University of Michiganioe481/ioe481_past_reports/18W02.docx · Web viewThe following section is an executive summary of the IOE 481 Radiation Oncology CT Simulator: Efficiency and

Radiation Oncology CT Simulator: Efficiency and Utilization

Final Report

Submitted to:

Dawn Johnson, Director of Clinical Operations, Radiation Oncology

Chris Alcala, Chief Radiation Therapist, Radiation Oncology

Zac Costello, Performance Improvement Engineer, Performance Improvement

Kate Sell, Performance Improvement Fellow, Performance Improvement

Dr. Mark Van Oyen, IOE 481 Professor, Industrial and Operations Engineering

Submitted by:

IOE 481 Team 2

Julia Clark

Alexander Mize

Maddie Price

Karan Shah

Date Submitted:

April 17, 2018

18W2-final-report

ii

Table of Contents

Executive Summary1

Introduction4

Background4

Current Situation4

Appointment Time Length Scheduling Guidelines5

CT Simulator Patient Flow5

Key Issues5

Goals and Objectives6

Project Scope6

Appointment Scheduling Guidelines Data Collection and Analysis Methods6

Appointment Time Length Data Collection7

Patient Attribute Data Collection7

Appointment Scheduling Guidelines Findings and Conclusions7

General Linear Model Regression7

Statistical Analysis8

30-Minute Appointments9



Appointment Scheduling Guidelines Recommendations20

Consider Five Significant Factors in Appointment Scheduling20

Pilot Reducing Appointment Time Length Increments from 30 to 15 Minutes20

Patient Flow Data Collection and Analysis Methods21

Historical Data Analysis21

Patient Flow Benchmarking21

Observations21

CT Simulator Patient Flow Process Map22

Time Studies22

Value Stream Map22

Patient Flow Findings and Conclusions23

Historical Delays23

Induction Room Benchmarking Research23

Main CT Simulator Patient Flow Tasks23

Patient Flow Bottlenecks24

Patient Flow Design Methods, Requirements, Constraints, and Standards25

Soft Design Constraints (Design Requirements)25

Hard Design Constraints 25

Design Standards26

Patient Flow Recommendations27

Designate Patient Flow Overflow Room27

Watch Educational Videos Prior to Room 1027

Assign Pre-Simulation Checklist to MAPSA28

Implement Induction Room28

Pugh Matrix Recommendation28

Expected Impact28

New Appointment Scheduling Guidelines29

Improved Patient Flow29

References31

Appendix A: Appointment Time Length Data Collection Sheet32

Appendix B: Appointment Time Length General Linear Model Regression33

Appendix C: Historical Data CT Simulator Delay Root Cause Frequency37

Appendix D: CT Simulator Patient Flow Process Map38

Appendix E: Time Study Data Collection Sheet41

Appendix F: Patient Flow Value Stream Map42

Appendix G: Patient Flow Design Constraints and Standards Matrix44

Appendix H: Future State Patient Flow46

Appendix I: Patient Flow Pugh Matrix48

List of Figures and Tables

Table 1: Mean and Standard Deviation of Actual Appointment Time Lengths9

Figure 1: 30-Minute Appointment Types Contributing to Unused Scheduled

Appointment Time9

Table 2: 30-Minute Appointment Type with Highest Unused Scheduled Time10


Appointment Time11

Table 3: 60-Minute Appointment Types with Highest Unused Scheduled Time12

Table 4: 60-Minute Appointment Types Unused Scheduled Time per Week12

Figure 3: Head and Neck Appointment Actual Time Length Distribution13

Table 5: Head and Neck Appointment Scheduled Time Comparison13

Figure 4: Prostate Appointment Actual Time Length Distribution14

Table 6: Prostate Appointment Scheduled Time Comparison14

Figure 5: Lung (No SDX) Appointment Actual Time Length Distribution15

Table 7: Lung (No SDX) Appointment Scheduled Time Comparison15

Figure 6: Spine Appointment Actual Time Length Distribution15

Table 8: Spine Appointment Scheduled Time Comparison16

Figure 7: Pelvis Appointment Actual Time Length Distribution16

Table 9: Pelvis Appointment Scheduled Time Comparison17


Appointment Time17

Table 10: 90-Minute Appointment Type with Highest Unused Scheduled Time18

Table 11: 90-Minute Appointment Types Unused Scheduled Time per Week18

Figure 9: Abdomen Appointment Actual Time Length Distribution18

Table 12: Abdomen Appointment Scheduled Time Comparison19

Figure 10: Abdomen Appointment Actual Time Length Distribution19

Table 13: Abdomen Appointment Scheduled Time Comparison20

Table 14: Value Stream Map Process Time, Wait Time, and First Time Quality Values24

Figure B-1: General Linear Model Regression with ACTV Patient Attributes34

Figure B-2: General Linear Model Regression with REVC Patient Attributes35

Figure B-3: General Linear Model Regression without Height, Weight, and BMI36

Table G-1: Soft Design Constraints (Design Requirements)44

Table G-2: Hard Design Constraints44

Table G-3: Design Standards45

Executive Summary

The following section is an executive summary of the IOE 481 Radiation Oncology CT Simulator: Efficiency and Utilization project which concluded on April 17, 2018.

Introduction

One of the vital resources in the Michigan Medicine Radiation Oncology Department is the CT Simulator: a machine that almost every patient must be scanned with to plan radiation dosages. Therefore, the optimization of the patient flow process and appointment schedule for the CT Simulator are important. The Radiation Oncology Department’s Director of Clinical Operations and the Chief Radiation Therapist requested the help of an IOE 481 student team from the University of Michigan to analyze and make recommendations for improving the CT Simulator appointment scheduling guidelines and CT Simulator patient flow.

Key Issues

The clients identified the following key issues:

· Frequent delays in the CT Simulator patient flow process

· Long patient wait time during CT Simulator appointments

· CT Simulator room idle time when schedule is delayed

· High variation in the difference between scheduled appointment time length and actual appointment time length

Goals and Objectives

The team’s primary goal was to create new appointment scheduling guidelines for optimizing appointment time lengths and design a more efficient CT Simulator patient flow process. The team’s recommendations aimed to achieve the following objectives:

· Decrease patient wait time during CT Simulator appointments

· Decrease CT Simulator room idle time

· Decrease the difference between scheduled appointment time length and actual appointment time length

· Increase patient throughput

Project Scope

The project included the process of scheduling patients for the CT Simulator and the process surrounding the CT Simulator, starting from the moment the patient who will be going through the CT Simulator enters the Radiation Oncology Department and ending when the patient leaves the department.

Appointment Scheduling Guidelines Data Collection and Analysis Methods

The team created a data collection sheet, shown in Appendix A, that the CT Simulator employees used to collect appointment time length data. The CT Simulator employees collected this data for a total of 255 patients from February 26, 2018 to March 28, 2018. The team’s coordinator obtained patient attribute data using the patient contact serial numbers (CSNs) for the patients included in the appointment time length analysis.

To analyze the collected data, the team used general linear model regression in Minitab to identify the significant factors affecting appointment time lengths to create new appointment scheduling guidelines. The team hypothesized that patient characteristics could be statistically significant in determining appointment times. Therefore, the team used general linear model regression to predict the length of the appointments using factors such as age, weight, and inpatient or outpatient status.

Appointment Scheduling Guidelines Findings and Conclusions

The team used 207 patient data records to perform a general linear model regression in Minitab to determine the factors that are significant at a significance level of 0.05. Variables that are statistically significant were: IV Contrast (yes or no), SDX training (yes/assess or no), number of treatment areas (isos), vacuum bag (yes or no), and Qfix (yes or no). However, the adjusted R-squared value was found to be only 34.41%. Therefore, the team concluded using a linear regression equation to predict appointment time lengths is infeasible.

The team then split the data by scheduled appointment time length (30, 60 or 90 minutes) and performed general statistical analysis. For each of these three groups of appointments, the team analyzed the appointment time lengths for each scan type. To identify the most important types of appointments to analyze, the team used the Pareto Principle which states that only a few appointment types contribute to a large percentage of the CT Simulator appointment scheduling optimization issue.

For 30-minute appointments, the mean of the actual time length was 29.11 minutes. The team concluded that the appointments are scheduled appropriately because the appointments only contributed to an additional 10.75 minutes per week in the CT Simulator schedule.

