University of Michigan Health System. Determining Patient Volumes, Staffing Volumes, and Patient-to-Staff Ratios in the Cardiac Procedures Unit

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1 University of Michigan Health System Determining Patient Volumes, Staffing Volumes, and Patient-to-Staff Ratios in the Cardiac Procedures Unit Final Report To: Robert Keast, Director of Cardiovascular Medicine Frankel Cardiovascular Center Janice Norville, Director of Clinical Operations Frankel Cardiovascular Center Katie Schwalm, Industrial Engineer Associate Frankel Cardiovascular Center Andrei Duma, Industrial Engineer Frankel Cardiovascular Center Mark P. Van Oyen, Professor Industrial and Operations Engineering From: IOE 481 Project Team #1 Jessica Cosentino Shubha Ranjan Konrad Thaler Date: April 21, 2015

2 Table of Contents EXECUTIVE SUMMARY... 1 Summary... 1 Background... 1 Key Issues... 1 Project Goals and Objectives... 1 Project Scope... 2 Methodology... 2 Findings and Conclusions... 2 Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # Recommendations... 3 INTRODUCTION... 4 BACKGROUND... 4 KEY ISSUES... 4 PROJECT GOALS AND OBJECTIVES... 5 PROJECT SCOPE... 5 METHODOLOGY... 5 Computing Staffing Volumes... 5 Collecting Staffing Dailies... 5 Analyzing Staffing Dailies... 6 Verifying EMR Data... 8 Collecting Observation Data and EMR Data... 8 Re-Organizing EMR Data... 8 Comparing EMR Data to Observed Data... 9 Computing Patient Volumes... 9 Collecting EMR Data... 9 Analyzing EMR Data... 9 Procedure #1: Steps to Calculate Patient Volumes at the Beginning of Each Hour... 9 Procedure #2: Calculating Patient Volumes at the Mid-hour Intervals Calculating Patient-to-Nurse Ratios for the Recovery Area Creating Graphs FINDINGS AND CONCLUSIONS Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # Finding and Conclusion # RECOMMENDATIONS EXPECTED IMPACT APPENDIX 1: Sample of a Daily APPENDIX 2: Graphs for EP Prep and Procedure Areas with Patient and Staffing Volumes... 18

3 APPENDIX 3: Graphs for EP Procedure Area with Patient Volumes and the Technician Capabilities APPENDIX 4: Graphs for Cath Prep and Procedure Areas with Patient and Staffing Volumes. 28 APPENDIX 5: Graphs for Cath Procedure Area with Patient Volumes and Technician Capabilities APPENDIX 6: Graphs for Recovery Area with Patient Volumes, Nursing Capabilities, and maximum number of patient-to-nurse Ratio... 38

4 List of Tables and Figures Table 1: Key to Convert Alphabetical Codes on Dailies to Shift Times.6 Table 2: Classification of Staff from dailies 7 Table 3: Patient Volume Data Collection Times.8 Table 4: Summary of the Verification Results for Prep, Procedure, and Recovery..14 Figure 1a: Formula used to determine if the patient s prep started before or at 7 am...10 Figure 1b: Formula used to determine if the patient s prep ended before or at 7 am 10 Figure 1c: Formula used to determine if the patient was present at 7 am.10 Figure 1d: Formula used to change the true or false value to a Boolean value.11 Figure 2a: Formula used to determine if the patient s prep started before or at 7:30 am..11 Figure 2b: Formula used to determine if the patient s prep ended before or at 7:30 am...11 Figure 2c: Formula used to determine if the patient was present at 7:30 am 12 Figure 2d: Formula used to change the true or false value to a Boolean value.12 Figure 3. Sample Daily Staffing Summary 16

