Michigan Medicine

Reducing Non-Value Added Time in RFID Tagging System

Final Report

To: Mamoon Nabilsi, Lead Supervisor, Patient Equipment Andrew Sweeney, Industrial Engineer, Logistics & Support Services

Allie Mukavitz, Industrial Engineering Fellow, Performance Improvement Mary Duck, UMHS IOE 481 Liaison, Program and Operations Analysis Dr. Mark P. Van Oyen, Ph.D., IOE 481 Faculty Instructor

From: IOE 481 Project Team #6 Tarini Arte Darnell Butler Samuel Epner Sarah Finley

Date: April 17, 2018

ID: 18W6-final-report

Table of Contents Executive Summary 1

Background and Key Issues 1

Data and Design Methods, Requirements, Constraints and Standards 2

Findings and Conclusions 2

Recommendations 3

Introduction 4

Background 4

Key Issues 5

Goals and Objectives 5

Project Scope 6

Data and Design Methods, Requirements, Constraints and Standards 6

Literature Search 6

Time Study Design 7

Historical Data Analysis 11

Interviews 12

Findings and Conclusions 12

Literature Search 12

Time Study Design 13

Historical Data Analysis 15

Interviews 18

Design Recommendations 20

Implementation of Standard Work Document for RFID Attachment 20

Distribution of Education Flyers 22

Further Investigation into Additional Methods of Reducing Alarm Frequency 23

Expected Outcome and Impact 23

References 24

Appendix 25

Appendix A: Interview Questions 25

Appendix B: Constraints & Standards Matrix 26

Appendix C: Timing and Location Trends Analysis 28

Appendix D: Standard Operating Procedures for Attaching RFID Tags 30

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List of Figures and TablesFigure 1: Data collection sheet with the front and back 8

Figure 2: Current state map of RFID retrieval process 13

Figure 3: Average relative length of each step in the RFID retrieval process 14

Figure 4: Frequency of dock alarms by type of asset 15

Figure 5: Resolution by type of item for five most commonly lost items 18

Figure 6: Adhesive tape attachment method 21

Figure 7: RFID placement of bracket 21

Figure 8: Informational flyer designed for nursing staff 22

Figure 9: Frequency of dock alarms by day of the week 28

Figure 10: Frequency of dock alarms by time of day 28

Figure 11: UH Main B2 resolution percent frequency 29

Figure 12: C&W resolution percent frequency 29

Table 1: Pugh Decision Matrix for selecting a data collection method 11

Table 2: Analysis of the frequency of the five most commonly lost pieces 16

Table 3: Frequency of the most commonly lost items weighted by total assets 16

Table 4: Percent frequency of each type of dock alarm resolution 17

Table 5: Constraints and Standards matrix for data collection method design 26

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Executive SummaryThe Patient Equipment Department at Michigan Medicine is responsible for the clinical ordering, managing and delivering of patient equipment throughout the hospital. In the past, the hospital has experienced problems with equipment being misplaced. To resolve this problem, the hospital deployed about 9,000 radio frequency identification (RFID) tags which allow the equipment’s relative location to be tracked in real time. When an RFID tag passes a boundary of the hospital, such as a loading dock, an alarm sounds and Patient Equipment supervisors are called to the dock to recover the RFID tag and the corresponding equipment, a process which is lengthy, difficult, and frustrating for the supervisors. To address this issue, the Lead Supervisor of Patient Equipment asked an IOE 481 student team from the University of Michigan to collect data to understand the current state of the RFID retrieval process, map and analyze the current process to identify root causes of dock alarms, and recommend methods for decreasing the frequency at which dock alarms due to RFID tags occur.

In order to accomplish this goal, the team performed a literature search, designed and conducted a time study, analyzed historical data, and conducted interviews. Through these methods, the team developed recommendations to reduce the frequency of dock alarms. With the implementation of these recommendations, the team believes employee morale and quality of work will be improved, and the hospital will benefit financially.

Background and Key IssuesRFID tracking of patient equipment is of significant importance. The hospital requires the tracking of patient equipment for logistical purposes and utilizes information about the location of equipment to better serve patients. Additionally, the department wants to prevent valuable equipment from being lost. However, there is currently a lack of standardization in processes relating to RFID tags, leading to inefficiency, wasted employee time, and staff dissatisfaction.

Goals and ObjectivesThe main goals of the project are to understand and quantify the current state of the RFID retrieval process, analyze historical data for trends, and conduct interviews to understand the qualitative factors influencing the alarms. The team’s recommendations aim to standardize RFID attachment procedures, raise awareness among hospital staff about RFID tags and their importance, and ultimately reduce the frequency of dock alarms in order to increase employee satisfaction and allow the hospital to save financially.

Project ScopeThe project focuses on the RFID tagging process for attaching RFID tags to equipment and the RFID retrieval process performed by supervisors in response to dock alarms. The project will also consider other groups interacting with these processes, such as Environmental Services (EVS), caregivers, and Patient Equipment staff. The project will not include any other uses for RFID tags such as clinical ordering or Patient Equipment management.

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Data and Design Methods, Requirements, Constraints and StandardsIn order to collect data, the team performed a literature search, designed and conducted a time study, completed historical data analysis, and conducted interviews with relevant parties.

Literature SearchThe team conducted literature searches through Google Scholar as well as the past IOE 481 Project Database using search terms, such as “RFID,” “process mapping,” and “time study.”

Time Study DesignThe team decided to perform a time study; however, because the docks alarms occur irregularly, the team needed a way to collect data without being onsite. Therefore, the team designed options for data collection methods by considering the constraints, standards, and requirements for design. The team used a Pugh decision matrix to select the best alternative, a data collection form, and implemented the method. Patient Equipment supervisors collected data for nine dock alarms over three weeks. The resulting data was analyzed in Microsoft Excel.

