Minimizing Built-in Waste in Michigan Medicine s New Clinical Inpatient Tower. Final Report. Submitted to:

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1 Minimizing Built-in Waste in Michigan Medicine s New Clinical Inpatient Tower Final Report Submitted to: Bob Harris, Associate Hospital Director, Facilities Planning and Operations Corrie Pennington-Block, Senior Facilities Lean Designer, Facilities Planning Taylor Pfershy, POA Fellow, Program and Operations Analysis Mary Duck, Michigan Medicine IOE 481 Liaison, Program and Operations Analysis Mark P. Van Oyen, Ph.D., IOE 481 Faculty Instructor Submitted by: IOE 481 Project Team #3 Sarthak Jain Mary Owczarczak Katherine Sell Jennifer Wenger Date: April 18, 2017

2 TABLE OF CONTENTS Executive Summary... 1 Introduction Background Goals and Objectives. 5 Key Issues.. 5 Project Scope.. 5 Methods.. 5 Literature Search... 6 Observations and Interviews.. 6 Study Using RFID Technology.. 7 Time Studies.. 8 Findings and Conclusions: All Departments. 9 Observations and Interviews.. 9 Distance and Epicenter Data from RFID Tag Study for All Units... 9 Findings and Conclusions: Individual Departments.. 10 Unit 4A Findings and Conclusions Unit 4C Findings and Conclusions Unit 4D Findings and Conclusions Pre-Op Department Findings and Conclusions.. 20 PACU Department Findings and Conclusions Anesthesiology Department Findings and Conclusions Adult Emergency Department Findings and Conclusions. 28 Recommendations.. 31 Comprehensive Recommendations Recommendations for Unit 4A.. 31 Recommendations for Unit 4C.. 32 Recommendations for Unit 4D.. 32 Recommendations for the Pre-Op Department.. 32 Recommendations for the PACU Department Recommendations for the Anesthesiology Department. 33 Recommendations for the Adult Emergency Department Innovative Recommendations Additional Study Recommendations Expected Impact References.. 36 ii

3 FIGURES AND TABLES Figure 1: Daily survey handed out at end of shift... 7 Figure 2: Example of RFID tag data Figure 3: Distances from epicenter and percentage of trips for Unit 4A key destinations 11 Figure 4: Unit 4A key destinations and their distances from the epicenter Figure 5: Screenshots of RFID data for Unit 4A subjects Figure 6: Distances from epicenter and percentage of trips for Unit 4C key destinations 14 Figure 7: Unit 4C key destinations and their distances from the epicenter Figure 8: Screenshots of RFID data for Unit 4C subjects.. 16 Figure 9: Distances from epicenter and percentage of trips for Unit 4D key destinations 17 Figure 10: Unit 4D key destinations and their distances from the epicenter Figure 11: Screenshots of RFID data for Unit 4D subjects Figure 12: Distances from epicenter and percentage of trips for Pre-Op key destinations Figure 13: Pre-Op key destinations and their distances from the epicenter Figure 14: Screenshots of RFID data for Pre-Op subjects. 22 Figure 15: Distances from epicenter and percentage of trips for PACU key destinations 23 Figure 16: PACU key destinations and their distances from the epicenter Figure 17: Screenshots of RFID data for PACU subjects.. 24 Figure 18: Distances from epicenter and percentage of trips for Anesthesiology key destinations. 25 Figure 19: Anesthesiology key destinations and their distances from the epicenter Figure 20: Screenshots of RFID data for Anesthesiology subjects Figure 21: Distances from epicenter and percentage of trips for Adult ED key destinations 28 Figure 22: Adult ED key destinations and their distances from the epicenter Figure 23: Screenshots of RFID data for Adult ED subjects. 30 Figure 24: Innovative recommendations visualization.. 34 Table 1: Mean and standard deviation of distances traveled per department Table 2: Epicenters for each department Table 3: MODAPTS and time studies for Unit 4A Table 4: MODAPTS and time studies for Unit 4C Table 5: MODAPTS and time studies for Unit 4D Table 6: MODAPTS and time studies for Pre-Op Department Table 7: MODAPTS and time studies for PACU Department Table 8: MODAPTS and time studies for Anesthesiology Department Table 9: MODAPTS and time studies for Adult ED Table 10: Primary and secondary key adjacencies for Unit 4A Table 11: Primary and secondary key adjacencies for Unit 4C Table 12: Primary and secondary key adjacencies for Unit 4D Table 13: Primary and secondary key adjacencies for Pre-Op Department Table 14: Primary and secondary key adjacencies for PACU Department Table 15: Primary and secondary key adjacencies for Anesthesiology Department Table 16: Primary and secondary key adjacencies for Adult Emergency Department iii

4 EXECUTIVE SUMMARY Michigan Medicine is building a new Clinical Inpatient Tower at its Ann Arbor location in order to expand adult services. There is a lean-led facility design effort in place by the Clinical Inpatient Tower Team to minimize wastes such as motion and transportation in the new tower s design. By minimizing these wastes, care providers can spend more time at the bedside, thus improving the patient experience. The Clinical Inpatient Tower Team has enlisted the help of an Industrial and Operations Engineering student team to analyze the wastes occurring in Michigan Medicine s current facilities, particularly departments in the University Hospital. The wastes examined by the team include excessive distance traveled by care providers and inefficient placements of key destinations and resources. After identifying these wastes and their causes, the student team formed recommendations to alleviate the wastes in the design and layout of the new tower. Background Michigan Medicine is experiencing an increase in demand for patient services in its renowned hospital system. To meet this demand and improve the patient experience, the organization is building a new Clinical Inpatient Tower on the corner of East Ann Street and Zina Pitcher Place. The architecture firm, HOK, is helping to design the new tower and is working with the Clinical Inpatient Tower Team to integrate lean-led design. The tower will help Michigan Medicine increase its market share as well as grow the adult services it can provide. Based on the current facility design, care providers are walking a long distance per shift. In addition, the time at the bedside is being constrained, and key resources and destinations are not efficiently placed. The student team identified motion and transporting wastes through measuring the total distance traveled during a care provider s shift, tracking the distance care providers travel within an hour, and quantifying the frequency of key destinations. To meet these objectives, the team analyzed the walking patterns of care providers, including doctors, nurses, and technicians. The analysis was conducted in the University Hospital, specifically in the Pre- Op, PACU, Anesthesiology, and Adult Emergency departments, as well as the 4A, 4C, and 4D inpatient units. The student team has created recommendations to minimize built-in waste in the design of Michigan Medicine s new Clinical Inpatient Tower through this intricate analysis. Methodology The student team completed the four following tasks to analyze built-in waste: Literature search: The team reviewed three academic papers that focused on minimizing walking distances for hospital care providers. Through this literature search, the team gained insight into design aspects that increase efficiency; lean-led design principles that improve processes and reduce waste; and time-consuming, non-value added activities that reduce time at the bedside care. Observations and interviews: The team spent 48 hours observing and interviewing 12 care providers in the University Hospital. Through the observation of nurses and technicians, the team was able to orient themselves with the departments and the workflow, as well as gain an understanding of how care providers move throughout the hospital. Through the interviews, the team gained insight into which destinations care providers felt they visited most frequently. 1

