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Reducing Unit Delays in Transporting Patients from UH Inpatient General Care UnitFinal ReportSubmitted To: Clients:Maria Ceo, Associate Hospital Director for Operations and Ancillary Services Amy Campbell, Senior Project Manager, Operations and Ancillary ServicesLaKita Pogue, Patient Transportation Manager Lisa Muscat, Business Systems Senior Analyst, LogisticsCoordinators:Zachary Costello, Continuous Improvement Specialist Intermediate, Continuous ImprovementJonathan Lee, Continuous Improvement Specialist Intermediate, Continuous Improvement Instructors:Mary Duck, IOE 481 Professor, Industrial Operations EngineeringDavid Hyatt, IOE 481 Professor, Industrial Operations Engineering Submitted By: IOE 481 Team 6:Courtney Bober Abigail Lorencz Jessica SherSophia TurnerDate Submitted: April 21, 2020 20W6-FinalTable of Contents TOC \h \u \z 1 Executive Summary PAGEREF _9o6xz7us61ia \h 32 Introduction PAGEREF _8uddm24nisd3 \h 53 Background and Key Issues PAGEREF _mlxz6prj8ls4 \h 54 Goals, Objectives, and Expected Impact PAGEREF _q2v6st3avepi \h 64.1 Goals PAGEREF _3jz1bw1yjras \h 74.2 Objectives PAGEREF _l672odtu1sup \h 74.3 Expected Impact PAGEREF _qkvy2ny8v8va \h 75 Project Scope PAGEREF _2v1svxfov7ou \h 76 Design Process PAGEREF _5ydkmv4jjai5 \h 86.1 Engineering Challenges PAGEREF _uxmu4arx2mc2 \h 86.2 Literature Search PAGEREF _by8usbpcgcb0 \h 86.3 Deliverables and Design Tasks PAGEREF _n1xvymklejd4 \h 96.4 Design Constraints PAGEREF _3eza8qytjn1u \h 106.5 Design Requirements PAGEREF _2sygjw5g2igb \h 116.6 Design Standards PAGEREF _pu3as0nacyek \h 117 Data Collection and Analysis Methods PAGEREF _hj3xl1nnzcfp \h 117.1 Shadowing Transporters PAGEREF _5h6xyxxqxahm \h 117.2 Interviewing Nursing Staff PAGEREF _5h6xyxxqxahm \h 127.3 Collecting Historical Data PAGEREF _5h6xyxxqxahm \h 128 Findings PAGEREF _rkpgervl34n5 \h 128.1 Shadowing Transporters PAGEREF _hj57gd2itm6 \h 128.2 Interviewing Nursing Staff PAGEREF _hj57gd2itm6 \h 128.3 Analyzing Historical Data PAGEREF _hj57gd2itm6 \h 139 Conclusions PAGEREF _w9rpi557j85h \h 1710 Alternatives Considered PAGEREF _873yugejidyv \h 1811 Recommendations PAGEREF _vxc41fvxpr0j \h 2012 Future Steps PAGEREF _7w4p47dpcx43 \h 2113 References PAGEREF _ivcy7kmq19ge \h 2214 Appendix A: Delay Codes PAGEREF _agzey822d3x \h 2315 Appendix B: Floor 5 Layout of Michigan Medicine General Care Unit PAGEREF _j3jbs281h8cp \h 2416 Appendix C: Questions for Interviews with Nursing Staff PAGEREF _ox9zu4xqvpcy \h 25List of Tables and Figures TOC \h \u \z Table 1: Constraints and Standards Matrix PAGEREF _u726dqqx26xt \h 10Table 2: Pugh Chart for Alternatives PAGEREF _oh2stecbrmm4 \h 19Figure 1: Typical flow for a Transport to Follow PAGEREF _vx08xuu2cjr6 \h 6Figure 2: Typical flow for transporters with the team’s scope highlighted PAGEREF _c9ksaz5oa81c \h 8Figure 3: Transports scheduled ahead of time (not spontaneous) PAGEREF _mwotckss7es7 \h 14Figure 4: Average late arrival of 10.8 minutes PAGEREF _cafwsjo7lfxi \h 15Figure 5: Average time of 43 minutes for total transport to arrival PAGEREF _q655jgd1h1im \h 15Figure 6: Average Delay Minutes per Transport Delay Entered PAGEREF _41u3h65q7ar \h 16Figure 7: Half of Transports with 1 or More Delay PAGEREF _o0bf186wb9lz \h 16Figure 8: A screenshot of the patient care report screen PAGEREF _k4crb0ja7nfu \h 191 Executive Summary Patients at Michigan Medicine are brought to and from appointments throughout the facility by hospital staff known as transporters. These transports are frequently experiencing delays resulting in underutilization of staff, missed appointments, cancellation of appointments and overall frustration for hospital staff and patients. The client has asked the IOE 481 team to examine these delays and get back the most time for the hospital. The team looked at the window of time between when the transporter arrives at the patient room and when the transporter leaves with the patient for the appointment in order to address the goal of decreasing delays at the patient room. With this goal in mind, the team had the objectives to decrease idle time of transportersdecrease late arrivals to appointmentsincrease patient satisfactionimprove communication between all stakeholders in the transportation of patientsIn order to do so, the team shadowed transportersinterviewed nursing supervisorsinterviewed nursing directorslooked at historical data of transports in the hospitalFrom shadowing, the team found that nurses often did not start patient prep until they saw the transporter physically at the patient room and that transporters were waiting at the room on almost every transport. The team also found from the interviews that there was a breakdown in communication between the transporters and the nursing staff. From the historical