation is attached in Appendix C. Locations The team created ten locations, which represent the ten units needed to be simulated: six inpatient nursing units grouped by floor (UH 4-UH 9), Radiology, CVC, CW and the Emergency Department. Entities The team created entities in the simulation named Patients. Arrivals The team used the arrival cycles function in ProModel to assign the percentage of transports requested by hour of day, day of week, and the origin unit using distributions for transport order volume. The team then created 70 arrivals of transport requests, one for each unit by day of week (for example, Radiology on Monday and Radiology on Tuesday, etc.). The arrival cycles were then assigned to each of the 70 arrivals. The arrivals of patients represent a transport order being placed. 7 Shifts The team created 21 shift schedules. A schedule was created for the Midnight (11:00 pm - 7:30 am), Afternoon (3:00 pm - 11:30 pm) and Day Shift (7:00 am - 3:30pm) for each day of the week. Resources The team created a variable number of resources, called Patient Transporters, depending on how many Patient Transportation employees transport patients on that particular shift. Resources were created for the Midnight, Afternoon and Day Shift for each day of the week. Shift schedules were assigned to the corresponding resource (for example, the resource created for the Midnight Sunday shift was assigned the shift calendar for Midnight Sunday).The number of each resource for each day of the week and shift are shown in Table 1 below. Table 1: Current Staffing Level - Number of Transporters per day and shift Midnight Afternoon Day 5 7 11 Sunday 5 14 29 Monday 6 16 34 Tuesday 6 16 33 Wednesday 6 16 31 Thursday 5 16 34 Friday 5 9 12 Saturday Processing Once a patient (entity) arrives at a unit, the patient will wait until a Patient Transporter (resource) is available. The entity will use the resource for the time that corresponds to the unit’s transporter dispatch-to-completion time (Appendix B). The resource is then released after this time has passed and the entity exits the system. Variables The team created variables to track the performance of different staffing level. These variables include Radiology wait time, UMHS wait time, overall transport time, total number of patients transported and total number of patients transported in Radiology. Macros To simulate the current and proposed staffing levels in ProModel’s Scenario Manager, the team created 21 macros for each shift by day of week. The baseline of the macros is the current staffing level, and these macros were changed while running different scenarios in Scenario Manager. 8 Validated Simulation Baseline Model To provide staffing level recommendations to Patient Transportation, the team validated the ProModel simulation to ensure the simulation accurately portrayed the current scenario. The team considered the current staffing schedule, shown in Table 1 above, and compared the model’s pending-to-dispatch time, dispatch-to-completion time and weekly transport volume to the same metrics found through analyzing the Transport Tracking data. Previous analysis done by a UMHS employee has shown that these three metrics follow Standard Normal distributions, so the team was able to generate 95% Confidence intervals for each of these metrics. The 95% confidence intervals, along with the ProModel simulation outputs, are shown for these three metrics in Table 2 below. Table 2: Comparison of Simulation Output Metrics to Real Life Scenario Lower 95% Upper 95% 2013 Transport Confidence Level of Confidence Level of Tracking Data Simulation Output Simulation Output Average 12.1 min 17.7 min. 12.6 min. Pending-to-Dispatch Time Dispatch-to-Completion 26.1 min 27.9min. 26.7 min. Time 2547 2608 2576 Weekly Transport Volume The 2013 Transport Tracking data’s pending-to-dispatch time was 12.6 minutes, which falls within the 95% confidence interval of the pending-to-dispatch data from the simulation output. The 2013 Transport Tracking data’s dispatch-to-completion time was 26.7 minutes, which falls within the 95% confidence interval of the dispatch-to-completion data from the simulation output. Lastly, the 2013 Transport Tracking data’s weekly transport volume was 2576 transports, which falls within the 95% confidence interval for weekly transport volume from the simulation output. The fact that these three output metrics are within their respective confidence interval confirms the model accurately represents the real life scenario. Adjusted Validated Simulation Baseline Model to Determine Impacts To provide Patient Transportation with as much information as possible on proposed staffing levels, the team considered ten different staffing level scenarios. The set of scenarios ranged from the baseline staffing level shown in Table 1 to the staffing levels proposed by a Junior Management Engineer at Program and Operations Analysis within UMHS, shown in Table 3 below. 