University of Michigan Health SystemProgram and Operations Analysis

Analysis of Patient Transportation Needs To and From UH Pre-Op and PACU

Final Report

Submitted To:Beverly Smith, Nurse Manager, UMH Post Anesthesia Care Unit-UH

Noreen Myers, Clinical Manager, UMH Post Anesthesia Care Unit-UHSharon Rombyer, Clinical Manager, UMH Post Anesthesia Care Unit-UH

Josh Pigula, Financial Analyst Senior, UH ORMark Van Oyen, IOE 481 Professor, University of Michigan

Submitted By:  IOE 481 Project Team 6

Michael HuVinayak MotwaniAnthony Tohme

Sophie White

Date:  April 22, 2014

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Table of Contents

Executive Summary……………………………………………………………………………….1Key Issues………………………………………………………………………………...2Primary Goal…………………………………………………………………………….2Methods………………………………………………………………………………...2Findings and Conclusions……………………………………………………………...3Recommendations………………………………………………………………………3Expected Impact…………………………………………………………………………3

Introduction……………………………………………………………………………………..…4

Background…………………………………………………………………………………….….4Key Issues………………………………………………………………………………....5Goals and Objectives……………………………………………………………………...5Project Scope………………………………………………………………………….…..6

Methods………………………………………………………………………………………....…6

Findings and Conclusions………………………………………………………………………7

Recommendations………………………………………………………………………………10

Expected Impact………………………………………………………………………………..10

Appendix A: Map of Pre-Op and PACU Areas…………………………………………….…..11

Appendix B : Beeper Study Data Sheet…………………………………………………….…..11

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Figures and Tables

Figure 1: Patient transport flow diagram……………………………………………………..3

Figure 2: Task distribution for 22 PCTs & Aides shows substantial time spent on transportation 4

Table 1: Classification of 23,714 patients found in 2013 ORMIS data set……………………5

Figure 3: Nearly all transports entering Pre-Op originate from a UH location…………5

Figure 4: Nearly all transports exiting PACU go to a UH location………………5

Figure 5: Average number of transports per day entering Pre-Op is approximately 6……6

Figure 6: Average number of transports per day exiting PACU is approximately 41……7

Figure 7: Distribution of transports into Pre-Op is bimodal with peak hours at 6AM and 10AM..8

Figure 8: Distribution of transports out of PACU has peak hours at 4PM, 7PM, and 8PM……8

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EXECUTIVE SUMMARY

The University Hospital (UH) Pre-Operative Unit (Pre-Op) and Post Anesthesia Care Unit (PACU) provide care to patients before and after surgery, respectively. The Nurse Manager of the Pre-Op and PACU has observed longer than expected patient transportation times coming into and going out of these locations. The Nurse Manager has also recognized suboptimal allocation of Patient Care Technical Assistants (PCTs) and Nurse Aides (Aides). As a result, the Nurse Manager asked an IOE 481 student team to measure the transportation workload in the current system in full-time equivalents (FTEs) and identify how the workload is affected by factors such as time of day, location of transport, and level of care administered. This information will help the Nurse Manager optimize staff scheduling and potentially improve patient care. This report presents the team’s methods findings, conclusions, and recommendations.

Key IssuesThe following key issues are the primary motivation for this project:

Patient transport times to and from Pre-Op and PACU are longer than desired resulting in suboptimal quality of patient care.

PCTs and Aides assist in patient transportation when insufficient personnel are available, thereby diverting PCTs and Aides from their primary task of direct patient care.

Primary GoalThe primary goal of this project is to:

Calculate the current workload associated with patient transportation to and from Pre-Op and PACU taking into account the time of day, the number and types of assistive personnel needed, and the location of transport.

Methods

The team approached the project from four perspectives to ensure the accuracy of the findings. These four perspectives provide both a quantitative and qualitative evaluation of the patient transportation workload. In particular, the team conducted observations and data collection, interviewed hospital personnel involved in patient transportation, implemented a beeper study, and analyzed the transportation data collected from the beeper study and existing hospital software systems.