For 60-minute appointments, the mean of the actual time length was 35.05 minutes. Head and neck, prostate, lung (no SDX), spine, and pelvis appointments contributed to 2.38, 2.10, 1.46, 1.12, and 1.02 unused scheduled hours per week, respectively. After analyzing the distribution of these appointment types, the team concluded that the scheduled appointment time lengths for these appointments could be shifted down to 45 minutes.

For 90-minute appointments, the mean of the actual time length was 62.10 minutes. Liver and abdomen appointments contributed 1.10 and 0.97 unused scheduled hours per week, respectively. After analyzing the distribution of these appointment types, the team concluded that the scheduled appointment time lengths for these appointments could be reduced to 75 minutes.

Appointment Scheduling Guidelines Recommendations

In the long-term, the team recommends that the department decrease the scheduled appointment time increments from 30 minutes to 15 minutes. From the statistical analysis performed, the team recommends that department conduct a pilot that decreases the scheduled time for head/neck, prostate, lung (no SDX), spine, and pelvis appointments to 45 minutes and decreases the scheduled time for liver and abdomen appointments to 75 minutes.

Patient Flow Data Collection and Analysis Methods

The team used historical CT Simulator data for 102 patients to determine root causes of CT Simulator room delays. Additionally, the team performed benchmarking research to understand the organization of other CT Simulator patient flows at hospitals around the United States. Then, the team observed the CT Simulator patient flow for 32 patients in order to create a process map in Microsoft Visio, shown in Appendix D, and the time study data collection sheet, shown in Appendix E. The team collected time studies for 90 patients over 102 hours in the department. Finally, a value stream map, shown in Appendix F, was created using the process map framework. The team calculated the average process times, wait times, and first time quality values from the time study data.

Patient Flow Findings and Conclusions

From the value stream map, the team found that, on average, the patient flow process time was 62 minutes, the wait time was 48 minutes, the lead-time was 110 minutes, and the total first time quality was 42%. The time identified three bottlenecks from the value stream map. First, the team identified patient check-in as an initial bottleneck for patients due to high wait time. The second bottleneck is the completion of the pre-simulation tasks due to the high wait time, high process time, and low first time quality. The third bottleneck is the pre-scan set up and patient positioning step due to the high process time. The team concluded that these three bottlenecks would be the focus of the patient flow recommendations and improved patient flow design.

Patient Flow Design Methods, Requirements, Constraints, and Standards

The main design task the team performed was creating an improved patient flow process. When designing this process, several design requirements, constraints, and standards guided the team’s approach. Details on the design considerations of the project are described in Appendix G.

Patient Flow Recommendations

Overall, four key recommendations were made to improve patient process flow. The future state patient flow map is shown in Appendix H. First, adding a second patient preparation room (similar to Room 10) could decrease customer wait time after checking in. Second, ensuring that the patients watch CT Simulator videos prior to their appointment could decrease patient process time in Room 10. Third, allowing MAPSA employees the role of highlighting and listing next day missing patient questionnaire tasks could decrease process and wait times in Room 10. Fourth, implementing a separate patient positioning room could increase overall CT Simulator patient flow efficiency. The team used a Pugh Matrix, shown in Appendix I, to recommend that the department heavily focus on changing the process for watching the educational videos in the patient flow.

Expected Impact

The team expects the new appointment time length pilot will increase patient throughput by 288 patients and decrease CT Simulator room idle time and the difference between the scheduled appointment length and actual appointment length by 234 hours.

Additionally, the team expects the four patient flow recommendations to decrease patient flow process time by 24%, from 62 minutes to 47 minutes, primarily from the decrease in time spent in Room 10 watching the video. After removing unnecessary patient wait time in the check-in process, the patient wait time is expected to decrease by 35%, from 48 minutes to 31 minutes. The lead time is expected to decrease by 29%, from 110 minutes to 78 minutes. Since patients would experience fewer delays when a MAPSA employee is assigned to document the next day patient missing questionnaire, the first time quality is expected to increase from 42% to 57%.

Introduction

The Michigan Medicine Radiation Oncology Department treats cancer patients through personalized radiation therapy treatment plans and radiation therapy. Radiation therapy is used to treat cancer and relieve pain for patients, which ultimately improves the quality of life for the individual receiving treatment. One of the vital resources in the Radiation Oncology Department is the CT Simulator: a machine that almost every patient must be scanned with prior to radiation therapy. The CT Simulator is used to simulate the patient’s exact position for treatment. Therefore, the patient is immobilized on the CT Simulator table using several devices such as cushions and masks. After the patient is positioned, a CT scan is taken of the treatment area. The CT scan is then used by a team of dosimetrists to precisely plan the radiation treatment dosage that minimizes radiation to the surrounding healthy organs. The optimization of the appointment schedule and the patient flow for the CT Simulator are important because the machine is vital in the treatment planning process.

The Radiation Oncology Department’s Director of Clinical Operations and the Chief Radiation Therapist have seen issues such as frequent delays in the CT Simulator patient flow process, long patient wait times during the CT Simulator appointments, CT Simulator room idle time when schedule is delayed, and high variation in the difference between scheduled appointment length and actual appointment length. Therefore, the clients requested the help of an IOE 481 student team from the University of Michigan to analyze and make recommendations for improving the patient flow and appointment scheduling guidelines. The team collected qualitative and quantitative data by observing the process to understand the patient flow and conducting time studies to analyze the appointment time lengths and bottlenecks in the patient flow. Using this data, the team performed analysis to create new appointment scheduling guidelines for optimizing appointment time lengths and design a new patient flow process. This final report documents the background, key issues, goals and objectives, scope, methods, findings and conclusions, recommendations, and expected impact of the project.

Background

This section of the proposal describes the current appointment scheduling guidelines and patient flow for the CT Simulator in the Radiation Oncology Department.

Current Situation

The CT Simulator is open Monday through Friday, from 7:00 AM to 6:00 PM. Most days, the CT Simulator is booked for all 11 hours. The department sees both inpatients and outpatients; therefore, several other hospital departments are often involved with the CT Simulator patient flow such as transportation services and anesthesiology. Because of the important and urgent nature of the simulation, the department does not cancel appointments for any reason. Therefore, if one patient is delayed, the following scheduled patients for that day are also delayed. During most delays, the CT Simulator is not being used. Thus, any delays in the schedule not only extend patient wait times, but also cause CT Simulator room idle time.

Currently, appointment time lengths are scheduled based on the anatomical location of the tumor on the body. The appointments are either 30, 60, or 90 minutes long. The client identified that the scheduling guidelines may not be accurate. The department does not consider factors such as age, weight, and inpatient or outpatient status, which could affect appointment length. Additionally, patients often finish early or late. This trend could signal that the appointment time length scheduling guidelines should be adjusted to provide accurate appointment time lengths. New appointment time length scheduling guidelines could minimize the difference between scheduled appointment length and actual appointment length and increase patient throughput.

Appointment Time Length Scheduling Guidelines

Once a patient has attended a consultation in the Radiation Oncology Department, a CT Simulator appointment is scheduled through the Medical Assistants and Patient Services Associates department (MAPSA). The length of each appointment is either 30 minutes, 60 minutes, or 90 minutes depending on the anatomical location of the tumor. The physician indicates the length of the appointment on the patient’s directive. The physician-recommended appointment time length is used to schedule the appointment.

CT Simulator Patient Flow

The patient’s first step after diagnosis is to have a consultation with a physician in the Radiation Oncology Department. If the physician recommends radiation therapy, the patient is scheduled immediately for a CT Simulator scan.

The patient is asked to arrive one hour prior to the appointment start time to complete pre-simulation tasks. The tasks that need to be completed prior to simulation include a physician signature on the patient directive, verification of consent, verification of an IV form if necessary, verification of a face photo, and education through a 20-minute radiation therapy video. After the patient checks in at the clerk desk, the clerk reviews the pre-simulation checklist to see which tasks the patient needs to complete. Then, the patient is taken to a holding room (Room 10) to change into a gown and complete the uncompleted tasks. The patient waits in the holding room until the CT Simulator is prepared.