5 EXECUTIVE SUMMARY Summary The Cardiac Procedures Unit (CPU) at the University of Michigan s Frankel Cardiovascular Center diagnoses and treats cardiovascular conditions. The CPU does not have organized data or summary visuals that show patient and staffing volumes in the preparation, procedure, and recovery areas. Further, the CPU does not know how often they meet their target patient-to-nurse ratio of 3:1 in the recovery area. The New York-Presbyterian Hospital shared a staffing efficiency analysis with the Frankel Cardiovascular Center that showed patient-to-nurse ratios over standard operating hours. The University of Michigan s CPU wanted to conduct a similar analysis. Therefore, an IOE 481 student team from the University of Michigan was asked to analyze patient and staffing volumes. The team obtained and analyzed patient and staffing volumes, determined whether the CPU was meeting the target 3:1 patient-to-nurse ratio in the recovery area, and created visuals to display the data. Background The CPU consists of the prep area, Cardiac Catheterization (Cath) lab, Electrophysiology (EP) lab, and recovery area. The recovery area consists of short-term recovery and overnight observations (obs). The CPU does not currently have organized data or summary visuals showing patient and staffing volumes as a function of time, day, and CPU area. The Directors of Cardiovascular Medicine and Clinical Operations requested detailed patient and staffing volume data, which resulted in the need for this project. The Directors of Cardiovascular Medicine and Clinical Operations shared a staffing efficiency analysis done for another hospital that showed the nurse-to-patient ratios in 30-minute increments throughout their standard operating hours. The CPU would like to perform a similar analysis, at a deeper level, that considers multiple types of staff (nurses and technicians), multiple days of the week (Monday through Friday), and multiple lab areas (preparation, procedure, and recovery). Key Issues The following key issues resulted in the need for this project: Lack of organized data connecting staff scheduling to patient volumes Excess number of staff scheduled compared to patient volume could lead to unnecessary staffing costs Inadequate number of staff scheduled compared to patient volume could lead to lengthy wait times for patients Project Goals and Objectives The primary goal was to determine patient and staffing volumes for the prep, procedure, and recovery areas; assess whether the CPU was meeting the target 3:1 patient-to-nurse ratio in the recovery area; and summarize the data with visuals. To achieve this goal, the team had the following objectives: Determine patient volumes Determine staffing volumes Compute patient-to-nurse ratios in the recovery area Analyze whether recovery area was meeting the 3:1 patient-to-nurse target ratio Display patient volumes, staffing volumes, and recovery ratios using visuals 1

6 Project Scope The scope of this project included the prep area, two labs in the CPU (EP and Cath), and the recovery area on the third floor of the Cardiovascular Center. Patient and staffing volume data was collected from 7:00 am through 11:00 pm Monday through Friday, for the months of November 2014, January 2015, and February The project did not consider areas on floors other than the third floor of the Cardiovascular Center. The project did not consider any other type of staff besides the nurses and the technical staff. Furthermore, the procedure types and specific details about the procedures were not included. The team did not collect any data for Saturdays and Sundays. Methodology This section describes the team s approach to completing this project in detail. 1. Collected staffing dailies from supervisors: The team requested staffing dailies from supervisors, who provided the data in an Excel format. The team received 51 dailies corresponding to November 3, 2014 through November 26, 2014, and January 1, 2015 through. 2. Analyzed dailies to compute staffing volumes: The team interviewed CPU supervisors to learn which alphabetical schedule codes correspond to which shift times. Then, the team computed the average frequency of each code to determine the number of nurses and technicians that were working in each area at each 30-minute time interval from 7:00 am through 11:00 pm. 3. Verified EMR data: The team observed patient volumes in the CPU, requested corresponding EMR data from the observation days, and determined how closely these datasets agreed. More specifically, the team collected observation data by going to the CPU and recording the number of patients in prep, procedure, and recovery areas in 30- minute time intervals on February 17, February 19, February 26, and February 27. In total, the team collected 22 hours of data. The team requested EMR data for those same days and compared the observations to the EMR data. 4. Used EMR data to compute patient volumes: After the EMR data was verified, the team requested additional EMR data from coordinators. The team requested data from November 3, 2014 through November 26, 2014, and January 1, 2015 through February 16, The team used this EMR data to compute average patient volumes in prep, procedure, and recovery areas on different weekdays. 5. Computed patient-to-nurse ratios in recovery area: The team used the patient and staffing volumes to compute the patient-to-nurse ratios in the recovery area. More specifically, the team found the maximum number of patients in each 30-minute time interval from 7:00 am to 11:00 pm for each day of the week, and divided these maximum patient volumes by the average number of nurses working during the corresponding time period. 6. Created visuals to display results: The team created graphs to show the average patient and staffing volumes Monday through Friday in prep, procedure, and recovery areas. Findings and Conclusions The team calculated patient and staffing volumes for all areas of the CPU, as well as patient-tonurse ratios in the recovery area. This section describes the team s findings and conclusions. 2

