University of Michigan Health SystemProgram and Operations Analysis

Analysis of Room Turnover Process for EP and Cath Labs

Final Report

Submitted to:

Janice Norville, MSN, MSBA, RN, Director of Clinical OperationsSheryl Wagner, Nursing Supervisor

Jim Bloom, Allied Health Senior SupervisorColleen Lucier, Allied Health Senior Supervisor

Katie Schwalm, Industrial EngineerAndrei Duma, Industrial Engineer

Mary Duck, UMHS IOE 481 LiaisonMark Van Oyen, IOE 481 Faculty Instructor

Submitted by:

IOE 481 Project Team #1Scott Agnew

Kimberly CusmanoBrandon DubaHolly Johnston

Date submitted: December 13, 2016

Table of Contents

Executive Summary 3Introduction 6Background 6Key Issues 6Goals and Objectives 7Project Scope 7Methods & Findings 7

Literature Search 7Historical Data Analysis 8Time Study of Current Processes 10Process Maps 11Staff Interviews 11

Conclusions 12Task Inside Labs Consistently Completed Before Next Patient Arrives 12

Current Turnover Times Longer than Goal 12Variation in Turnover Processes 12Lack of Parallel Workflow 13Tasks Completed Before Wheels Out 13Valuable MiChart Fields Not Consistently Entered 13Techs Leave Lab to Retrieve Equipment 13

Recommendations 13Further Investigation 14

Time between Room Ready and Wheels In 14EP Technologist Equipment Retrieval 14Temporary Colored Badges for CPU Staff Identification 14

New Turnover Process 14Cath 15EP 15

Future Tracking 16Expected Impact 16References 17Appendices

Appendix A: Data Analysis 18Appendix B: Log of Observation Hours 20Appendix C: Data Collection Sheet 21Appendix D: Current State Cath Process Map 22Appendix E: Current State EP Process Map 23Appendix F: Staff Interview Questions 24

List of FiguresFigure 1. Historical data breakdown showing relative size of relevant and

excluded data types 8Figure 2. Turnover start time, organized by lab type 9

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Figure 3. Turnover time, organized by lab type 10Figure 4. Future State Cath Turnover Process 15Figure 5. Future State EP Turnover Process 16Figure A-1. Cath lab turnover organized by patient class transition between cases 18Figure A-2. EP lab turnover organized by patient class transition 18Figure A-3. Turnover time organized by lab type and anesthesia type 19Figure A-4. “Room Start-Room Ready” Room Ready values only existed for EP records 19Figure B-1. Number of hours observed on each day of the week 20

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Executive SummaryThe Cardiac Procedures Unit (CPU) at the University of Michigan Hospital System (UMHS) performs Electrophysiology (EP) and Heart Catheterization (Cath) procedures. The CPU has noted abnormally long turnover times that often lead to patient, physician, and staff dissatisfaction. The CPU Director of Clinical Operations asked a team of industrial and operations engineering students to evaluate the current EP and Cath turnover processes, identify key wastes within these processes, and establish means to facilitate future EP/Cath turnover evaluation. For the purposes of this project, turnover is defined as the process between the last patient being wheeled out of the lab room and the next patient being wheeled into the same lab room. This report summarizes the findings, conclusions, and final recommendations for this project.

BackgroundThe Cardiac Procedures Unit (CPU) at the University of Michigan Hospital is a procedural unit within the Division of Cardiovascular Medicine, located in the Cardiovascular Center (CVC). Specifically, the CPU performs Electrophysiology (EP) and Heart Catheterization (Cath) procedures. The CPU noted abnormally long turnover times that often lead to patient, physician, and staff dissatisfaction. However, staff within the CPU have differing perceptions of what the turnover process entails. The CPU Director of Clinical Operations requested that the team explore the turnover process and the potential causes of extended turnover times.

The CPU Director’s main concerns included understanding the current processes, identifying process waste, and establishing the means to evaluate future turnovers. The team addressed these concerns through five methods: literature search, analysis of historical data, time studies, development of process maps, and interviews with CPU staff. Based on initial observations, the team developed process maps to display and record the current state of EP and Cath turnover processes. The process maps were further refined after data was collected through time studies and staff interviews. This information helped identify probable causes of delays and get a comprehensive understanding of the processes. Additionally, to gain further background information and ideas for potential solutions, the team analyzed historical CPU turnover data and performed a literature search.