5 Study using RFID technology: The team used Radio Frequency Identification (RFID) tags to conduct a detailed, data-driven study. The study was conducted for six weeks, and data was gathered from over 100 participants. The RFID tags were worn by care providers for the duration of their shift, and the tag data was exported to a software program that the team used for the analysis. The RFID tag located itself in the hospital every minute and plotted a point at its location. The team was then able to see a map of the hospital with all of the points plotted. The team used string and large copies of hospital blueprints to trace walk paths and determine the distances traveled. The following four key metrics were found for each department: (1) total distance traveled by a care provider over their tagged duration, (2) mean distance traveled each hour, (3) the most frequently visited destinations in each department and the department s epicenter, and (4) distances from the epicenter to those frequently visited destinations. Time studies: For each department, the team identified an epicenter and key frequented destinations. The team conducted time studies using the epicenter as the starting location and the key destination as the endpoint. The team timed 37 different walking paths using a stopwatch and used MODAPTS to validate the time studies. Findings and Conclusions Through observations and interviews, analysis of the RFID data, and time studies, the team found that the distance traveled varied greatly by department. Certain care providers, such as the nurses in Pre-Op and PACU, traveled less, while the University Hospital anesthesiologists took extensive walking distances and routes. Specifically, the average distance traveled per shift for care providers tagged ranged from 987 feet to 5743 feet. The average distance traveled per hour for care providers tagged ranged from 132 feet to 598 feet. In both cases, PACU nurses and technicians were at the low end and anesthesiologists were at the high end of the respective distances. The epicenters were found for each of the seven departments studied. The epicenter was deemed to be the location that was most frequently visited by the care providers. The inpatient units 4A, 4C, 4D, as well as the Pre-Op and PACU departments, all had the charge station as the epicenter. The Anesthesiology Department had office region H as its epicenter, and the Adult Emergency Department had the main computer area as its epicenter. Additionally, each department had different frequently visited destinations. In Unit 4A, these included the conference room, team/computer room, med room, and equipment room. In Unit 4C, the key destinations included the team/computer room, conference room, equipment room, med room, computer desk, and clean and sterile supply room. In Unit 4D, the care providers frequented the computer desk, staff room, clean holding room, med room, and soiled holding room. In the Pre-Op and PACU departments, the destinations included the computer area, supply room, break room, and clean room. In the Anesthesiology Department, the key destinations were the operating room, equipment room, conference room, logistics room, Pre-Op, and PACU. In the Adult Emergency Department, these included the equipment hallway, EMS linen room, and computers. These frequently visited destinations all ranged from 7 to 360 feet from department epicenters and became the key adjacencies recommended for the new Clinical Inpatient Tower. 2

6 Recommendations The team s findings and conclusions led to a series of recommendations. The recommendations include comprehensive ideas stemming from the RFID tag study, primary and secondary key adjacencies for each department, innovative recommendations drawn from the team s observations and literature search, and suggestions for additional analysis methods. The first comprehensive recommendation is to place the patient rooms as close to the department s epicenter as possible. This is estimated to save up to 54 feet per trip for care providers. A second recommendation is to place the anesthesiologists offices between the Pre- Op and PACU departments, saving 180 feet per trip. Lastly, the Pre-Op department should have its own break room, and this would save 195 feet per trip. The primary key adjacencies describe destinations that should be placed near the department s epicenter. Secondary key adjacencies should also be considered, but are not as critical as the primary key adjacencies. The following adjacencies are recommended for each department: Unit 4A: Primary adjacencies include the conference room and computer room. Secondary adjacencies include the med room and equipment room. Unit 4C: Primary adjacencies include the team room/computers and conference room. Secondary adjacencies include the med room, equipment room, computer desk, and clean and sterile supply room. Unit 4D: Primary adjacencies include the computer area and staff room. Secondary adjacencies include the med room, clean holding room, and soiled holding room. Pre-Op Department: Primary adjacencies include the computer area and clean room. Secondary adjacencies include the break room. PACU Department: Primary adjacencies include the supply room. Secondary adjacencies include the break room. Anesthesiology Department: Primary adjacencies include the PACU, equipment room, and operating room. Secondary adjacencies include the conference room, equipment room, and Pre-Op. Adult Emergency Department: Primary adjacencies include the equipment hallway. Secondary adjacencies include the EMS linens room and the computers. In addition, the team developed three innovative recommendations that would decrease the distance care providers travel. These stemmed from ideas gathered during observations, interviews, and the literature search. Locating two computer areas on opposite ends of each inpatient unit Locating supply rooms and closets far enough away from patient rooms to discourage unnecessary and inefficient trips Allowing frequented rooms to have two-hallway access, meaning that care providers can access the room regardless of what side of the unit they are currently in The team recommends additional analysis of the walking distances of Michigan Medicine s care providers. Due to inefficiencies with the RFID tags and data, pedometers are suggested to be an alternative tool. 3

