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Cost Analytics and Decision Intelligence:Cardiology Diagnostic Vascular Unit Final Report Submitted to:Cathy Twu Wong, Senior Project Manager, University of Michigan Medical Group (UMMG)Andreea Duma, CI Specialist, Performance ImprovementKate Sell, PI Fellow, Performance ImprovementMary Duck, UMHS IOE 481 Liaison, Performance ImprovementDr. Mark P, Van Oyen, Ph.D, IOE 481 Faculty Instructor Submitted by:IOE 481 Team 1Henry ChenJason HananiAmy JosephShakila Khan Date Submitted:December 11, 2018 18F1 - Final Report Table of ContentsExecutive Summary1Background and Key Issues1Goals and Objectives1Project Scope1Data2Methods2Design Methods, Requirements, Constraints, and Standards2Design Requirements2Design Constraints2Design Standards3Findings3Conclusions and Recommendations3Expected Impact5Introduction6Background6Key Issues6Goals and Objectives6Project Scope7Data 7Interviews7Time Studies8Observations8System Data8Methods9Process Maps9Costing Rules9Design Methods, Requirements, Constraints, and Standards9Design Requirements11Design Constraints11Design Standards12Findings12Cardiovascular Center13University Hospital16Domino’s Farms20Conclusions and Recommendations22Costing Rules22Recommendations23Best Practices23Improvement Opportunities24CVC25UH25MiChart (All Locations) 25Expected Impact26References 27Appendix A: Cardiovascular Center DVU Process Map28Appendix B: University Hospital DVU Bedside Process Map29Appendix C: University Hospital DVU In-Lab Process30Appendix D: Domino’s Farms DVU Process Map31Appendix E: Data Collection Methods, Constraints and Standards Matrix32List of Tables and FiguresTable 1: Improvement Opportunities and Recommendations4Table 2: Summary of DVU Costing Rules22Table 3: Example Total Patient Cost Calculations23Table 4: Improvement Opportunities and Recommendations 24Figure 1: Option 3, Gemba walk and system data pull, comprise the best option for data collection10Figure 2-1: CVC Preparation Process Map14Figure 2-2: CVC Procedure and Wrap-Up Process Map15Figure 3: CVC Procedures Pareto Chart16Figure 4-1: UH Bedside Preparation Process Map 17Figure 4-2: UH Bedside Procedure and Wrap-Up Process Map 18Figure 5-1: UH In-Lab Preparation Process Map 18Figure 5-2: UH In-Lab Procedure and Wrap-Up Process Map 19Figure 6: UH Procedures Pareto Chart19Figure 7-1: Domino’s Farms Preparation and Procedure Process Map 20Figure 7-2: Domino’s Farms Wrap-Up Process Map 21Figure 8: Domino’s Farms Procedures Pareto Chart21Executive SummaryThe University of Michigan Medical Group (UMMG) is currently working with Michigan Medicine to map the current state of their Diagnostic Vascular Units (DVUs) at a detailed level. The DVU includes Michigan Medicine’s Frankel Cardiovascular Center DVU (CVC), University of Michigan Hospital DVU (UH), and University of Michigan Hospital Domino’s Farms DVU, as well as other DVUs that are out of scope for this project. Currently, the costing data does not provide detail at an activity level. Therefore, the Performance Improvement team asked an IOE 481 team to observe the current state of three DVU locations, determine the process flow, and then tie in payroll data to find an accurate costing figure for patient procedures. Ultimately this data identifies DVU process steps and time allocation of every step for each role. The purpose of this project is to help Michigan Medicine better understand patient-related costs which will aid in making meaningful operational and business decisions within the DVUs. By utilizing the general ledger for payroll and observations, the student team created costing rules and documented areas of improvement. Background and Key IssuesThe Diagnostic Vascular Units (DVUs) of Michigan Medicine provide a full range of diagnostic vascular studies ordered by physicians across the hospital system. The current healthcare cost accounting methods used in the DVUs and other departments in Michigan Medicine do not accurately reflect the true cost of specific procedures attributed to the patient. Therefore, University of Michigan Medical Group (UMMG) started the Cost Analytics and Decision Intelligence (CAnDI) initiative to better determine costing information and make informed business and operational decisions. Goals and ObjectivesThe primary goal of the project was to produce accurate activity-based cost accounting methods. An additional goal was to make recommendations for improvement opportunities within the DVUs. The following objectives outline key project steps:Define and mapped out the current state of the DVUs processes.Develop appropriate costing rules for process steps.Identify best practices and opportunities for process improvement within the DVUs. Project ScopeThe scope of this project included operational and business processes for Healthcare Administration & Support and Patient Care Services within the DVUs at the UH, the CVC, and Domino’s Farms. Other departments such as Physical Therapy, OR ICU, and CVC procedures such as Echo and EKG were not included in scope. DVU locations outside of those mentioned above were also not in scope. DataThe team collected data from 3 sources: interviews, time studies, and observations. Interviews helped the team understand the current scheduling process. Time studies were conducted to identify the process flow of a patient during their visit. Observations were conducted at all three locations by two team members per observation period. The team observed 6 patients at UH, 4 patients at CVC, and 2 patients at Domino’s Farms.Additionally, the team obtained