Methods & FindingsThe team employed five methods to evaluate the current turnover processes. This section outlines these methods and associated findings.

Literature SearchThe team performed a literature search on past IOE 481 projects and relevant research. The reports were beneficial to understanding the background of the Cardiac Procedure Unit [1, 2], parallel workflow, staffing level increases, and definition of work roles that limit downtime and balance workloads [3]. The articles detailed the process researchers took to standardize definitions and collect turnover time data for baseline management [4]; strategies to reduce turnover times in an operating room, and using lean tools, such as a variability analysis; and the single minute exchange of dies method to improve operating room turnover times [6]. The articles helped guide the team with analysis methods.

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Historical Data AnalysisThe project coordinators provided the team a set of historical data capturing CPU operations from January to August 2016 taken from MiChart, an electronic medical record used by UMHS staff to log data. The data included parameters such as room start/stop times, anesthesia used, lab room, patient class, and procedure type for both Cath and EP labs. The team processed and analyzed the data to gain insight about the turnover processes. The team found the median turnover values for Cath and EP labs, which helped inform current state evaluation. It was also found that certain metrics critical to turnover are not consistently documented.

Time Study of Current ProcessesTo gain a better understanding of the tasks and staff coordination involved in the turnover process, the team observed the turnover processes for approximately 53 hours in the EP and Cath labs, witnessing 30 turnovers collectively. The team used stopwatches to track the start and end times of each step in the process. These times were used to determine the length of the current processes, act as a baseline number for future comparisons and create the process maps. The team found that there was no standard procedure; some tasks occurred before the turnover began and EP lab rooms were not consistently stocked. Numerical data from the time study were used to develop the process maps described below.

Process MapsBased on results from observations and interviews, the team developed process maps of the current EP and Cath turnover processes. The process maps were developed with a “swim lane” approach, showing each staff member involved in the processes and their specific tasks during turnover. The maps allowed the team to capture data of sequential and simultaneous tasks. Furthermore, the team determined there was an uneven workload balance between the staff.

Staff InterviewsThe team interviewed key staff in the CPU to obtain different perspectives on the turnover processes. The team collected data from different staff members and used their responses to inform recommendations and develop process maps. The nurses reported that the process of locating and securing patients can be confusing and time consuming. For EP, the staff reported there are communication difficulties between the nurses and CRNAs. Staff also reported that physician preference for procedure intricacies could elongate turnovers.

ConclusionsFindings obtained from the literature search, time study, historical data analysis, turnover process maps development, and CPU staff interviews led to determine seven conclusions that provide critical insight into the current state of turnovers within the CPU, which are as follows:

1. Tasks performed inside the EP and Cath labs are not the cause of long turnovers. Through findings from the time study, process map development, and staff interviews, the team determined that tasks completed inside the EP/Cath labs are performed efficiently and without waste; they therefore are not the cause of long turnover times.

2. Current turnover times are longer than goal turnover times, in both EP and Cath labs.Through findings from the historical data analysis and time study, the team has substantial evidence that the current median turnover time for both EP and Cath labs do not meet the goal turnover times set by the client.

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3. Variation exists within the turnover process for both EP and Cath.Through findings from the literature search, time study, and staff interviews, the team found that turnover tasks in EP/Cath labs are not always executed in a consistent order.

4. A lack of parallel workflow exists between technician roles during turnovers.Through findings from the literature search and process map development, the team determined there is an unbalanced workload between Scrub and Monitor Techs for EP turnover, and Circulator and Monitor Techs in Cath turnover.

5. Some turnover tasks are performed before a patient is wheeled out of the lab.Through findings from the literature search, time study, and process map development, the team concluded some turnover tasks, such as removing trash bags or cleaning lab equipment, is completed before “wheels out” occurs.

6. Valuable MiChart fields are not consistently entered.Through findings from the historical data analysis and time study, the team found that electronic documentation of lab turnover is not always consistent, which limits how the process can be evaluated.

7. EP techs leave the lab to retrieve equipment during turnovers.Through findings from the time study and process map development, the team concluded a primary factor in EP turnover is EP technologists leaving the lab to retrieve equipment.

RecommendationsFrom the conclusions described above, the team developed three recommendations to reach the project’s goals. The team recommends the following to improve the turnover processes in the EP and Cath Labs.