9 Table 3: Proposed Staffing Level - Transporters per day and shift Midnight Afternoon Day 9 12 15 Sunday Monday 9 18 34 Tuesday Wednesday Thursday Friday Saturday 12 11 11 10 11 18 19 18 19 14 33 37 33 35 19 In addition to the baseline staffing level and the proposed staffing level, the team also considered a number of different staffing levels for the current staffing schedule. These staffing levels, their Full Time Equivalent (FTE) increase, and their FTE percentage increase are shown in Table 4 below. A Full Time Equivalent employee is an employee that works five, 8-hour shifts in a week. Table 4: Different Staffing Levels Considered Staffing Level FTE Increase FTE% Increase Baseline 0 0% Scenario 1 2.4 3.73% Scenario 2 5.8 9.01% Scenario 3 8.6 13.35% Scenario 4 11.0 17.08% Scenario 5 13.4 20.81% Proposed Staffing Level 15.0 23.21% Scenario 7 17.4 27.02% Scenario 8 19.4 30.12% Scenario 9 23.4 36.34% The team also considered scenarios with modified start and end times for the three different shifts, which was proposed by the client. The modified shift times are shown in Table 5 below. Table 5: Modified Shift Times Shift Start Time End Time 11:00 A.M. 7:30 P.M. Day 3:30 A.M. Midnight 7:00 P.M. Afternoon 3:00 A.M. 11:30 A.M. 10 These shifts were considered with the same baseline and proposed staffing levels, along with the scenarios shown in Table 4 above. FINDINGS This section discusses the findings from the analysis of the Transport Tracking data and the proposed staffing model. Transport Tracking Data To develop an adequate ProModel simulation, extensive analysis of Patient Transportation’s 2013 Transport Tracking data was required. The team found the number of completed transports by origin and by destination for the ten units considered, which is shown in Figure 1 below. Figure 1: Completed Transports by Origin sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System The 2013 Transport Tracking data was also stratified to represent to transport volume by: destination unit, entire trip, day of week, and hour of day. The average and standard deviation of transporter dispatch-to-completion times were also calculated. In addition, the team calculated the percentage of transport requests for all ten units by hour of the day and by the day of week. The results of the analysis are summarized in Appendix B. From Proposed Staffing Model The team looked at ten staffing level scenarios for the current shift times ranging from the 11 baseline staffing level, to the proposed staffing level (with an increase of 15 FTE), to a staffing level with an increase of 23.4 FTE. The average transport request acceptance time (the time from when a patient requests a transport until a transporter accepts the transport) for these scenarios is shown in Figure 2 below. The average transport request acceptance time is a representation of room turnaround time. Figure 2: Average Transport Request Acceptance Times for 10 Staffing Levels simulation length: 26 weeks, scenarios: 10, source: ProModel simulation Figure 3 below shows the average radiology transport request acceptance time for the 10 scenarios. The radiology transport request acceptance time directly relates to ancillary test equipment utilization. Figure 3: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels simulation length: 26 weeks, scenarios: 10, source: ProModel simulation 12 The average patient transport time for each of the 10 scenarios is shown in Figure 4 below. Figure 4: Average Patient Transport Times for 10 Staffing Levels simulation length: 26 weeks, scenarios: 10, source: ProModel simulation From Modified Shift Schedules The team looked at the ten staffing level scenarios for the modified shift times. The average transport request acceptance time (the time from when a patient requests a transport until a transporter accepts a transport) for each of these metrics is shown in Figure 5 below. The average transport request acceptance time is a representation of room turnaround time. Figure 5: Average Transport Request Acceptance Times for 10 Staffing Levels at Modified Shift simulation length: 26 weeks, scenarios: 10, source: ProModel simulation 13 The average radiology transport