7 INTRODUCTION Michigan Medicine is a premier academic medical center and contains the leading hospital system in the state of Michigan [1]. Michigan Medicine consists of hospitals, clinics, health centers, and the University of Michigan Medical School. In total, Michigan Medicine employs more than 26,000 faculty, staff, students, trainees and volunteers and includes three hospitals at its main Ann Arbor location [2]. Each year, there are upwards of 47,000 hospital stays in 1,000 beds, thus depicting the hospitals drastic demand for patient care [2]. To meet this demand, and provide more quality care for patients, Michigan Medicine has decided to build a new Clinical Inpatient Tower to facilitate the overall growth of adult services, improve colocation of similar services and patients, and improve the patient experience. To optimize the design of the new tower, the health system would like to examine the wastes that are currently occurring in its University Hospital layout. These wastes include excessive distance traveled by care providers (including doctors, nurses, and technicians) and inefficient placements of key destinations and resources. The result of these wastes has been a decrease in time care providers spend at the bedside and in the operating rooms, which includes all direct patient care tasks such as giving medications and taking vital signs. Michigan Medicine s Clinical Inpatient Tower Team has enlisted the help of an Industrial and Operations Engineering 481 student team from the University of Michigan to identify and quantify these wastes and their causes. The team formed recommendations on how these issues could be addressed and alleviated in the new tower s design. Michigan Medicine is passionate about creating a lean tower design, which aims to minimize unnecessary built-in wastes such as motion and transporting [3]. A lean facility plan will increase time care providers spend at the bedside, therefore adding value for the patient. To identify the wastes, the team examined and collected data from three aspects of daily care: the total distance traveled by care providers per shift, the mean distance traveled by care providers per hour, and the most frequented destinations. To meet these objectives, the team observed and interviewed Michigan Medicine care providers, performed a literature search, conducted a Radio-Frequency Identification (RFID) tag study, and conducted time studies. The purpose of this report is to provide background information on the project, clearly state the project s goals and objectives, detail the team s engineering methods for the project, and to document the findings, conclusions, and recommendations which stemmed from the team s intricate analysis. The project was completed between January 24th and April 11th of BACKGROUND Michigan Medicine s flourishing hospital conglomerate constantly faces maximum capacity, and is looking to expand physically to meet growth demands. Due to its geographic restrictions, Michigan Medicine has decided to build its new Clinical Inpatient Tower on the corner of Ann Street and Zina Pitcher Place. Additionally, Michigan Medicine has hired an architecture firm, HOK, to design the new Clinical Inpatient Tower. There is ongoing analysis to help determine the different departments that will move to the new Clinical Inpatient Tower. Michigan Medicine aims to utilize lean-led facility design to minimize waste and add value for patients. Lean-led hospital design can help maximize efficiency and improve the quality of 4

8 hospital facilities [3]. Furthermore, the unnecessary transportation of people and materials, as well as unnecessary motions, are two of the largest contributors to wastes in the healthcare industry [3]. Problems in Michigan Medicine s current facilities include long walking distances for care providers, constrained time at the bedside, and inefficiently placed resources and destinations. Through lean improvements, these problems can be minimized in the new Clinical Inpatient Tower. GOALS AND OBJECTIVES The primary goal of the project was to develop a series of recommendations to minimize built-in waste in the design of Michigan Medicine s new Clinical Inpatient Tower. To identify potential waste, the team analyzed the following data from the current hospital layout: Total distance traveled during a care provider s shift Mean distance traveled per hour of the shift Destinations visited most frequently by care providers KEY ISSUES The following three issues drove the need for this project: Care providers walk a long distance during shifts Time at the bedside is constrained due to motion and transporting activities Key resources and destinations are not efficiently placed in current facility layouts PROJECT SCOPE The project scope included analyzing walking patterns from a variety of hospital care providers such as doctors, nurses, technicians, clerks, pharmacists, and residents. The team conducted the RFID tag study for seven departments within the University Hospital: Unit 4A, Unit 4C, and Unit 4D, as well as the Pre-Op, PACU, Anesthesiology, and Adult Emergency departments. The team collected RFID tag data from all shifts during the work week. Hospital employees that did not interact with patients were not included in this study. Additionally, the team did not focus on Michigan Medicine buildings other than the University Hospital. METHODS To meet the project s goal of minimizing built-in waste in the design of Michigan Medicine s new Clinical Inpatient Tower, the team completed four main tasks, including a literature search, observations and interviews, a study using RFID technology, and time studies. These tasks were chosen to gather information about walking distances, walking patterns, and most frequently visited destinations within each of the seven departments analyzed. Literature Search The team analyzed three academic papers to gain a deeper understanding of methods to reduce non-value added tasks performed by hospital care providers. The literature focused on 5

9 minimizing walking distances. Through the literature search, the team gained insight into design aspects which increase efficiency, lean-led design principles that improve processes and reduce waste, and time-consuming, non-value added activities that reduce time at the bedside care. The first paper examined the effects of the experience of nurses and the unit shape on the walking behavior of the nurses. The researchers noticed that experienced nurses had more unnecessary stops than newer nurses because they knew more people in the hospital and had more interactions while walking. Another observation made by the researchers was that despite the east wing of the hospital being a smaller unit than the west wing unit, the nurses in the east wing walked more. This was because the nurses in the east wing traveled back and forth between the medicine closet and the nurse station instead of efficiently making trips only when needed. Therefore, instead of trying to determine the unit shape or trying to predict a nurse s walking behavior, the team should examine the characteristics of the path that connects functional spaces such as patient room and medication area [4]. The second paper discussed the characteristics of a lean hospital and the principles that can be used to create a lean design. The four rules of lean-led design touch upon the topics of activities, connections, pathways, and continuous improvement. It also explained the eight wastes of lean. The team focused on two of these wastes: transportation and motion. Additionally, the team will employ lean-led design principles to create the most supportive, patient focused physical environment possible [3]. The third paper was a time and motion study performed in 36 hospitals examining how nurses spend their time. According to the research, the nurses spent two-thirds of their time on nonvalue added activities such as documentation, medication administration, and care coordination, and only a third of their time on value added activities such as patient care activities and patient assessment [5]. The paper helped the team identify which nursing activities added value. Observations and Interviews The team observed nurses and technicians within the University Hospital to gain an understanding of how they move throughout their departments and where key destinations, such as supply rooms, are located. The team worked with the project coordinator to establish times and departments to observe. The observations took place in Unit 4A, Unit 4D, and the Pre-Op and PACU departments. Every team member observed in each of these four departments for three hours, totaling 12 hours of observations per team member. The team observed nurses, who care for patients, and technicians, who assist the nurses and perform tasks such as transporting patients and setting up beds. By observing the care providers and their tasks, the team oriented themselves with the departments and workflow. The team used this information to gain a better understanding of the current system, the way care providers move throughout the system, and the locations of key destinations. During the observations, the team members talked to the care providers about their walking patterns and which destinations they felt they visited most frequently. The notes taken during the interviews were given to the project coordinator as a reference for future projects. 6