Further InvestigationThe team recommends that the CPU perform additional investigation of several key metrics related to turnover, including tasks that occur outside the lab during turnover, and the root cause for EP Techs leaving the lab during turnover. Additionally, if a student group or other external team is brought in for this investigation, providing staff with color-coded badges will help the team identify staff roles.

Recommended Turnover ProcessThe team has developed recommended turnover processes for EP and Cath turnover. The processes balance workload between technologists, and maximize the amount of work completed before a patient is wheeled out of the lab.

Future TrackingThe team recommends the CPU perform thorough tracking of future turnovers. Technologists and nurses will have additional avenues of communication to ensure critical turnover metrics are always entered in turnover documentation. Additionally, statistical analysis and quarterly reports will provide managerial staff the information needed to access future turnover.

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IntroductionThe Cardiac Procedures Unit (CPU) at the University of Michigan Hospital System (UMHS) performs Electrophysiology (EP) and Heart Catheterization (Cath) procedures. The CPU has noted abnormally long turnover times that often lead to patient, physician, and staff dissatisfaction. Turnover is defined as the process between the last patient being wheeled out of the lab room and the next patient being wheeled into the same lab room. In addition, staff within the CPU have differing perceptions of what the turnover process entails. The CPU Director of Clinical Operations has expressed concern that coordination issues within the unit could be causing long turnover times. The CPU is looking to explore the current turnover process and identify the potential causes and improvement opportunities.

The Director asked a team of industrial engineering students to observe the operations involved in EP and Cath turnovers and identify wastes within the processes. The Director would like to understand the current turnover processes and determine improvements that will reduce waste and coordination problems. Furthermore, the Director wanted a baseline indicator of turnover time and a method to evaluate future turnover performance.

The team observed, evaluated, and analyzed the current turnover processes. Based on initial observations, process maps were created to display and record the current EP and Cath turnover process. These were updated throughout the duration of the project as the team collected more data. During further observations, team members recorded the length of various tasks and other data within the turnover processes to determine causes of delays. Additionally, the team interviewed staff to gain insight into potential problem areas. This final report summarizes the findings, conclusions, and recommendations for this project.

BackgroundTwo procedure types occur within the CPU: EP and Cath. These procedures have unique scheduling, staff, and processes. In addition to EP labs and Cath labs, the CPU has 36 preparatory and recovery rooms, including beds for overnight observation. The CPU schedules procedures from 8am-4pm with a bulk of the procedure turnovers occurring between 11am-3pm.Currently, no written standard procedure exists for EP and Cath lab turnover.

The CPU Director had concerns regarding the current turnover processes. Physicians and staff are dissatisfied when procedures do not start on time as it often lengthens the work day. The lengthy turnover times can increase patient wait times, leading to unnecessary stress. Additionally, the Director worried that the various roles involved in the turnover process foster miscommunication and coordination problems between the staff. Miscommunication exacerbates the issues of differing staff perceptions of the turnover processes. The CPU does not have a system to evaluate and analyze turnover processes and times.

Key IssuesThe following key issues are driving the need for this project:

● Long turnover times lead to patient, physician, and staff dissatisfaction.● Physicians and staff have different perceptions of the turnover processes.● There is no documented process for EP and Cath turnover.

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● There is no system to assess turnover effectiveness, both in terms of overall time required, and time required for each step of the turnover process.

Goals and ObjectivesTo determine the primary factors of long turnover times, the team achieved the followinggoals:

● Evaluate the current turnover process for both EP and Cath.● Identify waste and coordination problems within the current turnover processes.● Establish means to evaluate future turnover effectiveness.

From this information, the team achieved the following objectives:● Develop process maps for the current turnover processes.● Perform time studies and conduct interviews with EP and Cath laboratory staff to

measure key performance metrics of current turnover procedures.● Establish a standard procedure for performing and coordinating turnover tasks efficiently.● Develop a method CPU managerial staff can use to track and assess performance metrics

of turnover procedures.● Make recommendations to CPU managerial staff regarding implementation of the above

objectives; recommendations may differ between EP and Cath procedures.

Project ScopeAny activity or task to prepare the room for the following procedure was considered in the scope of this project. The turnover process includes transporting the current patient to recovery and thenext patient into the lab room; therefore, these activities were also within the project scope. The team only analyzed the turnover process for EP labs 1-5 and the Interventional Cath labs. The Heart-Failure Cath Lab and EP Lab 6 were excluded from this project. Patient processes prior to the procedure were not investigated; however, delays and late arrivals were noted.