Observations and Data CollectionThe team collectively observed the Pre-Op and PACU areas in the University Hospital for 15 hours. The specific data that were collected include patient transportation times, distribution of transportation workload over time, and the general process flow. These data validated the hospital data captured by Centricity and ORMIS.

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Staff InterviewsThe team collectively interviewed five PCTS, three Aides, and two Charge Nurses who all have firsthand involvement with transportation to and from Pre-Op and PACU. The team collected the employees’ opinions on the workload and personnel requirements associated with patient transportation. This information validated the team’s observations and the hospital data.

Beeper StudyThe team developed a beeper study to measure the amount of time employees spend on patient transportation. The team used this study’s results to better estimate the amount of time that employees spend on various tasks.

Statistical Analysis of Existing DataThe team also analyzed data from the hospital’s ORMIS and Centricity data. These data analyses demonstrate how the transportation workload is affected by the time of day, location of transportation, and mode of transportation.

The team conducted an 80th percentile analysis. First, the transportation data were broken down by location (Pre-Op vs. PACU) and by weekday vs. weekend to capture any effects those factors may have on transportation parameters. Additionally, pivot tables were created to show the average and 80th percentile of the number of transportations during each hour of the day throughout 2013.

Findings and Conclusions

The approaches discussed in the Methods section provided the team with an in-depth and holistic view of the current transportation processes to and from the Pre-Op and PACU areas. The resulting analyses provided valuable insights into the current transportation workload.

Staff Interviews The interviews conducted with various Pre-Op and PACU staff confirm that patient transportation in the area is a problem that needs to be addressed. In particular, patient transportation often experiences delays and one specific issue affecting patient transportation is a shortage of staff to meet transportation needs.

Beeper StudyApproximately 40% of PCTs’ and Aides’ time is spent on tasks relating to transportation. This means the current transportation workload is 4.8 FTEs. Furthermore, the fact that 40% of PCTs’ and Aides’ time is spent on transportation tasks also means more time is spent on transportation than any other individual task category. This is concerning because PCTs’ job description indicates that patient care is their main duty, not patient transportation.

Statistical Analysis of Existing DataThe team examined the distribution of the number of transportations with respect to the time (hour) of day. In particular, the average number, 80th percentile, and percentage of daily transports that occur in every hour of the day were calculated.

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The largest number of transports into Pre-Op occur at 6AM and 10AM. Furthermore, the majority of transports into Pre-Op occur between the hours of 6AM and 4PM. On the other hand, there are relatively few transports into Pre-Op between the hours of 5PM and 5AM. The largest number of transports out of PACU occur at the hours of 4PM, 7PM, and 8PM. Furthermore, the majority of transports out of PACU occur between the hours of 1PM to 11PM. On the other hand, there are relatively few transports out of PACU between the hours of 12AM to 12 PM.

The provided Centricity data set initially contained information about 5,562 patients, however, after deleting records for patients with missing information, the final number of patients with valid information was 3,797. These data indicate that the majority of transports take place via beds (77%) versus stretchers (23%). Based on these findings, the Centricity data seems to be very unreliable. Not only did a large portion of the original data have missing information, but the distribution of the transportation modes does not seem realistic according to the Nurse Manager’s personal experience. Thus, the team concluded that no strong analytical conclusions could be made from analyzing the Centricity data set.

Recommendations

The team’s findings suggest that PCTs are often drawn away from their primary duties of patient care to perform transportation tasks due to a lack of other available personnel. Thus, the team recommends one of two options. One option is to simply hire more staff who are able fulfill currently unsatisfied patient transportation demand. Another option is to more optimally schedule the current pool of transportation staff such that more staff are available at peak hours.

On a similar note, the findings from the team’s analyses of the ORMIS data set provide valuable information that enable optimal staffing decisions to be made more easily. The team recommends that staff scheduling decisions be made in a manner that assigns more transportation staff during periods where transportation demand is expected to be higher.

Lastly, the team recommends that future data collection be conducted more carefully. The sample Centricity data set demonstrated that data is often collected incorrectly, if at all. Future projects that may use these data sets would benefit greatly from having more reliable data and not having to clean the data as thoroughly.