request acceptance time for each of the 10 scenarios is shown in Figure 6 below. These times are a representation of ancillary test equipment utilization. Figure 6: Average Radiology Transport Request Acceptance Times for 10 Staffing Levels at Modified Shift Times simulation length: 26 weeks, scenarios: 10, source: ProModel simulation The average patient transport time for each of the 10 scenarios is shown in Figure 7 below. Figure 7: Average Patient Transport Times for 10 Staffing Levels at Modified Shift Times simulation length: 26 weeks, scenarios: 10, source: ProModel simulation 14 CONCLUSIONS The simulation was run using both Patient Transportations current shift schedules and modified shift schedules, seen in Table 5. The results of both of these simulations are summarized below. Standard Shift Times The simulation was first run using the current shift schedules seen in Table 5 for Patient Transportation. The simulation was run for 26 weeks, using 10 scenarios of staffing levels. The results are summarized below. UMHS Transport Request Acceptance Time The value represented by UMHS transport request acceptance time is the room turnaround time for UMHS as a whole, and can be seen in Figure 2. Room turnaround time can be represented by UMHS transport request acceptance time because the acceptance time represents the amount of time that a patient is waiting for transportation while there is no transporter currently available. Reducing this time will allow for more patients to move throughout the hospital. The transport request acceptance time using the current staffing levels of Patient Transportation was simulated to be 19 minutes. When the staffing levels were incremented upwards, there is an exponential drop off of transport request acceptance time. The performance goal of the Patient Transportation department is to have 90% of these values less than 5 minutes. The transport request acceptance time associated with the proposed increase of staff, 15.0 FTE, is 1.2 minutes. Radiology Transport Request Acceptance Time The value represented by the Radiology transport request acceptance time is the increase in ancillary test machine utilization, and can be seen in Figure 3. Ancillary test machine utilization can be represented by Radiology transport request acceptance times because it represents the time that a patient in Radiology is waiting for transportation while there are no patient transporters currently available. Decreasing this time will allow for patients to be transported to and from ancillary test machines more quickly. The Radiology transport request acceptance times using the current staffing levels of the Patient Transportation was simulated to be 20.7 minutes. These times also drop off exponentially when the staffing levels are incremented. The transport request acceptance time associated with the proposed increase of staff, 15.0 FTE increase, is 1.4 minutes. Having a higher ancillary test machine utilization rate will increase UMHS revenue because there will be less wasted time for the machines. 15 Overall Transport Time The overall transport time represents the time that passes between a transport order being placed and when the transport is completed and confirmed, and can be seen in Figure 4. The simulated overall transport time using the current staffing levels of the Patient Transportation department is 46.1 minutes. The overall transport times decreased exponentially when the staffing levels were increased. When the staffing levels were increase to the proposed levels, 15.0 FTE increase, the overall transport time decreased to 28.3 minutes. Modified Shift Times The simulation was then changed to run using the modified shift times seen in Table 5 for Patient Transportation. The simulation was run for 26 weeks using ten scenarios of staffing levels. The results are summarized below. UMHS Transport Request Acceptance Time Compared with the standard shift times, the modified shift times did not perform as well as the current shift times when the increase was below 6.0 FTE. However, the modified shift times performed better than the current shift times for increases above 6.0 FTE. Since the performance goal of the Patient Transportation department is to have 90% of these values less than 5 minutes, an increase above 6.0 FTE should be considered, as can be seen from Figure 2 and Figure 5. The transport request acceptance time associated with the proposed increase of staff with modified shift times is 0.5 minutes, which is 0.7 minutes less than the standard shift times. Radiology Transport