Historical Data AnalysisThe project coordinators pulled data from Michigan Medicine databases using SQL queries for dock alarms over a four and half month period. The team cleaned the data to remove duplicates and dock alarms not relevant to the scope. The data was analyzed in Microsoft Excel.

InterviewsThe team conducted interviews with supervisors and the manager to understand their insights into the RFID retrieval process. The team also interviewed the tag team and observed two experienced members perform the RFID tag attachment process.

Findings and ConclusionsAfter collecting the data, the team analyzed the data to determine findings and conclusions that helped craft the team’s recommendations.

Literature SearchThe team found a gap in knowledge in current literature surrounding RFID tag implementation in hospitals and focused their project on filling this void. They also researched examples of standard operating procedures created for hospital processes to learn best practices.

Time StudyOverall, the team found that the average total time for the process was 44.5 minutes, which helped the team to quantify their expected impact in supervisor time and hospital money saved. The team also found that travel time to the dock takes 14.5% of the total process time. For alarms tripped in error, this travel time could be eliminated by letting the supervisor know immediately

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that the alarm was a mistake. Additionally, the team found that EVS noted which bin set off the alarm only 44% of the time, even though the Patient Equipment department considered this to be standard practice, implying confusion between parties about EVS’s responsibilities.

Historical Data AnalysisThe team found that 1.15 dock alarms occurred per day. The data showed that five devices accounted for over 90% of the dock alarms, so the team concluded that targeting these equipment types was essential. For these five items, the alarm was most frequently resolved by finding the RFID tag but not the equipment or tripped in error. The team concluded that improving the attachment of tags to equipment and reducing the alarms tripped in error should be prioritized.

InterviewsSupervisors agreed that reducing the frequency at which the retrieval process needed to occur would be helpful. They shared that most often only the RFID tag is found in retrievals, not the equipment itself, confirming findings from the historical data analysis. The team concluded that the root cause of the problem was with the attachment of the RFID tags to the equipment -- either they are falling off or being taken off intentionally by the hospital staff. The team learned that there was a lack of standardization for how tags were attached and identified the attachment process as an area where standard procedures could be created to ensure more consistent, higher quality attachment. The team also learned that many hospital staff are not unware of the uses of RFID tags and may remove tags from equipment or throw out tags found on the ground.

RecommendationsFrom these findings, the team developed three recommendations to achieve the project goals.

Standard Operating Procedures for RFID AttachmentThe team recommends the implementation of a standard work document for attaching RFID tags to equipment. The team designed a 13-page document which describes how to choose the attachment location and attachment method as well as how to attach the tags themselves.

Flyer to Educate CaregiversThe team also recommends circulating educational flyers to increase awareness among hospital staff as to what RFID tags are and why they are important. The team designed a flyer that they recommend placing in high traffic areas in order to inform caregivers and hospital staff.

Additional Ideas for Further InvestigationDue time constraints, the team could not explore all factors affecting the frequency of the dock alarms. The team recommends the department further investigate reducing alarms tripped in error, creating standard work procedures for EVS’s role in the retrieval process, and installing exciters in additional locations to sound alarms earlier in the process and simplify retrieval.

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IntroductionThe Patient Equipment Department of Michigan Medicine is responsible for the clinical ordering, managing and delivering of patient equipment throughout the University of Michigan Medical Center. Patient equipment includes everything from IV pumps to beds to incubators. In the past, the hospital has experienced problems with equipment being misplaced or stolen. To resolve this problem, the hospital has invested in and deployed about 9,000 radio frequency identification (RFID) tags. These tags are attached to assets that are high-value, high-volume, or at high-risk of disappearance or theft and allow the equipment’s relative location to be tracked in real time. As documented in the article “Passive RFID Asset Monitoring System in Hospital Environments,” the use of RFID tags is becoming increasingly popular for hospitals, given the positive return on investment they can bring by preventing equipment from being lost or stolen [1].

One of the capabilities of the Michigan Medicine RFID tags is that an alarm will sound if an RFID tag passes a boundary out of the hospital. Most frequently, these alarms are triggered at the loading docks when items such as trash and laundry are being taken out of the building. When an alarm sounds, Patient Equipment supervisors are called to the dock and are responsible for recovering the RFID tag and the corresponding equipment. Currently, there are no official standard procedures for RFID tag attachment or the division of responsibilities with Environmental Services (EVS). The Lead Supervisor of the Patient Equipment Department expressed concerns regarding the inefficiency of the current process, particularly with the amount of time supervisors devote to RFID retrievals. Supervisors also expressed their dissatisfaction with the current state, stating that they spend too much of their on-duty time tending to the triggered RFID tags and that the time could be better spent attending to other matters that could directly improve patient care quality. Therefore, the Patient Equipment Department and supervisors wanted to determine how to decrease this time investment by reducing the frequency of triggered dock alarms.

In order to accomplish this goal, the Lead Supervisor wanted to determine the root causes of the alarms at loading docks and use these insights to create new standard operating procedures for the Patient Equipment department. To address this question, the Lead Supervisor asked an IOE 481 student team from the University of Michigan to collect data to understand the current state, map and analyze the current process to identify pain points, and recommend areas for improvement. The team first looked to understand the core factors driving the frequency of RFID alarms at the loading docks and then secondly, designed new processes and corresponding documentation to decrease the frequency of occurrences. This report presents all the content the team generated throughout the project, including the data the team has collected and analyzed, the findings and conclusions from the data, and the team’s final recommendations and expected impact.

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BackgroundMichigan Medicine considers RFID tracking of patient equipment to be of significant importance for multiple reasons. To begin with, Michigan Medicine requires the tracking of patient equipment for logistical purposes and utilizes information about the location of equipment to better serve patients and improve their care. Additionally, the department wants to prevent valuable equipment from being lost. The inadequate tracking of equipment could have a large financial impact on the hospital if not addressed appropriately. However, there is a lack of awareness about RFID tracking outside of the Patient Equipment department. Many of the caregivers and hospital staff are unaware of the process for tracking patient equipment and the importance of RFID tags.