Methods & FindingsThe team used five primary research and evaluation methods to complete this project: literature search, analysis of historical CPU data, time study of the current process, process maps, and interviews with key staff. Each method yielded several findings used to generate the team’s conclusions. This section details each of the methods and the corresponding findings.

Literature SearchThe team performed a literature search and found reports for similar projects conducted by past student groups. Along with the past student report, the team also found articles on Google Scholar.

The findings from the literature search are:1. Implementing parallel workflow during turnover eliminates waste of waiting by

technologists [3].2. Standardizing the definition of turnover time reduces confusion and removes

misconceptions [4]. 3. Streamlining cleaning processes before the patient exits the operating room increases

short turnovers by 176% [5].

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4. Simulation and balancing workloads led to a 45% decrease in turnover time [6].

Historical Data AnalysisThe project coordinators provided the team a set of historical data that cover CPU operations from January to August 2016. This data set was taken from MiChart, an electronic medical record used by UMHS staff to log data. These records contain data for both EP and Cath labs, and include parameters such as room start/stop times, anesthesia used, lab room, patient class, and procedure type.

The data set provided to the team contained 4888 records. The team used the programming language “R” to process, visualize, and analyze the data. The first step was data processing, which involved excluding records not useful to the project. Lab procedures occurring on weekends, and lab procedures within the Heart Failure Transplant (HFT) lab are both outside the scope of the project as defined by the Director. These records were therefore excluded from further analysis. Records with errors such as negative time values or data entered incorrectly into MiChart were deemed “unusable” and also excluded from further analysis. Additionally, the team excluded records that were the first case of each day, because such cases have no turnover. Records with turnover greater than 2 hours were also excluded; the coordinators suggested such records would not be useful in assessing turnover, and could be erroneous similar to the “unusable data”. The team considered all remaining data useful, and used it to evaluate turnover within the CPU. Figure 1 details all data types found within the provided records, both usable records and the records the team excluded, and the number of records that make up each data type.

Figure 1. Historical data breakdown showing relative size of usable and excluded data typesSample Size: 4888; Source: MiChart; Collection Period: January-August 2016

After viewing initial histograms, the coordinators recommended that any records containing turnover greater than 2 hours should be excluded. This was to remove the outliers and obtain a more normal distribution. The team filtered out such records, resulting in a final RD of 2320 records (47.5% of the original set of records).

The team applied programming techniques to calculate and generate histograms for turnover time, turnover start time, and “room ready-room start” time. These histograms provide insight

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into EP and Cath lab operations, and were used to guide the team’s development of recommendations. All histograms display the data’s median value (also known as the second quartile), the first and third quartiles of the data, and absolute sample size. The first quartile (also called the 25th percentile) is a value under which the smallest 25% of turnover records reach. The third quartile (also called the 75th percentile) is a value under which the smallest 75% of turnover records reach.

Figure 2 shows the distribution of times at which EP and Cath turnovers begin (i.e. the time aprocedure ends). It is important to note this figure omitted two records (one EP and one Cath) with extremely early start times. This plot allowed the team, as well as future researchers, to determine when tobest observe the highest frequency of turnovers. Based on evaluating the 25th percentile and 75th percentile of the data, it is evident that the middle 50% of all turnovers happen between 12:15 and 16:15 for Cath labs, and between 12:43 and 16:44 for EP labs. Therefore, observations during these times will maximize the number of turnovers observed.

Figure 2. Turnover start time, organized by lab typeSample Size: 2318; Source: MiChart; Collection Period: January-August 2016

Figure 3 displays turnover time distributions for both EP and Cath labs. This figure validates theEP turnover time values the team has observed, and the turnover time estimates provided by the Director of roughly 30 and 45 minutes for Cath and EP labs, respectively. These histograms show similar median values, and the large sample sizes of 1094 records for Cath, and 1226 records for EP, validate the data’s representativeness.

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Figure 3. Turnover time, organized by lab typeTotal Sample Size: 2320; Source: MiChart; Collection Period: January-August 2016

Figure 3 also served as a starting point for the rest of the team’s data analysis. The team analyzed turnover times further by splitting the data based on certain categorical variables within the relevant data set such as patient class transition, and anesthesia type. The team also performed analysis based on the difference between a room’s “start time” and “ready time.” These remaining figures are shown in Appendix A.