                   Expected Impact

The staff interviews, beeper study, and data analyses have yielded valuable information about the distribution of patient transportation with respect to the time of day and location of transport. This information provides the client with the quantitative data necessary to optimize staff scheduling. In turn, this will help the Pre-Op and PACU areas more easily meet the demand for patient transportation. This enhanced knowledge of the current transportation workload will allow the Pre-Op and PACU areas to refocus its efforts on addressing patient transportation issues to focusing more on patient care.

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Introduction

The University Hospital (UH) Pre-Operative Unit (Pre-Op) and Post Anesthesia Care Unit (PACU) provide care to patients before and after surgery, respectively. The Nurse Manager of the Pre-Op and PACU has observed longer than expected patient transportation times coming into and going out of these locations. The Nurse Manager has also recognized suboptimal allocation of Patient Care Technical Assistants (PCTs) and Nurse Aides (Aides). As a result, the Nurse Manager asked an IOE 481 student team to measure the transportation workload in the current system in full-time equivalents (FTEs) and identify how the workload is affected by factors such as time of day, location of transport, and level of care administered. This information will help the Nurse Manager optimize staff scheduling and potentially improve patient care. This report presents the team’s methods findings, conclusions, and recommendations.

Background

UH Pre-Op and PACU both operate 24 hours a day, 365 days a year. As shown in the hospital map in Appendix A, Pre-Op contains 17 patient beds and PACU contains 37 patient beds. The 37 patient beds in PACU are divided into two rooms: 28 beds in one room and 9 beds in the other room. The assignment of patients to beds follows a standard procedure; however, patients may be relocated at the discretion of the Charge Nurse on duty. Pre-Op and PACU currently employ 12 assistive personnel – 4 PCTs and 8 Aides – whose primary duties involve patient care. Additionally, over 50 Nurses administer the majority of patient care in Pre-Op and PACU.

The patient transportation process has four primary steps. First, a PCT/Aide leaves the Pre-Op area to retrieve a patient. Second, the PCT/Aide transports the patient back to the Pre-Op area. Third, a PCT/Aide transports a patient from the PACU to a destination within the hospital. Fourth, the PCT/Aide returns to the PACU area. These steps are highlighted in Figure 1 below. Patient transportation occurs between five major areas for this process: University Hospital, Cancer Center, C.S. Mott Children’s Hospital/Van Voigtlander Women’s Hospital, Taubman Center, and the Cardiovascular Center (CVC).

Figure 1: Patient transport flow diagram

Pre-OP and PACU currently have two software systems that record patient data: Centricity and ORMIS. While both of these systems contain data associated with the patient transportation process (e.g. time of departure from PACU, origin/destination of transport, type of personnel performing transport, etc.), the data are not wholly contained in either system, but rather split between the two systems. Furthermore, due to the fact that certain data fields recorded in Centricity and ORMIS are not crucial to the hospital’s billing process, some data are unreliably collected. For example, time stamps for when PCTs/Aides return to the PACU after finishing transports occasionally indicate times before the transportation even began. This suggests that

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Bed is Mad e Ava ilabl e

PCT /Aid e Lea ves Pre -Op

PCT /Aid e Ret urns to P re-O p with Pati ent

Pre -Op/OR/ PAC U

PCT /Aid e Lea ves

PAC U with

Pati ent

PCT /Aid e Ret urnsTo P ACU

certain portions of the Centricity and ORMIS data cannot be used for analyses.

Despite inconsistencies in Centricity and ORMIS, the two data sets still provide methods of analyzing the workload associated with the current transportation process. The workload is quantified by assistive personnel work hours, which are defined in terms of full-time equivalents (FTEs). Time of day impacts the patient transportation workload because of peaks in demand during mornings and evenings. During these peaks, the Nurse Manager believes PCTs and Aides spend more time transporting patients rather than providing patient care. Additionally, the number of personnel required for patient transportation depends on the mode of transportation (bed vs. stretcher). Thus, transportation workload is a function of the time of day as well as the mode of transportation.