10 Study Using RFID Technology The team had access to ten RFID tags provided by the hospital to track the walking paths of care providers during their shifts. The care providers wore the tags to track their movement within the hospital. The team collected data for 6 weeks, and tagged over 100 participants. Each week, the team scheduled ten participants to wear the RFID tag for the entire work week. If the shift was completed by part-time employees, different employees were tagged each day. If the participant was a full-time employee, then the tag was worn by that employee for all five days. To drop off and pick up the tag each day, an IOE 481 team member brought the tag to the participant at the beginning of their shift, and picked up the tag at the end of their shift. This allowed the team to keep track of the tags and ensure the tags did not get lost. The team collected data from seven departments: Unit 4A, Unit 4C, and Unit 4D, as well as the Pre-Op, PACU, Anesthesiology, and Adult Emergency departments. At the end of each participant s shift, the IOE 481 team member gave the participant a short survey to fill out. The team developed the survey to assist in the analysis of the RFID tag data. The team used knowledge from previous course work to create the survey. Two of the survey questions used a Likert Scale. The Likert Scale assumed that the strength of an experience is linear and provides the respondent with five to nine pre-coded options to choose from. These options include a neutral choice in between negative and positive options. The survey created by the team was used to keep track of when the participant wore the tag, determine where the participant traveled most frequently, and gauge how busy the participant was during their shift. A copy of the daily survey can be seen in Figure 1. Figure 1: Daily survey handed out at end of shift The team used software provided by the hospital to view the RFID tag data. The RFID tag would plot its location with a pre-determined frequency. During the first three weeks of data collection, the RFID tag plotted its location every five minutes. After analyzing the early data, the team decided to change this frequency to every minute to gather a more detailed walking pattern of the care provider. 7

11 An example of the data collected from an RFID tag for one care provider s shift is provided in Figure 2. The diamonds represent a point plotted by the RFID tag, indicating where the RFID tag is located within the hospital during that time. The lines between the points follow the time sequence with which the data was collected. As seen from the figure, the lines between the points cut through walls, making it difficult to determine the distance and path walked between the two points. The RFID software had several limitations, including the lack of data analysis and exportation tools. As a result, the team developed a hands-on method to determine the distance traveled between the points. First, the team received large copies of the blueprints for each level of the University Hospital. With these large blueprints, the team used string to trace the walk path, moving the string from one point to the next, following the hallways between the points. This method continued for each point along the time sequence. After each hour of data points, the string was measured against the blueprint scale to determine the distance traveled each hour. Additionally, the team tallied the number of times each room was visited during each tagging duration. This was later used to determine the key destinations, defined as the destinations that were visited most frequently by the care providers. Figure 2: Example of RFID tag data After analyzing the RFID tag data, the team developed findings and conclusions for the seven departments. The team analyzed the walk paths collected by the RFID tags to determine four key metrics for each department: (1) Total distance traveled per shift (2) Mean distance traveled per hour (3) Epicenter and the most frequently visited destinations (4) Distances from the epicenter to these destinations Time Studies The team performed time studies to further analyze the care provider s walk paths. The team timed 37 different walking distances using a stopwatch. For each department, an epicenter was identified. The team defined the epicenter as the location visited most frequently in that department, apart from the patient rooms. After identifying the epicenter for each department, the team performed a time study of how long it took to walk from the epicenter to each key destination. The team used the epicenter as the starting location for each time study, and the key destinations as the endpoints. 8

12 To validate the time studies, the team used MODAPTS to determine the normal time it would take to walk from the epicenter to the key destinations. MODAPTS is a predetermined time system based on the analysis of body motions and ensures equivalent conversions. By knowing the distance (in linear feet) between two locations, the normal time it takes to walk that distance can easily be determined. The MODAPTS results and time study results were compared side-byside. FINDINGS AND CONCLUSIONS: ALL DEPARTMENTS The team gathered a significant amount of data from observations and interviews, the RFID tag study, and the time studies. This section presents the details of the team s findings and conclusions that can be applied to all departments. Observations and Interviews After conducting 48 total hours of observations, the team found that care providers had great insight into the hospital processes and most frequented destinations. The care providers discussed their experiences and greatest frustrations, which included making trips to destinations that were out of their way and accommodating at-capacity units. Many voiced concerns about a lack of storage for resources such as patient beds and medicine carts. Furthermore, the team gained insight into the orientation of the units and the overall facility design. This included the location of the charge stations and the most frequented walking routes by care providers. Distance and Epicenter Data from RFID Tag Study for All Units The mean distances traveled by care providers in each department are summarized in Table 1. The team analyzed the walking paths of at least four care providers from each of the departments involved in the study. The length of the shifts that were analyzed varied from seven hours for the nurses in Unit 4A, Unit 4D, and Pre-Op, to eleven hours for the nurses in Unit 4C. The results show that the mean distance traveled by a care provider per hour was the greatest for the anesthesiologists, and the lowest for care providers working in the Pre-Op and PACU departments. Table 1: Mean and standard deviation of distances traveled per department 9