Within the Patient Equipment department, there has recently been a high rate of personnel turnover. In the past few months, there have been changes in management with the hiring of new supervisors, as well as turnover in staff due to many of the department’s employees being part-time or temporary staff. According to the Lead Supervisor, because supervisors do not have a standard process for most activities, including how RFID tags are attached to equipment, they spend a lot of time training new employees, and the training these new employees receive is inconsistent.

With the personnel changes, the supervisors’ time is especially valuable. However, current supervisors stated that they felt overworked due to the number of dock alarms they are required to respond to. The supervisors asked that more efficient processes be put into place to help reduce their time expenditure for RFID tracking. This project aims to address these concerns by determining the root causes of RFID dock alarms and then developing recommendations that will reduce the frequency at which dock alarms occur.

Key IssuesThe following key issues drove the need for this project:

● A lack of standardized processes within the Michigan Medicine network, specifically for supervisors and staff, leading to wasted time for employees.

● Significant supervisor time being spent on the RFID retrieval process.● Inconsistencies in how operations related to the RFID tagging process are performed.● Dissatisfaction among supervisors due to the large amount of on-duty time that is

spent on RFID retrievals and the inconveniences of the process.● A desire for standardization that will improve both communication and employee

training.● A need for widespread education about RFID tracking and its importance.

Goals and ObjectivesTo determine potential changes that could reduce the frequency with which the RFID retrieval process occurs and thus the time investment in the process, the team completed the following tasks:

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● Understood the current state of the RFID retrieval process.● Designed a time study to quantify time spent in the current retrieval process.● Analyzed historical data on RFID dock alarms and retrievals.● Interviewed supervisors and staff to get qualitative feedback on the RFID tagging and

retrieval processes. With this information, the team developed recommendations to:

● Standardize the RFID tagging procedures within the Michigan Medicine system to improve the consistency and quality of tagging.

● Create awareness among nursing staff, janitorial staff, and other similar entities about the importance of RFID tags.

● Reduce the frequency of dock alarms, thereby increasing on-duty time available for supervisors to dedicate to their duties outside of the RFID retrieval system.

● Validate and quantify the amount of time saved by switching to a future state process.● Increase overall supervisor and manager satisfaction.

Project ScopeThis project focused on the RFID tagging process for attaching RFID tags to equipment and the RFID retrieval process performed by supervisors in response to RFID alarms triggered at hospital loading docks. The tagging process begins when the Patient Equipment department receives a piece of equipment and ends once the RFID tag is attached to the equipment and its attachment has been documented. The retrieval process begins when the RFID exciter receives a signal that sets off the alarm, and ends when the supervisor concludes the search for the RFID tag and resolves the alarm. The project will also consider other entities surrounding these processes, including but not limited to EVS staff, nursing and janitorial staff, and Patient Equipment staff.

Any task not connected to the RFID tagging or retrieval process in the Michigan Medicine network is not included in this project. Specifically, any other use of the RFID tags, such as clinical ordering and Patient Equipment management, or any other tasks or activities performed by supervisors will not be studied under this project. However, the goal is that the findings from this project and the designed standardized processes and corresponding documentation could be extended as a template for the standardization of similar processes at the Michigan Medicine in the future.

Data and Design Methods, Requirements, Constraints and StandardsThe team used a number of data analysis methods to progress their project, including a literature search, designed time study, historical data analysis, and interviews, which are described in the following section.

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Literature SearchThe team conducted literature searches using Google Scholar, as well as the past IOE 481 Project Database, to obtain a more holistic sense of previous research in this field. The literature searches were performed throughout the project in order to help develop in the team’s project plan and focus. Relevant sources were found using appropriate search terms in the databases and search engines, such as “RFID,” “process mapping,” and “time study.”

Time Study Design The sections below describe the team’s design methods, recommendations, constraints, and standards when designing the time study.

IntroductionThe team wanted to gather time study data of the end to end RFID retrieval process in order to better understand the process and influence the team’s final design based recommendations. However, because the docks alarms occur irregularly and their occurrences can not be predicted, the team needed a creative way to collect relevant data without being onsite. Therefore, the team looked to design their own data collection method.

The team’s design task was to create a method that would allow for time study data collection while the team was remote. After brainstorming with the project coordinators, the team decided that one option would be to provide supervisors with a paper form that they could fill out with time data while completing the retrieval process. The team first brainstormed relevant data points that they wanted to capture from the data collection process and then designed the form to incorporate those fields. In order to anticipate questions from the supervisors before they occurred, the team decided to include a completed example of the time sheet on the back of the form for guidance. The resulting data collection sheet is shown in Figure 1.

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Figure 1: Data collection sheet with the front (left) and back (right) shown above

In order to ensure proper usage of the data collection form and provide supervisors with context for the project, the team also designed an accompanying instruction manual. The instructions explained why the team was collecting data as well as why they needed assistance from the supervisors. The document also described how the team planned to use the data and the benefits the end results of the project could have for the supervisors. Finally, the team included instructions for data collection itself and definitions of each process step listed on the form.

Additionally, the team brainstormed other alternatives for time study data collection. Another option the team considered was using video recordings in order to collect data. This tool would allow the supervisors to videotape the retrieval process while narrating the steps they executed.

In order to decide between these two options, the team used a Pugh matrix to compare the options in the “Concluding Documentation of the Design Process in Action” section.