existing system data from the general ledger for payroll (DVU Payroll Info_for Students.xlsx) [1], average procedure times (Diagnostic Vascular Lab Procedures.doc) [2], and procedure frequencies (DVU Numbers2018.xlsx) [3]. MethodsUsing the data, the team created process maps (Appendix A, B, C, and D) and costing rules for the current state. The process maps were created by analyzing the time studies and observations. They identify the process steps, data sources, and supplies utilized. System data pulled from payroll along with time study findings were used to identify the percentage of time and cost allocated per task per role. The labor rates were multiplied by the time for each step to create costing rules. Design Methods, Requirements, Constraints and StandardsDue to time and scheduling limitations, the team was not able to make observations for all operational hours of the DVUs. Therefore, the team designed a data collection methodology from which accurate data could be extrapolated and analyzed. The team conducted preliminary time studies and observations of a few typical procedures at the three DVUs in scope. Based on these preliminary observations, four data collection methods were compared to select the best approach for this project. Design RequirementsDesign requirements include: accuracy, ease of implementation, acceptability by coordinators and healthcare professionals, acceptability by patients, and volume of data. These requirements were upheld by consulting with the project coordinators, healthcare professionals, and patients. Design ConstraintsTo collect time studies and observations, the team scheduled blocks of time during the week according to the open hours of each DVU location. However, certain procedures were missed when the team could not be present. Other design constraints included budget, zero disruptions to regular activity, and the project time frame. Design StandardsThe team complied to the following standards: HIPAA, Michigan Medicine Standards, Best Practices, IEEE, and MLearning. The team also protected Protected Health Information (PHI). Findings Findings from interviews, observations, and time studies helped the team to better understand the process flows at each DVU location. The interviews outlined patient-related tasks performed by Clerks, Technicians, and other healthcare professionals who are not directly under the DVUs. The interviews also gave insight into what schedulers do during a patient’s appointment and how the patient is tracked in the system during their visit. Detailed process maps of CVC, UH, and Domino’s Farms were created based on the team’s observations and time studies at each location. All patient-related activities were categorized into three major segments of a patient’s visit: preparation, procedure, and wrap-up. In addition, the healthcare professionals involved, data source, time needed per task, costing rules, and supplies were recorded on the process maps. The time measured for only patient-related tasks will be used to formulate the costing rules for each DVU location. Costing rules were calculated based on the time study data collected and payroll data. By multiplying the time a technician spent on a patient for the most common procedures with the healthcare professional’s payroll information, the team calculated the average cost for patients receiving a procedure at the DVUs. These costing rules are also summarized in the process maps. The sum of costing rules in one process is the cost attributed to a single patient. The costing rules developed by the team are static, based on average observation times and procedure times from the Diagnostic Vascular Lab Procedures [2].Conclusions and RecommendationsBased on the findings, the team developed costing rules to capture the direct cost to patients based on healthcare professional activity. Additionally, opportunities for improvement for the DVU process at each location were identified and discussed.Table 1: Improvement Opportunities and RecommendationsLocationImprovement OpportunitiesRecommendationsCVCRegistered Vascular Technician idle time between patients.Propose that scheduler make an optimized schedule for each Tech. Identify additional work to utilize talent, possibly taking on TA’s role.CVCClerk downtimeTake on TA’s role of transporting patients. UH - BedsidePatient with other care teams when technician arrives at room leads to technician lag timeBuild system to identify if patient is being seen by healthcare provider / some sort of status identifier on MiChartUH Only one phone shared between two clerks, limit one call at a time.Install one phone per clerkUHPhysicians at UH often order bedside tests because it is assumed that it will produce results faster than if the patient is sent to the lab, though it is not always the case.Verify need for bedside procedure with doctor and check information on patient’s chart. If in-lab procedure is viable, request transport for patient and prepare for procedure.All LocationsMiChart queue does not update in real time. Technician must remember to refresh frequently to see if patients are queuedFind way to refresh automatically whenever an order is pushed into queue. All LocationsInvestigate issues further by performing time studies, observations, and interviews Assign tasks and goals to a new project team that outlines recommendations Expected ImpactThe team’s recommendations are expected to have an impact in two main ways. First, by breaking down the processes into their smaller steps, the team was able to create more accurate costing rules based on