7 Finding and Conclusion #1 The team discovered that actual staffing volumes may differ from scheduled staffing volumes. For example, the dailies may not accurately reflect staffing volumes if staff members leave work early due to low patient volume, call in sick, or work overtime. Fortunately, these events do not significantly affect the analysis due to the large sample size (1,947 instances of staff members and codes). Finding and Conclusion #2 The dailies do not have a standard format, which prevents someone from using a macro to quickly compute the number of staff working in each area of the CPU. If a macro could be used, the team believes that the likelihood of counting errors would decrease and the overall repeatability of the analysis would increase. The team proposes a staffing summary sheet that would be compatible with a macro in the recommendations section. Finding and Conclusion #3 The team determined that the EMR data is representative of patient volumes in all areas of the CPU. For the prep and procedure areas, between 93.75% and 100% of the observations agreed with the EMR data within two patients, on average. For the recovery area, approximately 70% of the observations agreed with the EMR data within two patients, on average. The team discussed these results with the Directors of Cardiovascular Medicine and Clinical Operations, who confirmed that the data was verified. Finding and Conclusion #4 Patient and staffing volumes vary based on day of the week, staff type, and area of the CPU. Appendices 2 through 6 contain 25 graphs to illustrate this finding. Administrators can utilize the graphs to investigate areas of potential over- and under- staffing. Finding and Conclusion #5 Out of the total 165 time intervals, there were only six instances where the ratio of maximum number of patients to average number of nurses exceeded 3:1. The team talked with the recovery supervisor and determined that these outliers were not indicative of actual patient-to-nurse ratios, since the supervisor will simply have a nurse work overtime hours to handle the extra patient volume. Therefore, the recovery area always maintains a 3:1 patient-to-nurse ratio. Recommendations First, the team recommends that administration repeat this experiment over a longer period of time to capture more data points as well as detect trends in other months and seasons that may indicate under- or over- staffing in the CPU. In addition, the team recommends adding a qualitative component to the methodology. If potential areas of under- or over- staffing are identified, the supervisors and staff in the CPU should be interviewed about their perceived workload during these time periods to determine if true under- or over- staffing occurs. If the CPU continues this project, the team recommends that the supervisors fill out an additional form at the end of each workday called a daily staffing summary. Due to the variability in the structure of the current dailies, the team was unable to write a macro that would analyze each daily automatically. The team believes that the staffing analysis would be easier to repeat and would have fewer errors if a macro were used. 3

8 INTRODUCTION The Cardiac Procedures Unit (CPU) at the University of Michigan s Frankel Cardiovascular Center diagnoses and treats cardiovascular conditions. The CPU does not have organized data or summary visuals that show patient and staffing volumes in the preparation, procedure, and recovery areas. Further, the CPU does not know how often they meet their target patient-to-nurse ratio of 3:1 in the recovery area. The New York-Presbyterian Hospital shared a staffing efficiency analysis with the Frankel Cardiovascular Center that showed patient-to-nurse ratios over standard operating hours. The University of Michigan s CPU wanted to conduct a similar analysis. Therefore, an IOE 481 student team from the University of Michigan was asked to analyze patient and staffing volumes. The team obtained and analyzed patient and staffing volumes, determined whether the CPU was meeting the target 3:1 patient-to-nurse ratio in the recovery area, and created visuals to display the data. The primary goal of this project was to determine patient and staffing volumes for the preparation (prep), procedure, and recovery areas; assess whether the recovery area was meeting the 3:1 patient-to-nurse ratio; and show the data using visuals. The team has completed the project, and the purpose of this report is to describe the project goals and scope as well as the team s methodology, findings, conclusions, and recommendations. BACKGROUND The CPU consists of the prep area, Cardiac Catheterization (Cath) lab, Electrophysiology (EP) lab, and recovery area. The recovery area consists of short-term recovery and overnight observations (obs). The CPU does not currently have organized data or summary visuals showing patient and staffing volumes as a function of time, day, and CPU area. The lack of organized data may result in unnecessary staffing costs (if overstaffed) or lengthy wait times (if understaffed). The Directors of Cardiovascular Medicine and Clinical Operations requested detailed patient and staffing volume data, which resulted in the need for this project. The Directors of Cardiovascular Medicine and Clinical Operations shared a staffing efficiency analysis done for another hospital that showed the nurse-to-patient ratios in 30-minute increments throughout their standard operating hours. The CPU would like to perform a similar analysis, at a deeper level, that considers multiple types of staff (nurses and technicians), multiple days of the week (Monday through Friday), and multiple lab areas (preparation, procedure, and recovery). KEY ISSUES The following key issues resulted in the need for this project: Lack of organized data connecting staff scheduling to patient volumes Excess number of staff scheduled compared to patient volume could lead to unnecessary staffing costs Inadequate number of staff scheduled compared to patient volume could lead to lengthy wait times for patients 4