Design Requirements (Soft Constraints)In order to evaluate the merits of each alternative, the team considered seven soft constraints that were used to score the two options. The labels correspond to entries in the Constraints and Standards Matrix in Appendix B. The soft constraints were:

● Reliability: The data collection method should result in data that is as accurate as possible and avoid unnecessary opportunities for human error. (C-E-1)

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● Minimal Time to Review: The team wanted to limit the time investment required in order to translate data from the collection medium of either paper forms or video into a clean data set in Excel that is ready to be analyzed. (C-F-1)

● Ease of Implementation: The method should be easy to implement, and require minimal equipment set-up as well as few if any changes to the loading dock layout. (C-E-2)

● Usefulness: The data collection method should result in data that is as useful to the project’s final recommendations as possible by providing a view of the RFID retrieval process in as much detail as possible. In addition to data on the amount of time spent on each step, collecting information on how exactly the steps are performed would also be helpful to the project. (C-F-2)

● Acceptability to the Coordinators: The data collection method must be acceptable to the project’s coordinators and be a next-step that they are comfortable supporting and advocating for to the department. (C-F-3)

● Ergonomics: The data collection tool must consider ergonomics when being designed. It should be easy for the supervisors to complete and must not be mentally or physically taxing. (C-C-1)

● Quantity of Observations: The method should allow the team to collect data on a high percentage of dock alarm occurrences with minimal effort required from the supervisors. (C-H-1)

The team used these seven constraints to evaluate the two options for data collection methods using their Pugh matrix in “Concluding Recommendations on the Design Process in Action” section.

Hard Constraints on the DesignWhen brainstorming data collection methods, the team identified that the chosen solution needed to follow the requirements set by the scope of the project, in addition to the requirements set forth by the client. The team considered six hard constraints that needed be satisfied. The labels correspond to entries in the Constraints and Standards Matrix in Appendix B. The hard constraints were:

● Budget: The data collection method must not cost the Patient Equipment department any money, as the department has limited funds available. (R-D-1)

● Compliance: The method must not violate any patient protection standards. (R-G-1)● Remote: Due to the unpredictable nature of the dock alarms, the data collection method

must be able to occur without the student team being present onsite. (R-E-1)● Accessibility: All tools and equipment necessary to collect data must be able to be

carried on the supervisors’ person while they are on the move. (R-E-2)● Timing: The collection method must be able to be completed within the timeframe of the

semester-long project. (R-A-1)● Thorough: The data collection method must, at minimum, collect information on all

steps in the RFID retrieval process. (R-E-3)

The team only considered alternatives that met these six hard constraints. Both the paper self-collection method and the video recording method satisfied all of these constraints.

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StandardsThe team identified a number of professional and governmental standards that were relevant to the data collection method design process. The labels correspond to entries in the Constraints and Standards Matrix in Appendix B. The standards were:

● S-1 OSHA: This standard says that all places of employment, passageways, storage rooms and walking surfaces must be kept clean, orderly and in sanitary condition. This is related to the project at hand because any data collection methods must ensure that they do not compromise the order of the hospital. Any tools or equipment must be out of the way of the walkways to ensure no employees are put in potential danger [2]. (S-6-1)

● S-2 OSHA: According to this standard, employees should avoid awkward positions that involve twisting while pulling because that can cause muscle strains and spinal injuries. Therefore, any data collection methods that the team develops should ensure that they do not require any awkward movements from the supervisors [3]. (S-6-2)

● S-3 HIPPA: This standard says that sensitive patient information must always be kept safe and confidential. This is relevant for the current project because, when collecting equipment information during the time study, the data collect method must ensure that patient information is kept separate and not compromised [4]. (S-1-1)

Additionally, on March 11, 2018, the team searched the multiple websites for additional standards using the keywords walking, distance, and standing time, but found no other relevant standards from OSHA, MiOSHA, HIPPA, ASTM, or ANSI.

Concluding Documentation of the Design Process in ActionIn order to compare the two alternatives for data collection methods, the team created a Pugh matrix. The matrix, shown in Table 1, compares the two options by scoring them based on a number of criteria and assigning a weight to the importance of each criterion.

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Table 1: Pugh Decision Matrix for selecting a data collection method

Option 1:Paper Self-Collection Option 2: Video Recordings

Selection Criteria

Weighting Raw Score Weighted Score Raw Score Weighted Score

Reliability 5 2 10 5 25

Minimal Time to Review

2 3 6 1 2

Ease of implementation

2 5 10 1 2

Usefulness 5 3 15 5 25

Acceptability to coordinators

10 5 50 1 10

Ergonomics 4 5 20 2 8

Quantity of Observations

3 2 6 5 15

Total 117 87

As can be seen from the Pugh matrix above, the paper self- collection method received a higher score when evaluated based on the relevant criteria. The team decided to select this option for data collection and distributed the data collection form and instructions to supervisors on February 28, 2018. The five supervisors collected data from February 28, 2018 to March 14, 2018. This time study collection resulted in nine overall completed dock alarm sheets.

Analysis of the Time Study DataThe results from the time study sheets were used to create a current state process map of the RFID retrieval process to better understand pain points in the process, analyze EVS and other parties’ involvement, and assess potential areas for improvement in the future. The team used Microsoft Excel to analyze the data using averages, boxplots, and waterfall graphs.

Historical Data AnalysisThe team acquired the historical data from the project coordinators, who obtained the data from the Michigan Medicine databases using SQL queries. The data was pulled on February 16. The data contained information on dock alarms during the time range of November 1, 2017 to February 16, 2018. The original dataset contained over 700 data points.

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The team cleaned the data by removing all of the duplicates and removing alarms at locations that were not relevant to the scope of the project. To begin with, the team removed all locations that were not “C&W” or “UH Floor 2,” and created a macro that made the assets sortable. At this point, the data was ready to be manipulated. Next, the team cleaned the data. To accomplish this, duplicate alarms were removed. After performing these tasks, 134 relevant instances of dock alarms were found, and this was used as the final data set.