data, the team found that most transports are scheduled between about 46 minutes and 3 hours before being assigned to a transporter; however, 80% of the transports are late to patient appointments. The team found that the average lateness of these late arrivals is between 2 and 15 minutes and each delay code entered by the transporters lasts between 4 and 8 minutes. Additionally, the team found that the average number of delays per transport in our scope is between 1 and 2. Overall, the team concluded that the information that the transporters had needs to be accessible to the nurses. While very few transports were actually spontaneous, from the nurse perspective, the majority of transports present as spontaneous tasks due to a lack of transparency in the transportation process and therefore are unprepared to meet the transportation schedule expectations. The team's final recommendation is that Michigan Medicine implements a note of all scheduled transports in MiChart, an area nurses already check daily for information. This alternative will help meet the objectives of decreasing transporter idle time, decreasing appointment delays, and improving communication between nurses and transporters. The recommendation will change transports from the nurses perspective and address chronic hand off delays that impact on-time arrivals at destinations. However, COVID-19 has affected the team’s ability to contact other stakeholders in the transportation process such as the IT department, dispatch team, and the patients who experience these transports. Because of this, the objectives of improving communication between all stakeholders was not completely met. As such, the team recommends that the project should move forward with interviewing other stakeholders such as the IT department and testing the alternatives when the time permits. 2 IntroductionMichigan Medicine is a large network of patient services ranging from emergency services, child services, general care, cancer treatment and more. Within this network, patients often stay overnight or longer to receive various care and require an escorted transport to appointments. All transports require a nurse to begin and end the process and an escort from a “transporter” from their room to the appointment. Transporters utilize a “Ticket to ride” (Information packet relating to the patients’ transportation needs, destination, etc) given to them in paper form by the nurse in the patient’s unit. All other transportation data including assignment of patients, transportation progress updates, and mode of transport required are electronically communicated via ‘iPods’ the transporters carry on their person. Within this process, frequent delays can happen resulting in underutilization of staff, missed appointments, cancellation of appointments and overall frustration for hospital staff and patients. The client has asked the IOE 481 team to examine the frequency of the delays and the most common causes of these delays for all transports within Michigan Medicine. The team has collected data by shadowing transporters on Michigan Medicine general care units and conducted interviews with Michigan Medicine staff including Clinical Nursing Director and Nursing Supervisor. Additionally, the team has compiled and analyzed the historical transport dispatch data. Using this data, the team has formed conclusions and recommendations to achieve the goal to decrease delays and late arrivals to appointments. This final report documents the project’s background, goals, objectives, and scope, as well as the team’s design process, data collection and analysis accomplishments, comparisons of alternatives, conclusions, and final recommendation.3 Background and Key IssuesThe transport process starts once a transporter receives a notification of transport. They then locate equipment such as beds, stretchers, or wheelchairs, travel to the patient room, locate the nurse, get all documentation (“Ticket to ride”) needed, wait for the patient to be fully prepped, and finally take the patient to their appointment. This process is illustrated below in Figure 1. Figure 1: Typical flow for a Transport to Follow Most aspects of patient transportation are able to be tracked via users (transporters) inputting their progress with transporting trip into an iPod. Input codes include “In Progress”, “Job Complete”, and several delay codes such as “Handoff of patient not ready”, “Elevator Delay”, and “Nurse unavailable delay”. When the transporter experiences one of these delays, they log it in their iPod from a drop down menu on the iPod. In the drop down menu, here are approximately 20 delay codes the transporters can choose from. These delay codes are shown in Appendix A. The timing of patient transportation is crucial for on-time appointments and discharges