Request Acceptance Time The current shift time model performed better when the increases was below 6.0 FTE and the modified shift time model performed better for increases above 6.0 FTE. Since the performance goal of the Patient Transportation department is to have 90% of transport request acceptance times less than 5 minutes, an increase above 6.0 FTE should be considered, as can be seen from Figure 3 and Figure 6. The Radiology transport request acceptance time associated with the proposed increase of staff with modified shift times is 0.5 minutes, which is 0.9 minutes less than the standard shift times at the same increase in staffing. Overall Transport Time The standard shift time model has a better performance when the increase was below 6.0 FTE, while the modified shift time model does better above an increase of 6.0 FTE. Since the performance goal of the Patient Transportation department is to have 90% of these values less than 30 minutes, an increase above 6.0 FTE should be considered, as can be seen from Figure 4 and Figure 7. The transport request acceptance time associated with the proposed increase of staff with modified shift times is 27.6 minutes, which is 0.7 minutes less than the standard shift times. 16 RECOMMENDATIONS Based on the results of the team’s simulation the team has developed the following recommendations: Increase in Patient Transportation Staffing Levels Increasing the Patient Transportation staffing levels will allow for the Patient Transportation department to make their current performance goals, according to the team’s simulation. The team recommends an increase in their current staffing levels of 13.4 FTE. This increase in staffing levels corresponds to transport request acceptance times of 30-60 seconds, regardless of shift schedule. Modification in Patient Transportation Shift Schedules The team discovered that when the shift times were changed to the modified shift schedules seen in Table 5, in conjunction to an increase of 13.4 FTE, the performance of the Patient Transportation department was higher than the simulation using the current shift schedules at the same increase in staffing. This can be seen by comparing the values of Figure 2 and 3 at the 13.4 increase in FTE. When the current shift schedules were used, the transport request acceptance time was 1.2 minutes, while the modified shift schedules showed this value was 0.5 minutes. EXPECTED IMPACT Should the Patient Transportation department implement the recommendations provided above, they can expect to see the following improvements: ● Increase of patient throughput ● Decrease in room turnaround time ● Increase in ancillary test equipment utilization While it’s difficult to know how well the model would represent Patient Transportation’s workload if there was a staffing level increase and/or shift schedule modification, the team believes their model will accurately portray the Patient Transportation department. The expected impact on Patient Transportation’s staffing, with regards to their performance goals, is shown in Table 6 below. 17 Table 6: Expected Impact on Patient Transportation with Regards to Performance Goals Performance Goal Current Performance FTE Increase Resulting Performance 90% transport request acceptance times < 5 mins 95% completion rate 2.5 transports per hour per transporter 51% < 5 mins 12.6 min (average) 13.4 90% < 5 mins 0.5 min (average) 84% 13.4 95% 1.53 (average) 13.4 2.17 (average) 18 Copy available upon request APPENDIX A: PREVIOUS ANALYSIS 19 APPENDIX B: ACCESS DATABASE ANALYSIS RESULTS Figure B1: Completed Transports by Origin sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System Figure B2: Completed Transports by Destination sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System 20 Figure B3: Completed Transports by Day of Week sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System Figure B4: Completed Transport by Hour of Day (12am through 11am) sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System 21 Figure B5: Completed Transports by Hour of Day (12pm through 11pm) sample size: 132,618 completed transports, date: January-December 2013, source: Transport Tracking System Table B1: Mean and Standard Deviation of Transport Times by Origin Transport Time (minutes) Origin Average Standard Deviation 31.4 10.8 Emergency Department 23.31 11.52 Radiology 27.37 9.15 UH 4 27.83 9.71 UH 5 28.34 15.18 UH 6 27.76 10.9 UH 7 28.44 9.9 UH 8 27.19 8.67 UH 9 29.29 10.96 CVC 27.41 12.54 CW 22 APPENDIX C: SCREENSHOT OF SIMULATION Figure C1: Screenshot of Simulation Model 23