The team analyzed this data in Microsoft Excel. The team utilized pivot tables, cluster column graphs, box and whisker plots, histograms, and macros to analyze the data and identify relationships between variables.

InterviewsThe team performed preliminary interviews with two supervisors to understand parts of the process that frustrate them and the areas that need improvement. Additionally, follow-up interviews were conducted with two supervisors to understand how potential recommendations could impact the process and the people closely involved. Similarly, the project team interviewed the tag team staff who perform RFID attachments to equipment. For interviews with the tag team, the project team followed the ‘go to the gemba’ strategy and observed two experienced members from the tag team perform the tagging attachment process using multiple method. Some examples of the questions asked to both supervisors and the tag team are outlined in Appendix A.

Findings and ConclusionsAfter performing the methods discussed in the previous section, the team analyzed the resulting data to gain insight into the RFID retrieval process and the factors that influence it.

Literature SearchIn order to better understand the current knowledge surrounding RFID tags and standardizing processes in hospitals, the team conducted a literature search to become familiar with past research and projects in these fields. To begin with, the team found an article titled “Passive RFID Asset Monitoring System in Hospital Environments” that investigated the benefits of RFID technology and recommended procedures for its implementation [1]. The article conducted Return on Investment (ROI) Analysis to show that RFID tags are a practical business investment that pays off monetarily for hospitals in the long run [1]. Additionally, when describing methods for implementation, the article described the usefulness of having an alarm system to notify the hospital if equipment left the premises [1]. However, the article did not establish any standard operating procedures for recovering the RFID tag in situations where the alarm goes off or for attaching tags to equipment in the first place. The team recognized this to be a gap in knowledge in current literature surrounding RFID tags in hospitals and chose to focus their project on filling this void. This source helped the team to determine the scope of their project.

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Additionally, the team conducted research to understand the approaches used by past IOE 481 projects in the Michigan Engineering project database. The team found an article titled “SWAT Patient Flow and Personnel Workload at the University of Michigan Hospital,” which utilized similar techniques for data collection and analysis to those relevant in this project. The article described the past team’s usage of process mapping to understand the current state and the anticipated future state of the SWAT team’s scheduling process [5]. The article also described the past team’s recommendation to create standard operating procedures and the benefits and challenges that such a document created for the department [5]. While the areas of application within the hospital were different for this past project than they are for the current team’s project, learning from how previous IOE 481 students applied similar techniques to their project was helpful in understanding the methodology and its potential impacts on a system. The current team used this information to help craft their own recommendations and standard operating procedures for the RFID tagging process.

Time Study Using the time study data collected by the supervisors, the team looked to better understand the current state of the RFID retrieval process. The team used the data gathered from the time studies to create a current state map for the process. The map, shown below in Figure 2, documents each step in the RFID retrieval process and provides an estimate for the amount of time that it takes.

Figure 2: Current state map of RFID retrieval process (n=9, March 29, 2018, IOE 481 Time Study)

From the current state process map, the team was able to get a clearer idea of the steps in the process and better understand the time investment required by supervisors. Additionally, from the time studies, the team saw that only 33% of tags were actually found by the end of the process. This shows a lack of efficiency in the time consuming RFID retrieval process. Therefore, a large percentage of the retrievals are really non-value added time for the supervisors

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and for the Patient Equipment department as a whole so reducing the frequency at which these alarms occur would be very beneficial.

Overall, based on the time study data, the team found that the average total time for the process was 44.5 minutes. This information helped the team to quantify their expected impact in supervisor time saved as well as in dollars saved by considering the expected number of retrievals avoided due to the team’s recommendations to reduce frequency.

Next, the team analyzed the relative weight of each step in the process, as shown in Figure 3 below.

Figure 3: Average relative length of each step in the RFID retrieval process (n=9, March 29, 2018, IOE 481 Time Study)

From this analysis, the team found that identifying the bin with the tag is the longest step in the process. They also found that travel time to the dock takes a surprisingly large portion of the total process time at 14.5%.

Furthermore, in analyzing EVS’s role in the process, the team found data which implied that EVS does not always mark which bins set off the alarm. In the time study, the bins were only set aside 44% of the time, while the Patient Equipment department believed that this should have been standard procedure. This implies a lack of communication to EVS about what their responsibilities are in the process. In cases where EVS did not set aside the bin, the process became more time-consuming, as the supervisors had to spend time determining which bin contained the RFID tag. This was a contributing factor to the finding that identifying the bin with the tag in it was the longest step in the process. Thus, better documenting EVS’s role and

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ensuring clear communication to employees about their responsibilities could have a noteworthy reduction on process time for the retrieval.

Historical Data AnalysisThe team’s goal for the historical data analysis was to identify trends in the circumstances under which dock alarms occurred. The team performed analysis to gain insight in three areas: the frequency, the resolution type, and the time and location.

FrequencyOn average over the data collection period, the team found that roughly 1.15 dock alarms occurred per day. The team then looked to understand the frequency of dock alarms by asset type, shown in Figure 4.

Figure 4: Frequency of dock alarms by type of asset (n=134; November 1, 2017 to February 16, 2018, Michigan Medicine databases)

The team realized that the top five most commonly lost pieces of equipment accounted for a large portion of the total dock alarms. To better understand their contribution, the team looked specifically at the alarms for each type, shown below in Table 2.

