University of Michigan Health System

Analysis of Pathology-Chemistry Laboratory Workflow

Final Report

To: Sue Stern, Chemistry Manager, scstern@umich.edu

Kristina Martin, Clinical Pathology Operations Coordinator, martkris@umich.edu

Dr. David Keren, Clinical Pathology Director, dkeren@umich.edu

Mary Martin, Associate Director of Operations, mkpalazz@umich.edu

Professor Mark P. Van Oyen, vanoyen@umich.edu

Mary Duck, Industrial Engineer Expert/Lean Coach, mgduck@umich.edu

Bijal Shah, MQS Fellow, shahbb@med.umich.edu

From: IOE 481 – Team3 – Fall 2014 – IOE 481 Students

Julian Benzer

Mark Cilia

Micah Grand

December 9, 2014

Table of Contents

Executive Summary 1

Background 1

Methods 2

Findings and Conclusions 2

Recommendations 3

Introduction 4

Background 4

Key Issues 5

Goals and Objectives 5

Project Scope 6

Methods 6

Observations 6

Interviews 7

Existing Data 7

Literature Search 7

Time Studies 7

Hypothesis Tests to Determine Statistical Significance of Busy Days 8

Linear Program for Optimal Batch Size 8

Findings and Conclusions 9

Lack of Standardized Work Process 9

Lack of Optimal Batch Size for Tacro Test 10

Turnaround Time Is Not Dependent on Day 10

Recommendations 11

Expected Impact 12

Appendices 14

Appendix 1: Current State Map of Toxicology Laboratory 14

Appendix 2: Existing Data Source 15

Appendix 3A: Data Self Collection Worksheets – Data Self-Collection 16

Appendix 3B: Data Self Collection Worksheets – Data Self-Collection Faults 17

Appendix 3C: Incoming Volume by Hour and Day 18

Appendix 4: Hypothesis Testing for Statistical Significance of Busy Days 19

Appendix 5: Linear Program for Optimal Batch Size 20

Appendix 6: Tacro Detailed Process Notes 21

Appendix 7: Future State Map of Toxicology Laboratory 22

Appendix 8: Recommendation Simulations 23

List of Figures and Tables

Table E-1: Average Elapsed Time Per Step 3

Table E-2: Summary Statistics of Batch Time 3

Table 1: Average Elapsed Time Per Step 9

Table 2: Summary Statistics of Batch Time 9

Table 3: Mean Turnaround Time Is Independent of Day 11

Table 4: Variance of Turnaround Time Is Independent of Day 11

Table 5: Comparing Recommended Batch Sizes 12

Table 6: Comparing Recommendation Step Times 12

Table 7: Summary of Expected Impacts from Future State Map 13

1

Executive Summary

The Chemical Pathology Department at the University of Michigan Health System (UMHS)

processes blood and urine samples from patients in UMHS as well as hospitals throughout the

state. Upon entering the laboratory, a sample is sent to one of three areas: Toxicology, Special

Chemistry, or Automated Chemistry. After assessing the current state of the laboratory, the team

focused on the Toxicology section of the laboratory because a significant portion of the testing

done in this area requires manual batch preparation compared to the largely automated processes

in the rest of the laboratory. Within the Toxicology section, the laboratory technicians are trained

to perform approximately 30 tests. The volume of each type of test varies greatly; the Tacrolimus

based Immunosuppression (Tacro) level testing makes up the vast majority of this volume with

57.6% of average weekly samples. Due to this volume, the team decided to focus its analysis on

the Tacro testing process.

The manual batch preparation has led to concerns regarding turnaround time when large (30+)

size batches are run. To address these concerns, the Chemistry Manager and the Clinical

Pathology Operations Coordinator asked an IOE 481 student team from the University of

Michigan to analyze the workflow of the laboratory and recommend improvements.

After building a Current State Map (CSM) of the Tacro process and conducting a time study in

the laboratory, the team developed recommendations to improve the efficiency of processing

these samples. Increased efficiency will reduce the turnaround time, giving doctors faster results

so that they can more effectively treat patients.

Background

The team completed an initial tour and was introduced to key laboratory staff in early September

2014. The high-volume automated system showcased the efficient processing of the majority of

samples, and did not require any further consideration. Through observations and interviews

during the initial tour, the team chose to focus on the Toxicology area based on the amount of

manual work performed, as well as the larger volume of tests processed in this area compared to

other areas that practice manual processing.

The day supervisor of the Toxicology section creates a weekly schedule that assigns laboratory

technicians to perform the variety of tests each day. Each technician’s assignment changes daily,

however, one person is assigned solely to the Tacro samples. The Tacro test involves five

manual steps to prepare the samples for the Liquid Chromatography/Mass Spectrometry (LCMS)

Machine for testing and one manual results step after the LCMS machine testing.

Each technician’s techniques vary. Two main issues cause this variability: (1) small differences

in the manual preparation steps between each technician and (2) a lack of a standard batch size.