According to the Nurse Manager, the current process is inefficient and results in prolonged transportation times, which negatively affect the quality of patient care. To address this problem, the team has calculated the workload associated with patient transportation in the current system.

Key IssuesThe following key issues are the primary motivation for this project:

Patient transport times to and from Pre-Op and PACU are longer than desired resulting in suboptimal quality of patient care.

PCTs and Aides assist in patient transportation when insufficient personnel are available, thereby diverting PCTs and Aides from their primary task of direct patient care.

Goals and ObjectivesThe primary goal of this project is to:

Calculate the current workload associated with patient transportation to and from Pre-Op and PACU taking into account the time of day, the number and types of assistive personnel needed, and the location of transport.

To achieve this goal, the team established the following objectives:

Observe current transportation processes to supplement existing data associated with transportation times and personnel requirements.

Interview relevant hospital staff members (PCTs, Aides, and Charge Nurses) to obtain both quantitative and qualitative data regarding the transportation process.

Conduct a beeper study to collect additional data regarding how much time PCTs and Aides are dedicating to patient transportation.

Consolidate and analyze the transportation data collected from the beeper study and existing hospital software systems.

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Project ScopeThis project includes only the process of patient transportation to and from the Pre-Op and PACU areas. In particular, the primary focus of the project involves identifying the workload needed to satisfy patient transportation requests.

Any task not related to patient transportation to or from the Pre-Op and PACU areas is not included in this project. This project does not examine communication within the Central Transport department. Furthermore, this project does not investigate methods using the determined transportation workload to optimize staff scheduling. Any transportation that does not involve the Pre-Op or PACU as an origin or destination is not examined in this project. However, the hope is that the findings and methods presented can be extended to other similar units in the future.

Methods

The team approached the project from four perspectives to ensure the accuracy of the findings. These four perspectives provide both a quantitative and qualitative evaluation of the patient transportation workload. In particular, the team conducted observations and data collection, interviewed hospital personnel involved in patient transportation, implemented a beeper study, and analyzed all of the transportation data collected from the beeper study and existing hospital software systems.

Observations and Data CollectionThe team collectively observed the Pre-Op and PACU areas in the University Hospital for 15 hours. Observations and data for approximately 10 patient transports were collected. To minimize the adverse effect of anomalies and to capture the effect that time of day has on transportation demand, the team collected data over a week and at various times of day. The specific data that were collected include patient transportation times, distribution of transportation workload over time, and the general process flow. These data validated the hospital data captured by Centricity and ORMIS.

Staff InterviewsThe team collectively interviewed five PCTS, three Aides, and two Charge Nurses who all have firsthand involvement with transportation to and from Pre-Op and PACU. The team collected the employees’ opinions on the workload and personnel requirements associated with patient transportation. This information validated the team’s observations and the hospital data.

Beeper StudyThe team developed a beeper study to measure the amount of time employees spend on patient transportation. The study spanned two weeks and 22 PCTs and Aides participated. The beepers randomly went off on average 3.2 times per hour throughout each employee’s shift at which time the employee indicated on a data sheet whether he/she was transporting patients, experiencing a transportation delay, administering patient care, or undergoing another task. The team used this study’s results to better estimate the amount of time that employees spend on various tasks.

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The team designed the data sheet used in the beeper study. The data sheet is shown in Appendix B. Information recorded on the data sheet include the employee’s job title (PCT or Aide), date, beeper start/end time, and the number of times the employee was transporting patients, experiencing a transportation delay, administering patient care, or undergoing other tasks when the beeper went off.

The task categories of Patient Transportation, Transportation Delay, Patient Care, and Other were carefully defined to establish data reporting standards across all study participants. Patient Transportation was defined as any necessary tasks during the patient transportation process in and out of Pre-Op and PACU (preparing a patient for transport, physically transporting a patient, cleaning/dressing stretchers, etc.). Transportation Delay was defined as tasks during patient transportation that are not directly related to transportation (waiting for an elevator, waiting for a patient to be prepped for transportation, etc.). Patient Care was defined as any tasks related to patient care that do not involve transportation. Other was defined as any tasks that do not fall into the previous three categories.