When the CT Simulator room is prepared, the simulation team retrieves the patient from the holding room. A team of radiation therapists, radiation therapy students, and other medical staff then immobilize the patient for the scan. The comfort of the patients during the CT scan is extremely important because the patient must be in the same exact position for every radiation treatment in order for the treatment to work effectively. Once the scan is complete, the simulation team pages a physician to approve the scans. After physician approval, the simulation team takes photos of the reference marks and patient setup for reproducibility. After the patient has completed the CT Simulator scan, the patient will go on a tour of the Radiation Oncology Department. The purpose of the tour is to show the patients where they will be coming every day for the next two to four weeks to receive their radiation therapy treatments. The CT Simulator patient flow is complex because of the varying medical needs of each patient. Therefore, the team observed the patient flow process and focused on the most common delays when designing the improved patient flow.

Key Issues

The key issues causing CT Simulator inefficiency that drove this project were:

· Frequent delays in the CT Simulator patient flow process

· Long patient wait time during CT Simulator appointments

· CT Simulator room idle time when schedule is delayed

· High variation in the difference between scheduled appointment time length and actual appointment time length

Goals and Objectives

The primary goal of this project was to create new appointment scheduling guidelines for optimizing appointment time lengths and design a more efficient CT Simulator patient flow process. To reach this goal, the team completed the following 12 tasks:

· Analyzed historical data

· Attended Radiation Oncology Lean meetings

· Observed current patient flow

· Created current patient flow process map

· Conducted preliminary time studies

· Conducted time studies of the pre-simulation process

· Conducted time studies in the CT Simulator room

· Recorded appointment time length and patient characteristic data

· Interviewed key team members of the CT Simulator

· Created value stream map for current patient flow

· Designed future state patient flow

· Created new appointment time length scheduling guidelines

The team aimed to achieve the following design objectives using the qualitative and quantitative information gathered:

· Decrease patient wait times during CT Simulator appointments

· Decrease CT Simulator room idle time

· Decrease the difference between scheduled appointment time length and actual appointment time length

· Increase patient throughput

Project Scope

The project included the process of scheduling patients for the CT Simulator and the process surrounding the CT Simulator, starting from the moment the patient who will be going through the CT Simulator enters the Radiation Oncology Department and ending when the patient leaves the department. The team solely analyzed the pre-simulation and during-simulation patient flow process for the CT Simulator.

Any process outside the CT Simulator, such as the MRI Simulator process and treatment room patient flow, was out of scope. Any factors that are not within the control of the Radiation Oncology Department were not considered and were out of scope for this project, such as transportation services, in-patient procedures, and other departments.

Appointment Scheduling Guidelines Data Collection and Analysis Methods

This section describes the data collection methodology and analysis techniques used to formulate findings and conclusions leading to recommendations for the appointment scheduling guidelines. The team created a data collection sheet that the CT Simulator employees used to collect appointment time length data and obtained patient characteristic data from the IOE 481 coordinator.

Appointment Time Length Data Collection

An obstacle regarding the appointment scheduling guidelines was gathering enough data to run a general linear model regression analysis that identified statistically significant factors. To mitigate this obstacle and obtain a large sample size, the team asked the employees in the CT Simulator room to collect data. The team designed an appointment time length data collection sheet using a format similar to other forms used in the CT Simulator room. Therefore, the data collection had minimal interference with normal employee work flow. The data collection sheet is shown in Appendix A. The CT Simulator employees collected data for a total of 255 patients from February 26, 2018 to March 28, 2018. Of the total 255 patients, 207 data records were used for analysis. The remaining data records were eliminated due to missing data values.

To analyze the collected data, the team used general linear model regression in Minitab to identify the significant factors affecting appointment time lengths to create new appointment scheduling guidelines. The team hypothesized that patient characteristics could be statistically significant in determining appointment times. This hypothesis was supported by a study performed on the consideration of patient characteristics to improve medical resource utilization in a hospital radiology department [1]. Therefore, the team planned to use a regression equation to predict the length of the appointment using factors such as age, weight, and inpatient or outpatient status, in addition to the anatomical location of the tumor on the body.

Patient Attribute Data Collection

The IOE 481 team’s coordinator pulled patient attribute data using the patient contact serial numbers (CSNs) for the patients included in the appointment time length analysis. The data was pulled from the Health System Warehouse’s activity (ACTV) and revenue (REVC) data tables [2]. The team used the ACTV height, ACTV weight, ACTV body mass index (BMI), REVC height, REVC weight, and REVC BMI. The team requested this information as patient characteristic data to test whether these factors are statistically significant in determining appointment time length.

Appointment Scheduling Guidelines Findings and Conclusions

Findings from the appointment time length and patient attribute data analysis helped the team form conclusions that supported appointment scheduling recommendations. This section describes the findings and conclusions for the appointment scheduling guidelines.

General Linear Model Regression

First, the team used general linear model regression in Minitab to determine which factors listed in Appendix B are significant for predicting the actual appointment time length with a significance level of 0.05. The actual appointment time length is the time between when the patient enters the CT Simulator room and when the CT Simulator employees finish cleaning the room.

The team did not have height, weight, and BMI data for all patients. Therefore, to use Minitab general linear model regression, the team had to subset the data. The team created two subsets: (1) patients with ACTV height, ACTV weight, and ACTV BMI records and (2) patients with REVC height, REVC weight, and REVC BMI records. For the second subset of data, none of the patients needed a body fix positioning and, therefore, the body fix factor was excluded from the analysis.

Using the first subset of data, the team found that the statistically significant variables were ACTV height, ACTV weight, whether or not the patient needed IV contrast, whether or not the patient needed or needed to be assessed for SDX training, the number of scan locations (isolations), and whether or not the patient needed to be positioned with a vacuum bag. The output from the general linear model regression with the first subset is shown in Appendix B, Figure B-1. The adjusted R-squared, the amount of variation in the response variable described by the factors tested, was only 31.82%.

Using the second subset of data, the team found that the statistically significant variables were age, whether or not the patient needed IV contrast, whether or not the patient needed or needed to be assessed for SDX training, and whether or not the patient needed to be positioned with a vacuum bag. The output from the general linear model regression with the second subset is shown in Appendix B, Figure B-2. The adjusted R-squared, the amount of variation in the response variable described by the factors tested, was only 40.24%.

Since general linear model regression with the first two subsets of data yielded low adjusted R-squared values, the team chose to run a general linear model regression without the height, weight, and BMI factors. Therefore, all 207 patient data records were included in the analysis. With the actual appointment time length as the response variable, the team tested to see whether the factors listed in Appendix B are significant for predicting the actual appointment time length with a significance level of 0.05. The Minitab output is shown in Appendix B, Figure B-3. The team found that the following variables are statistically significant:

· IV Contrast (yes or no)

· SDX training (yes/assess or no)

· Number of treatment areas (isos)

· Vacuum Bag (yes or no)

· Qfix (yes or no)

Although these three variables are significant in determining the actual appointment time length, the adjusted R-squared value, which is the amount of variation in the response variable described by the factors tested, is only 34.41%. This adjusted R-squared value is also low. Therefore, the team concluded that using a linear regression equation to accurately determine appointment time lengths for patients in the Michigan Medicine Radiation Oncology Department is infeasible. However, because these variables are statistically significant, the department should consider the five factors above when scheduling appointments.

Statistical Analysis

Due to the infeasibility of a linear regression equation to predict appointment time lengths, the team used general statistical analysis to make recommendations for appointment time lengths. The appointment time length data was analyzed using Microsoft Excel. First, the team split the data by scheduled appointment time length and identified the mean and standard deviation of actual appointment time lengths, shown in Table 1 below. The actual appointment time length is the time between when the patient enters the CT Simulator room and when the CT Simulator employees finish cleaning the room.

Table 1: Mean and Standard Deviation of Actual Appointment Time Lengths

Scheduled Length (minutes)

Mean of Actual Length (minutes)

Standard Deviation of Actual Length (minutes)

% of Appointments Shorter Than Scheduled Time

% of Appointments Longer Than Scheduled Time

30

29.11

11.21

62%

38%

60

35.05

18.35

85%

15%

90

62.10

20.33

83%

17%

Sample size: 207; Source: University of Michigan Radiation Oncology Department; Time Period: February 26, 2018 to March 28, 2018

As shown in Table 1, on average, the actual appointment time lengths are lower than the schedule appointment time lengths. Hence, the CT Simulator room is experiencing idle time when time is scheduled for appointments but not used. However, the standard deviations of the actual appointment lengths from the scheduled lengths are wide which indicates that the actual time lengths vary. Therefore, the team decided to break down the analysis further by looking at appointments grouped by scheduled time length.