Currently, the process takes an average of 2 hours and 48 minutes with batch sizes ranging from

19 to 48 and an average batch size of 33.

2

Methods

The team performed six types of tasks to evaluate and improve the workflow of the Toxicology

laboratory:

1. Observations & Interviews: The team performed fifteen observations of the Toxicology area

during both the day and night shifts. After the project focus was narrowed to the Tacro test

process, observation time was used to create a list of the detailed process steps required to

perform these tests. The team used this list to create a CSM using Visio software. The team

also interviewed the staff, specifically the Chemistry Manager, the Day Manager and five

laboratory technicians.

2. Existing Data: The team received historical Toxicology data from the Chemistry Manager to

supplement the observations and time studies. This data included total volume of Tacro

samples and the corresponding turnaround times. This data also contained testing information

sorted by test type and day of week, along with an estimate of average test volumes made by

a technician with lean experience. From this data, the team focused its analysis to the Tacro

test and conducted time studies.

3. Literature Search: The team found materials pertaining to 5S and Lean in a laboratory

setting, as well as studies that dealt with finding optimal batch sizing in a number of different

settings. This literature helped confirm that the team would need to conduct time studies.

4. Time Studies: The team performed a time study to document the task times on the CSM. The

laboratory technicians completed the data self-collection sheets. The team showed all

laboratory technicians the beginning and end triggers of each step of the process in order to

ensure accuracy. The time study took place from November 3 to November 21, and included

data for 38 batches. The information collected in the time studies was used to determine

whether there is a statistically significant difference of mean turnaround time on peak days

versus non-peak days and to develop recommendations to improve laboratory efficiency.

5. Hypothesis Tests to Determine Statistical Significance of Busy Days: The team used the data

collected in the time studies to perform six hypothesis tests to determine if there is a

statistically significant difference in turnaround time on Tuesday (highest sample volume)

and the rest of the week. The team conducted 2-Sample T Test for Means and 2-Sample F

Test for Variances. These tests were conducted independently for total elapsed time, setup

time, and machine run time. 7 samples were collected for Tuesdays and 31 samples were

collected for the rest of the week.

6. Linear Program for Optimal Batch Size: The team formulated a linear program to determine

an optimal batch size that would minimize the sample turnaround time. The linear program

uses the data collected in the time study to determine an optimal batch size where time spent

in the LCMS machine is equal to the time to perform all other manual processes.

Findings and Conclusions

The team created a current state map based on observation and interview data. Through the

previously listed methods, the team concluded:

1. A standardized work process did not exist.

2. An optimal batch size for the Tacro test did not exist.

3. Turnaround time is not dependent on day.

3

Table E-1 shows the results of the time study and includes the average time per step and the time

per sample at each step, where applicable. 38 self-collection sheets were analyzed.

Table E-1: Average Elapsed Time Per Step. Source: Time Studies. Sample = 38

Step Number Elapsed Time Time Per Sample

1 0:03:51 0:00:07

2 0:18:28 0:00:34

3 0:02:38 N/A

4 0:13:40 N/A

5 0:05:28 0:00:10

6 1:39:17 0:03:01

7 0:24:43 0:00:45

Below, Table E-2 displays the summary statistics of all observed batch runs.

Table E-2: Summary Statistics of Batch Time. Source: Time Studies. Sample = 38

Summary Statistic Total Time Time Per Sample

Mean 2:48:05 0:05:15

Median 2:36:00 0:04:47

Standard Deviation 0:49:51 0:01:21

Min 1:43:00 0:03:39

Max 5:31:00 0:09:11

At a confidence level of 99.99%, hypothesis testing showed that there was no statistically

significant difference in mean turnaround time or variance of turnaround time between Tuesday

and the other days of the week. The linear program formulated by the team found that the

optimal batch size is 15 samples, resulting in an expected standard process time of 1 hour and 21

minutes.

Recommendations

Based on the findings from the workflow analysis of the Toxicology Laboratory, the team

recommends using a batch size of 15 samples to minimize the turnaround time. Considering that

this batch size approximately doubles the number of test runs per day, the team has developed an

alternative recommendation: use a timed approach and conduct the test every 2 hours. Based on

the linear program, this results in a batch size of 24. Based on these two recommendations and

the team’s analysis, the following impacts are expected:

Predictable and repeatable workflow of the Toxicology laboratory.

Decreased turnaround time from 2 hours and 48 minutes to 1 hour and 21 minutes or 2

hours.

Expedited delivery of results to hospital entities.

Increased clinic/bed turnover within hospital.