The team inputted the results of the beeper study into Excel. Then the team used these results to calculate the percentage of time PCTs and Aides were spending on Patient Transportation, Transportation Delays, Patient Care, and Other.

Statistical Analysis of Existing DataThe team analyzed data from the hospital’s ORMIS and Centricity data sets for this part of the study. These data analyses demonstrate how the transportation workload is affected by the time of day, location of transportation, and mode of transportation.

The relevant data fields in the ORMIS data set include transportation travel times, transportation origins/destinations, and the number of patients transported per day. The data include 23,714 patients spanning from January 2013 to December 2013. The large sample size and span of time covered by the data ensure that the team’s findings are statistically significant.

Statistical analyses of the data were conducted in Excel. In particular, the team conducted an 80th percentile analysis following its coordinator’s recommendation. First, the transportation data were broken down by location (Pre-Op vs. PACU) and by weekday vs. weekend to capture any effects those factors may have on transportation parameters. Additionally, pivot tables were created to show the average and 80th percentile of the number of transportations during each hour of the day throughout 2013.

The relevant data fields in the Centricity data set include type of personnel (PCT, Aide, and Nurse) performing patient transportations as well as the mode of transportation (bed vs. stretcher). This data set includes valid information for 3,797 patients and spans from January 2013 to March 2014. Although the data was provided in an Excel file, the data was largely in text format. Thus, the team used Excel functions such as IF, ISERR, and FIND to parse the data and categorize transportations according to the type of personnel performing the transportation and the mode of transportation.

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Findings and Conclusions

The approaches discussed in the Methods section provided the team with an in-depth and holistic view of the current transportation processes to and from the Pre-Op and PACU areas. The resulting analyses provided valuable insights into the current transportation workload.

Description of Key MetricsThere were two key metrics used in the study. The primary metric was current workload. Workload was measured by the number of transportations occurring in the Pre-Op and PACU areas. A secondary metric used to better understand the workload is the time of day at which transportations occur. The secondary metric describes the effect that time of day has on the transportation workload, which may allow Nurse Managers and other staffing decision-makers to make staffing decisions according to workload needs. These metrics were chosen to align with the study’s primary goal of calculating the current workload associated with patient transportation to and from Pre-Op and PACU.

Staff Interviews Interviews with hospital employees (five PCTs, three Aides, and two Charge Nurses) provided firsthand insight into current patient transportation processes to and from the Pre-Op and PACU areas. Both Charge Nurses who were interviewed reported frequent delays in patient transportation. For example, after a patient transportation request is made, there may be a wait of approximately 30 minutes before the transportation begins. All of the PCTs reported participating in patient transportation duties more often than their job descriptions suggest. Four of the PCTs and all of the Aides reported insufficient staffing levels to meet current transportation needs. Collectively, all of the interviews conducted with various Pre-Op and PACU staff confirm that patient transportation in the area is a problem that needs to be addressed. In particular, patient transportation often experiences delays and one specific issue affecting patient transportation is a shortage of staff to meet transportation needs.

Beeper StudyThe results of the beeper study indicate how Aides and PCTs in the Pre-Op and PACU areas distribute their work among the following categories (as defined previously): Patient Transportation, Transportation Delay, Patient Care, and Other. The team found that Aides and PCTs spend 34% of their time on Patient Transportation, 6% of their time on Transportation Delay, 33% of their time on Patient Care, and 27% of their time on Other. This distribution of time spent on different tasks is illustrated in Figure 2 below.

34%

5%33%

27% Patient Transporta-tionTransportation Delay

Patient Care

Other

Figure 2: Task distribution for 22 PCTs & Aides shows substantial time spent on transportation

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Aggregating the Patient Transportation and Transportation Delay categories indicates that approximately 40% of PCTs’ and Aides’ time is spent on tasks relating to transportation. Combining this 40% figure with the current 12.0 FTE pool means the current transportation workload is 4.8 FTEs. Furthermore, the fact that 40% of PCTs’ and Aides’ time is spent on transportation tasks also means more time is spent on transportation than any other individual task category. This is concerning because PCTs’ job description indicates that patient care is their main duty, not patient transportation.