30-Minute Appointments

First, the team created a Pareto chart, shown in Figure 1 below, of the total positive difference between the scheduled appointment time and actual appointment time by scan type for the appointments scheduled in 30-minute time slots. Therefore, the y-axis in the graph displays the amount of extra time available for additional appointments during the data collection period by 30-minute appointment type. The type of scan is determined by the area of the body that needs radiation therapy treatment.

Figure 1: 30-Minute Appointment Types Contributing to Unused Scheduled Appointment Time


In order to identify the types of appointments that were most important to the CT Simulator schedule, the team used the Pareto Principle which states that only a few appointment types contribute to a large percentage of the CT Simulator appointment scheduling optimization issue. Therefore, the team focused on the spine appointments, which contribute to a large amount of the extra time available, as shown in the graph in Figure 1. From this analysis, the team calculated the mean and standard deviation of the spine appointments, displayed in Table 2 below.

Table 2: 30-Minute Appointment Type with Highest Unused Scheduled Time

Appointment Type

Mean of Actual Time Length (minutes)

Standard Deviation of Actual Time Length (minutes)

Spine

21.40

5.08


As shown in Table 2, the mean of the actual appointment time lengths is 21.40 minutes and the standard deviation is 5.08 minutes for spine appointments. The team concluded that the spine appointments could, on average, provide an additional 10.75 minutes in the CT Simulator room each week. Since this time is minimal, the team concluded that the 30 minutes currently scheduled for spine appointments is appropriate.


For the appointments scheduled for 60 minutes, approximately 50% of appointments lasted 30 minutes or less and 77% of appointments lasted 45 minutes or less. The team created a Pareto chart, shown in Figure 2, of the total positive difference between the scheduled appointment time and actual appointment time by scan type for the appointments scheduled in 60-minute time slots. Therefore, the y-axis in Figure 2 displays the amount of extra time available for additional appointments during the data collection period by 60-minute appointment type.



Similar to the analysis with the 30-minute appointments, in order to identify the types of appointments that were most important to the CT Simulator schedule, the team used the Pareto Principle. This principle states that only a few appointment types contribute to a large percentage of the CT Simulator appointment scheduling optimization issue. Therefore, the team decided to investigate the distributions of the five appointment types contributing to the most extra time: head/neck, prostate, lung (no SDX), spine, and pelvis. The team calculated the mean and standard deviation of these five appointment types, displayed in Table 3 below.

Table 3: 60-Minute Appointment Types with Highest Unused Scheduled Time

Appointment Type



Head/Neck

36.17

14.43

Prostate

30.35

22.92

Lung (No SDX)

28.09

11.41

Spine

26.50

10.60

Pelvis

31.67

16.83


As shown in Table 3 above, the mean appointment times are significantly lower than the 60-minute scheduled time. Each week, these five appointment types contribute to over eight hours of extra time collectively, as shown in Table 4 below.

Table 4: 60-Minute Appointment Types Unused Scheduled Time per Week

Appointment Type

Unused Scheduled Appointment Time per Week (hours)

Head/Neck

2.38

Prostate

2.10

Lung (No SDX)

1.46

Spine

1.12

Pelvis

1.02


Given the large amount of opportunity available, the team analyzed the individual actual time length distributions for each appointment type. First, the team created a histogram of actual appointment time lengths for the head and neck appointments to determine the appropriate time length to schedule for the appointments, shown in Figure 3 below.

Figure 3: Head and Neck Appointment Actual Time Length Distribution


As shown in the histogram above, most of the appointments are shorter than the scheduled 60-minute appointment time. Table 5 below shows the percentage of head and neck appointments above and below the scheduled time currently and the effect of changing the appointment time length to 45 minutes.

Table 5: Head and Neck Appointment Scheduled Time Comparison

Scheduled Time



60 Minutes (Current)

96%

4%

45 Minutes (New)

75%

25%


As shown in Table 5 above, 75% of appointments would still finish earlier than expected if the scheduled appointment time were 45 minutes instead of 60 minutes. Although 25% of the appointments would be finishing after the scheduled time, the CT Simulator room idle time would be decreased and the department would be able to schedule more head and neck patients.

Second, the team analyzed the distribution of actual appointment time length for the prostate appointments. A histogram of the actual appointment times observed is shown in Figure 4 below.

Figure 4: Prostate Appointment Actual Time Length Distribution


After finding that most of the prostate appointments finish before 60 minutes, as shown in Figure 4 above, the team investigated the expected effect of reducing the scheduled appointment time from 60 minutes to 45 minutes, as shown in Table 6 below.

Table 6: Prostate Appointment Scheduled Time Comparison

Scheduled Time




88%

12%

45 Minutes (New)

71%

29%


As shown in Table 6 above, the team expects that 71% of the prostate appointments would finish early if the scheduled appointment time length was 45 minutes. Although 29% of the appointments would finish late, the extra time would allow patient throughput to increase. However, since the prostate patients require a full bladder for the CT Simulator scan, the department would need to closely communicate with MAPSA and the patients prior to the appointment to ensure the patient drinks at the correct time to avoid delays.

Third, the team investigated the distribution of the actual appointment time lengths for lung (no SDX) appointments, as shown below in Figure 5.

Figure 5: Lung (No SDX) Appointment Actual Time Length Distribution


After observing that all of the lung (no SDX) appointments finished before the 60 minutes scheduled, the team analyzed the anticipated effect of changing the scheduled appointment time from 60 minutes to 45 minutes, shown in Table 7 below.

Table 7: Lung (No SDX) Appointment Scheduled Time Comparison

Scheduled Time




100%

0%

45 Minutes (New)

100%

0%


As shown above in Table 7, 100% of lung (no SDX) appointments would be expected to finish early if the appointment time length was scheduled as 45 minutes. Therefore, the team expects that decreasing the scheduled time would allow the department to schedule more lung (no SDX) appointments and increase patient throughput.

Fourth, a histogram for the actual appointment time lengths for spine appointments was analyzed, as shown in Figure 6 below.

Figure 6: Spine Appointment Actual Time Length Distribution


As shown in the histogram above, all of the appointments were shorter than the scheduled 60-minute appointment time. Table 8 below shows the percentage of spine appointments above and below the scheduled time currently and the effect of changing the appointment time length to 45 minutes.

Table 8: Spine Appointment Scheduled Time Comparison

Scheduled Time




100%

0%

45 Minutes (New)

100%

0%


As shown in Table 8 above, 100% of appointments would still be expected to finish earlier than the scheduled appointment time if the scheduled appointment time was changed to 45 minutes. Therefore, the CT Simulator room idle time would be decreased and the department would be able to schedule more spine appointments and increase patient throughput.

Fifth, the team analyzed the distribution of actual appointment time lengths for the pelvis appointments. A histogram of the actual appointment times observed is shown in Figure 7 below.

Figure 7: Pelvis Appointment Actual Time Length Distribution


As shown in the histogram above, most of the appointments were shorter than the scheduled 60-minute appointment time. Table 9 below shows the the effect of changing the appointment time length to 45 minutes.

Table 9: Pelvis Appointment Scheduled Time Comparison

Scheduled Time




100%

0%

45 Minutes (New)

78%

22%


As shown in Table 9 above, the team expects that 78% of the pelvis appointments would finish early if the scheduled appointment time length was 45 minutes. Although 22% of the appointments would finish late, the extra time would allow patient throughput to increase through additional pelvis appointments.


Following the same analysis methodology used for the 30-minute and 60-minute appointments, the team created a Pareto chart, shown in Figure 8 below, of the total positive difference between the scheduled appointment time and actual appointment time by scan type for the appointments scheduled in 90-minute time slots. The y-axis in Figure 8 displays the total positive amount of extra time available for additional appointments during the data collection period by 90-minute appointment type.



The team used the Pareto Principle which states that only a few appointment types contribute to a large percentage of the CT Simulator appointment scheduling optimization issue. The team found that liver and abdomen appointments contribute to a large percent of the extra time available, as shown in Figure 8. Following this finding, the team calculated the mean and standard deviation of the liver and abdomen appointments, displayed in Table 10 below.

Table 10: 90-Minute Appointment Type with Highest Unused Scheduled Time

Appointment Type



Liver

66.09

19.56

Abdomen

43.40

13.94


As shown in Table 10 above, the mean appointment times were significantly lower than the 90-minute scheduled time. Each week, these appointments contribute to almost two hours of extra time collectively, as shown in Table 11 below.