4

Introduction

The University of Michigan Health System’s Chemical Pathology Department consists of three

areas: Toxicology, Special Chemistry and Automated Chemistry. This project focused on the

Toxicology area, specifically the Tacrolimus immunosuppressant (Tacro) tests, which receive the

highest volume of daily samples. While parts of the workflow are automated, much is still done

manually. The Tacro samples require manual preparation before they are placed in the LCMS for

testing. The LCMS requires 2 minutes and 43 seconds per sample, which has led to concerns

regarding turnaround time when large (30+) size batches are run. To address these challenges,

the Chemistry Manager and the Clinical Pathology Operations Coordinator have requested that

an IOE 481 student team from the University of Michigan analyze the workflow of the Chemical

Pathology Laboratory and give recommendations to improve efficiency.

The team observed the current process and was provided data to develop a current state map

(CSM) of the Tacro test process, see Appendix 1. Using staff interviews, time studies, and Excel

for batch size optimization, the team has created a future state map (FSM). This report contains

the methods used to collect and analyze information, a future state map and standard work model

of the Toxicology Laboratory, findings and conclusions from the team’s analyses,

recommendations to improve the efficiency of the laboratory, and supporting documentation.

The supporting documentation included in this report includes a current state map, a future state

map, details on the hypothesis testing and linear program, and a simulation of the performance of

the team’s recommendations versus the current process.

Background

The team completed an initial tour and was introduced to key laboratory staff in early September

2014. During the initial meeting, the team became familiar with the sections of the laboratory.

The high-volume Automated Chemistry system showcased the efficient processing of the

majority of samples, and did not require any further consideration. The team also observed the

separate Toxicology and Special Chemistry areas, which required more manual setup and

processing than the automated system. Through observations and interviews during the initial

tour, the team determined that the Toxicology area receives a higher volume of samples than the

Special Chemistry area; therefore, the team chose to focus on the Toxicology area to improve

efficiency.

The Pathology-Chemistry laboratory is the largest laboratory in the hospital and is depended

upon for many critical patient test results every day. The Toxicology section analyzes patient

samples to find detectable levels of a variety of drugs. Within Toxicology, Tacro tests account

for the largest volume of average weekly samples (57.6%, Appendix 2). Tacrolimus has become

the preferred type of immunosuppression for transplant patients (a change from cyclosporine),

which explains the large volume of tests that Toxicology handles. To perform the Tacro tests, a

laboratory technician must first complete five manual steps to prepare samples before they are

placed in the Liquid Chromatography/Mass Spectrometry (LCMS) Machine. The LCMS

identifies the level of Tacro in each sample and compares it to an acceptable level. The

5

laboratory technicians assess these results and report their findings to the doctors. It is also

important to note that Tuesdays are the highest volume days for Tacro testing.

The laboratory technicians do not currently adhere to a standard batch size. Interviews revealed

that technicians observe the stock of unprocessed samples that have accumulated and use their

discretion to decide on a batch size; from the data, the team calculated the average batch size to

be 33. The majority of this processing time is taken up by the LCMS, which requires 2 minutes

and 43 per sample. The machine can hold up to 48 samples at once. The team identified this long

turnaround time as an area where the Toxicology section had a significant opportunity for

improved efficiency.

Key Issues

In order to improve the efficiency of their laboratory, the Chemical Pathology department

requested an IOE 481 team to analyze the laboratory workflow.

The following key issues drove the urgency of this project:

1. Chemical Pathology Department wished to improve efficiency in its laboratory.

2. Important tests that the laboratory performs require manual set up.

3. Unnecessary wait time in the testing process.

Goals and Objectives

The primary goals of this project were to analyze the workflow of the Toxicology section of the

Pathology-Chemistry laboratory and make recommendations to improve the efficiency of the

manual testing. To achieve these goals, the team has addressed the following objectives:

Develop a current state map of the Toxicology laboratory.

Carry out a time study in order to find the amount of time laboratory technicians are

spending on each step of the testing process.

Identify an optimal batch size to minimize turnaround time for Tacro tests.

Develop a future state map of the Toxicology laboratory based on time studies.

The student team has completed the following tasks to determine the causes of the inefficiencies:

Observed current process to understand workflow.

Conducted interviews with the Chemistry Manager, the Day Manager and five laboratory

technicians.

Researched relevant literature to further understand Lean Six-Sigma in hospital settings.

Performed time studies to develop a current state map.

Analyzed existing data.

Developed future state map.

The team has analyzed and collected information to provide recommendations to:

Reduce sample turnaround time.

Implement standard processing techniques.

6

Project Scope

After initial observations of the entire Chemical Pathology laboratory, the scope of this project

was narrowed to the Toxicology area, specifically the Tacro tests, because they account for the

highest volume of daily samples (57.6%). The Tacro testing process begins when the samples

first arrive in the laboratory and ends after the results from the LCMS are recorded in the

computer.

Any test performed outside of the Toxicology section of the laboratory was not included in this

project. Individual patient information was not within the scope of this project nor was any

process outside of the control of the Pathology Department. A potential move to a new site

location was not considered in the recommendations.