activity directly involving the patient. This data will allow Michigan Medicine to better understand costs and inform meaningful operational and business decisions. Additionally, the team identified system triggers that can be used by the CAnDI team to create more dynamic costing rules in the future. Second, the data collected gives light to best practices and potential areas of improvement. Best practices at one site can be replicated and implemented in the other DVU sites if applicable. The team made recommendations to address the areas of improvement as summarized in Table 1. Performance Improvement at Michigan Medicine can use this data to standardize processes and increase efficiency across DVU sites. IntroductionThe University of Michigan Medical Group (UMMG) is currently working with Michigan Medicine to map the current state of Diagnostic Vascular Units (DVUs) at a detailed level. The DVU includes Michigan Medicine’s Frankel Cardiovascular Center DVU (CVC), University of Michigan Hospital DVU (UH), and University of Michigan Hospital Domino’s Farms DVU, as well as other DVUs that are out of scope for this project. Currently, the costing data does not provide detail at an activity level. Therefore, the Performance Improvement team asked an IOE 481 team to observe the current state of three DVU locations, determine the process flow, and then tie in payroll data to find an accurate costing figure for patient procedures. Ultimately this data identifies DVU process steps and time allocation of every step for each role. By utilizing the general ledger for payroll and observations, the student team created costing rules and documented areas of improvement. The purpose of this report is to outline the team’s methodology, findings, and final recommendations. The outcome of this project will help Michigan Medicine better understand patient-related costs which will aid in making meaningful operational and business decisions within the DVUs. BackgroundCurrently, the cost accounting systems used by Michigan Medicine and standard systems within the healthcare provider industry provide limited costing details at the patient level to inform operational and business decisions. The Cost Analytics and Decision Intelligence (CAnDI) project is working to shift towards a detailed cost accounting system. The project identifies new costing rules to address current limitations. The CAnDI project began about two years ago, starting for the operating room (OR), and has since expanded to encompass several other departments. Key IssuesThe current cost system in the DVUs does not explain the cost allocation of each process step at a detailed level. Although procedure data is tracked through the MiChart system, the current costing system does not accurately reflect healthcare provider activity. Goals and ObjectivesThe primary goal of the project was to produce accurate activity-based cost accounting methods. An additional goal was to make recommendations for improvement opportunities within the DVUs. The following objectives outline key project steps:Define and map out the current state of the DVUs processes.Developed appropriate costing rules for process steps.Identify potential best practices and opportunities within the DVUs.To meet these objectives, the team performed the following tasks:Conducted interviews with key personnel including schedulersPerformed time-studies at each DVU locationDeveloped a reliable time data collection tool for process stepsCreated detailed process maps on DVU processes Obtained general ledger for payrollAcquired average Diagnostic Vascular Lab Procedure dataProject ScopeThe scope of this project included operational and business processes for Healthcare Administration & Support and Patient Care Services within the DVUs at the UH, the CVC, and Domino’s Farms. Other departments such as Physical Therapy, OR ICU, and CVC procedures such as Echo and EKG were not in scope. DVU locations outside of those mentioned above were also not in scope. The roles that are in scope are listed as follows:Patient Service Assistants/Associates (Clerks and Schedulers)Patient Care Tech Assistants (Tech Assistant)Registered Vascular Technicians (RVT) Data The team collected data from 3 sources: interviews, time studies, and observations. Additionally, the team obtained existing system data from the general ledger for payroll, average procedure times, and procedure frequencies. InterviewsTo understand the current scheduling process, the team interviewed clerks and schedulers in UH, CVC, and Domino’s Farms. The team asked the following questions to the healthcare professionals: What tracking system do you utilize at this location?How is the patient tracked throughout their visit?What is the usual process flow of a patient?How do the healthcare professionals communicate with each other?What are the most frequent tasks performed?How do you prepare for a procedure?Who do you need to contact?What are the estimated times for each task?Follow-up questions were added for clarification of processes and tasks. Time StudiesTo identify the process flow of a patient, the team conducted time studies in two-hour periods and recorded activities in one-minute intervals [4]. At UH the team conducted time studies for the following procedures:TranscranialRefluxLower Extremity Deep Vein Thrombosis (DVT)Upper Extremity DVTLower Extremity Arterial Doppler with Digit Pressures At CVC the team was able to conduct time studies for the following procedures:Carotid Duplex Lower Extremity DVT Femoral popliteal (lower arterial and toes) At Domino’s