Statistical Analysis of Existing DataAs stated previously, the ORMIS data set contained records of 23,714 patients from January 2013 to December 2013. The classification of these patients into inpatients (IPs), outpatients (OPs), and admit patients (APs) is shown in Table 1 below.

Table 1: Classification of 23,714 patients found in 2013 ORMIS data setType of Patient Number of PatientsInpatients 4,396Outpatients 11,382Admit patients 7,936Total Patients 23,714

Among the IPs, there were a total of 20,572 transports into the Pre-Op area. Among the IPs and APs, there were a total of 20,569 transports out of the PACU area. Following the recommendation of the team’s client and coordinator, OPs were omitted from the analyses due to their hospital visits being structured substantially differently.

To provide a high-level characterization of the types of transportations being performed, the team first examined the distribution of origins for transportations entering Pre-Op. This distribution is shown in Figure 3 below. Similarly, the distribution of destinations for transportations exiting PACU is shown in Figure 4 below.

Motts1.9%

CVC0.7%

UH97.4%

Figure 3: Nearly all transports entering Pre-Op originate from a UH location

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Motts1.4%

UH98.0%

CVC0.4%

Other0.2%

Figure 4: Nearly all transports exiting PACU go to a UH location

Figures 3 and 4 indicate that the vast majority of transports entering Pre-Op and exiting PACU originate from and go to UH locations, respectively.

To better understand transportation needs, the team then performed a frequency analysis of the number of transports that occur every day. The number of transports per day entering Pre-Op is illustrated in Figure 5 below. Similarly, the number of transports per day exiting PACU is shown in Figure 6 below.

1 2 3 4 5 6 7 8 9 10 11 More05

1015202530354045

Number of Transports Per Day

Freq

uenc

y (d

ays)

Figure 5: Average number of transports per day entering Pre-Op is approximately 6(sample size = 261 days, data source = ORMIS calendar year 2013)

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20 25 30 35 40 45 50 55 More0

10

20

30

40

50

60

70

80

Number of Transports Per Day

Freq

uenc

y (d

ays)

Figure 6: Average number of transports per day exiting PACU is approximately 41(sample size = 261 days, data source = ORMIS calendar year 2013)

Figures 5 and 6 indicate that there are approximately 7 times more transports exiting PACU than transports entering Pre-Op. This knowledge can be used by the Nurse Manager and Charge Nurses to appropriately allocate transportation staff between Pre-Op and PACU in a manner such that the differing transportation demands in the areas are satisfied.

Inspecting transportation needs at a more granular level, the team then examined the distribution of the number of transportations with respect to the time (hour) of day. In particular, the average number, 80th percentile, and percentage of daily transports that occur in every hour of the day were calculated. Graphical representations of these results for transportations entering Pre-Op and exiting PACU are shown in Figures 7 and 8, respectively.

12:00 AM

2:00 AM

4:00 AM

6:00 AM

8:00 AM

10:00 AM

12:00 PM

2:00 PM

4:00 PM

6:00 PM

8:00 PM

10:00 PM0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

0.0

0.5

1.0

1.5

2.0

2.5

Transport Percent-age

Average

80 Per-centile

Time of Day

Perc

enta

ge o

f Dai

ly T

rans

port

s

Num

ber o

f Tra

nspo

rts

Figure 7: Distribution of transports into Pre-Op is bimodal with peak hours at 6AM and 10AM(sample size = 261 days, data source = ORMIS calendar year 2013)

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12:00 AM

2:00 AM

4:00 AM

6:00 AM

8:00 AM

10:00 AM

12:00 PM

2:00 PM

4:00 PM

6:00 PM

8:00 PM

10:00 PM0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

Transport Percent-age

Average

80 Per-centile

Time of Day

Perc

enta

ge o

f Dai

ly T

rans

port

s

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f Tra

nspo

rts

Figure 8: Distribution of transports out of PACU has peak hours at 4PM, 7PM, and 8PM(sample size = 261 days, data source = ORMIS calendar year 2013)

Figure 7 illustrates that the largest number of transports into Pre-Op occur at 6AM and 10AM. Furthermore, the figure shows that the majority of transports into Pre-Op occur between the hours of 6AM and 4PM. On the other hand, there are relatively few transports into Pre-Op between the hours of 5PM and 5AM.