13 After determining which rooms in each department were most frequently visited, the team identified the epicenter for each department, as shown in Table 2. The epicenter is defined by the team as the location visited most frequently per department. Table 2: Epicenters for each department The charge station was determined to be the epicenter for Unit 4A, Unit 4C, Unit 4D, Pre-Op Department, and PACU Department. This is the location where the schedules are kept, the charge nurse is located, and the med room is nearby. Office Region H was identified as the Anesthesiology Department epicenter. Finally, a large computer area was defined as the epicenter for the Adult Emergency Department. FINDINGS AND CONCLUSIONS: INDIVIDUAL DEPARTMENTS The team analyzed RFID tag data for each department separately. In this section, the findings for each individual department are summarized. It should be noted that for the Pre-Op, PACU, and Anesthesiology departments, the RFID tags located themselves every five minutes, while for the remaining departments, the tags located themselves every minute. The departments were analyzed in the order of: Unit 4A, Unit 4C, Unit 4D, and then the Pre-Op, PACU, Anesthesiology, and Adult Emergency departments. For each department, three visuals are used to depict the findings and conclusions. These visuals are: 1) Distances from Epicenter and Percentage of Trips for Key Destinations Bar Graph/Pie Chart: The bar graph displays the key destinations found for each department along the x-axis. For all departments, the patient rooms/bay stations were included in the analysis. For each key destination, their distance from the epicenter is displayed. The epicenters of each unit are shown above in Table 2. The pie chart displays the percentage of trips that are taken to each of the key destinations during the care provider s shift. The team used both diagrams to understand which key destinations led to the care providers walking the farthest and which destinations were visited most often. 2) Key Destinations and Their Distances from the Epicenter Visual: This visual displays the blueprint of each department, highlighting the key destinations that were most frequented. The distances from the department s epicenter to each key destination are displayed in the visual. The star on the visual depicts the epicenter. 10

14 3) MODAPTS and Time Study Table: This table displays the distance from the epicenter to each key destination, the team s time study results, and the MODAPTS values. MODAPTS determined the normal time, and this value was compared to the team s time study value. In addition to the key destinations, the team conducted a time study for the time it took to walk from the epicenter to the farthest patient room/bay station. In addition, screenshots of RFID tag data from the software are included for each department. These screenshots depict the walking paths of each subject the team analyzed. Unit 4A Findings and Conclusions Unit 4A is located on the fourth floor of the University Hospital and operates as a neuro inpatient unit with approximately 26 patient rooms. The team tagged nurses and technicians within this unit. The unit s epicenter proved to be the centrally located charge station. Unit 4A s key destinations were determined to be the conference room, team room/computers, med room, and equipment room. The team found the distances from the epicenter to each of the key destinations. Additionally, the team measured the mean distance traveled to the patient rooms. Figure 3 below shows that the farthest distance care providers in Unit 4A walk are to the patient rooms. This mean distance to the patient rooms was 61 feet. The farthest of the key destinations was the equipment room, while the shortest was the med room. These distances were 51 feet and 14 feet, respectively. Figure 3: Distances from epicenter and percentage of trips for Unit 4A key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 3. The greatest percentage of the care provider s trips were to patient rooms, totaling 54%, while the smallest percentage was to the equipment room, totaling 2%. 11

15 Unit 4A s key destinations are highlighted against the department s layout in Figure 4. A star is used to represent the department s epicenter: the charge station. The visual confirms that the med room is closest to the department epicenter, while the farthest destinations were the patient rooms along the hallways. Figure 4: Unit 4A key destinations and their distances from the epicenter The time studies conducted in Unit 4A are depicted in Table 3. Out of all the key destinations, the farthest path from the epicenter was to the equipment room, totaling 51 feet and taking approximately seconds. Additionally, the shortest path from the epicenter was to the med room, totaling 14 feet and taking approximately 3-4 seconds. The distance from the epicenter to the farthest patient room was 93 feet, taking approximately seconds. Table 3 validates the team s data collection method; all values from the time studies aligned within 0-2 seconds of the MODAPTS values. Table 3: MODAPTS and time studies for Unit 4A 12

16 The walking paths of five care providers were analyzed in this unit, and their locations were tracked through the RFID technology. Figure 5 depicts the five different subjects and their RFID plots throughout Unit 4A. The dates and times of the tagging are included in the figure. Figure 5: Screenshots of RFID data for Unit 4A subjects 13

17 Unit 4C Findings and Conclusions Unit 4C is located on the fourth floor of the University Hospital and operates as a neuro inpatient unit with approximately 29 patient rooms. The arrangement of the department is similar to that of Unit 4A s. The team tagged nurses and technicians to determine which destinations the care providers visited most frequently, and how far they traveled per shift and hour. The epicenter for the unit was found to be the charge station. Unit 4C s key destinations were identified as the clean and sterile supply room, computer desk, med room, equipment room, conference room, and team room/computers. Figure 6 shows that, on average, care providers in Unit 4C walked the farthest distance to patient rooms at a mean distance of 75 feet. For the key destinations, the clean and sterile supply room proved to be the farthest distance from the epicenter at 68 feet, while the med room proved to be the closest at 14 feet. Figure 6: Distances from epicenter and percentage of trips for Unit 4C key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 6. The greatest percentage of the care provider s trips were to patient rooms, totaling 42%, while the smallest percentage was to the clean and sterile supply room, totaling only 4%. Unit 4C s key destinations are highlighted against the department s layout in Figure 7. A star is used to represent the department s epicenter: the charge station. The visual confirms that the med room is closest to the department s epicenter, while the farthest destinations were the patient rooms along the hallways. 14

18 Figure 7: Unit 4C key destinations and their distances from the epicenter The time studies conducted in Unit 4C are depicted in Table 4. Out of all the key destinations, the farthest path from the epicenter was to the clean and sterile supply room, totaling 68 feet and taking approximately seconds. Additionally, the shortest path from the epicenter was to the med room, totaling 14 feet and taking approximately 3 seconds. The distance from the epicenter to the farthest patient room was 143 feet, taking approximately seconds. Table 4 validates the team s data collection method; all values from the time studies aligned within 0-4 seconds of the MODAPTS values. Table 4: MODAPTS and time studies for Unit 4C The walking paths of six care providers were analyzed in Unit 4C, and their locations were tracked through the RFID technology. Figure 8 depicts the six subjects and their RFID results throughout Unit 4C. The dates and times of the tagging are included in the figure. 15