Farms, two time studies were conducted for a Lower Extremity Reflux Study. Time studies conducted by the team were compared to the Diagnostic Vascular Lab Procedures and their estimated times provided by the DVU [2]. All time studies conducted is consistent with the estimated time data provided by the project client with small variations due to the condition of the patient being studied. In addition, system data was pulled to verify that the process times that the team captured were accurate. ObservationsDuring observations, two team members were present at each location. One team member sat with the clerks and schedulers for interviews. Notes were taken on the patient tracking system used, how patients were entered into the system, and how supplies were logged. The second team member observed the technician, including patient interactions and the supplies used for the observed procedures. Observations were performed at all three locations and with all patient populations. The team has observed six different patients at UH, four different patients at CVC, and two different patients at Domino’s Farms. System DataThe project team received the general ledger for payroll for UH DVU, CVC DVU, and Domino’s Farms DVU [1]. The team also received existing data from the coordinators which included a list of all the Diagnostic Vascular Lab Procedures [2] performed at each location along with the time scheduled and brief procedure descriptions, and a list summarizing the frequency of each procedure in the year 2018 [3]. MethodsThe team analyzed the time studies and system data to create detailed process maps and develop costing rules. In addition, the interviews and observations documented potential process improvement opportunities to be discussed further during data analysis.Process MapsTime studies and observations were compiled to create process maps for the three locations (Appendix A, B, C, and D). Process maps identify the process steps, the data sources (patient tracking system), and supplies utilized for a DVU patient visit. Also, the CVC DVU process map includes a swimlane process since the patient interacted with more than just the Vascular Technician. Costing RulesUsing the time studies and system data that is pulled from payroll, the team identified the percentage of time and cost per task per role. The labor rate per minute for each in-scope role was calculated based on the payroll data. The labor rates were then multiplied with the time data for each step to create the costing rules. Design Methods, Requirements, Constraints, and StandardsThe team needed to have a robust understanding of typical DVU operations and procedure times in relation to the healthcare professionals involved. Due to time and scheduling limitations, the team was not able to make observations of the DVUs during all operational hours. Therefore, the team designed a data collection methodology from which accurate data could be extrapolated and analyzed. The team conducted preliminary time studies and observed a few typical procedures at the three DVUs in scope. While these provided valuable insight into the general process flow of the department, time study data resulted in high variance due to outlier events such as MiChart system outage and emergency patient scheduling delay. This required a more accurate method of data collection that yielded a higher volume of samples. There were four viable options that could be used to collect the relevant data. The first option was to create data self-collection forms for all the healthcare professionals involved where they would document the time and action taken during their interactions with a patient. This would allow for a fuller, more detailed picture of the involvement of the various roles during patient care and yield a high volume of accurate data.The second option was to utilize system data such as historical schedules from Snapboard and timestamped triggers from MiChart. The requested data were:The general ledger for payroll at the three DVU locations [1]The average time spent for each procedure [2]The frequency of each specific procedure within a certain time period (Snapboard) [3] This additional data allowed the team to have a higher volume of accurate time estimates for healthcare professionals’ interaction with patients. The third and fourth options were a combination of one of the above options with a Gemba walk (observations). To decide between the four options, the team used a Pugh matrix (Figure 1) to compare the alternatives in the Concluding Documentation of Design Process in Action section below. The Pugh matrix utilizes a weighted parameter scoring system on a percent scale. The design parameters were accuracy of data, ease of implementation, acceptability by involved personnel, acceptability by patients, and volume of data collected. The four options were assigned additive or subtractive points depending on their accordance with the requirement parameters. Final scores are used to determine the efficacy of the various options. Figure 1: Option 3, Gemba walk and system data pull, comprise the best option for data collection Design RequirementsThe team considered the following design requirements to judge between the four options. The labels correspond to those in the Constraints and Standards Matrix in Appendix E. Accuracy: The data collection method should produce accurate data which better translates to the ultimate cost accounting figures for the department. (R-E-1)Ease of Implementation: The data collection method should be easily implemented in terms of equipment