The findings from the historical data analysis are:1. Median turnover time for EP and Cath laboratories are 28 and 40 minutes, respectively. 2. A small portion of cases, 8.2% of the usable data (190 records), fill out an important

MiChart field (“Room Ready” time). “Room Ready” is the time when all turnover activities within the lab are complete. All of these cases are EP cases; no Cath case within the usable data has a completed “Room Ready” field.

Time Study of Current ProcessesTo gain a better understanding of the tasks and staff coordination involved in the turnover process, the team observed the turnover processes for approximately 53 hours in the EP and Cath labs, witnessing 30 turnovers collectively. A detailed log of dates the team observed can be found in Appendix B. The team used stopwatches to map the start and end times of each step in the process and recorded it on a data collection sheet. The data collection sheet is attached in Appendix C. The major steps are outlined below:

Steps Inside Lab:1. Patient wheeled out of room2. Room is cleaned and trash is disposed of

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3. New equipment for next lab is laid out4. Patient and inventory information is entered into computer5. Patient wheeled into room

Steps Outside of Lab:1. RN wheels patient out of room and transports to recovery2. Hook up equipment for patient3. Relay post-lab data to other RN4. Dispose medical waste5. Find next patient6. Return with next patient

The team then analyzed this data to find a baseline for the current state. To find the full turnover time the team averaged all of the full turnover times (12 for Cath, 5 for EP). To find the length of each step, the team averaged the time for each step (with varying sample sizes). These numbers have been utilized to create the appropriate process maps.

The findings from the time study are:1. The median turnover time for Cath is 15.99 minutes, while for EP the median turnover

time is 48.33 minutes.2. The order in which the techs complete tasks deviates between turnovers.3. 52% of observations show the techs begin turnover tasks before the patient is wheeled out

of the lab.4. 87% of observations show the room was ready before the patient wheeled in.5. 80% of EP observations show EP techs leave the lab and return with equipment.

Process MapsBased on the results from initial observations and interviews, the team developed process maps of the EP and Cath turnover processes, shown in Appendix D and E. These maps show each staff member involved in the processes and the specific tasks during the turnover. These maps were created using a “swim lane” setup, which organizes tasks by the staff type responsible for it. The diagrams allowed the team to capture data of sequential and simultaneous tasks. Tasks that are not consistent in all turnovers are indicated on the diagrams with stars, accompanied by a brief explanation below each diagram. In order to make the diagrams as accurate as possible, the team continuously improved the diagrams as more data was collected.

The findings from the process maps are:1. There is not an even workload balance between the techs in the turnover processes.2. On average, 47% of the Cath and 39% of the EP turnover time, the room is ready and

waiting for the next patient to be wheeled in.3. There are tasks completed before wheels out in both the EP and Cath processes.4. On average, EP Techs have to leave the room for 2 minutes to retrieve stock that is not in

the room.

Staff InterviewsThe team conducted interviews with key staff in the CPU to gain more perspectives of the turnover process. Initially, the team conducted informal interviews while observing in the labs.

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Later, the team created a standard question set for interviews with staff members involved in the turnover processes, attached as Appendix F. The team interviewed 2 Cath Nurses, 2 Cath Techs, 4 EP Nurses, 2 EP techs and 1 EP CRNA in person. The team also asked 3 Cath physicians and 3 EP physicians questions about their role during turnovers through email.

The findings from the staff interviews are:1. Both interviewed nurses indicated there is no standard way to communicate between EP

nurses and CRNAs regarding the patient’s pre-op status.2. According to 1 EP Tech, no one has the responsibility of recording room ready time in

MiChart, which has been confirmed by the clients.3. Physicians have unique equipment set-up preferences. Physician preference for procedure

intricacies and equipment needs can increase turnover. CPU staff must accommodate these physicians’ requests, but are not always alerted to the requests until some time into the turnover process.

ConclusionsThe previously discussed methods and findings led the team to seven conclusions. This section highlights the main issues in the current turnover processes and summarizes the conclusions that led to the team’s recommendations.

Tasks Inside Lab Consistently Completed Before Next Patient ArrivesThe turnover process includes activities inside the lab and activities outside the lab. Considering Finding #4 from the time studies and Finding #2 from the process maps, the team determined that long turnover times are not caused by the required tasks inside the lab. Additionally, Finding #1 from the staff interviews shows that there are communication issues between the RN and CRNA in the EP process, which could be a cause of patient delay.