Table 11: 90-Minute Appointment Types Unused Scheduled Time per Week

Appointment Type

Unused Scheduled Appointment Time per Week (hours)

Liver

1.10

Abdomen

0.97


Given the opportunity for increased patient throughput with the extra time available shown in Table 11 above, the team analyzed the individual actual time length distributions for each appointment type.

First, the team created a histogram of actual appointment time lengths for the liver appointments to determine the appropriate time length to schedule for the appointments, shown in Figure 9 below.

Figure 9: Abdomen Appointment Actual Time Length Distribution


As shown in Figure 9, the majority of the appointments observed during data collection were shorter than the scheduled 90-minute appointment time. Table 12 below shows the percentage of liver appointments above and below the scheduled time currently and the effect of changing the appointment time length to 75 minutes.

Table 12: Abdomen Appointment Scheduled Time Comparison

Scheduled Time




83%

17%

75 Minutes (New)

58%

42%


As shown in Table 12, the team expects 58% of the appointments to end early if the appointment time length is changed to 75 minutes. Although 42% would still finish late, the change would decrease overall CT Simulator room idle time and the department would be able to schedule more liver appointments annually.

Second, the team analyzed the distribution of actual appointment time length for the abdomen appointments. A histogram of the actual appointment times observed is shown in Figure 10 below.

Figure 10: Abdomen Appointment Actual Time Length Distribution


After finding that all abdomen appointments finished before 90 minutes, as shown in Figure 10 above, the team investigated the expected effect of reducing the scheduled appointment time from 90 minutes to 75 minutes, as shown in Table 13 below.

Table 13: Abdomen Appointment Scheduled Time Comparison

Scheduled Time




100%

0%

75 Minutes (New)

100%

0%


As shown in Table 13 above, the team expects that 100% of the abdomen appointments would finish early if the scheduled appointment time length was 75 minutes. Therefore, scheduling abdomen appointments for 75 minutes would reduce CT Simulator room idle time and would allow patient throughput to increase.

Appointment Scheduling Guidelines Recommendations

This section describes the recommendations the team has for the Radiation Oncology Department’s appointment scheduling guidelines. First, the recommendations resulting from the general linear model regression are described. Then, the section details the recommendations for reducing scheduled appointment time lengths for specific appointment types.

Consider Five Significant Factors in Appointment Scheduling

The general linear model regression showed that the following factors were statistically significant in determining the appointment time length:

· IV Contrast (yes or no)


· Number of treatment areas (isos)


· Qfix (yes or no)

Although a linear function cannot be used to accurately predict the actual appointment times, the Michigan Medicine Radiation Oncology Department should consider these five factors when scheduling. Currently, the IV contrast, SDX training, vacuum bag, and Qfix are considered. The team recommends that the department require multiple appointments for patients with more than one treatment area (iso). This process would also require physicians to complete a simulation directive for each treatment area. By considering these factors, the department will be able to collect data for using regression to accurately predict appointment time lengths in the future.

Pilot Reducing Appointment Time Length Increments from 30 to 15 Minutes

After performing statistical analysis on the appointment time length data collected from February 26, 2018 to March 28, 2018, the team recommends that the Michigan Medicine Radiation Oncology Department reduce specific appointment time lengths to minimize the amount of CT Simulator room idle time, decrease the difference between scheduled appointment length and actual appointment length, and increase patient throughput. The team found that few appointment types contribute heavily to the unused scheduled time in the department. Therefore, the team recommends that the department take measures to reduce the appointment time increments from 30 minutes to 15 minutes.

In the short-term, the team recommends that the department perform a pilot test for reducing the time increments from 30 minutes to 15 minutes. For the pilot, the team suggests the department reduce the scheduled time for head and neck, prostate, lung (no SDX), spine, and pelvis appointments from 60 minutes to 45 minutes. Additionally, the team recommends that the department reduce the scheduled time for liver and abdomen appointments from 90 minutes to 75 minutes.

After initiating a pilot of these new appointment time lengths, the team recommends that the Radiation Oncology Department continue to collect appointment time length data for all appointment types. The team predicts an additional three to four months of data will be needed for full analysis of all appointment types and for full integration of 15-minute appointment time length scheduling increments.

Patient Flow Data Collection and Analysis Methods

This section describes the data collection methodology and analysis techniques used to formulate findings and conclusions leading to recommendations for the CT Simulator patient flow. The team analyzed historical data, performed benchmarking research and observations, created a patient flow process map, collected time study data, and designed a value stream map.

Historical Data Analysis

The Radiation Oncology Department collected data in the CT Simulator room from January 5, 2018 to January 18, 2018. The team was provided with this historical data for 102 patients. The CT Simulator employees kept handwritten notes regarding the delays that occurred during the patient’s appointment, the time the patient entered the CT Simulator room, and the time the patient exited the CT Simulator room. Of the 102 patients in the CT Simulator room during the data collection period, 58 patients experienced some type of delay.

The team used this data to determine root causes of CT Simulator room delays during the period that the data was collected. The team analyzed this information by categorizing the delays into delay types. Using Microsoft Excel, the graph shown in Appendix C was created from this analysis to understand the relative frequency of each delay type.

Patient Flow Benchmarking

The team performed benchmarking research to understand how CT Simulator patient flow is organized at other hospitals in the United States. The team found a research study from the Ohio State University Medical Center’s James Department of Radiation Oncology. The study was performed in collaboration with the Academy for Excellence in Healthcare in August 2014 [3]. The team used the findings from the benchmarking research to inform recommendations for alternative patient flows in the Michigan Medicine Radiation Oncology Department.

Observations

To understand the current CT Simulator patient flow process and detect possible bottlenecks and areas for improvement, the team observed the CT Simulator patient flow process for 32 patients from January 26, 2018 to February 23, 2018. During the observations, two team members were present. One team member sat at the front desk and took notes when the patient checked into the department to track the pre-simulation checklist tasks. The second team member sat in the CT Simulator control room to observe and take notes on any delays or variations during the scan. The notes were taken by hand and the team members switched places halfway through each observation period. At the end of the observation period, notes were compared to understand the full patient flow process. The observations were used to create the patient flow process map, shown in Appendix D, and the time study data collection sheet, shown in Appendix E.

CT Simulator Patient Flow Process Map

One team member attended the Radiation Oncology Department’s Lean meetings on Tuesday, January 30, 2018 and Tuesday, February 6, 2018 from 9:30 AM to 10:30 AM in the Radiation Oncology Department at the University of Michigan Hospital. The clients, the Director of Clinical Operations and the Chief Radiation Therapist, participated in the meeting along with staff across various groups in the department such as Physics, Dosimetry, and Medical Assistants and Patient Services Associates. The employees at the meeting mapped out the current patient flow process for the CT Simulator using post-it notes on the walls of the conference room.

The IOE 481 project team utilized the employee-perspective patient flow process in addition to their observations to create a process map for the CT Simulator patient flow process in Microsoft Visio. The patient flow process map is shown in Appendix D. The process map begins when the patient is ready to be taken into the CT Simulator room. The process map was used as a framework for the value stream map.

Time Studies

To record task times for use in the value stream map, the team collected time study data in the Radiation Oncology Department from March 5, 2018 to March 30, 2018. The team used the data collection sheet shown in Appendix E. Time study data was collected for 90 patients during a total of 102 hours in the department.

An obstacle regarding time studies was that the team members had to observe for long time periods to collect data because the CT Simulator appointments are either 30, 60, or 90 minutes. This obstacle was mitigated by staggering the team members’ observation times so the team could record data for a maximum number of patients. Each team member recorded time study data independently each week. The team member stood in the CT Simulator control room and recorded data on the time study data collection sheet. Every hour, the team member went to the clerk desk and requested time data from the MAPSA employees regarding when the patient checked in, when the patient was moved to the holding room, and what pre-simulation tasks needed to be completed.

The team entered the time study data in Microsoft Excel and utilized general statistical analysis to obtain the metrics needed to create a value stream map for the patient flow.

Value Stream Map

The team created a value stream map to identify the current bottlenecks in the patient flow process, which led to recommendations for the future state patient flow process. The team used a 2010 IOE 481 team’s patient flow value stream map as a reference for the CT Simulator value stream map [4]. The value stream map is shown in Appendix F. Ultimately, the value stream map was used to identify key bottlenecks in the current process so the team could make useful and specific recommendations when designing the improved patient flow process.