within the hospital. The transporters are responsible for getting patients to appointments, so when delays occur in this transportation process, patients can miss appointments, or their scheduled services become behind schedule. These delays amount to about 13 cumulative hours of collective transporters’ time lost per day. This hospital needs to recover this crucial time to be a more efficient and effective hospital. In light of the novel Coronavirus COVID-19, this project has become even more important as the hospital takes on a very high volume of patients. With this increase in patients, transporters will be in higher demand and need to communicate well with all nurses in order to get patients where they need to be in a timely fashion. 4 Goals, Objectives, and Expected ImpactThis section describes the main project goals, objectives, and expected impact of the team’s recommendations as well as the process to arrive at these goals. 4.1 Goals Transporters are currently often found waiting at the patient rooms for their patient to be ready for transport due to a variety of delays. These delays not only cause the transporters to be underutilized, but they also cause patients to arrive late to appointments and for appointments to be canceled altogether. The goal of this project is to decrease the time lost due to delays at the patient room. 4.2 Objectives The team has the following objectives with the goal to decrease delays at the patient room: Decrease idle time of transportersDecrease late arrivals to appointmentsIncrease patient satisfaction Improve communication between all stakeholders in the transportation of patients4.3 Expected Impact The team believes that meeting the goals and objectives of this project will enable the hospital to have a more streamlined communication between nurses and transporters that will enable the hospital to run more efficiently and complete more appointments on time. The team also believes that this will help to improve the relationship of all stakeholders and mitigate some of the frustrations that have been experienced by staff at the hospital. 5 Project ScopeThe scope of this project includes:Transporter arrival to patient roomGeneral Care units in Michigan Medicine (Appendix D)Any delays or causation of delays between the transporter’s arrival and the transporter’s moving of the patientTransporter training for how to select which delay code to use Transporter beginning to move patientNurse prep of patientsScope does NOT include:Dispatch assigning transporterICU, Mott Children’s hospital, emergency, or high-risk units in Michigan MedicineTransporter assignment to arrival time delays (i.e. elevator delays or looking for equipment)Transporter motion after moving patient from room to when they reach their destination‘iPod’ screen or screen codes displayedTracking methodScheduling of breaks and appointmentsFigure 2 below outlines illustrates the portion of current process flow that is considered in scope by the team. Figure 2: Typical flow for transporters with the team’s scope highlightedWhile the COVID-19 outbreak has impacted many of the operations within Michigan Medicine at this time, it has not affected the scope of the team’s project. 6 Design ProcessThe following sections discuss the engineering challenges that this project presents along with results from the literature search that the team conducted, the project deliverables, and the design constraints, requirements, and standards that affect this project.6.1 Engineering ChallengesThe main engineering challenges inherent in this project are regarding process improvement. The team utilized lean principles in the evaluation of this process. Finally, the team utilized the Five Why tool to diagnose the root causes of issues.6.2 Literature SearchThe team utilized the past IOE 481 project reports to learn how similar projects have operated and what has been successful. The team used strategies from these reports to perform data collection as well. Reducing Non-Value Added Time in RFID Tagging SystemThe team used information from this past IOE 481 project to understand time studies and how to conduct them. The project on radio frequency identification (RFID) tags mapped out a process, compared how long each point in the process took and identified the main source of delay. The team’s project used the general process the RFID team used to attack the data. The team instead compared the different delays that can happen when the transporter arrives to the patient room and the time these delays each took up. The team mimicked the technique to standardize the process in which RFID tags are used in order to standardize patient transportation preparation and identify areas that are causing the greatest number of delays. The team also took inspiration from the visual