Current Turnover Times Longer than GoalThe Director wanted the team to evaluate the turnover processes because the turnover times were longer than the desired 15 minutes and 30 minutes for EP and Cath, respectively. The Director also estimated that the current process took 30 minutes and 50 minutes, respectively. The team was able to determine the current state of the processes through time study and historical data analysis. Based on Finding #1 from the time studies, and Finding #1 from Historical Data Analysis, the team was able to validate the information obtained through MiChart with our observed time study data.

The historical data had thousands of data records, but many involved cases that were outside the scope of this project, which reduced the magnitude of data the team was able to evaluate effectively. Furthermore, there is ambiguity as to who recorded the data and when it was recorded. Conversely, the sample size for the time studies was small, but the team was able to record exactly when and where tasks in the EP and Cath turnover process occurred. While the resulting metrics from the historical data and the time study differed slightly the team was able to validate that the current state turnover is longer than desired.

Variation in Turnover ProcessesThe team observed many tasks were performed in different sequences. Most turnovers contained similar processes but specific steps occurred at different times or not at all. Finding #2 from the

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literature search outlined the importance of defining turnover and making sure that the tasks are clearly defined. The streamlined process increases the efficiency and reduces time.

Similarly, the different physicians performing the procedure have different preferences. For example, some physicians wish to see the patient before the operation. This interferes with the nurse who is performing pre-op. Another example is that some physicians require unique equipment. This changes the stock that the techs must have in the room and can add an element of confusion.

Lack of Parallel WorkflowFinding #1 from the process maps shows that there is a lack of parallel work between the techs in both EP and Cath labs. Explicitly, the work is not standardized and is not balanced between tech 2 or 3 techs performing the turnover activities. This lack of balance increases the room ready time. This importance of parallel workflow and defined roles and balanced workload were learned from Findings #1 and #4 from the literature search.

Tasks Completed Before Wheels OutThe team recorded all the tasks that occurred during the turnover processes. Finding #3 from the time studies shows that some tasks are completed before the patient is wheeled out of the room, which is when the turnover starts. Finding #6 from the process map indicates the same tasks are completed before the turnover begins. The team determined that these pre-turnover tasks could save time during the turnover and that more tasks could potentially be completed before turnover starts. Tasks such as wiping down the table, disposing of old supplies, taking out the trash and cleaning the equipment could be performed while the nurse are transferring the patient to the transporting bed and rewiring equipment. Finding #4 from the literature search explains the process and value of performing tasks pre-turnover.

Valuable MiChart Fields Not Consistently EnteredFinding #2 from the historical data analysis showed that a small amount of MiChart data that was usable contained room ready time. This time is when the techs finish their work in the lab turning the room over. Room ready time is an important factor because it shows when the room is ready, and therefore can be used to calculate how long the room is ready before the nurse returns with the next patient. Finding #2 from the staff interviews also shows that no one records room ready time. Therefore, MiChart did not describe this delay despite it being a large contributor to turnover times.

Techs Leave Lab to Retrieve Equipment The team also concluded that a source of delay of getting the room ready was techs leaving the EP lab to retrieve equipment. Finding #5 from the time studies shows the number of times that the staff left the room and Finding #4 from the process maps shows the time delay in turning the room over the room.

RecommendationsBased on observations, interviews and data analysis the team recommends the following changes to improve the turnover processes in the EP and Cath Labs.

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Further InvestigationDue to time constraints and scheduling conflicts, the team was not able to further investigate and determine the root cause of the delay between room ready and wheels in and the reasons for EP techs leaving the lab. The team believes the topics listed below would be useful to investigate further.

Time between Room Ready and Wheels InDuring observations the team noted long times between the tasks inside the lab being completed and the next patient arriving, as detailed in Finding #2 from the process maps. 87% of all turnovers observed had the room ready before the patient was wheeled in, indicating that the tasks inside the lab are not the cause of long turnover times. The team recommends that the CPU staff further investigate the reasons behind the delay. The team was unable to observe a sufficient sample size of processes outside of the lab room. Two complete Cath turnovers from the perspective of the nursing staff were observed. Both observations showed that the nurses waited an average of 8.5 minutes for pre-op processes to be completed. From interviews the team noted a lack of communication between the CRNA’s and the nurses. The nurses are occasionally unsure whether a patient has been seen by the CRNA. The team believes these are key areas to observe and analyze in future investigations.