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Table 2: Analysis of the frequency of the five most commonly lost pieces (n=134; November 1, 2017 to February 16, 2018, Michigan Medicine databases)

As shown above, these five devices accounted for over 90% of the dock alarms. Therefore, the team concluded that it would be more efficient for the Patient Equipment department to focus specifically on reducing the frequency at which these five pieces are lost by understanding why they are lost more frequently than other types of equipment. However, the team realized that the number of occurrences for each type of tag could skewed by the number of devices of each type tagged by the hospital. For example, if the hospital owns two of Device A but 1000 of Device B, one could expect Device B to set off the dock alarm more often than Device A. In order to directly compare items with different total numbers of assets, the team weighted the dock alarm occurrences by the total number of items in the hospital’s fleet, shown below in Table 3. Table 3: Frequency of five most commonly lost items weighted by total assets (n=134; November 1, 2017 to February 16, 2018, Michigan Medicine databases)

After weighting the items, the team found that SCDs and T-pumps were the equipment pieces that caused the largest number of alarms for their relative proportion of the hospital’s fleet and concluded that the department should prioritize reducing the frequency of alarms due to these two items. These findings led the team to further investigate the reason why these two items were lost with such a high frequency.

Resolution TypeIn order to understand the problem from a different angle, the team looked at the frequency of each type of resolution for dock alarms. Note that the “Other” category bucketed events into

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three categories: Test Tag, Battery was low, and no tag or equipment was found. The findings are shown below in Table 4. Table 4: Percent frequency of each type of dock alarm resolution (n=134; November 1, 2017 to February 16, 2018, Michigan Medicine databases)

The team found that the most common resolution to a dock alarm is “Tag only found in trash,” implying that often, tags are found without their corresponding piece of equipment. The team concluded that this finding pointed to a problem with how RFID tags are attached to the equipment, since the two frequently separate from each other. The team decided to further investigate the reasons why this separation could be occurring in interviews with supervisors and the tag team.

The team also found that over 20% of the dock alarms are due to alarms being tripped in error, which is a very high percentage. The team concluded that through additional training and education to raise awareness among staff about RFID tags and the exciters at loading docks, this proportion could be reduced. Additionally, for alarms tripped in error, it is current standard practice that the supervisor must still travel to the loading dock to determine in person that the alarm was a mistake. Thus, the team realized that there was a need for a better system to let the supervisors know that the alarm was tripped in error before they invest the time to report to the dock, especially given the finding from the time study that travel accounted for 14.5% of the total retrieval process. One option could be making it standard practice for EVS to immediately page supervisors and let them know that the alarm was a mistake. Next the team looked to understand how the frequency of resolution types varied based on the type of asset. Note that the team removed dock alarm occurrences with resolution types of “NULL” and “Other” as they were not conducive to the analysis. “NULL” had 2 data points and “Other” had 39 data points so sample size for this graph was 93. The resulting graph is shown in Figure 5.

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Figure 5: Resolution by type of item for five most commonly lost items (n=93; November 1, 2017 to February 16, 2018, Michigan Medicine databases) For all of the five most commonly lost items, the “Alarm Tripped in Error” and “Tag only found in trash” resolutions accounted for the vast majority of dock alarm resolutions. SCDs and Carefusion Modules were the most likely assets to have lost only their RFID tag, implying that the attachment of RFID tags to these devices is not effective and frequently results in tags that fall off. Therefore, the team saw a need for improving and subsequently standardizing the attachment process for connecting RFID tags to these pieces of equipment.

It is also noteworthy that any alarms for Adult Stryker Beds were almost always tripped in error. This supports the theory that alarms tripped in error are likely due to equipment being placed or moved too close to the alarm system. Training and education could help to solve this issue.

Timing and LocationThe team performed additional analysis to understand trends in the time of day, day of week, and location of the alarms, but found no actionable trends that directly contributed to the team’s final recommendations. This analysis can be found in Appendix C.

InterviewsThe team conducted interviews with all five of the supervisors at multiple points in the process to discuss their concerns about the RFID retrieval process and gather their insights on ways to improve the process. Supervisors were unanimous in expressing their dislike for the current

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process and its inefficiencies, and agreed that reducing the frequency at which the retrieval process needed to occur would be helpful.

Additionally, the supervisors provided insight into the reasons why the dock alarms occur. Anecdotally, they agreed that it is rare to find the actual equipment when conducting the retrieval process -- most often, the RFID tag is found alone without even the connection bracket. This assessment aligned with the team’s findings from their analysis of the historical data. This information led the team to conclude that the root cause of the majority of dock alarms is a problem with the attachment of the RFID tags to the equipment -- either the tags are falling off or being taken off intentionally by the hospital staff. When the team asked the supervisors about the state in which the tags are found, the supervisors agreed that most frequently, the tags do not appear to have been cut off or tampered with, so the team concluded that the main reason for lost tags is an ineffective method of attaching the tags to the equipment. Although less common, the supervisors said they still see a significant number of instances where tags have been removed from equipment or been thrown in the trash. This speaks to a need for education of the caregivers and hospital staff about RFID tags and their importance.

Following this analysis, the team interviewed the manager of Patient Equipment to learn more about the process for attaching the RFID tags to different types of equipment. The team learned that there is a lack of standardization for how tags were attached. The method of attachment used often varies depending on the type of equipment as well as the employee who performs the attachment. Sometimes the tag is attached using zip ties while other times adhesive tape is used because there is not a standard documented practice for how to decide on a method of attachment. Similarly, there is no standard location on the equipment where the RFID tags are attached -- the decision is made on a case to case basis. Furthermore, the department has no formal training process for new employees. Instead, current staff members train new employees in person on attachment methods with no formal guidelines of content to cover. The team identified the attachment process as an area where standard procedures could be created and documented in order to ensure more consistent, higher quality attachment.

Based on this conclusion, the project team coordinated a time to meet with the tag team and shadow them as they went about the process of attaching RFID tags to equipment. The project team observed the techniques used by the tag team, and took both notes on their process and pictures of the tagging procedure. As expected based on their conversations with the manager of Patient Equipment, the project team found that multiple methods for attachment were used; however, there was not a standard process describing when each method should be used. The team concluded that this was a gap in knowledge, and identified this as an area for recommendations and deliverables.