communications within this report. SWAT Process Improvement at Michigan Medicine The team also used information from a past IOE 481 project on SWAT transports to model our study. SWAT transports are concerned with high-risk patients and require special training. While this is not in the scope of our project, the SWAT transporters experienced delays and staff underutilization similar to those within the team’s project such as patients not being ready when the transporters arrive. As such, the team learned many things from the SWAT project. The SWAT transporters follow the same process of scheduling, preparing material, traveling to bedside, preparing the bedside, and traveling to the procedure area. This project uses observations, shadowing, interviews, time studies, and data analysis in order to understand the current process flow and identify areas of improvement such as coordination between units. These methods, as well as the recommendations for increased communication and adjusted policy on timing, served as a guide for the team’s project on the transport of moderate-risk patients by routine transporters. 6.3 Deliverables and Design TasksThe team has presented the client with a detailed report containing all of the work performed, including:Data collection methods such as shadowing, interviews, and historical dataData analysis on all data collection Findings from data analysis about frequency and main causes of delays Alternatives to address the goal of reducing lost time due to patient transport delaysPugh matrix to quantify success of alternatives Final recommendation of selected alternativePlan on how to move forward to implement recommendation6.4 Design ConstraintsThe constraints the team worked within the duration of the project are summarized below in Table 1. Table 1: Constraints and Standards MatrixFrom Table 1 above, the team was constrained by the length of the semester, causing them to deliver a recommendation in this 11-week time frame. The recommendation could not require extra staffing or interfere with the scheduling of patient appointments. Additionally, the team could not assign any clinical duties to the transporter or to any staff other than a nurse. The team dealt with scheduling constraints. As the peak hours of transports occur on weekdays from 9am to 3pm, a time of day that is also peak hours for University of Michigan classes, the team had to work to find times that they could observe transports that did not interfere with their academic calendar. Finally, the team was limited by the restrictions placed on the hospital due to the COVID-19 outbreak. While this did not impact scope, the hospital stopped allowing in person data collection including shadowing, times studies, interviews, and in-person surveying. Because of this, the team was limited in the data they could collect and had to rely heavily on what they were able to obtain. 6.5 Design RequirementsThe project used the number of instances of delays as the most important metric by which we measure our success in reducing in unit patient transports. The alternative designs were measured by the decrease in the number of transport delays and decrease in number of canceled appointments due to these delays. 6.6 Design StandardsThe team followed all industry standards such as HIPAA (Health Insurance Portability and Accountability Act) and OSHA (Occupational Safety and Health Act) regulations. As shown in above in Table 1, these standards are for ethical, organizational, and safety measures. The team did not use any data in which patients are identifiable and aggregated all findings. The team also followed all COVID-19 state mandates such as the stay at home order and the reduced access to the hospital in order to protect the safety of all. 7 Data Collection and Analysis MethodsThe team collected data through shadowing of transporters, interviewing of nursing supervisors and directors, and collecting historical data. This section outlines the team’s process for collecting the data and analyzing the findings. Collection was through stored historical data reported by transport tracking devices and electronic records of transport. Data was analyzed primarily in Excel pivot tables to explore relationships between variables and frequency of actions. Due to COVID-19 the team was not able to do additional interviews or data collection with stakeholders such as the IT department, dispatchers, and nurses. 