19 Figure 8: Screenshots of RFID data for Unit 4C subjects 16

20 Unit 4D Findings and Conclusions Unit 4D of the University Hospital is a neuro intensive care unit with approximately 15 patient rooms. This unit shares similarities with Unit 4A and Unit 4C in terms of design and organization. The team tagged nurses and technicians from the department to determine which destinations the care providers visited most frequently, and how far they traveled per shift and hour. The epicenter for Unit 4D was found to be the charge station. The key destinations for Unit 4D were determined to be the med room, clean holding room, staff room, soiled holding room, and computer area. Figure 9 pictured below shows that, on average, care providers in Unit 4D walk a mean distance of 62 feet to patient rooms. Out of the key destinations, the med room proved to be the closest distance from the epicenter at 7 feet, while the farthest distance was the computer area at 104 feet. Figure 9: Distances from epicenter and percentage of trips for Unit 4D key destinations The percentage that each of the key destinations are visited can be visualized in Figure 9. The greatest percentage of the care provider s trips was to the computer area and charge station, totaling 24% each, while the smallest percentage was to the soiled holding room, totaling 5%. The distances and the most direct path from the epicenter to each key destination are depicted in the visual below in Figure 10. The epicenter for this unit, much like Unit 4A and Unit 4C, was the charge station and is depicted by a star on the visual. The closet key destination is the med room while the farthest is the computer area down the hall. 17

21 Figure 10: Unit 4D key destinations and their distances from the epicenter The time study results conducted in Unit 4D are shown in Table 5. Out of all the key destinations, the farthest path from the epicenter was to the computer area, totaling 104 feet and taking approximately 24 seconds. Additionally, the shortest path from the epicenter was to the med room, totaling 7 feet and taking approximately 2 seconds. The distance from the epicenter to the farthest patient room was 125 feet, taking approximately 27 seconds. The team s data collection method was verified by comparing the MODAPTS values; all values from the time studies aligned within 0-2 seconds of the MODAPTS values. Table 5: MODAPTS and time studies for Unit 4D The walking paths of five care providers were analyzed in this unit, and their locations were tracked through the RFID technology. Figure 11 depicts the RFID results of the five subjects. The dates and times of the tagging are included in the figure. 18

22 Figure 11: Screenshots of RFID data for Unit 4D subjects 19

23 Pre-Op Department Findings and Conclusions The Pre-Op Department, located on the first floor of the University Hospital, prepares patients for surgery. Due to its nature, there are fluctuations on when the Pre-Op Department is busy throughout the day. The epicenter for this department was found to be the charge station. The key destinations for the Pre-Op Department were identified as the clean room, PACU break room, and two computer areas. Figure 12 below shows that care providers in the Pre-Op Department walk an average distance of 52 feet to the bay stations. Out of the key destinations, the PACU break room was the farthest distance from the epicenter at 245 feet. During our analysis, the team noticed that the Pre-Op care providers walked to the PACU break room once during their shift. Figure 12: Distances from epicenter and percentage of trips for Pre-Op key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 12. The majority of the care provider s trips were to the bay stations and charge station, each at 39% and 22% respectively. The smallest percentage was a trip to the PACU break room during lunch time, while the clean room and both computer areas total 11% each. Pre-Op s key destinations are highlighted against the department s layout in Figure 13. A star is used to represent the department s epicenter: the charge station. The visual confirms that the clean room is closest to the department epicenter, while the farthest destination is the PACU break room. 20

24 Figure 13: Pre-Op key destinations and their distances from the epicenter The time study results conducted in the Pre-Op Department are shown in Table 6. Out of all the key destinations, the farthest path from the epicenter was to the PACU break room, totaling 245 feet, and taking approximately 54 seconds. Additionally, the shortest path from the epicenter was to the clean room, totaling 35 feet and taking approximately 7 seconds. The distance from the epicenter to the farthest bay station was 75 feet, taking approximately 16 seconds. The team s data collection method was verified by comparing the time study values against the MODAPTS values; all values from the time studies aligned within 0-6 seconds of the MODAPTS values. Table: 6: MODAPTS and time studies for Pre-Op Department The walking paths of four Pre-Op care providers were analyzed, and their locations were tracked through the RFID technology. Figure 14 depicts the four participants and their RFID results throughout the Pre-Op department. The dates and times of the tagging are included in the figure. 21

25 Figure 14: Screenshots of RFID data for Pre-Op subjects PACU Department Findings and Conclusions The PACU Department, located on the first floor of the University Hospital, provides patient bay stations for recovery following surgeries. Due to its nature, there are fluctuations on when the PACU Department is busy throughout the day. The epicenter for this department was the charge station. The key destinations for the PACU Department were found to be the supply room and the PACU break room. Figure 15 pictured below shows that, on average, care providers in the PACU Department walk a distance of 68 feet to patient bay stations. Out of the key destinations, the supply room proved to be the farthest distance from the epicenter at 60 feet, while the closest key destination was the break room at 55 feet. 22

26 Figure 15: Distances from epicenter and percentage of trips for PACU key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 15. The greatest percentage of the care provider s trips was to the bay stations, at 50%. The smallest percentage was to the supply room, at 8%. The PACU key destinations are highlighted against the department layout in Figure 16. The epicenter for this department, much like the Pre-Op Department, was the charge station, which is highlighted with a star. The supply room and break room are located on opposite ends of the department, each at 60 feet and 55 feet from the epicenter respectively. Figure 16: PACU key destinations and their distances from the epicenter 23