9 PROJECT GOALS AND OBJECTIVES The primary goal was to determine patient and staffing volumes for the prep, procedure, and recovery areas; assess whether the CPU was meeting the target 3:1 patient-to-nurse ratio in the recovery area; and summarize the data with visuals. To achieve this goal, the team had the following objectives: Determine patient volumes Determine staffing volumes Compute patient-to-nurse ratios in the recovery area Analyze whether recovery area was meeting the 3:1 patient-to-nurse target ratio Display patient volumes, staffing volumes, and recovery ratios using visuals PROJECT SCOPE The scope of this project included the prep area, two labs in the CPU (EP and Cath), and the recovery area on the third floor of the Cardiovascular Center. Patient and staffing volume data was collected from 7:00 am through 11:00 pm Monday through Friday, for the months of November 2014, January 2015, and February The project did not consider areas on floors other than the third floor of the Cardiovascular Center. The project did not consider any other type of staff besides the nurses and the technical staff. Furthermore, the procedure types and specific details about the procedures were not included. The team did not collect any data for Saturdays and Sundays. METHODOLOGY This project affects the CPU, because all patient and staffing volumes are from this unit. The primary project goal was to determine patient and staffing volumes for the prep, procedure, and recovery areas; assess whether the recovery area was meeting the target 3:1 patient-to-nurse ratio; and summarize the data with visuals. The team computed staffing volumes by collecting and analyzing staffing dailies (dailies). The team verified the electronic medical record (EMR) data by collecting and re-organizing the data, then comparing the EMR data to observed data. The team computed patient volumes by collecting and analyzing EMR data. The team calculated the maximum number of patients to the average number of nurses to determine whether the recovery area was meeting their target 3:1 patient-to-nurse ratio. Finally, the team displayed all results with visuals. Computing Staffing Volumes This section describes the process of collecting and analyzing the staffing dailies, which was necessary to compute staffing volumes. Collecting Staffing Dailies The team requested dailies from supervisors and then used the dailies to determine the average staffing volumes of nurses and techs in the CPU. The team requested a total of 51 dailies corresponding to November 3, 2014 through November 26, 2014, and January 1, 2015 through. Each daily contains the names of the nurses and techs working in each area, 5

10 as well as an alphabetical scheduling code that denotes their assigned shifts. An example daily is contained in Appendix 1. Analyzing Staffing Dailies The first step in determining staffing volumes was to convert the alphabetical codes on the dailies into shift times. The team interviewed CPU supervisors on February 19th and April 2nd to learn which codes correspond to which shift times. The conversion key is provided in Table 1, below. Table 4: Key to Convert Alphabetical Codes on Dailies to Shift Times Staff Type Code Shift Time EP Techs A 6:30am - 7:00pm a Q D 9:00am - 7:30pm 6:30am - 5:00pm 7:00am - 2:30pm EP Nurses A 6:30am - 7:00pm a Q D G 9:00am - 7:30pm 6:30am - 5:00pm 6:00am - 2:30pm 9:00am - 9:30pm Cath Techs A 7:00am - 7:30pm a Q D 9:00am - 7:30pm 7:00am - 5:30pm 8:00am - 4:30pm Cath Nurses A 7:00am - 7:30pm a Q D 9:00am - 7:30pm 7:00am - 5:30pm 8:00am - 4:30pm 6

11 Next, the team analyzed each daily using the follow procedure: Ignored data related to Charge Nurses, Sheath Pullers, ECHO Charges, Supervisors, Cath Calls, and EP Calls because these staff were not part of the project scope Used Role to classify remaining staff members as either Nurses or Techs Used Location to determine whether each nurse was working in EP Prep and Procedure, Cath Prep and Procedure, or Recovery Used Location to determine whether each tech was working in EP Procedure or Cath Procedure Table 2, below, shows the CPU area for each combination of location, role, and staff type. The team collaborated with supervisors to create this table. Table 5: Classification of Staff from Dailies Location Role Staff Type CPU Area EP Lab Prep Nurse EP Prep and Procedure EP 1-5 RN Nurse EP Prep and Procedure EP 1-5 Tech Tech EP Procedure EP Turn Team Tech EP Procedure EP Procedure Room Nurse EP Prep and Procedure EP Lunches Nurse EP Prep and Procedure CPU RN Nurse Recovery Night Shift RN Nurse Recovery Cath Lab Prep Nurse Cath Prep and Procedure Cath Lab Prep Tech Tech Cath Procedure Cath 1,2,4,5 RN Nurse Cath Prep and Procedure Cath 1,2,4,5 Monitor Tech Cath Procedure Cath 1,2,4,5 Circulate Tech Cath Procedure Cath 1,2,4,5 Scrub Tech Cath Procedure OR/TAVR RN Nurse Cath Prep and Procedure OR/TAVR Monitor/Circulate/ Scrub Tech Cath Procedure 7