involved, parties affected, and complexity of set up. (R-E-2)Acceptability by Coordinators and Healthcare Professionals: The data collection method should be supported by both the project coordinators and the healthcare professional affected. (R-F-1)Acceptability by Patients: The data collection method should have little pushback by patients and allow them to feel comfortable during their procedures. (R-G-1)Volume of Data: The volume of data collected should be sufficient, accurate, and robust to develop the final cost accounting figures. (R-H-1) Design ConstraintsThe data collection methods must stay within and cover the scope of the project. The team considered the following design constraints throughout the duration of the project. The labels correspond to those in the Constraints and Standards Matrix in Appendix E. Budget: All data collection and time studies should be able to be conducted without the need for an allocated budget. (C-D-1)Zero Disruptions: The data collection and time studies should not disrupt any procedures conducted by the DVUs. (C-E-1)Observation Hours: All observations, time studies, and data collection must be conducted during the open hours of each DVU. The CVC is open 7:00 AM - 5:30 PM Monday-Friday, the UH is open 24/7, and Domino’s Farms is open 8:00 AM - 5:30 PM Monday-Friday. (C-H-1)Project Timeframe: All observations, time studies, and data collection must be completed within the timeframe of the University of Michigan Fall 2018 semester. The final report and all deliverables must be presented to the client by December 17, 2018. (C-H-2) The team identified various constraints that limit the ability to collect data and provide recommendations. The entire project and deliverables are due December 11, 2018. To collect time studies and observations the team needed to schedule blocks of time during the week according to the open hours of each DVU location. The CVC is open 7:00 AM - 5:30 PM Monday-Friday, the UH is open 24/7, and Domino’s Farms is open 8:00 AM - 5:30 PM Monday - Friday. Certain procedures were missed when the team is not present. Design StandardsThe team was required to abide by multiple policy standards such as HIPAA, Michigan Medicine’s standards and best practices, IEEE, and MLearning. The team was also required to protect Protected Health Information (PHI). The team shared all files through a University of Michigan MBox folder, and accessed secured files using CAEN machines. The team considered the following design standards throughout the duration of the project. The labels correspond to those in the Constraints and Standards Matrix in Appendix E. HIPAA: The HIPAA Privacy Rule establishes national standards protecting patient medical records and other personal health information including but not limited to patient identifiers, patient health plans, and patient transactions with health care providers. Thus, the data collection method must be secure and not breach any of the aforementioned standards. [5] (S-1-1)Michigan Medicine’s Standards: The CAnDI project being conducted by Michigan Medicine has documentation and templates that have been produced from studies in other Michigan Medicine Departments, provided by the team’s coordinators, that were followed by the team to document data for the DVUs. (S-2-1)Best Practices: The team’s coordinators have suggested templates and advice on the best way to perform time studies and interviews while shadowing healthcare professionals in the DVUs. (S-3-1)IEEE: In all formal documentation, the team used the Institute of Electrical and Electronics Engineers (IEEE) standards to cite literature used for analysis and developing recommendations. [4] (S-4-1)MLearning: The team has been trained on compliance, patient safety, and other important policies within the hospital via MLearning modules, the Michigan Medicine training portal. The team must adhere to these standards throughout the project. (S-5-1) Additional searches for relevant standards were made on OSHA, HIPPA, and ANSI websites, but none were found. FindingsFindings from interviews, observations, and time studies helped the team to better understand the process flows at each DVU location. This section summarizes the findings and assumptions made that aided the team in designing process maps and costing rules.Cardiovascular CenterSince CVC was the largest and complex DVU location, it was essential that the team interviewed the clerks at CVC to gain insight regarding scheduling and general process flow before conducting time studies. The clerks shared they primarily function as schedulers and overlook the CVC patient tracking system, specifically through Snapboard within the DVU and the Dot System outside of the DVU. They mentioned there is idle time between patients in which Registered Vascular Technicians(RVT) spend it leisurely. Observations of the patient process flow revealed that patients went through three main segments in their visit: preparation, procedure, and wrap-up. Preparation is from the point the patient checks in till they are taken to their DVU Study Room. First, the patient is checked in at the reception where they are either identified as non-clinic or clinic. If the patient has a scheduled appointment(clinic) with a physician in the clinic after their DVU procedure, the patient receives a pager while waiting for a Medical Assistant(MA) to take their vitals. Afterwards, the MA drops off the patient at the DVU lobby. If the patient does not have a clinic appointment(non-clinic), the patient waits for the TA to take them directly to the DVU lobby. The