27 The time study results conducted in the PACU department are shown in Table 7. Out of all the key destinations, the farthest path from the epicenter was to the supply room, totaling 60 feet, and taking approximately 12 seconds. Additionally, the shortest path from the epicenter was to the PACU break room, totaling 55 feet and taking approximately seconds. The distance from the epicenter to the farthest bay station was 90 feet, taking approximately 19 seconds. The team s data collection method was verified by comparing the time studies values against the MODAPTS values; all values from the time studies aligned within 0-1 seconds of the MODAPTS values. Table 7: MODAPTS and time studies for PACU Department The paths of four care providers were analyzed in the PACU, and their locations were tracked through the RFID technology. Figure 17 depicts the four subjects and their RFID results throughout the department. The dates and times of the tagging are included in the figure. Figure 17: Screenshots of RFID data for PACU subjects 24

28 Anesthesiology Department Findings and Conclusions The Anesthesiology Department of the University Hospital resides on the first floor near the Pre- Op and PACU departments. The team studied anesthesiologists supporting neurosurgery. These care providers are unique in that they visit the patient bay stations in the Pre-Op and PACU areas and travel to operating rooms. Their offices are clustered together in an area the team has deemed to be the Region H office area, due to the already existing room naming convention. The epicenter for the department was the Region H office area. The key Anesthesiology Department destinations were found to be the conference room, logistics room, equipment room, operating room, and Pre-Op and PACU departments. Figure 11 pictured below depicts that, on average, the anesthesiologists walk a distance of 360 feet to the Pre-Op Department and 195 feet to the PACU Department. Out of the key destinations, the operating room proved to be the farthest distance from the epicenter at 200 feet, and the conference room proved to be the closest distance from the epicenter at 50 feet. Figure 18: Distances from epicenter and percentage of trips for Anesthesiology key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 18. The greatest percentage of the care provider s trips was to the PACU, totaling 32% of trips, while the smallest percentage was to the conference room, totaling 5%. The Anesthesiology Department key destinations are highlighted against the department layout in Figure 19. The epicenter for this unit, the Region H office area, is depicted by a star. The distances to Pre-Op and PACU below end at the charge stations, as this represents an average distance that anesthesiologists might travel when visiting those units. 25

29 Figure 19: Anesthesiology key destinations and their distances from the epicenter Table 8 below depicts the time study results conducted for the anesthesiologists. Out of all the key destinations, the farthest path from the epicenter was to the Pre-Op charge station, totaling 360 feet and taking approximately 80 seconds. Additionally, the shortest path from the epicenter was to the conference room, totaling 50 feet and taking approximately 11 seconds. The values of the time studies aligned within 0-2 seconds of the MODAPTS values, thus verifying the team s data collection method. The one exception was the path from the epicenter to the Pre-Op Department. This path was heavily trafficked and congested, therefore the team s time study value was greater than that of the MODAPTS value. Table 8: MODAPTS and time studies for Anesthesiology Department The walking paths of five anesthesiologists supporting neurosurgery were analyzed, and their locations were tracked through the RFID technology. Figure 20 depicts the five different subjects and their RFID results. The dates and times of the tagging are included in the figure. 26

30 Figure 20: Screenshots of RFID data for Anesthesiology subjects 27

31 Adult Emergency Department Findings and Conclusions The Adult Emergency Department (Adult ED) is located on floor B1, or the first basement level, of the University Hospital. This department is expansive, and has an immediate path to the outside of the facility. The epicenter was the computer area located in the middle of the department at C264. The key Adult ED destinations were determined to be the EMS linens, equipment hallway, and computers. Figure 21 pictured below shows that, on average, the Adult ED care providers walk a distance of 60 feet to patient rooms. Out of the key destinations, the computers proved to be the farthest distance from the epicenter at 110 feet, while the EMS linen room was the closest to the epicenter at 90 feet. Figure 21: Distances from epicenter and percentage of trips for Adult ED key destinations The percentage that each of the key destinations are visited during a care provider s shift can be visualized in Figure 21. The greatest percentage of the care provider s trips, apart from the percentage to the epicenter, was to patient rooms at 23%. The smallest percentage was to the EMS linens room and computers, both at 9%. The key destinations for the Adult ED are highlighted in the layout in Figure 22. The epicenter for this unit was the computer area and is depicted by a star. The farthest key destination was the computers while the closest key destination was the EMS linens. 28

32 Figure 22: Adult ED key destinations and their distances from the epicenter The time studies conducted in the Adult ED are depicted in Table 9. Out of all the key destinations, the farthest path from the epicenter was to the computers, totaling 110 feet and taking approximately 22 seconds. Additionally, the shortest path from the epicenter was to EMS linens, totaling 90 feet and taking approximately 19 seconds. Table 8 confirms the team s data collection method; all values from the time studies aligned within 0-2 seconds of the MODAPTS values. Table 9: MODAPTS and time studies for Adult ED The walking paths of five care providers from Adult ED were analyzed, and their locations were tracked through the RFID technology. Figure 23 depicts the five subjects and their RFID results throughout floor B1. The dates and times of the tagging are included in the figure. 29