Methods

The team studied the process of testing patient blood samples for specific levels of Tacrolimus

based immunosuppressant. The results of these findings were used to make recommendations to

improve the efficiency of the Tacro tests; these results should be extended to all other tests in the

Toxicology area. The following seven sections outline the team’s process for completing this

project: (1) Observations, (2) Interviews, (3) Existing Data, (4) Literature Search, (5) Time

Studies, (6) Hypothesis Testing, and (7) Linear Programming.

Observations

Each team member observed the laboratory on 5 separate dates to determine sources of waste in

the testing process. Using the lean concept of a waste walk, team members observed all steps in

the process to pinpoint inefficiencies. In the lean concept of waste walk, waste is defined as

actions that do not add value to the process; these actions should either be removed entirely from

the workflow or rearranged to a different step within the workflow. The waste walk was

completed by the team members themselves, rather than by a technician reporting to the student

team, so that the team could fully understand the process.

Through observing the current process and examining previous data supplied by the laboratory

managers, located in Appendix 2, the team determined that the highest volume test in the

Toxicology area is the Tacrolimus immunosuppressant test. According to previously collected

data, the laboratory performs Tacro tests on an average of 216 samples every Tuesday (the

busiest day of the week), and on 112 samples every other day. In contrast, the next largest

volume test, Lead, was performed on an average of 65 samples daily. At this point, the team

turned its focus towards the Tacro testing process. All work done with Tacro testing was used as

a model for other testing processes in the Toxicology section.

The collected information was used to outline the steps for a current state map and to create a

self-collection time study sheet.

7

Interviews

During laboratory observations, team members interviewed key staff members to better

understand the testing process. Interviews with individual laboratory technicians helped the team

identify why process implementation may vary from person to person and also provided

rationale for why particular batch sizes are chosen for each run. The interviews provided

additional information about other laboratory functions that may occasionally interfere with

sample processing including: monthly staff meetings, scheduled laboratory cleaning days, and

holiday work scheduling. Specific staff interviewed included the Chemistry Manager, the Day

Manager and five laboratory technicians.

Existing Data

The team received historical Toxicology data from the Chemistry Manager to supplement the

observations and time studies. These data included total volume of Tacro samples from July

through September of 2013 and a sample of Tacro turnaround time from September 14, 2014

through September 27, 2014. These data contained testing information sorted by test type and

day of week, along with an estimate of average test volumes from June 26, 2014 through August

2, 2014 made by a technician with lean experience, see Appendix 2.

The Excel data pertaining to Tacro samples from July 2013 through September 2013 was useful

in examining changing incoming test volumes during different times of the year. The data

revealed day-by-day trends to address whether daily models must be developed.

Literature Search

The team studied materials from an online six-sigma course from American Medical

Technologists, ODC15: Lean Six Sigma (5S) - Implementing 5S in the Laboratory, which

focuses on implementing lean and 5S in a hospital laboratory setting. The team also researched

literature sources for materials related to optimal batch sizes in laboratories, projects similar to

this project, and general lean methodologies. The information that the similar projects provided

was helpful in conducting time studies.

Time Studies

The team completed a time study that was used to document the task times on the CSM.

Appendices 3A and 3B show the self-collection sheets that the laboratory technicians completed.

The first self-collection sheet, located in Appendix 3A, contains the seven steps of the Tacro

sample testing processing; the events that trigger the beginning and end of each step are in

parentheses. The seven steps are (1) Form Tacro List, (2) Rotate/Pipette Bullets, (3) Shaker/Inst.

List, (4) Centrifuge, (5) Jars and Crimp, (6) LCMS, and (7) Results. The second self-collection

sheet, located in Appendix 3B, appears on the backside of the first self-collection sheet, and was

distributed to the laboratory technicians. The purpose of the second sheet was to record issues

8

that occurred during each step of the sample testing process. The team showed all laboratory

technicians the beginning and end triggers of each step of the process before the sheets were

distributed in order to ensure accuracy.

These data were useful in generating average process times and down times for the steps

highlighted in the CSM. The team calculated the amount of time required per sample for

applicable process steps using data collected from different batch sizes. The time study data

provided the majority of the information that the team needed in order to compute an optimal

batch size for the Tacro tests. Time study data were collected for three weeks (November 3-

November 21), which totaled 38 samples.

Additionally, the team analyzed the incoming distribution of samples that were processed during

the duration of the time studies (November 3- November 21). A summary of this data can be

found in Appendix 3C.

Hypothesis Tests to Determine Statistical Significance of Busy Days

The team used the data collected in the time studies to perform six hypothesis tests to determine

if there is a statistically significant difference in turnaround time between Tuesdays and other

days. 7 samples were collected for Tuesdays and 31 samples were collected for days that are not

Tuesday.