12 After transforming scheduling codes to shift times and identifying work areas of staff members, the team manually counted the number of each alphabetical code on all 51 dailies for each area of the CPU, type of staff, and day of the week. Then, the team computed the average frequency of each code. At this point the team created formulas for each area and 30-minute time interval, since different codes were counted for different time intervals. For example, the formula for nurses working in Cath prep and procedure areas on Monday at 11:00 am summed the average frequency of a, A, Q, and D for Cath nurses. After conducting the staffing volume analysis, the team had determined the average number of staff (nurses and techs) working in each CPU area at each 30-minute time interval on each day of the week. The next step in the project was to verify the EMR data pertaining to patient volumes. Verifying EMR Data The team had access to EMR data chronicling the times that EP and Cath patients entered prep, procedure, and recovery areas. The team planned to use EMR data to derive patient volumes. However, the team could not reliably use EMR data until they determined that the data was representative of actual patient volumes in CPU areas. To verify the EMR data, the team observed patient volumes in the CPU, requested corresponding EMR data from the observation days, and determined how closely these datasets agreed. This section describes the data collection and verification of EMR data. Collecting Observation Data and EMR Data The team gathered observation data by recording patient volumes in prep, procedure, and recovery areas on four days, for a total of 22 hours, as shown below in Table 3. The team counted the number of patients in each area in 30-minute increments, and recorded results. Neither surveys nor additional research were required. Date Table 6: Patient Volume Data Collection Times Data Collection Times Tuesday, February 17 Thursday, February 19 Thursday, February 26 Friday, February 27 9 am 1 pm, 2 pm 5 pm 9 am 1 pm, 2 pm 5 pm 9:15 am 1:15 pm, 3 pm 5 pm 2 pm 4 pm In addition, EMR data was required for the verification. The team requested EMR data from the coordinators for the same dates that the observations occurred. The data (provided in an Excel format) contained the times that each patient entered and exited each of the three CPU areas. Re-Organizing EMR Data As previously stated, the EMR data consisted of the times that EP and Cath patients entered and exited prep, procedure, and recovery areas. The observed data, on the other hand, consisted of the number of patients that were present in each of the three CPU areas at half-hour intervals. The EMR data needed to be in the same format as the observation data before they could be 8

13 compared, which meant the enter and exit times needed to be converted to number of patients in each area at each half-hour time interval. The team manually converted the EMR data so that it was in the same format. In addition, the team deleted the data points that were missing an entering or exiting time for any of the areas. Comparing EMR Data to Observed Data After changing the form of the EMR data, the team compared the observed data to the EMR data to determine how well they agreed. More specifically, the team calculated the percentage of time periods where the number of patients observed exactly matched the number of patients present in each area according to the EMR data. The team also performed sensitivity analysis by calculating the percentage of time periods where the number of patients observed was within one patient or two patients of the EMR data. The EMR data was verified, as described in more detail in the findings and conclusions section later in the report. Computing Patient Volumes After verifying the EMR data, the team used data from November 3, 2014 through November 26, 2014, and January 1, 2015 through to approximate patient volumes. This section describes the process of collecting and analyzing EMR data to compute patient volumes. Collecting EMR Data The team requested EMR data from the coordinators covering the time periods of November 3, 2014 through November 26, 2014, and January 1, 2015 through. The coordinators gathered the data and provided it to the team in an Excel format. The data, which was recorded by nurses, contains the times that EP and Cath patients entered and exited prep, procedure, and recovery areas. The data collection did not require surveys or additional research. Analyzing EMR Data The team used Excel formulas to manipulate the entry and exit times to determine the number of patients in each CPU area at each 30-minute interval from 7 am to 11 pm, Monday through Friday. Then, the team found the 85th percentile of patient volume for each half-hour interval for each of the five days of the week in each of the CPU areas. Two different procedures were used to calculate patient volumes. The first procedure was used to compute the number of patients at the beginning of each hour (e.g. 7 am, 8 am, etc.). The second procedure was used to compute the number of patients at the beginning of each mid-hour (e.g. 7:30 am, 8:30 am, etc.). Procedures #1 and #2 explain the computations for the Cath procedure area on Monday, November 3rd, 2014 at 7:00 am and 7:30 am (respectively). Procedure #1: Steps to Calculate Patient Volumes at the Beginning of Each Hour 1. Opened the Excel file that contained the EMR time-stamped data 2. Filtered the Room column so that only data for Cath Lab rooms were displayed 3. Filtered the Day column so that only data for Mondays was displayed 4. Copied the Room, Date, Day, Estimated Arrival in Prep, and Estimated Prep End columns onto Sheet 2 in the same Workbook, where the remaining steps were performed 9