33 Figure 23: Screenshots of RFID data for Adult ED subjects 30

34 RECOMMENDATIONS Based on the findings and conclusions, the team has developed comprehensive recommendations stemming from the overall analysis of RFID tag data, as well as recommendations regarding key adjacencies in the new Clinical Inpatient Tower layout. Primary key adjacencies describe destinations that should be the top priority in terms of placement near each department s epicenter. Secondary key adjacencies should also be taken into consideration, but are not as critical as the primary destinations. Furthermore, the team has created innovative recommendations stemming from their observations, interviews, and literature search. Lastly, the team has formed recommendations regarding additional studies and analysis methods. Comprehensive Recommendations The team developed three recommendations derived from the RFID tag analysis that include: 1. Placing patient rooms as close to the department s epicenter as possible 2. Placing the Anesthesiology offices between the Pre-Op and PACU departments 3. Creating a break room specifically for the Pre-Op department Based on data collected from the departments on the fourth floor, patient rooms could span a large range of distances from a unit s epicenter. The farthest patient rooms had distances between 93 and 143 feet. Due to these large distances, the team recommends a unit configuration that decreases the overall distance between patient rooms and epicenters. This could save care providers from walking an additional 54 feet and spending 12 seconds per trip. The anesthesiologists spend time walking to the Pre-Op and PACU departments from the Anesthesiology Department epicenter, which proved to be the office region. Therefore, the team recommends placing the Anesthesiology office area between the Pre-Op and PACU units. This would save anesthesiologists 180 feet per trip and 39 seconds in route. Lastly, the team recommends that the Pre-Op Department have its own break room. Through the RFID tag study data, Pre-Op care providers were seen frequenting the PACU break room, which was 245 feet away from the Pre-Op epicenter. By creating a break room for the Pre-Op Department, care providers would save up to 195 feet per trip, depending on where the break room was configured inside the unit. This would correlate to 42 seconds of saved walking time. Recommendations for Unit 4A Data stemming from the team s RFID tag study determined the primary and secondary key adjacencies for the 4A neuro inpatient unit. In building an inpatient unit such as Unit 4A, the team recommends having these destinations close to the unit s epicenter. For Unit 4A in the current University Hospital, the epicenter was the charge station. These key adjacencies are depicted in Table 10 below. Table 10: Primary and secondary key adjacencies for Unit 4A Primary Key Adjacencies Secondary Key Adjacencies Conference Room Computer Room Med Room Equipment Room 31

35 Recommendations for Unit 4C Similar to Unit 4A, the team analyzed the RFID findings to develop recommendations for the 4C neuro inpatient unit. The epicenter for this unit was also the charge station. The primary and secondary key adjacencies are depicted in Table 11. These rooms should be located as close as possible to the epicenter. Table 11: Primary and secondary key adjacencies for Unit 4C Primary Key Adjacencies Secondary Key Adjacencies Team Room / Computers Conference Room Med Room Equipment Room Computer Desk Clean & Sterile Supply Room Recommendations for Unit 4D The primary and secondary key adjacencies for the 4D neuro intensive care unit are depicted in Table 12. These destinations should be taken into account when designing the new inpatient tower. The primary key adjacencies should take priority over the second key adjacencies, due to the primary destinations being traveled to more frequently. This was supported in the team s findings from the RFID tag study. Table 12: Primary and secondary key adjacencies for Unit 4D Primary Key Adjacencies Secondary Key Adjacencies Computer Area Staff Room Med Room Clean Holding Room Soiled Holding Room Recommendations for Pre-Op Department The team s RFID tag study determined the primary and secondary key adjacencies for the Pre- Op Department. When building a Pre-Op Department in the new Clinical Inpatient Tower, the team recommends having the primary and secondary key adjacencies, depicted in Table 13 close to the department s epicenter. The Pre-Op epicenter is currently the charge station. Table 13: Primary and secondary key adjacencies for the Pre-Op Department Primary Key Adjacencies Secondary Key Adjacencies Computer Area Clean Room Break Room 32

36 Recommendations for PACU Department Similar to the Pre-Op Department, the RFID tag study data highlighted the primary and secondary key adjacencies for the PACU Department. When building a PACU Department in the new Clinical Inpatient Tower, the team recommends having the primary and secondary key adjacencies, depicted in Table 14 close to the department s epicenter. For the PACU Department in the University Hospital, the epicenter was the charge station. Table 14: Primary and secondary key adjacencies for the PACU Department Primary Key Adjacencies Secondary Key Adjacencies Supply Room Break Room Recommendations for Anesthesiology Department Data from the team s RFID tag study determined the primary and secondary key adjacencies for the Anesthesiology Department in the University Hospital. In building a similar department in the new tower, the team recommends including the key adjacencies close to the department s epicenter, which was determined to be Office Region H, where the anesthesiologists resided. These key adjacencies are depicted in Table 15 below. Table 15: Primary and secondary key adjacencies for the Anesthesiology Department Primary Key Adjacencies Secondary Key Adjacencies PACU Equipment Room Operating Room Conference Room Logistics Room Pre-Op Recommendations for Adult Emergency Department Data collected from the team s RFID tag study determined the primary and secondary key destinations for the Adult Emergency Department in the University Hospital. In building a department, such as Adult ED s, the team recommends the new tower be designed to have these specific key adjacencies in mind. For the Adult ED department, the epicenter was the computer area. These primary and secondary key adjacencies are depicted in Table 16 below and should be located as close to the epicenter as possible. Table 16: Primary and secondary key adjacencies for the Adult Emergency Department Primary Key Adjacencies Secondary Key Adjacencies Equipment Hallway EMS Linens Computers 33

37 Innovative Recommendations Although the team formed recommendations based on the findings from the data-driven RFID tag study, the team also created innovative suggestions based on observations, interviews, and literature search. Three out-of-the-box suggestions for the new Clinical Inpatient Tower layout, depicting visually in Figure 24, include: 1. Placing two computer rooms on opposite ends of the inpatient units 2. Moderately spacing the patient rooms and supply/med rooms 3. Affording two-hallway access to frequented rooms Figure 24: Innovative recommendations visualization As seen through the team s observations, a great deal of care providers time was consumed by using computers. Tasks such as charting and keeping track of medications must be conducted in a timely fashion using a computing station. Due to this responsibility, care providers must have easy and optimal access to computer rooms at all times. For inpatient units, such as Unit 4A and Unit 4C, which are long and slim, the team recommends building not one, but two computing spaces one fourth of the distance from each end of the unit. This would ensure that no matter where the care provider is in the unit, they would be, at most, 1/4 of the unit away from a computer. This would save care providers a walking distance of 60 feet and a walking time of 13 seconds per trip. From the team s literature search, it was found that psychological aspects are involved with how nurses and care providers transport and retrieve medical supplies. The literature proved that because nurses in smaller units had supply closets nearby, they frequented those destinations more often [5]. They were more inefficient in their trips due to the close proximity of the destination. Therefore, the team suggests placing supply rooms a moderate distance away from the patient rooms as to discourage unnecessary and inefficient trips. This would save care providers a minimum of 12 feet per trip, correlating to 3 seconds of saved walking time per trip. 34