Below, total elapsed time refers to steps 1 through 7, setup time and results refers to steps 1

through 5 and 7, and machine run time refers to step 6; please refer to Appendices 3A and 6 for

better understanding of these steps. The six tests conducted were as follows:

1. Total Elapsed Time – 2-Sample T Test for Means

2. Setup Time – 2-Sample T Test for Means

3. Machine Run Time and Results – 2-Sample T Test for Means

4. Total Elapsed Time – 2-Sample F Test for Variances

5. Setup Time – 2-Sample F Test for Variances

6. Machine Run Time and Results – 2-Sample F Test for Variances

For a detailed list of formulae used in these hypothesis tests, please refer to Appendix 4.

Linear Program for Optimal Batch Size

The team formulated a linear program to determine the optimal batch size that minimizes the

total testing time, see Appendix 5. The linear program aggregates information from the time

studies, observations, and interviews and seeks an optimal batch size where time spent in the

LCMS machine is equal to the time to perform all other tests. Through observing the laboratory,

it was determined that the times to complete steps 3 and 4 are independent of batch size; the time

to complete steps 1, 2, 5, 6, and 7 are dependent on batch size. These dependencies are reflected

in the linear programming model.

9

Findings and Conclusions

Through these methods, the team observed (1) a lack of a standardized work process and (2) a

lack of a standard batch size used for testing. The next three sections will discuss the current

process and these two observations in detail.

Lack of Standardized Work Process

The team created a current state map and conducted a time study of the current process. The

current process as observed by the team is as follows:

1. Gather samples and scan them into a computer to form a list of Tacro samples.

2. Rotate samples and number plastic bullets based on batch size. Then, use auto pipetter to

extract correct amount of blood and reagents for each bullet.

3. Place bullets into vortexer to be shaken. Then re-enter list of Tacro samples at separate

computer attached to the LCMS.

4. Place shaken bullets into the centrifuge.

5. Take centrifuged bullets, pour them into jars and crimp a lid on them.

6. Put rack of sealed jars into the LCMS.

7. Print results from the LCMS and enter them into the computer used in Step 1.

The average times of each step determined by the time study are displayed in the following table:

Table 1: Average Elapsed Time Per Step. Source: Time Studies. Sample = 38

Step Number Elapsed Time Time Per Sample

1 0:03:51 0:00:07

2 0:18:28 0:00:34

3 0:02:38 N/A

4 0:13:39 N/A

5 0:05:28 0:00:10

6 1:39:17 0:03:01

7 0:24:43 0:00:45

Below, Table 2 displays the summary statistics of all observed batch runs.

Table 2: Summary Statistics of Batch Time. Source: Time Studies. Sample = 38

Summary Statistic Total Time Time Per Sample

Mean 2:48:05 0:05:15

Median 2:36:00 0:04:47

Standard Deviation 0:49:51 0:01:21

Min 1:43:00 0:03:39

Max 5:31:00 0:09:11

For a more detailed description of each step, please refer to Appendix 6.

10

The team observed that not all laboratory technicians followed the same procedure when

performing the Tacro testing. Specifically, some technicians numbered the plastic bullets at a

step different than the predefined Step 2. The team also noticed that the second half of Step 3, re-

entering the list of Tacro samples into the LCMS computer, was performed during varying steps

of the testing process. Time studies revealed total time standard deviation of 49 minutes and 51

seconds. Establishment of a standardized work process will improve the efficiency of the

laboratory technicians and the turnaround time for testing as well as reduce the large variability

in process time between batches. The team’s ideas for standardizing this process are highlighted

in the FSM.

Lack of Optimal Batch Size for Tacro Test

The team observed that Step 6, the LCMS step, of the Tacro test has the longest duration, as it is

the most dependent on batch size; each sample takes 2 minutes and 43 seconds in the LCMS

machine. Laboratory technicians have varying estimations of an optimal batch size that

minimizes the amount of time waiting for the LCMS to finish. The team used time studies and a

linear program to determine that a batch size of 15 samples minimizes this wait time, and

decreases the total testing time from 2 hours and 48 minutes to 1 hour and 21 minutes.

Turnaround Time Is Not Dependent on Day

Through hypothesis testing, the team has concluded that the total turnaround time is not

dependent on the day of the week, even though Tuesdays are the busiest day by testing volume.

The sample sizes for Tuesdays and other days of the week were 7 and 31 respectively.

A null hypothesis is an assertion that is assumed to be true for purposes of statistical testing. A

significance level of 0.0001 means that there is a 0.01% probability that the test results are

incorrect, and the null hypothesis is rejected when it should not be. Critical values are intervals

that if exceeded by the test statistic, lead to a rejection of the null hypothesis.

When testing for whether the mean time of Tuesday is larger than that of any other day of the

week, there is only one critical value; therefore, if the test statistic is greater than this, the

conclusion is to reject the null hypothesis. The null hypothesis was that the average turnaround

times were equal (2 hours 28 minutes and 2 hours 46 minutes for Tuesdays and non-Tuesdays

respectively); the alternative hypothesis was that Tuesday turnaround times exceed turnaround

times for the rest of the week.