The team also asked both the supervisors and the manager of Patient Equipment about the role of EVS in the RFID retrieval process. According to the manager, the standard protocol is that EVS should be marking the bin that set off the alarm at the loading docks and setting it aside for Patient Equipment supervisors to perform the retrieval process on. However, according to the supervisors, in practice, this does not always occur, a claim that was supported by the team’s time study data which found that bins were set aside less than 50% of the time. The team found that there is a lack communication between the Patient Equipment department and EVS so that

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the expectations for EVS are not clear to both parties. The team identified a need for a standard work document that could clearly lay out the responsibilities of each party in the RFID retrieval process.

Design RecommendationsAfter reviewing the findings, the team created three recommendations: (1) Implementation of a standard of work document outlining how to properly attach an RFID tag to any piece of equipment, (2) Distribution of an educational flyer around the hospital explaining what an RFID tag is, why it is important, and who to contact if it is found, and (3) Further investigation into additional methods to reduce the frequency of dock alarms.

Implementation of Standard Work Document for RFID Attachment The team recommends the implementation of a standard work document that describes the steps that should be performed when attaching RFID tags to equipment. Through interviews with supervisors and historical data analysis, the team found that RFID tags were frequently falling off of equipment and correspondingly setting off dock alarms. The team also found that there was no standard documented process for RFID attachment, including choosing an attachment location and attachment method. The only existing documentation was part driven and described specifically where to attach the RFID tags to a few select types of equipment. Therefore, the team looked to fill this gap in knowledge.

The team sought out experienced members of the tag team to serve as experts from whom to learn steps that go into the equipment attachment process. In addition to learning current best practices, the project team further improved upon the process to make it even more effective. From interviews with supervisors, the team concluded that the main way an RFID tag is lost is when it un-clips from the bracket, given that RFID tags are usually recovered without their connection bracket or the equipment. To help mitigate this problem, the team recommends adding additional adhesive onto the back of every RFID tag before it is clipped into the bracket and the bracket is attached to the equipment. This way, if the tag does become unclipped from the bracket, the tag will still be bonded to the bracket by the tape and will be less likely to fall off. In Figures 6 and Figure 7, the new part of the process is shown.

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Figure 6: Adhesive tape application Figure 7: RFID placement in bracket

The team documented the current best practices for the RFID attachment process as well as this new innovation by designing a 13-page standard work document which gives step by step instructions on how to properly attach an RFID tag to any piece of equipment. The document is process driven. It describes the logic process that should be followed by staff performing the attachment by outlining, for each decision, the hard constraints and design requirements that should be considered. For example, when determining the attachment location, hard constraints such as that the tag cannot cover any stickers or serial numbers and design requirements such as that the tag should be placed in a location where it is hard to bump off should be considered. This methodology equips the Patient Equipment staff to perform the design process themselves whenever a new piece of equipment arrives, and therefore makes the document applicable to any piece of equipment.

The document includes three sections:

● Determining Attachment Location and Method: This section describe the logic process for determining the proper location to use for attachment of the tag as well as the logic process for determining the attachment method on a given piece of equipment.

● Attaching Using Zip Ties: This section describes the standard procedure that should be used to attach RFID tags using zip ties.

● Attaching Using Adhesive Tape: This section describes the standard procedure that should be used to attach RFID tags using adhesive tape.

The standard work document the team designed can be found in Appendix D. The team believes that standardizing this process with help to mitigate issues with tags falling off of equipment and ultimately reduce the frequency with which docks alarms occur.

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Distribution of Education FlyersThe team also recommends circulating educational flyers to increase awareness among hospital staff as to what RFID tags are and why they are important. From the team’s interviews with supervisors and the manager, the team learned that when RFID tags become unattached from equipment, hospital employees may accidentally throw out the tags, causing them to end up in the trash and therefore set off dock alarms. From these discussions, the team concluded that there is a need for more education on how RFID tags should be handled. This could help employees to know what to do when they see an RFID tag on the ground and equip them to choose to return the tag rather than throw it away.

The team spoke with supervisors and the manager in Patient Equipment to determine the current level of education that hospital employees have about RFID tags, and then the group brainstormed relevant information to include on the flyers. The team designed the flyer to be bright, colorful, and animated in order to attract attention and set the flyer apart from the multitude of other postings in the hospital. The flyer the team designed is shown in Figure 8.

Figure 8: Educational flyer designed for hospital staff

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The team recommends placing this flyer in high traffic areas such as in break rooms, nursing hubs, and around the elevators in order to inform caregivers, the nursing staff, and the janitorial staff. The team believes that this education will help to reduce problems with equipment being removed from equipment or thrown out and ultimately decrease the occurrence of docks alarms.

Further Investigation into Additional Methods of Reducing Alarm FrequencyDue to the time frame of the team’s project, there were a number of other factors influencing the frequency of the dock alarms which the team did not have the bandwidth to fully investigate. Therefore, the team recommends the Patient Equipment department continue to pursue these ideas in the future. First, the team suggests investigating why such a high percentage of dock alarms are being tripped in error. Secondly, the team recommends increasing communication between the Patient Equipment department and EVS in order to agree upon the division of tasks between the two departments in the RFID retrieval process and create standard work documents for each party. This could include making it standard practice for EVS to page supervisors immediately if alarms are tripped in error in order to save them the travel time to the loading dock. Finally, the team suggests that Patient Equipment look into implementing RFID exciters in other locations in the hospital so that alarms will sound earlier in the process before trash loads have been combined into large bins and simplify retrieval. The team suggests that a future IOE 481 group use these ideas as starting points in order for them to build their recommendations and deliverables.

Expected Outcome and ImpactThe team expects that implementing the project’s design recommendations will lead to:

● A reduction in the frequency of dock alarms due to RFID tags, which will enable supervisors to spend more on-duty time performing work which directly improves the quality of patient care.