Patient Flow Findings and Conclusions

Findings from the historical data analysis, benchmarking, process map, and value stream map helped the team form conclusions that informed the patient flow recommendations. This section describes the findings and conclusions for the CT Simulator patient flow.

Historical Delays

After analyzing the historical data using the bar graph in Appendix C, the team found that the patient was the cause of the greatest number of delays, including showing up late to the appointment time, asking many questions before the simulation, using the bathroom multiple times, drinking contrast slowly, and having pain or discomfort. This analysis, in addition to a review of the Radiation Oncology delays identified in an IOE 481 report from 2007 [5] and IOE 481 report from 2012 [6], helped the team conclude that the patient flow process varies by patient and is difficult to standardize. Therefore, the team needed to make general recommendations that would still allow all patient needs to be satisfied.

Induction Room Benchmarking Research

After benchmarking research, the team found that the James Department of Radiation Oncology at the Ohio State University Medical Center recently changed its CT Simulator patient flow process after a patient flow improvement study with the Academy for Excellence in Healthcare [3]. The result of the project was a patient flow change to a two-step immobilization process for CT Simulator appointments. The first step is to bring the patient to an induction room where the patient is immobilized with several devices. The second step is to bring the patient to the CT Simulator room for the scan. The researchers expect that this two-step process will optimize the CT Simulator room so patients can be scanned while complicated immobilizations are prepared in the induction room. The hospital predicts that this improved patient flow will allow 14 appointments in an 8-hour day. Therefore, the IOE 481 team concluded that an induction room could be an option for increasing patient throughput in the Michigan Medicine Radiation Oncology Department.

Main CT Simulator Patient Flow Tasks

From the patient flow process map shown in Appendix D, the team found that the patient flow process is complicated by various conditional tasks. For example, the physician, physics, or dosimetrists can be called at any point in the patient positioning and scanning process. Therefore, the team concluded that the value stream map must only include the main tasks for effective analysis purposes. Therefore, the team concluded that the process has 11 main tasks, which are listed below.

1. Patient check-in

2. Completion of pre-simulation tasks in Room 10

3. Team notified patient ready

4. Patient brought to CT Simulator room

5. Patient moved to CT Simulator table

6. Pre-scan set up and patient positioning

7. Scout

8. Scan

9. Physician scan approval

10. Patient exits CT Simulator room

11. CT Simulator room cleaned

These 11 tasks were used to create the value stream map which is discussed in the following subsection.

Patient Flow Bottlenecks

The team used the time study data for 90 patients to create a value stream map for the CT Simulator patient flow process, shown in Appendix F. The team calculated the average process time, wait time, and first time quality, for each step in the patient flow. Overall, on average, the patient flow process time was 62 minutes, the wait time was 48 minutes, the lead time was 110 minutes, and the total quality was 42%. Table 14 below lists the wait time (W/T), process time (P/T), and first-time quality (FTQ) for the 11 main tasks in the patient flow process.

Table 14: Value Stream Map Process Time, Wait Time, and First Time Quality Values

Task

P/T (minutes)

W/T (minutes)

FTQ (%)

Patient check-in

1.00

22.04

100.00%

Completion of pre-sim tasks in Room 10

25.00

13.94

66.30%

Team notified patient ready

1.00

8.74

100.00%

Patient brought to CT Simulator room

2.00

0.00

93.48%

Patient moved to CT Simulator table

1.27

0.00

98.81%

Pre-scan set up and patient positioning

11.68

0.00

94.57%

Scout

3.98

0.00

84.78%

Scan

3.24

0.00

93.48%

Physician scan approval

2.13

3.24

91.30%

Patient exits CT Simulator room

4.87

0.00

100.00%

CT Simulator room cleaned

5.41

0.00

100.00%

From the information in Table 14 above, the team identified three bottlenecks based on high process time, high wait time, or low first time quality. The three bottlenecks were later used to formulate patient flow recommendations.

The team identified patient check-in as the first bottleneck for patients. On average, patients wait approximately 22.04 minutes before they are brought to Room 10 for completion of pre-simulation tasks. Therefore, the team decided to focus on this bottleneck when making patient flow improvement recommendations for the department to decrease patient wait time.

The completion of the pre-simulation tasks in Room 10 is the second bottleneck, where the average process time is 25.00 minutes, wait time is 13.94 minutes, and first time quality is rated at 66.30%. The first time quality for this task is the percent of patients without any possible source of delay present including consent, face photo, or other pre-simulation tasks. Therefore, the team concluded that patient throughput and patient wait time could be reduced by addressing this bottleneck.

The value stream map informed the team that the pre-scan set up and patient positioning was the third bottleneck because of the high process time of 11.68 minutes on average. Therefore, the team concluded that this step in the patient flow process could be improved to increase patient throughput in the department.

Patient Flow Design Methods, Requirements, Constraints, and Standards

The main design task is to create an improved patient flow process for the CT Simulator. This section describes the design requirements, constraints, and standards for the CT Simulator patient flow that were used to create the alternative patient flows. The design requirements, constraints, and standards were identified through discussions with department stakeholders, observations, and online research. The alternative patient flows were analyzed using a Pugh Matrix. The three tables in Appendix G form the project’s constraints and standards matrix for the new patient flow process.

Soft Design Constraints (Design Requirements)The main design task was the improved patient flow process. When designing this process, several design requirements guided the team’s approach. The design requirements are listed below and were used to evaluate the alternative patient flows. The labels following the descriptions refer to an entry in the constraints and standards matrix in (Appendix G, Table G-1).

1. The new patient flow process should incur minimal costs to the Radiation Oncology Department. (R-D-1)

2. The new patient flow process should be easily implemented in the department and should not require intensive training. (R-E-1)

3. The new patient flow process should minimize interaction with new departments or new staff that are not already included in the CT Simulator patient flow. (R-E-2)

4. The CT Simulator staff should accept the new process in order for it to be successfully implemented and sustained. (R-F-1)

5. The new CT Simulator patient flow process should be acceptable to patients because the patient flow directly affects the experience of the patients in the Radiation Oncology Department. (R-G-1)

6. The average patient wait time should decrease as a result of the new patient flow process. (R-H-1)

7. The CT Simulator room idle time should decrease as a result of the new patient flow process. (R-H-2)

8. The CT Simulator throughput should increase as a result of the new patient flow process. (R-H-3)

Hard Design ConstraintsThe team identified several hard constraints that were adhered to when creating the new patient flow process. The hard constraints are listed below. The labels following the descriptions of the hard constraints refer to an entry in the constraints and standards matrix (Appendix G, Table G-2).

1. The new patient flow process must follow specific organizational policies. These policies require the patient to stay on the CT Simulator table until the physician approves the scan and require signed consent and a complete physician directive before the patient enters the simulation room. (C-A-1)

2. The new patient flow process must not violate the health or safety of patients. Therefore, the patient lift team, the anesthesiology team, the IV team, and other hospital departments will not be removed from the patient flow process. (C-C-1)

3. An additional CT Simulator cannot be purchased due to budget and space limitations. (C-D-1)

4. The new patient flow process must not disrupt any of the other processes in the Radiation Oncology Department. (C-E-1)

5. All process maps must be designed in Microsoft Visio so the Radiation Oncology Department can update the maps after the IOE 481 project is complete. (C-F-1)

6. Observations can only occur Monday through Friday from 7:00 AM to 6:00 PM, the days and times that the CT Simulator is open. (C-H-1)

7. The new patient flow process must be applicable to the CT Simulator Monday through Friday from 7:00 AM to 6:00 PM, the days and times that the CT Simulator is open. (C-H-2)

8. All observations and data collection must be completed within the University of Michigan semester time frame, allowing a final report including the new patient flow process to be delivered to the client by April 25, 2018. (C-H-3)

Design StandardsThe standards the team followed when designing the new patient flow process include HIPPA, Radiation Oncology Department standards, Michigan Medicine best practices, IEEE, and MLearning. The implications of the standards on the project are listed below. The labels following the descriptions of the standards refer to an entry in the constraints and standards matrix (Appendix G, Table G-3).