7.1 Shadowing TransportersAll four members of the team shadowed several transporters as they completed patient transports in various units of Michigan Medicine. The floor layout of a general care unit is shown in Appendix B. During this time, the team was able to see firsthand how the process works as well as several instances where the transport was delayed. Each member of the team experienced a slightly different perspective on transports as each member observed at a different time. The team would see the notification go out to the transporters, see a transporter accept it, and then the team member would go to the room the transporter was heading to and wait there to see the transport process. The team would wait outside of the room, take notes and ask questions when appropriate. 7.2 Interviewing Nursing StaffThe team asked the client to put them in contact with nursing staff in order for the team to get a nursing perspective. The client made the connection between the team and a nursing supervisor and a nursing director. The team created a list of questions to go through with both staff members separately. These questions are shown in Appendix C. 7.3 Collecting Historical DataData was collected from transporters’ iPod from February 2019 to February 2020. Two separate datasets, time stamp data and delay code data, were pulled. Both data sets were cleansed to include only transports originating in inpatient units. The time stamp data included time stamps relating when a transport was created in the computer system, when the appointment the transport was for was to be attended, when the appointment was officially queued for transporter assignment, when the appointment was assigned to a transporter, when the transporter accepted the assignment, total minutes delayed, total minutes in progress, and whether or not the appointment was on time. The delay code data included delay codes entered and length of time of each delay. 8 FindingsFrom the shadowing, interview, and historical data analysis, the team identified several significant findings. This section outlines those findings. 8.1 Shadowing TransportersThe team saw transporters standing around waiting due to patients in the bathroom, patients finishing a meal, patients completing physical therapy, the doctor in the room talking to patients, patients not prepped, patients talking to family, and transporter waiting on the ticket to ride. The team observed that the nurses often did not start patient prep until they saw the transporter physically outside the door of the patient's room. The team also witnessed a large amount of idle time of transporters outside of the patient room and interacted with the transporters who explained that waiting around has become “just a part of the job”. 8.2 Interviewing Nursing StaffThe team was able to interview both a nursing supervisor and nursing director in order to gain two different perspectives on nurse experience during patient transports. Interviews with Nurse Supervisor From this interview the team got valuable information about the miscommunications that occur between the nurses and the transporters. The supervisor believes more significant communication can occur between the transport team and the nurse team. He believes a note in the patient care report would be a great first step, however he believes even more drastic measures could be taken. The supervisor suggested a notification or a text message alerted to the nursing staff 30 minutes or more prior to the appointment. He told us in his opinion it is the “ad hoc” transports that tend to be delayed. When it is a regular appointment like morning dialysis the nurses are aware of the appointments and they are typically prepared ahead of time. However, he believes the more random appointments are the source of delays. Overall, the supervisor believes the more communication the better. Interview with Nursing Director The interview shed light on how the nurses paging system works as well as what the Patient Care Report screen entails. There is no way to signify that the transporter specifically is at the room to pick up the patient, the call light is the same as if the patient were to signal for the nurse. The nursing director brought up the possibility of the transporter being able to contact the nurses directly from the iPod that they carry. The director believes that this implementation could allow nurses to be notified even earlier. The director also brought up the possibility of changing the call light color to signal transporter arrival. He brought up the Patient Care Report screen on MiChart and suggested that a scheduled transport be added to the “Events” dialogue box or as a message below where the fall risk assessment is located at the top of the report. This information should be available to the nurses as soon as the appointment is scheduled, and/or as soon as it is canceled, for any reason. The director’s main point was that the information that the transporters have needs to be accessible to the nurses.8.3 Analyzing Historical DataThe time stamp data included timestamps throughout the transport process. From this data set, the team found that most transports were scheduled further