For testing for a difference of variances, there is a critical value range. If the test statistic falls

within this range, the conclusion is not to reject the null hypothesis. The null hypothesis was that

the variance turnaround times were equal; the alternative hypothesis was that the variance of

Tuesday turnaround times was not equal to that of the rest of the week.

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At a significant level of 0.0001, the team has concluded:

1. There is insufficient evidence to reject the claim that the total average turnaround time is

independent of the day of the week

2. There is insufficient evidence to reject the claim that the total variance of turnaround time

is independent of the day of the week

Although the Setup Time and Results test for mean differences proved to be statistically

significant, the remaining five tests proved to be statistically insignificant. Therefore, the team

concluded that there was no statistically significant difference between Tuesday and the other

days of the week. Table 3 shows the rejection regions and test statistics obtained from the three

hypothesis tests for means:

Table 3: Mean Turnaround Time Is Independent of Day. Source: Time Studies. Sample = 38

Test Test Statistic Critical Value

Total Elapsed Time 3.905 t > 4.374

Setup Time and Results 10.396 t > 4.374

Machine Run Time -1.571 t > 4.374

Table 4 shows the rejection regions and test statistics obtained from the three hypothesis tests for

means:

Table 4: Variance of Turnaround Time Is Independent of Day. Source: Time Studies. Sample =

38

Test Test Statistic Critical Value

Total Elapsed Time 25.458 F < 0.131 or F > 46.834

Setup Time and Results 39.886 F < 0.131 or F > 46.834

Machine Run Time 12.882 F < 0.131 or F > 46.834

Recommendations

The linear program has determined that the optimal batch size is 15, which results in a total time

per batch of 1 hour and 21 minutes. Using this batch size will standardize the testing process and

result in a 207% improvement in turnaround time. This batch size allows a technician to prepare

a batch for the LCMS in the exact amount of time that the LCMS runs. Therefore, as soon as the

LCMS is finished running, a new batch can immediately be placed into the LCMS. Please see

Appendix 5 for linear programming details.

Considering that this batch size approximately doubles the number of test runs conducted daily,

the team has developed an alternative recommendation: use a timed approach and conduct the

test every 2 hours. Based on the linear program, this results in a batch size of 24. This results in a

140% improvement in turnaround time. The above information is summarized in Table 5.

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Table 5: Comparing Recommended Batch Sizes. Source: Time Studies. Sample = 38

Test Type Batch Size Process Time

Current State 33 2 hours and 48 minutes

Optimal Batch Size 15 1 hour and 21 minutes

Standard Time Increment 24 2 hours

Table 6 shows a comparison of both recommendations and the current state of the laboratory

broken down by time per step.

Table 6: Comparing Recommendation Step Times. Source: Time Studies. Sample = 38

Step Current Optimal Batch (15) 2 Hour Standard (24)

1 0:03:51 0:01:46 0:02:49

2 0:18:28 0:08:26 0:13:30

3 0:02:38 0:02:38 0:02:38

4 0:13:40 0:13:40 0:13:40

5 0:05:28 0:02:30 0:03:60

6 1:39:17 0:40:46 1:05:13

7 0:24:43 0:11:17 0:18:03

TOTAL

TIME 2:48:05 1:21:00 1:59:50

The future state map in Appendix 7 shows additional process changes as kaizen bursts and may

further improve turnaround time. Appendix 8 displays a simulation that compares the team’s two

recommendations to the current state using actual batch sizes and test times from November 5,

2014. The simulation illustrates that both of the team’s recommendations result in decreased

turnaround times as compared to the current process.

Expected Impact

The team has provided recommendations to improve and maximize the productivity and

efficiency of the Toxicology laboratory of the Pathology Department. The recommendations

reduce the batch size from the current 33 to 15 or 24 and reduce turnaround time of samples from

2 hours and 48 minutes to 1 hour and 21 minutes or 2 hours.

Table 7 shows a summary of expected impacts by process step from the Future State Map. For

additional clarification, please see Appendix 7.

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Table 7: Summary of Additional Considerations from Future State Map

Recommendation Impact

Standard Batch Size/Time approach Reduce turnaround time significantly

Prepare bullet numbering externally Reduce internal process time

Enter LCMS list at Step 4 Centrifuge downtime provides more adequate time than

shaker downtime

Use smaller centrifuges Reduce centrifuge process time

Use standard tube/bullet Eliminate extra processing waste

Automate results step Eliminate major non value added process time

The recommendations will result in:

Predictable and repeatable workflow of the Toxicology laboratory.

Decreased turnaround time from 2 hours and 48 minutes to 1 hour and 21 minutes or 2

hours.

Expedited delivery of results to hospital entities.

Increased clinic/bed turnover within hospital.