patient waits in the DVU lobby until the TA takes them into the designated DVU Study Room where the patient meets the RVT. In the procedure segment, the RVT starts the ultrasound machine [BEGIN STUDY], puts gel on the area being scanned and captures images and videos. The procedure time is approximately the same time as it mentions in the Diagnostic Vascular Lab Procedures [2]. It can range from 20-60 minutes depending on the procedure.The wrap up segment starts from when Once the procedure is completed [END STUDY], non-clinic patients go home while clinic patients wait in the DVU lobby for their clinic appointment. During this time the RVT completes the post-study report. Once a room opens up in the clinic, the TA drops the clinic patient off for their appointment.Figure 2-1 and 2-2 summarize the CVC process flow in a swimlane process map including the times associated with each segment which will aid in creating the costing rules. The full process map can be found in Appendix A. Arrows in the process map identify the flow of non-clinic patients. Figure 2-1: CVC Preparation Process MapFigure 2-2: CVC Procedure and Wrap-Up Process MapHistorical procedure frequency data was received by Michigan Medicine IT staff from the MiChart system database. It included the number of times each procedure was performed in the past year by month at each location. The data shows that procedure frequency roughly fits a pareto distribution where a few common procedures are most common at the CVC DVU. For example, roughly 20% of the procedures make up 80% of the scheduled appointments in the past year for each location. The pareto chart below in Figure 3 shows the frequency of CVC DVU procedures performed in descending order. The most common procedure at CVC is the ABI DVU Toe/Ankle Brachial Index (ANKLE BRACHIAL INDEX 2520).Figure 3: CVC Procedures Pareto ChartUniversity Hospital For UH, separate maps were made for bedside and in-lab processes due to significant differences in preparation. For both maps seen in Figure 4-1, 4-2, 5-1, and 5-2, the major three steps involved are preparation, procedure, and wrap-up. The full process map for bedside patients is shown in Appendix B and the full process map for in-lab patients is shown in Appendix C. At UH, registered vascular technicians (RVTs) are the only healthcare providers involved in their DVU processes. The RVT begins by refreshing the work order queue on MiChart to update the list of patients queued in the system, then determines which patient to see depending on the priority level reported in MiChart. There are three priority levels: “STAT”, “URGENT”, and “ROUTINE”; however, “ROUTINE” cases are only taken care of during the day shift. Once the RVT selects a patient, he or she may call the physician to confirm that a patient should be studied bedside. Once that is confirmed, the RVT will click “Begin Exam” for the selected patient(s) on MiChart and begin preparing for the procedure.” If the RVT is performing a bedside procedure, he or she may visit more than one patient before returning to the work room. If multiple patients are studied on a bedside procedure, the RVT will click begin exam for all selected patients. If the RVT is conducting the study in one of the DVU exam rooms, he or she will prepare the room and wait until a transporter arrives with the patient. The RVT then clicks “Begin Study” on the machine to begin the procedure. The study length is currently based on DVU Procedure Index which contains estimated study lengths for each procedure type. At the end of the procedure, the RVT wipes the patient and disinfects all equipment to wrap up the procedure. After cleaning up and returning everything to its proper place in the DVU exam rooms, the RVT returns to the work room in UH DVU and writes a report on the conducted study. Once that is completed the RVT clicks “Prelim study” to save the report. The time spent on writing on the report varies with the type of procedure that was conducted. If there are any outstanding issues from the study, the RVT may page the physician who ordered the study. The process maps summarize the task times and labor rates which are used in the costing rules.Figure 4-1: UH Bedside Preparation Process MapFigure 4-2: UH Bedside Procedure and Wrap-Up Process MapFigure 5-1: UH In-Lab Preparation Process MapFigure 5-2: UH In-Lab Procedure and Wrap-Up Process MapFigure 6 below shows the YTD frequency distribution of UH procedures on a pareto chart. The most common procedure at UH is a LEVU DVU Lower Extremity DVT (ULTSND LWR EXTRM VEN 3500).Figure 6: UH Procedures Pareto ChartDomino’s Farms Domino’s Farms DVU is a Michigan Medicine satellite that caters to its surrounding clinics. This is the smallest location out of those in scope, with only a single reception clerk and vascular tech catering to its cardiovascular patients. Therefore, this process is relatively simple and similar to that at the CVC DVU. Scheduling is performed by the reception clerk who also takes basic patient identification and insurance information. This location primarily serves clinic patients who first check in at reception and are directed to the waiting room. There is no intake process, the patient moves directly to the cardiovascular study room led by the vascular tech. The study is performed and after completed, the patient is free to go while the vascular tech starts the post report write up. Room prep is simple and takes very little time, it consists of disposing of any rags and patient gowns as well as wiping down the machines and hospital bed. The study start