1. When collecting and analyzing the Radiation Oncology simulation data, the team must follow the Health Insurance Portability and Accountability Act of 1996 (HIPPA) standard. HIPPA includes a Privacy Rule “to assure that individuals’ health information is properly protected while allowing the flow of health information needed to provide and promote high quality health care and to protect the public's health and well being” [7]. Following this standard involves de-identifying patient data to refrain from potentially leaking protected health information (PHI). To protect PHI, the team and clients have access to a University of Michigan MBox folder to allow safe data and file sharing. When collecting time study data for the new patient flow process, the team has been labeling each patient with a unique random identifier and does not record any data such as gender, age, or weight. (S-1-1)

2. The Radiation Oncology Department has several standards of work surrounding the CT Simulator process that must be followed during the new patient flow process design. These departmental standards include processes surrounding patient education prior to the simulation, physician scan approval, and patient positioning techniques. These organizational standards were communicated by the team’s clients during the initial client meeting on January 19, 2018. (S-2-1)

3. The team’s coordinator, Zac Costello, provided the team with best practice templates for process maps and value stream maps. The team used these resources to construct the current patient flow process map and plans to utilize them again when designing the value stream map. The resources were provided to the team in an email following a coordinator meeting on February 15, 2018 [8]. (S-3-1)

4. The Radiation Oncology Department uses Microsoft Visio to construct process maps within the department. Therefore, the clients requested that the team use Microsoft Visio as a best practice to enable the department to update the maps after the IOE 481 project ends. (S-3-2)

5. When documenting the final report with the new patient flow process design, the team will use the Institute of Electrical and Electronics Engineers (IEEE) standards to cite literature used for analysis and developing recommendations [9]. (S-4-1)

6. The team has been certified through MLearning, the Michigan Medicine training portal on compliance, patient safety, and other important policies within the hospital. The team must adhere to these standards when observing, collecting data, and creating the new patient flow process [10]. (S-5-1)

The team searched the websites of OSHA, MiOSHA, NIOSH, ASTM, ANSI, SAE, and Mil-Specs on March 12, 2018 from 6:00 PM to 6:30 PM using the key words “patient,” “patient flow,” “data,” and “data collection” but did not find these to be relevant standards.

Patient Flow RecommendationsThe team used the findings from the value stream map in addition to the design requirements, constraints, and standards to formulate the CT Simulator patient flow recommendations. First, the team identified four patient flow recommendations associated with the three bottlenecks identified. A future state map with kaizen bursts is shown in Appendix H. Then, the team used a Pugh Matrix with the design requirements as criteria to determine the preferred patient flow alternative for the Michigan Medicine Radiation Oncology Department CT Simulator. This section describes the patient flow alternatives and the decision process leading to the final recommendation.

Designate Patient Flow Overflow Room

The first recommendation addresses the wait time after the patient checks in, prior to being brought to Room 10. This alternative patient flow includes designating a second room as a CT Simulator patient overflow room. This room could still be utilized by other patients, but it would be set as a higher priority for CT Simulator patients. Therefore, this room would only be used by non-CT Simulator patients if there were no other rooms available. With this patient flow, two CT Simulator patients could be completing pre-simulation tasks that were not completed prior to arrival at the same time. Therefore, the overall patient wait time would be decreased.

Watch Educational Videos Prior to Room 10

The second recommendation for the patient flow process is to ensure that patients watch the necessary educational videos prior to going into Room 10 to decrease patient wait time and delays. The team recommends that patients watch the videos with a nurse, doctor, or physician before leaving their initial consultation appointment, instead of in Room 10 before their CT Simulator appointment. Another option could be to have the patient watch the educational videos in the waiting room after check-in on a portable tablet such as an iPad. The current average wait time after check-in before the patient is brought to Room 10 is 22.04 minutes. Given that the main educational video is 20 minutes long, ensuring that patients watch the video in the waiting room would be an efficient utilization of space and patient wait time.

Assign Pre-Simulation Checklist to MAPSA

The third recommendation is for MAPSA employees to be assigned the task of preparing a “next day patient missing questionnaire” list that highlights pre-simulation tasks needed for all upcoming CT Simulator patients. Currently, this task is led by a CT Simulator staff member. Since MAPSA employees are responsible for ensuring the pre-simulation tasks are complete prior to the patient entering the CT Simulator room, the team believes assigning this task to MAPSA employees would better prepare the department for delay mitigation.

Implement Induction Room

The final alternative patient flow recommendation is for the department to implement a second patient positioning room similar to the two-step CT Simulator patient flow process at the Ohio State University Medical Center [3]. This process was identified through departmental benchmarking. In the two-step process, an additional room is available for complex immobilizations. After the patient is immobilized properly in the positioning room, the patient would be transported to the CT Simulator scan room. This process would decrease CT Simulator room idle time and increase patient throughput.

Pugh Matrix Recommendation

After creating alternatives for a new patient flow process, the team used a Pugh Selection Matrix, shown in Appendix I, to weigh the advantages of the new patient flow process alternatives when compared to the datum, the current process. The team identified the requirements of cost, training required, number of outside resources required, CT Simulator employee acceptability, patient acceptability, patient wait time, CT Simulator room idle time, and patient throughput as the design requirements for analysis. The weights for the requirements are shown in the Overall Importance column in the Pugh Selection Matrix. The weights represent percentages out of 100%. Each alternative was given a score of +1, 0, or -1 for each criterion compared to the datum.

As shown in Appendix I, the highest weighted alternative was the pre-watch video alternative. Therefore, the team highly recommends that the Radiation Oncology Department design the patient flow for patients to watch the educational video prior to entering Room 10. This change would reduce the amount of time the patient is in Room 10 and reduce the number of delays in the department. Additionally, the team recommends that the department have the patients watch the SDX video prior to the general video because the SDX video is a higher priority. The second highest weighted alternative was the MAPSA questionnaire preparation alternative. Therefore, MAPSA should take over the role of contacting patients for the next day and tracking incomplete pre-simulation tasks to reduce patient flow delays.

Expected Impact

This section describes the expected impact of the team’s recommendations for the Michigan Medicine Radiation Oncology Department regarding the CT Simulator appointment scheduling guidelines and the CT Simulator patient flow.

New Appointment Scheduling Guidelines

If the Michigan Medicine Radiation Oncology Department reduces the scheduled appointment time length increments from 30 minutes to 15 minutes as recommended, the team expects that the patient throughput will be maximized and the CT Simulator room idle time and the difference between the scheduled time length and actual time length will be minimized. Therefore, the team expects that these changes will directly address the current key issues associated with the appointment scheduling guidelines.

From February 26, 2018 to March 28, 2018, the appointment time length observation period, the team calculated the expected number of additional appointments that could be scheduled as a result of the pilot if the scheduled appointment time lengths were reduced as recommended. The team aggregated the accumulated unused scheduled appointment time for the appointments recommended to be included in the pilot and found that the pilot is expected increase patient throughput by 288 patients annually. The appointment type distribution of the expected 288 extra appointments is listed below.

· 96 Head/Neck

· 60 Prostate

· 36 Lung (No SDX)

· 24 Spine

· 36 Pelvis

· 24 Liver

· 12 Abdomen

Furthermore, the 288 additional appointments would decrease CT Simulator room idle time and the difference between the scheduled appointment length and actual appointment length by 234 hours.

Improved Patient Flow

The CT Simulator future state patient flow map, shown in Appendix H, was designed based on the four patient flow recommendations for eliminating the patient flow bottlenecks.

The recommendation of opening a second room for CT Simulator patients would decrease the wait time for patients between checking in and moving to Room 10 because an additional room would be available for the patient to complete the pre-simulation tasks if someone was occupying Room 10 at the time of check-in. The expected impact of having this overflow room for CT Simulator patients is a decrease in wait time after check-in to five minutes. The five minutes is estimated as the amount of time needed for the MAPSA employee to prepare the second room and bring the patient in.

The recommendation for the patient to complete any pre-simulation educational videos after their consultation appointment or on a tablet in the waiting room prior to entering Room 10 would reduce the process time of the patient in Room 10 to ten minutes because the patient would no longer need to watch the 20-minute educational video in Room 10. The only tasks necessary to complete in Room 10 would be talking with a nurse, changing clothes, and, if necessary, other tasks such as contacting the IV team or drinking contrast. Due to factors outside of Room 10, the wait time decrease cannot be estimated.