14

Appendix 1: Current State Map of Toxicology Laboratory

15

Appendix 2: Existing Data Source

16

Appendix 3A: Data Self Collection Worksheets – Data Self-Collection

TOXICOLOGY DATA SELF-COLLECTION SHEET

Date Name

Batch Size Laboratory Position

Task

Start

Time

End

Time Comments

1) Form Tacro List (Start: Get samples from

front, End: Scan last tube)

2) Rotate/Pipette Bullets (Start: Turn on rotator,

End: Pipette last bullet)

3) Shaker/Inst. List (Start: Turn shaker on, End:

Take samples out of shaker)

4) Centrifuge (Start: First sample in centrifuge,

End: All samples out of centrifuge)

5) Jars and Crimp (Start: Pour first bullet into

jar, Finish: Crimp last jar)

6) LCMS (Start: Jars in new tray, Finish: Take

tray out of LCMS)

7) Results (Start: Enter first result, Finish: Put

printed record in folder)

17

Appendix 3B: Data Self Collection Worksheets – Data Self-Collection Faults

TOXICOLOGY COLLECTION FAULTS

Date Name

Batch Size Laboratory Position

Step Number Reason

1

2

3

4

5

6

7

18

Appendix 3C: Incoming Volume by Hour and Day

Hour Monday Tuesday Wednesday Thursday Friday

Average

Standard

Deviation Average

Standard

Deviation Average

Standard

Deviation Average

Standard

Deviation Average

Standard

Deviation

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1 0.00 0.00 0.00 0.00 0.33 0.58 0.00 0.00 0.00 0.00

2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33 0.58

3 1.00 1.00 0.33 0.58 0.33 0.58 0.33 0.58 0.00 0.00

4 4.00 0.00 1.33 0.58 1.00 1.00 2.67 0.58 0.33 0.58

5 2.00 1.00 0.67 1.15 0.67 0.58 1.67 0.58 0.67 1.15

6 1.33 0.58 0.00 0.00 0.67 1.15 2.33 3.21 1.33 0.58

7 7.00 2.00 7.00 1.00 4.33 1.53 5.00 1.73 4.67 2.08

8 18.33 3.21 28.67 6.66 22.33 3.51 16.00 5.00 18.33 2.52

9 15.67 6.03 20.67 5.51 12.67 1.53 15.67 9.02 14.00 6.56

10 15.00 1.73 29.33 5.51 31.67 20.53 31.00 7.81 44.00 10.15

11 7.33 2.89 42.33 12.50 13.33 13.80 12.33 6.66 8.33 7.77

12 8.33 3.21 23.00 10.82 15.33 4.93 6.00 3.46 10.67 3.21

13 15.67 5.03 37.67 20.11 11.00 7.00 12.33 3.06 7.00 4.58

14 1.67 1.15 1.33 0.58 5.67 6.43 5.33 1.15 0.00 0.00

15 6.67 4.04 4.00 3.46 6.33 7.51 2.33 1.53 3.00 2.00

16 3.33 3.21 1.33 1.53 1.33 1.53 2.67 2.52 0.67 0.58

17 0.67 0.58 0.33 0.58 0.00 0.00 1.33 0.58 1.33 0.58

18 2.33 1.53 0.67 1.15 0.00 0.00 1.00 0.00 0.00 0.00

19 7.33 10.12 0.67 1.15 0.00 0.00 2.33 1.53 0.67 0.58

20 5.00 0.00 1.33 1.53 1.33 1.53 0.67 1.15 1.33 1.53

21 1.00 1.00 1.33 1.15 1.33 1.15 1.33 1.53 0.67 0.58

22 0.33 0.58 0.67 0.58 0.67 1.15 0.00 0.00 0.00 0.00

23 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

19

Appendix 4: Hypothesis Testing for Statistical Significance of Busy Days

All variables with subscript 1 refer to non-busy days; all variables with subscript 2 refer to

Tuesdays. The team is assuming that the number of Tacro tests is normally distributed.

Variable Declarations:

𝜇: Population average

𝜎: Population standard deviation

𝑥: Sample average

𝑠: Sample standard deviation

2 Sample T Test for Means - Test to see if the average turnaround time for busy days is greater

than that of normal days.

Null Hypothesis: 𝜇1 = 𝜇1

Alternative Hypothesis: 𝜇1 < 𝜇2

Pooled Standard Deviation: 𝑠 = √𝑠12/𝑛1 + 𝑠2

2/𝑛2

Degrees of Freedom: 𝑑𝑓 = 𝑛1 + 𝑛2 − 2

Test Statistic: 𝑡 =(�̅�1−�̅�2)

𝑠

Critical Value at 𝛼 = 0.01: 𝑡−1𝛼,𝑑𝑓

If the test statistic 𝑡 is less than the critical value 𝑡−1𝛼,𝑑𝑓, then there is insignificant evidence to

reject the null hypothesis. Otherwise, there is significant evidence to reject the null hypothesis.