in advance than an hour before patient appointment. These findings are shown below in Figure 3. Figure 3: Transports scheduled ahead of time (not spontaneous)From Figure 3, the team saw that most transports are scheduled between 1 and 8 hours before patient appointment. This is within the time of a nurses shift, meaning they are unable to be briefed at the beginning of their shift about upcoming transports the majority of the time. This may be a contributing factor to patient lateness. The average lateness of these transports is shown below in Figure 4. Figure 4: Average late arrival of 10.8 minutes From Figure 4, the average lateness of the late arrivals is 10.8 minutes. The time stamp data also showed that 80% of the transports are late to patient appointments. Total transport time from the time the transport is queued for assignment to a transporter to the time the patient arrives at the appointment. This is shown below in Figure 5. Figure 5: Average time of 43 minutes for total transport to arrival Figure 5 shows that the average total transport time is 43 minutes. The delay code data included which delay codes were entered and the length of time of each delay code indicated. From this data set, the team saw that 70% of the delays were considered in scope. These delay codes occurred between the time of arrival of the transporter at the patient’s room and the time the transporter exited the patient room with the patient en route to the scheduled appointment. The average time these delays take is summarized below in Figure 6. From this figure, each delay code entered by the transporters lasts averages 8.2 minutes. Figure 6: Average Delay Minutes per Transport Delay EnteredFinally, the team combined both the time stamp data and the delay code data by using the transport ID as a link. The findings from this analysis are shown below in Figure 7. Figure 7: Half of Transports with 1 or More Delay Figure 7 shows the number of delays per transport in our scope. Roughly half have 1 or more delay. However, it is important to note that while not empirically shown, shadowing with transporters hinted at a culture of entering one delay code upon first delay, but not re-assigning the delay type if it is a delay consecutive to a different delay. More shadowing would ideally be conducted to further investigate the integrity of the data specifically looking at the average number of delays and time lost per delay code.9 ConclusionsBased on the data analysis, the team was able to come to the following conclusions. From shadowing, the team concluded that the idle time of transporters was much too high, with transporters constantly waiting at the patient room for the patient to be prepped and ready for transport. The team also concluded that there was a significant lack of communication between the transporters and the nurses, as nurses often began to prep the patient only once they had physically seen the transporter at the room. From the interview with the Nursing Supervisor, the team concluded that this lack of communication between was also impacting the nurses drastically and that a notification system such as a note in the patient care report or text message alert could help nurses to prepare ahead of time, which would then decrease the wait time transporters. From the interview with the Nursing Director, the team again concluded the need for increased communication with the nurses and acknowledged that the information that the transporters have on their iPods must be made available to the nurses as well. This interview led the team to conclude that notifications on the Patient Care Report or changes in the nurse call light could also help nurses to prep patients in time for transports. From historical data analysis, the team saw that 70% of delays occur between the arrival of the transporter at the patient's room and the exit of the patient from the room and concluded that this time zone heavily indicates that nurse participation is a significant factor in decreasing the delays experienced by transporters. The team saw that most transports were scheduled about 46 minutes to 3 hours before assignment; however, 80% of transports were late to patient appointments. This late arrival was mainly due to delays such as patients not prepped at the patient room, resulting in transporters standing around waiting for nurses. This finding led the team to conclude that the lack of communication between nurses and transporters was significantly impacting the delays of transports. While very few transports were actually spontaneous (scheduled within 1 hour of patient appointment time), from nurse interviews, a primary complaint was the ‘surprise’ of transport appointments that did not give the nurses time to prepare patients. This phenomenon was assumed to be due to frequent