EP Technologist Equipment RetrievalFor EP turnovers, the team had observed that technologists would leave the room and return with extra equipment. 80% of the observed EP turnovers had techs leaving the lab room and returning with equipment. This occurred between 1-3 times per turnover and can be categorized as a waste of movement. The team was unable to determine the cause behind the EP techs leaving. The reasons for leaving could be due to a lack of equipment stocked in the labs, physicians requesting additional equipment, or technologists forgetting to bring a specific piece of equipment. The team recommends the CPU staff further investigate the possible reasons for techs leaving. The investigation would require observations and interviews of techs to determine stock needs.

Temporary Colored Badges for CPU Staff IdentificationThis recommendation was developed solely from the team’s observation experience, and is not explicitly connected to any particular finding. During observations, the team initially had difficulty identifying the roles each staff members. For example, nurses are not easily distinguishable from techs, which make accurately observing the current processes in the CPU difficult. If the Director decides to have an outside team evaluate part of the CPU in the future, adding color-coded stickers to staff member’s badges would ease the observation process.

Recommended Turnover ProcessThe historical data analyzed by the team proves that the current turnover process for both EP and Cath does not meet the goals of the clients. The current process maps indicate inefficient processes within EP and Cath labs. After performing time studies, the team noticed variation in the order of completed tasks and which staff member performed them. The team has created future state process maps for both EP and Cath, detailing process changes to improve turnover. The process maps utilize the balancing of workloads between the two technologists and defining roles for each with a standard set of tasks. Tasks are also streamlined to be completed before the previous patient is wheeled out.

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CathFor the Cath labs, the team identified an opportunity for a few turnover improvements. The team split one of the circulator tech roles between the monitor and circulator tech. The circulator tech is still responsible for laying out new bedding, but the monitor tech will wipe down new equipment. These two tasks can be completed before the patient exits. The two techs will work in parallel to clean the room. Once cleaning is complete, the monitor tech will begin entering last patient and next patient information while the circulator opens and drops new equipment. After equipment is opened this enables the circulator tech to take the inventory to be scanned into Q-Sight.

Figure 4. Future State Cath Turnover Process

EPThe changes to the EP process include balancing tasks between the monitor and scrub tech. The team has streamlined two tasks. Before, the scrub tech was tasked replace the trash bags and bedding. It has now been assigned to the monitor tech and will be completed before the patient leaves the room. This shortens the number of tasks for the equipment to be opened by the scrub tech. While the scrub tech is opening the new equipment, the monitor tech is entering information for the previous and next patient. No waste of waiting occurs as the scrub tech can transition into scanning inventory while the scrub tech organizes the equipment.

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Figure 5. Future State EP Turnover Process

Future TrackingThe team was asked to develop a methodology for staff to track and evaluate future turnovers. The CPU has a system in place (MiChart) that records specific timestamps of the labs including when the lab is complete and when the patient enters and leaves the lab. These data points will be efficient to track future turnovers. The nursing staff is in charge of updating patient records, which includes timestamps of room start and room stop. From interviews, this process is straightforward, however the nurses are unable to complete the room ready section. The nurses are not in the room when techs complete the cleaning and setup of labs and no relay of information exists between the staff members. The historical data is thus found to be incomplete. It is recommended that the technologists write down the room ready time on a whiteboard within the lab. When the nurses return with the patient and fill out MiChart data, the room ready time will be visible and inputted. From the inputted data, managerial staff can then statistically analyze the turnovers and a report will be posted quarterly for reference.

Expected ImpactAfter implementing the recommendations, the CPU staff will have a recommended turnover procedure for both EP and Cath, and the means to document and evaluate the effectiveness of future turnovers. Accordingly, the recommendations will benefit the EP and Cath labs in the following areas:

● A comprehensive understanding of the current turnover processes for EP and Cath.● Reduction of wasted actions and time of staff within the EP and Cath labs.● Means to evaluate future turnover effectiveness.● Standardization of turnover process for EP and Cath laboratory staff.● Implementation plans for the recommendations described above.

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References

[1] Johnson, J., Liang, J., & Stumpos, L. (2015, October 13). Time-Out Data Verification and Late Starts in the Cardiac Procedures Unit Project Proposal. 1-8. Retrieved September 16, 2016.