2 Sample F Test for Variances - Test to see if the variance of turnaround time for busy days is

different than that of normal days.

Null Hypothesis: 𝜎12 = 𝜎2

2

Alternative Hypothesis: 𝜎12 ≠ 𝜎2

2

Degrees of Freedom: 𝑑𝑓1 = 𝑛1 − 1, 𝑑𝑓2 = 𝑛2 − 1

Test Statistic: 𝐹 = 𝑠12/𝑠2

2

Critical Values at 𝛼 = 0.01: 𝐹1−

𝛼

2,𝑑𝑓1,𝑑𝑓2

−1 , 𝐹𝛼

2,𝑑𝑓1,𝑑𝑓2

−1

If the test statistic 𝐹 is within 𝐹1−

𝛼

2,𝑑𝑓1,𝑑𝑓2

−1 < 𝐹 < 𝐹𝛼

2,𝑑𝑓1,𝑑𝑓2

−1 , then there is insignificant evidence to

reject the null hypothesis. Otherwise, there is significant evidence to reject the null hypothesis.

20

Appendix 5: Linear Program for Optimal Batch Size

Input Variables:

𝑡𝑖 ∀𝑖 ∈ {3, 4} (Minutes to perform step 𝑖) 𝑡𝑗 ∀𝑗 ∈ {1, 2, 5, 6, 7} (Minutes per sample to perform step 𝑗)

Decision Variable: 𝑥 (Batch size)

Objective: Minimize ∑ 𝑡𝑖∀𝑖 + 𝑥 ∗ ∑ 𝑡𝑗∀𝑗 (minimize total testing time)

Constraints:

1. 𝑥 ∗ (𝑡6) = 𝑡3 + 𝑡4 + 𝑥 ∗ (𝑡1 + 𝑡2 + 𝑡5 + 𝑡7) (Time required for machine run time should

equal to setup time and results)

2. 1 ≤ 𝑥 (Minimum batch size must be greater than or equal to 1)

3. 𝑥 ≤ 48 (Maximum batch size cannot exceed 48 per laboratory specifications)

21

Appendix 6: Tacro Detailed Process Notes (Early morning procedures included in italics)

-Turn gasses on

-Get internal standard from fridge (Used to measure against other samples for validity)

1. Pull available Tacro samples from fridge and create list on main computer using a

barcode scanner. Technician prints this out and brings it with them to each step just in

case the samples get out of order.

2. A.) Use Fisher Hematology/Chemistry Mixer (Model 346) to rotate the all Tacro samples

simultaneously. Recommended time is approximately 2 minutes.

B.) Retrieve plastic bullets, while A) is running, from drawer and number the bullets

according to the number of samples. Place these bullets in new tray.

Take the previously rotated samples and use the Hamilton Microlab 500 Series Dual

Auto Pipetter to automatically withdraw the exact amount of blood needed for the bullets

along with two reagents (100 micrograms of zinc and 50 micrograms of the internal

standard)

-Additional note: The first run of the day will also include 3 bullets that are just for

machine calibration. Every run also includes two other bullets that are placed at the

beginning and end of the sample list. These are used for Quality Control.

3. Place the bulleted samples into Fischer Scientific Multi-tube Vortexer to be shaken (there

is a button that will automatically shake the samples for 1 minute, which is all that is

needed, but often technicians will just turn it on and leave it for some non standard

amount of time). While this is being shaken, technicians will go over to the computer

attached to the LCMS and reenter the list of samples using another barcode scanner then

turn on the gas flow to the machine. After this they will return to get the shaken samples.

4. Set a kitchen timer for 10 minutes to alert them when that amount of time has elapsed.

There is a 10-minute wait time between taking the samples out of the shaker before they

can be put into the centrifuge. Then, using the Abbott centrifuge, the technicians will take

each individual bullet out of the tray and place it into a spot in the centrifuge, making

sure to preserve the order that the bullets were originally in. The centrifuge is set to run

for 4 minutes. After this time, the technician will take out each individual bullet and place

it in order back into the tray.

5. Take each bullet and pour it into a tiny glass jar. The glass jar is then sealed using an 11

mm crimp tool. These jars are then placed in a new tray that is designed to fit into the

LCMS (the maximum number of jars it can hold is 24)

6. Place this tray, or trays depending on the size of the run, into the LCMS. The LCMS can

hold two trays so the maximum size a run can be is 48 samples. It takes the machine

exactly 2 minutes and 43 seconds per sample (not the 3-4 minutes which the team was

previously told).

7. Remove samples from the machine and return to the original computer after the LCMS

finishes all the samples and the 2 QC’s. Entered each sample’s results into the computer

manually to verify that the drug levels in each sample are at an acceptable level.

22

Appendix 7: Future State Map of Toxicology Laboratory

23

Appendix 8: Recommendation Simulations