spontaneity. Overall, we have concluded the following recontextualization of our original problem statement (Transports are not arriving to appointments on time) to: From the nurse perspective, the majority of transports present as spontaneous tasks due to a lack of transparency in the transportation process and therefore are unprepared to meet the transportation schedule expectations (patient ready at time of pick up). This in depth problem statement explains the underlying factor that leads to chronic hand off delays impacting patients’ on-time arrival at destinations. This underlying factor is what the recommendations in this report will address.10 Alternatives Considered The team evaluated 3 different alternatives. These alternatives were chosen by the team due to our conclusions from the data analysis mentioned above. The team believes that these alternatives target the goal to decrease idle time lost to delays at the hospital. Alternative 1: Nurse notification systems70% of delays occur in the “nurse required” step of the transport process. Nurse leadership perceives transport jobs as spontaneous, or surprises, and therefore difficult to plan around and be present for in a time orchestrated manner. The nurse notification system would send notifications of transports directly to the nurses phone or pager. By creating a nurse notification system, the “surprise” factor (Nurses perceiving transports as being scheduled spontaneously, e.g. within one hour of patient appointment time) should decrease, meaning more nurses will be able to prep patient rooms closer to the transporter arrival time at the room and possibly have related tasks prepared shortly ahead of the transporter arrival. This would reduce overall delay. Alternative 2: X minutes or more policyBased on the given data, nurses are voicing concerns about spontaneous transports that give them limited time to prep their patients for appointments. An on-time goal can be set by adjusting the time that transports are pending in the queue. Therefore, if changing nothing else of the process, scheduling all transports X minutes ahead of an appointment should increase on time patient arrivals. However, it is unknown how this change may impact transporter resource allocation.Alternative 3: Note in MiChart The nurses spend the majority of their time in the Patient Care Report tab of MiChart. Currently, there is no callout to the nurses of any transports that patient has that day. The team believes that adding a note to the Patient Care Report or an “event” for the transports that patient has that day would be a great way to improve the communication of transports to nurses (Figure 8). Figure 8: A screenshot of the patient care report screen-533399114300The team has constructed a Pugh Chart shown below in Table 2 in order to evaluate the possible alternatives the team has discussed up until this point. Table 2: Pugh Chart for Alternatives WeightCurrent StateAlt 1: Nurse NotificationAlt. 2: X Minutes or MoreAlt. 3: Note in MiChartCriteriaCriteriaWeightCurrent StateAlt. 1Alt. 2Alt. 3Ease of Implementation 150-++Transporter Idle Time250+-+Cost of Implementation 100-0-Late arrival to appointments250+++Communication with Nurse250-0+Total 10000065The team scored each alternative based on the following criteria:Ease of implementation How difficult will it be to make this change? What training will need to be done? Will it inhibit other positions? CostWhat cost is associated with this change? Are there new parts that need to be purchased? New staff that needs to be hired?Transporter Idle time Will it decrease Transporter idle time? Appointment delay impactHow impactful would this alternative be? Will it decrease the number of late appointments/canceled appointments?Nurse CommunicationHow well is communication between stakeholders? How are nurses impacted? 11 Recommendations Based on the shadowing, interviews, data analysis and input from the client, the team is recommending that the hospital adopts Alternative 3, the note in MiChart. The team used the Pugh matrix in Table 2 as well as our conclusions to develop this recommendation. Alternative 1 would be the most dramatic increase in communication to nurses. It would be a notification in real time that one of their patients needs to be prepped for transport. This does not allow a nurse to be prepared for the transport, but it would give a strong reminder of the transport which would allow for decrease in late arrivals to appointments and decreased idle time. While Alternative 1 would improve the communication of transports to nurses, it could also be hectic for the nurses. Since they are constantly running around, this would still feel like a more “random event” as opposed to something they can plan into their day. This alternative would also be the most