Figure 8 illustrates that the largest number of transports out of PACU occur at the hours of 4PM, 7PM, and 8PM. Furthermore, the figure shows that the majority of transports out of PACU occur between the hours of 1PM to 11PM. On the other hand, there are relatively few transports out of PACU between the hours of 12AM to 12 PM.

This information about the distribution of transportation demand with respect to the time (hour) of day can be used by the Nurse Manager and Charge Nurses to optimize their transportation staff scheduling to appropriately meet the expected demand. In particular, information about the 80th percentile of the number of transports allows staffing decisions to be made in a manner that avoids staffing shortages even when the transportation demand is higher than normal.

The team’s final analysis was done on the Centricity data set. The provided Centricity data set initially contained information about 5,562 patients, however, after deleting records for patients with missing information, the final number of patients with valid information was 3,797. These data indicate that the majority of transports take place via beds (77%) versus stretchers (23%). Based on these findings, the Centricity data seems to be very unreliable. Not only did a large portion of the original data have missing information, but the distribution of the transportation modes does not seem realistic according to the Nurse Manager’s personal experience. Thus, the team concluded that no strong analytical conclusions could be made from analyzing the Centricity data set.

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Recommendations

The results of the staff interviews suggest that there are frequent shortages of staff available and able to perform patient transportation. This aligns with the results of the beeper study, which indicate that PCTs are participating in patient transportation-related activities more than they are supposed to according to their job description. Collectively, the team’s findings suggest that PCTs are often drawn away from their primary duties of patient care to perform transportation tasks due to a lack of other available personnel. Thus, the team recommends one of two options. One option is to simply hire more staff who are able fulfill currently unsatisfied patient transportation demand. Another option is to more optimally schedule the current pool of transportation staff such that more staff are available at peak hours.

On a similar note, the findings from the team’s analyses of the ORMIS data set provide valuable information that enable optimal staffing decisions to be made more easily. The team recommends that staff scheduling decisions be made in a manner that assigns more transportation staff during periods where transportation demand is expected to be higher.

Lastly, the team recommends that future data collection be conducted more carefully. The sample Centricity data set demonstrated that data is often collected incorrectly, if at all. Future projects that may use these data sets would benefit greatly from having more reliable data and not having to clean the data as thoroughly.

                   Expected Impact

The staff interviews, beeper study, and data analyses have yielded valuable information about the distribution of patient transportation with respect to the time of day and location of transport. This information provides the client with the quantitative data necessary to optimize staff scheduling. In turn, this will help the Pre-Op and PACU areas more easily meet the demand for patient transportation. This enhanced knowledge of the current transportation workload will allow the Pre-Op and PACU areas to refocus its efforts on addressing patient transportation issues to focusing more on patient care.

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Appendix A: Map of Pre-Op and PACU Areas

Source: University of Michigan Medical Center

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Appendix B: Beeper Study Data Sheet

Role (circle one): Nurse Aide / PCT Date:Beeper Start Time: _______________ Beeper End Time: _______________

Directions: When the beeper goes off, please place a tally in the box corresponding to the task you are performing at that time. The beeper should go off on average 2-4 times per hour. General descriptions of the task categories follow:

Patient Transportation: Any necessary tasks during the patient transportation process in and out of Pre-Op and PACU (e.g. preparing a patient for transport, physically transporting a patient, cleaning/dressing stretchers, etc.)

Transportation Delay: Tasks during patient transportation that are not directly related to transportation (e.g. waiting for an elevator, waiting for a patient to be prepped for transportation, etc.)

Patient Care: Any tasks related to patient care that do not involve transportation.

Other: Any other tasks that do not fall into the other categories (please specify)Patient

TransportationTransportation

DelayPatient Care Other

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***Please return the beeper and sheet to the charge nurse desk at the end of your shift***