Next, the recommendation for MAPSA employees to be assigned the task of preparing a “next day patient missing questionnaire” list that highlights pre-simulation tasks needed for all upcoming CT Simulator patients would increase the visibility of patients with outstanding pre-simulation tasks. Since MAPSA employees are responsible for bringing the patients to Room 10 and ensuring completion of pre-simulation tasks, the team expects the first time quality percentage in Room 10 to increase to 90%. The team does not expect 100% first time quality because some delay sources are outside the control of MAPSA and the CT Simulator team, such as a myelogram recovery delay.

For the two-step immobilization patient flow process recommendation based on the Ohio State University Medical Center study, the team was unable to predict the expected changes in process time and wait time because of the scale of the change. However, since the CT Simulator room would only be needed for the scan, which takes 3.24 minutes on average, the team expects that patient throughput would increase.

Overall, the expected impact of the improved patient flow is that the process time will decrease by 24%, from 62 minutes to 47 minutes, primarily from the decrease in time spent in Room 10 watching the educational videos. The patient wait time is expected to decrease by 35%, from 48 minutes to 31 minutes, after removing unnecessary patient wait time in the check-in process. The lead time is expected to decrease by 29%, from 110 minutes to 78 minutes. The first time quality is expected to increase from 42% to 57%, since patients would experience fewer delays when a MAPSA employee is assigned to document the next day patient missing questionnaire.

The average patient flow lead time is expected to decrease by 32 minutes. The data collected is the average of all appointment types and lengths. Therefore, these recommendations and expected time decreases will affect different types of appointments and scheduled lengths with variation in the actual change in time. However, on average, the lead time is expected to decrease regardless of appointment type or scheduled length.

References

[1] Y. L. Huang and J. Marcak. (2008, February). “Radiology Scheduling with Consideration

of Patient Characteristics to Improve Patient Access to Care and Medical Resource Utilization.” Health Systems. [Online]. vol. 2 (no. 2), pp. 93-102. Available: https://www.tandfonline.com/doi/full/10.1057/hs.2013.1?scroll=top&needAccess=true

[2] Z. Costello. “RE: IOE 481 - Coordinator Meeting Wednesday 3/28” Personal email (March 29, 2018).

[3]A. McCabe and M. Pennington. “Improved CT Simulation and Patient Access at the James.” The Ohio State University: Columbus, Ohio. 11, Mar. 2015.

[4] A. R. Hershberger, D. Y. Lee, D. J. Jansen, and P. Raswono, “Improving Patient Flow

within the Physical Medicine and Rehabilitation and Anesthesiology Clinics,” IOE 481,Univ. Michigan, Ann Arbor, Final Report. Apr. 21, 2010. [5] S. Mowlavi, Z. Shoup, and A. Wang, “Utilization Study of Linear Accelerators in theRadiation Oncology Department,” IOE 481, Univ. Michigan, Ann Arbor, Final Report.

Apr. 16, 2007.

[6] J. Card, A. Conlin, H. Frey, and K. Ogrodzinski, “Improving the Scheduling Process ofReferred Radiation Oncology Patients,” IOE 481, Univ. Michigan, Ann Arbor, FinalReport. Apr. 17, 2012.

[7] United States Department of Health and Human Services, Health Information Privacy,

Health Information Privacy, 2018. [Online]. Available: https://www.hhs.gov/hipaa/index.html. [Accessed: 12 March, 2018].

[8] Z. Costello. “RE: IOE 481 - Coordinator Meeting Tomorrow” Personal email (February 15, 2018).

[9] IEEE, IEEE Standards, IEEE Standards Association, 2018. [Online]. Available: http://standards.ieee.org. [Accessed: 12 March, 2018].

[10] University of Michigan Health System, MLearning, Training Portal, 2018. [Online].

Available: https://trainingportal.med.umich.edu/Saba/Web/Main. [Accessed: 12 March,

2018].

Appendix A: Appointment Time Length Data Collection Sheet

Patient Sticker

Time sim room set up begins

Time sim room clean up ends

IV contrast on directive?

4D sim on directive?

SDX on directive?

Appendix B: Appointment Time Length General Linear Model Regression

Response variable: Actual appointment time length

Categorical variables:

· Gender

· IV contrast (yes or no)

· 4DX scan (yes or no)


· Inpatient or outpatient

· Number of isos

· 3-point mask (yes or no)

· 5-point mask (yes or no)

· Dental guards (yes or no)

· Bite block (yes or no)

· Bolus (yes or no)

· Cradle (yes or no)


· Accuform (yes or no)

· Qfix (yes or no)

· Body fix (yes or no)

Covariates:

· Age

· ACTV height

· ACTV weight

· ACTV BMI

· REVC height

· REVC weight

· REVC BMI

Minitab Output:

Figure B-1: General Linear Model Regression with ACTV Patient Attributes

Figure B-2: General Linear Model Regression with REVC Patient Attributes

Figure B-3: General Linear Model Regression without Height, Weight, and BMI

Appendix C: Historical Data CT Simulator Delay Root Cause Frequency

Sample size: 58 patients

Source: University of Michigan Radiation Oncology Department

Data collection period: January 5, 2018 to January 18, 2018

Appendix D: CT Simulator Patient Flow Process Map

Note: The team has provided a Microsoft Visio version of the CT Simulator Patient Flow process map to the clients. The process map below may be difficult to read due to space constraints.

Appendix E: Time Study Data Collection Sheet

Process

Start Time

End Time

Reasons for delays

Patient check in

_ _ : _ _

N/A

Late

At another appointment

Patient taken to Room 10

_ _ : _ _

N/A

Room 10 not available

Taken to a different room

Patient Questionnaire Filled?

N/A

N/A

Face Photo

Consent Paged: _ _ : _ _ Arrival: _ _ : _ _

IV Team Needed Paged: _ _ : _ _ Arrival: _ _ : _ _

Anesthesiology Needed Paged: _ _ : _ _ Arrival: _ _ : _ _

NPO Requested Wait Time: _ _ : _ _

Oral Contrast

SDX Training

Myelogram Needle out time: _ _ : _ _

Simulation team notified patient ready

_ _ : _ _

N/A

Patient enters simulation room

_ _ : _ _

N/A

Myelogram recovery

Not finished drinking oral contrast

Transportation team

Patient moved to simulation table

_ _ : _ _

_ _ : _ _

Lift team Paged: _ _ : _ _ Arrival: _ _ : _ _

Pre-scan set up / patient positioning

_ _ : _ _

_ _ : _ _

IV Contrast Start: _ _ : _ _ End: _ _ : _ _Power port? Y/N

SDX Training Start: _ _ : _ _ End: _ _ : _

Physician needed Paged: _ _ : _ _ Arrival: _ _ : _ _

Physics needed Paged: _ _ : _ _ Arrival: _ _ : _ _

Dosimetry needed Paged: _ _ : _ _ Arrival: _ _ : _ _

Patient needed to go to bathroom

Complicated bolus

Could not complete SDX training

> 1 iso?

Inaccurate or incomplete simulation directive

Scout

_ _ : _ _

_ _ : _ _

> 1 repositioning needed




Patient scan

_ _ : _ _

_ _ : _ _

Input error




Physician scan approval

_ _ : _ _

_ _ : _ _

Physician Paged: _ _ : _ _ Arrival: _ _ : _ _

Physics Paged: _ _ : _ _ Arrival: _ _ : _ _

Dosimetry Paged: _ _ : _ _ Arrival: _ _ : _ _

Patient exit

_ _ : _ _

N/A

Room cleaned

_ _ : _ _

N/A

Appendix F: Patient Flow Value Stream Map

Appendix G: Patient Flow Design Constraints and Standards Matrix

This appendix contains three tables that map the soft design constraints, hard design constraints, and design standards for the new patient flow process. Together, these three tables form the project’s constraints and standards matrix for the new patient flow process.

Table G-1: Soft Design Constraints (Design Requirements)

Entry #

1

2

3

R-A. Organizational Policy

N/A

R-B. Ethical

N/A

R-C. Health & Safety

N/A

R-D. Economic

(R-D-1)

R-E. Implementability

(R-E-1)

(R-E-2)

R-F. User Acceptance

(R-F-1)

R-G. Patient Acceptance

(R-G-1)

R-H. Task Duration

(R-H-1)

(R-H-2)

(R-H-3)

Table G-2: Hard Design Constraints

Entry #

1

2

3

C-A. Organizational Policy

(C-A

University of Michiganioe481/ioe481_past_reports/18W02.docx · Web viewThe following section is an executive summary of the IOE 481 Radiation Oncology CT Simulator: Efficiency and

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