● A reduction in the non value-added work done by supervisors, which will contribute to an increase in supervisor job satisfaction.

● An increase in the level of standardization of the RFID attachment process, which will decrease training time for new employees and further increase employee morale.

These positive outcomes can be quantified in terms of the amount of supervisor time that would be saved due to the reduction in alarm frequency. Based on the team’s historical data analysis, RFID retrievals occurred on average 1.15 times per day. From the time study, the team found that the RFID retrieval process takes, on average, 44.5 minutes. Thus, assuming that the process occurs uniformly over the course of a year, this amounts to 316 hours of supervisor time spent on the RFID retrieval process per year. This is equivalent to over $8,500 per year wasted on non-value added work. Therefore, any decrease in the frequency of the alarms would save the hospital money and have a positive financial impact.

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References[1] H. Hakim, R. Renouf and J. Enderle, "Passive RFID Asset Monitoring System in

Hospital Environments," Proceedings of the IEEE 32nd Annual Northeast Bioengineering Conference, Easton, PA, USA, 2006, pp. 217-218. doi: 10.1109/NEBC.2006.1629830

[2] Osha.gov. (2018). General requirements. - 1910.22 | Occupational Safety and Health Administration. [online] Available at: https://www.osha.gov/pls/oshaweb/owadisp.show_document?p_table=STANDARDS&p_id=971 4#1910.22(a)(1) [Accessed 11 Mar. 2018].

[3] Osha.gov. (2018). Ergonomics eTool: Solutions for Electrical Contractors - Materials Handling: Pushing, Pulling and Carrying. [online] Available at: https://www.osha.gov/SLTC/etools/electricalcontractors/materials/pushing.html [Accessed 11 Mar. 2018].

[4] HHS.gov. (2018). Summary of the HIPAA Privacy Rule. [online] Available at: https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations/index.html [Accessed 11 Mar. 2018].

[5] N. Bartecki, J. Jasper, R. Shah, and E. Sweet, “SWAT Patient Flow and Personnel Workload at the University of Michigan Hospital,” Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA, 2016.

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Appendix

The following sections contain additional information on the team’s project, including interview questions, the constraints and standards matrix for the design process, analysis of the time and location trends of dock alarms, and the team’s standard operating document for attaching RFID tags.

Appendix A: Interview QuestionsBelow are the interview questions that the team asked the Patient Equipment supervisors and the manager:

● What pieces of equipment do you tend to have the most dock alerts for?○ Do you generally find the tag by itself or attached to the bracket?

● Do you feel the RFID tags could be placed better on common equipment? If so, how?● Do you believe training EVS staff on locating the tags would be useful or beneficial?● What is the standard process for tagging equipment with RFID tags currently, if any?

○ What are your thoughts on the current placement of the RFID tags?● What is the process you use to determine how to attach the RFID tags to equipment?

○ Who do you talk to?○ What factors do you take into account?

Described below are the interview questions asked of the tag team:

● What is your role in the RFID tag attachment process?○ How comfortable are you with this process?

● What different methods of attachment do you use?○ How do you decide which method to use in what circumstance?

● Do you have any suggestions for improving the current RFID tag attachment process?

The team used these questions to better understand the RFID retrieval process and the process the department uses to attach RFID tags to equipment.

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Appendix B: Constraints & Standards MatrixThe team created a constraints and standards matrix to summarize the factors influencing the design of data collection methods.

Table 4: Constraints and Standards matrix for data collection method design

Entry # 1 2 3

Requirements R-A. Organizational Policy

(R-A-1)

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-E-3)

R-F. User Acceptance N.A.

R-G. Patient Acceptance (R-G-1)

R-H. Task Duration N.A.

Entry # 1 2 3

Constraints

C-A. Organizational Policy

N.A.

C-B. Ethical N.A.

C-C. Health & Safety (C-C-1)

C-D. Economic N.A.

C-E. Implementability (C-E-1) (C-E-2)

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C-F. User Acceptance (C-F-1) (C-F-2) C-F-3)

C-G. Patient Acceptance N.A.

C-H. Task Duration (C-H-1)

Entry # 1 2 3

Standards S-1. HIPPA (S-1-1)

S-2. Organization's Std. N.A.

S-3. Best Practice N.A.

S-4. ASTM N.A.

S-5. Code N.A.

S-6. OSHA (S-6-1) (S-6-2)

Standards that are not of consequence

ANSI MiOSHA

Appendix C: Timing and Location Trends AnalysisThe team looked to identify trends in how the frequency of dock alarms varied by time of day and by day of the week. Both analyses are shown below:

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Figure 9: Frequency of dock alarms by day of the week (n=134; November 1, 2017 to February 16, 2018; Michigan Medicine databases)

Figure 10: Frequency of dock alarms by hour of the day (n=134; November 1, 2017 to February 16, 2018; Michigan Medicine databases) The team found that there were slightly lower numbers of dock alarms on weekends than on the weekdays. When looking at the frequency by hours of the day, the team found that the frequency mirrors the schedule of EVS. For example, the frequency is lower around breaks, such as lunch, and when shift changes are occurring, such as 6-7 pm. Alarms peak at 9 am when shifts are starting and the employees have the most work.

Next, the team looked at how the resolutions of dock alarms varied with location by comparing the two docks of interest: UH B2 and C&W. Both graphs are shown below:

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Figure 11: UH Main B2 resolution percent frequency (n=99; November 1, 2017 to February 16, 2018; Michigan Medicine databases)

Figure 12: C&W resolution percent frequency (n=34; November 1, 2017 to February 16, 2018; Michigan Medicine databases) The team found that the two locations have similar percentages of tag recovery from the trash. C&W has a slightly higher error percentage for tripping the alarm. UH has more problems with tags and equipment being located in Bio Hazard.

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Appendix D: Standard Operating Procedures for Attaching RFID Tags

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