[2] Sivakumar, A., Martinez, T., Ovis, A., & Sura, J. (n.d.). Analysis of clerical workloads in the Cardiac Procedures Unit. 1-8. Retrieved September 16, 2016.

[3] King, N. (n.d.). Executive Summary: Turnover Times in the EP Lab. Retrieved September 13, 2016.

[4] Mazzei, W. J. (1994). Operating room start times and turnover times in a university hospital. Journal of clinical anesthesia, 6(5), 405-408.

[5] Kodali, B. S., Kim, D., Bleday, R., Flanagan, H., & Urman, R. D. (2014). Successful strategies for the reduction of operating room turnover times in a tertiary care academic medical center. journal of surgical research, 187(2), 403-411.

[6] Leslie, M., Hagood, C., Royer, A., Reece, C. P., & Maloney, S. (2006). Using lean methods to improve OR turnover times. AORN journal, 84(5), 849-855.

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Appendix A: Data Analysis

Figure A-1. Cath lab turnover organized by patient class transition between cases

Figure A-2. EP lab turnover organized by patient class transition

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Figure A-3. Turnover time organized by lab type and anesthesia type

Figure A-4. “Room Start-Room Ready” Room Ready values only existed for EP records

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Appendix B: Log of Observation Hours

Hours of Observations

Wednesday, September 21st – 4 hoursWednesday, September 28th – 4 hoursThursday, September 29th – 2 hours

Thursday, October 6th – 4.5 hoursWednesday, October 12th – 4 hoursThursday, October 13th – 4 hoursThursday, October 20th – 2 hoursWednesday October 26th – 4 hoursThursday, October 27th – 2 hoursFriday, October 28th – 2.5 hours

Wednesday November 2nd – 4 hoursThursday, November 10th – 2 hoursTuesday, November 15th – 2 hoursWednesday November 16th – 4 hoursThursday, November 17th – 3 hoursFriday, November 18th – 3 hours

Monday, December 5th – 2 hours

Total: 53 hours

Figure B-1. Number of hours observed on each weekday

Appendix C: Data Collection Sheet

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Appendix D: Current State Cath Process Map

Turnover time median of 15.99 minutes is based on time studies (sample size = 12). Task times are based time studies and feedback from staff interviews.

Turnover time median of 28 minutes is based on historical data from January 2016 – August 2016 (sample size = 1,094).

*Scrub Tech not always present during process**Sometimes there is missing data; Monitor Tech has to consult another tech or the physician***Some labs require a new equipment arrangement, Scrub Tech will have to re-arrange equipment

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Appendix E: Current State EP Process Map

Turnover time median of 48.33 minutes is based on time studies (sample size = 5). Task times are based on time studies and feedback from staff interviews.

Turnover time median of 40 minutes is based on historical data from January 2016 – August 2016 (sample size = 1,226).

*Some labs require a new equipment arrangement, Scrub Tech will have to re-arrange equipment.**CRNA was not observed during turnover. CRNA tasks and task times are based on 1 interview with an EP CRNA and client feedback.

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Appendix F: Staff Interview Questions

Interviews with Cath Physicians:1. How do you decide the next procedure to conduct?2. How do you communicate with non-physician staff before the next procedure begins?

Interviews with EP Physicians:1. How do you communicate with non-physician staff before the next procedure begins?

Interviews with EP & Cath Techs, Nurses, and CRNAs:General Questions:

1. Which labs do you work in?2. Could you please describe your thoughts on this diagram (show printed process map for

EP or Cath)? Are there any elements missing, or any elements you feel are incorrectly sized or placed?

3. Please describe your knowledge of how MiChart data is entered. Do you have any problems with the current process?

Tech Questions:1. Do you scan items in before they are set up in the lab room? Or do you scan the item’s

bag after it has been set up?2. Sometimes staff must leave the room to find a needed item the room is missing. Is there a

specific type of item this happens a lot with?3. Are there any specific pieces of equipment or lab supplies you feel could be located in a

more efficient or accessible spot?

RN Questions:1. When and how do you check who the next patient is?2. Do the display boards have any elements that are confusing? Is there any info you wish

they displayed but currently do not?3. What type of staff do you interact with between when you check who the next patient is,

and when you wheel that patient to their lab room?4. How are the display boards updated?

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