difficult to implement and the most costly. While Alternative 2, the X minutes or more policy, would be very easy to implement and have a minimal cost, this alternative would not decrease the idle time of transporters, improve communication with nurses, or improve communication between the nurses and the transporters. As mentioned above, the main problem within the transportation process is due to a lack of transparency that creates spontaneous transports from the nurses’ perspective. This alternative does not combat this problem so that the nurses are able to meet the transportation schedule expectations. There was also some feedback from the transporters that it can be a negative to arrive at appointments too early as well and thia alternative would cause many early arrivals. The team also found out an alternative similar to this one was tested about one year prior and it showed little to no improvement. Alternative 3, the note in MiChart, would also be easy to implement and have a minimal cost. This alternative would make appointments more transparent to the nurses, and therefore help to decrease transporter idle time, decrease appointment delay and improve nurse experience. This alternative was also the most endorsed by the client. This is how the team decided to make this alternative their recommendation. The team believes that Alternative 3, the note in MiChart, will meet the objectives of decreasing transporter idle time, decreasing late arrivals to appointments , and improving communication with nurses. The team also believes this alternative will contribute to the objective of increasing patient satisfaction, although due to COVID-19 the team was not able to investigate this through additional data collection from stakeholders such as patients and additional nurses. This alternative directly combats the lack of transparency that leads to chronic hand off delays impacting patients’ on-time arrival at destinations. The team recommends to start with a note in the “events” section with all the info for the transport, what kind of transport it is and what time the appointment is. If this still is not enough communication the team suggests making this a “banner” at the top of the patient care report screen for an even more streamlined message to the nurses. This alternative also scored the highest in the team’s Pugh matrix shown in Table 2. 12 Future Steps As COVID-19 drastically impacted access to staff in Michigan Medicine, the team has recommendations for future steps to take in relation to this project. The team was unable to reach out to MiChart IT in order to discuss possible plans for how the note in MiChart would be implemented and what exactly it would look like. The team was also not able to interview nurses in order to gain insight on what type of notification they believe would work best. The team therefore suggests that going forward, this project continues on with these next steps in mind with the team’s final recommendation. 13 References[1] Tarini Arte, Darnell Butler, Samuel Epner, Sarah Finley (2018, April 17). “Reducing Non-Value Added Time in RFID Tagging System” University of Michigan. [Online][2] Julian Covos, Tasha Gillum, Alexandra Mukavitz, and Thomas White. (2017, April 18). “SWAT Process Improvement at Michigan Medicine.” University of Michigan. [Online] Appendix A: Delay CodesHand off not readyNursing UnavailableDoctor W/ PatientStaff in RestroomNursing Unaware of AppointmentPatient Not PreparedPatient in RestroomWrong InformationEquipmentElevatorCode PinkCode (general)Respiratory TXBlood DrawnWaiting for Transport AssistanceWaiting or Lift TeamWayfindingPharmacyRound Trip15 Appendix B: Floor 5 Layout of Michigan Medicine General Care Unit INCLUDEPICTURE "" \* MERGEFORMATINET 16 Appendix C: Questions for Interviews with Nursing Staff1. What does the transporter process mean to you?a. What are your thoughts around it?b. How does it impact your team?c. Have there been any complaints?d. Have there been any kudos? 2. What portion of a nurse’s day, if you had to ballpark it, is spent preparing transports?3. What is the nurse’s involvement in printing the ticket to ride?4. Do you ever meet with a transport team lead?5. Do you have any communications with dispatch?6. What technology do nurses have a hold of (phones, pagers)?7. What is the training for prep for Nurses in relation to the transporters?8. Can you explain the paging system with the call button?9. Would it be more helpful to have notifications of transports as soon as they are scheduled? An hour before? Or when the transporter arrives?10. Ask to see an example of the Michart “Patient Care Report” screen. Can we add an alert or message box on this screen? What about the “events” box on that page. ................
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