and end times follow the same procedure as described above. The vascular tech enters time triggers into MiChart as well as pressing “Begin Study” on the machine used. This information is cataloged and stored in system databases. This process is summarized in the process map in Appendix E.Figure 7-1: Domino’s Farms Preparation and Procedure Process MapFigure 7-2: Domino’s Farms Wrap-Up Process MapFigure 8 below shows the YTD frequency distribution of the Domino’s Farms procedures. Like that of UH, the most common procedure at this location is a LEVU DVU Lower Extremity DVT (ULTSND LWR EXTRM VEN 3500). Figure 8: Domino’s Farms Procedures Pareto ChartConclusions and RecommendationsFindings from the process maps helped the team form conclusions that support the costing rules. This section discusses best practices of the DVUs and the team’s recommendations. Costing RulesTimes from both the time studies and procedure times noted on the DVU Procedure Index were used to create costing rules. The costing rules were calculated by multiplying the time spent on an activity by the labor rate of the involved role. Note that in some cases multiple people can perform the same task at the same time. For this example, a single UH Registered Vascular Technician spends 2 minutes to select a patient. The UH RVT has a labor rate of $0.44 per minute. The following equation shows the dollar amount allocated to selecting a patient that one RVT performed:2 minutes * $0.44 per minute * 1 UH RVT = $0.88 The sum of these costs in a single process flow sums up to the total cost attributed to a single patient through the DVU. As another example, if “Procedure X” conducted by an RVT is reported as taking 30 minutes according to the DVU Procedure Index, the cost allocated to that procedure is:30 minutes for “Procedure X” * $0.44 / minute * 1 UH RVT = $13.22 The following table summarizes all the costing rules developed for the three DVU locations. Table 2: Summary of DVU Costing RulesThe costing rules listed in Table 2 were used to find the total cost for a single patient going through the DVU. Table 3 below shows the calculations for total patient cost for the most common procedure at each location.Table 3: Example Total Patient Cost CalculationsLocationMost Common ProcedureCosting RuleTotal Patient CostCVCABI DVU Ankle Brachial Index (ANKLE BRACHIAL INDEX 2520)TA: 6 min * $0.24/minRVT: [6+30+15] min * $0.44/min$23.88UHLEVU DVU Lower Extremity DVT (ULTSND LWR EXTRM VEN 3500) RVT: [2+6+30+10] min * $0.44/min$21.12Domino’s FarmsLEVU DVU Lower Extremity DVT (ULTSND LWR EXTRM VEN 3500) RVT: [10+30+7] min * $0.61/min$28.67Currently all costing rules are static, based on either average observation times or reported average procedure times. This is a foundation to create dynamic costing rules, in the future, based on timestamps pulled from system triggers throughout the process.RecommendationsThe team documented potential opportunities for improvement based on observations and interviews with the healthcare professionals involved.Best PracticesRVT took an average of 5 to 7 minutes to move patient to wrap up and transport patient to transport bay (UH)RVT reviews priority of patients after physicians have submitted order into queue before selecting patients (UH)Dot System for tracking patient flow in larger departments with more personnel and patients (CVC)At UH, RVTs were efficient when moving patients from lab procedures to transport bay. RVT performed this task between 5 and 7 minutes. Furthermore, RVTs are responsible for reviewing priority of patients who are being treated. The three priority levels are “STAT”, “URGENT”, and “ROUTINE”. RVT’s then select patients based off physician recommendations as well as their own judgment. This double-checked method is a good way to make sure patient priority is standardized and validated. RVTs can make informed decision based on priority levels.The Dot System is a patient tracking system used by the CVC DVU within Snapboard and MiChart. The CVC DVU is the only location in scope to utilize the Dot System because of its relative size and complexity compared to UH and Domino’s Farms DVUs. The project team views the Dot System as a best practice for departments involving multiple healthcare personnel, patients, and rooms to be scheduled. While this system would not improve smaller processes such as that of the Domino’s Farms DVU, it would improve the scheduling and patient flow tracking of larger departments. Improvement OpportunitiesTable 4 below summarizes the improvement opportunities we observed at each location and our recommended solution to resolve the issue.Table 4: Improvement Opportunities and RecommendationsLocationImprovement OpportunitiesRecommendationsCVCRegistered Vascular Technician idle time between patients.Propose that scheduler make an optimized schedule for each Tech. Identify additional work to utilize talent, possibly taking on TA’s role.CVCClerk downtimeTake on TA’s role of transporting patients. UH - BedsidePatient with other care teams when technician arrives at room leads to technician lag timeBuild system to identify if patient is being seen by healthcare provider / some sort of status identifier on MiChartUH Only one phone shared between two clerks, limit one call at a time.Install one phone per clerkUHPhysicians at UH often order bedside tests because it is assumed that it will produce results faster, though it is not always the case.Verify need for bedside procedure with doctor and check information on patient’s chart. If in-lab procedure is viable, request transport for patient and prepare for procedure.All LocationsMiChart queue does not update in real time. Technician must refresh frequently to update patient queueFind way to refresh automatically whenever an order is pushed into queue. All LocationsInvestigate issues further by performing time studies, observations, and interviews Assign tasks and goals to a new project team that outlines recommendations CVCAt the CVC, RVTs and Clerks experience idle time during two common periods. The first period is when no patients are scheduled for an appointment; CVC DVU experience peak hours (mornings) and downtimes when no patients are scheduled. At the CVC this is caused primarily by patient preference for appointment times. The second time is in between patient appointments where there is no work needed to be done by RVTs post report. The team recommends further investigations such as root cause analysis on worker idle time to inform possible improvements. UHRVTs at UH are sometimes turned away from patient rooms when there is another care team with the patient. For example, an RVT may be called to a bedside patient; after clicking beginning study in their computer and walking to the patient room, the RVT’s study may be delayed due to care teams already working with the patient. The team recommends a system to track patient occupancy status to communicate with separate departments involved with the patient. An identifier in MiChart detailing patient status would alleviate this issue. There are two clerks at UH DVU. Clerks can only take one call at a time because there is only one shared phone. Clerks interact with inpatient departments such as ER, Transport, Doctors, outpatient department, and insurance companies. Insurance calls range from 10 to 30 minutes. Generally, each clerk will call 6 to 8 insurance companies. Providing one phone per clerk would allow for better utilization of time as the clerks will be able to work in parallel.Physicians at UH often order bedside tests because it is assumed that it will produce results faster than if the patient is sent to the lab; however, this is not always the case. To determine whether it is appropriate to perform a bedside procedure, the RVT can verify it by contacting the physician who ordered the procedure. Additionally, the RVT may be able to identify whether a bedside study is necessary based on information on the patient’s chart. If after these two steps, the RVT believes it is viable to perform the procedure in the lab, the RVT should request a transporter to bring the patient to the lab and prepare for the procedure while waiting for the patient to arrive.MiChart (All Locations)MiChart patient queue does not update in real time and must be repeatedly refreshed to see the most recent patient queue. A system change in which MiChart patient queue is continuously updated would provide RVT’s with more accurate information on patient status. Expected ImpactThe team’s recommendations are expected to have an impact in two main ways. First, by breaking down the processes into their smaller steps, the team was able to create more accurate costing rules based on activity directly involving the patient. This data will allow Michigan Medicine to better understand costs and inform meaningful operational and business decisions. Additionally, the team identified system triggers that can be used by the CAnDI team to create more dynamic costing rules in the future. Second, the data collected gives light to potential areas of improvement. By cross-referencing best practices at the different DVU sites, best practices at one site can be replicated and implemented in the other DVU sites. Performance Improvement at Michigan Medicine can use this data to make processes standardized and more efficient across DVU sites.Our findings are expected to be integrated into the CAnDI initiative and built upon by future teams. The data collected and methodology from this project can be used as a basis for future improvement and cost accounting initiatives within the three DVUs. Ultimately the CAnDI initiative will aid operational leaders in making informed business decisions in all areas of Michigan Medicine. References[1]K. Sell. “IOE 481” Personal email (December 6, 2018)[2]S. Brown. “FW: DVU Procedure Index” Personal email (December 10, 2018)[3]S. Brown. “RE: [IOE 481] Validation Meeting Request” Personal email (November 15, 2018)[4]O'Leary KJ, Liebovitz DM, Baker DW, Hospitalist Time‐Motion. J. Hosp. Med 2006;2;88-93. doi:10.1002/jhm.88[5] Carroll, Nathan, and Justin C. Lord. “The Growing Importance of Cost Accounting for Hospitals.” Journal of Health Care Finance, 2016, index.php/johcf/article/view/109.Appendix A: Cardiovascular Center DVU Process MapAppendix B: University Hospital DVU Bedside Process Map Appendix C: University Hospital DVU In-Lab Process Map Appendix D: Domino’s Farms DVU Process MapAppendix E: Data Collection Methods, Constraints and Standards Matrix Entry #123 R-A. Organizational PolicyN/A R-B. EthicalN/A R-C. Health & SafetyN/A RequirementsR-D. EconomicN/A R-E. Implementability(R-E-1)(R-E-2) R-F. User Acceptance(R-F-1) R-G. Patient Acceptance(R-G-1) R-H. Task Duration(R-H-1) C-A. Organizational PolicyN/A C-B. EthicalN/A C-C. Health and SafetyN/A ConstraintsC-D. Economic(C-D-1) C-E. Implementability(C-E-1) C-F. User AcceptanceN/A C-G. Patient AcceptanceN/A C-H. Task Duration(C-H-1)(C-H-2) S-1. HIPAA(S-1-1) S-2. Organization’s Standards(S-2-1) StandardsS-3. Best Practice(S-3-1) S-4. IEEE(S-4-1) S-5. M-Learning(S-5-1) ................
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