14 5. Used the formula shown in Figure 1a, below, to determine if the patient s prep started before 7 am Figure 1a: Formula used to determine if the patient's prep started before or at 7 am 6. Used the formula shown in Figure 1b, below, to determine if the patient s prep ended at or before 7 am Figure 1b: Formula used to determine if the patient s prep ended before or at 7 am 7. Used the formula shown in Figure 1c, below, to determine if the patient was present at 7 am based on steps 5 and 6 Figure 1c: Formula used to determine if the patient was present at 7 am 8. Used the formula shown in Figure 1d, below, to convert the true or false from step 7 into a Boolean value 10

15 Figure 1d: Formula used to change the true or false value to a Boolean value 9. Inserted a row after all the data points for 11/3/ Summed values in column I (using the Sum function) to find total number of patients present at 7 am 11. Repeated steps 1 through 10 for each hour (e.g. 7:00 am, 8:00 am, etc.) and Monday in the data set (e.g. 11/10/2014, 11/17/2014, etc.) 12. Copied all the sums for the Mondays to a new Excel Workbook (Workbook #2) 13. Computed the 85th percentile for the number of patients using the Percentile function 14. Determined the maximum number of patients using the Max function 15. Repeated steps 1 through 14 for each day of the week and area of the CPU to calculate the remaining patient volumes, 85th percentiles, and maximum values Procedure #2: Calculating Patient Volumes at the Mid-hour Intervals 1. Used the formula shown in Figure 2a, below, to determine if the patient s prep started before 7:30 am Figure 2a: Formula used to determine if the patient s prep started before or at 7:30 am 2. Used the formula shown in Figure 2b, below, to determine if the patient s prep ended at or before 7:30 am Figure 2b: Formula used to determine if the patient s prep ended before or at 7:30 am 11

16 3. Used the formula shown in Figure 2c, below, to determine if the patient was present at 7:30 am based on steps 1 and 2 Figure 2c: Formula used to determine if the patient was present at 7:30 am 4. Used the formula shown in Figure 2d, below, to convert the true or false from step 3 into a Boolean value Figure 2d: Formula used to change the true or false value to a Boolean value 5. Inserted a row after all the data points for 11/3/ Summed values in column M (using the Sum function) to find total number of patients present at 7:30 am 7. Repeated steps 1 through 6 for each mid-hour (e.g. 7:30 am, 8:30 am, etc.) and Monday in the data set (e.g. 11/3/2014, 11/10/2014, etc.) 8. Copied all the sums for each Monday to Workbook #2 9. Computed the 85th percentile for the number of patients using the Percentile function 10. Determined the maximum number of patients using the Max function 11. Repeated steps 1 through 10 for each day of the week and area of the CPU to calculate the remaining patient volumes, 85th percentiles, and maximum values With the staffing and patient volume analyses complete, the team computed patient-to-nurse ratios in the recovery area. Calculating Patient-to-Nurse Ratios for the Recovery Area One of the project goals was to determine if the CPU was meeting the 3:1 patient-to-nurse ratio, even when operating at maximum capacity. To determine patient-to-nurse ratios in the recovery area, the team found the maximum number of patients in each 30-minute time interval from 7:00 am to 11:00 pm for each day of the week, and divided these maximum patient volumes by the average number of nurses working during the corresponding time period. 12

17 Creating Graphs To visualize the results of the patient and staffing volume analyses, the team graphed the data for each day of the week (Monday through Friday) for each area and type of staff: Cath nurses (prep and procedure), Cath techs (procedure), EP nurses (prep and procedure), EP techs (procedure), and recovery area nurses. There are 25 graphs in total, which are displayed in Appendices 2 through 6. Each graph displays the 85th percentile of patient volume, maximum number of patients, and staffing capability for a specific day of the week. The 85th percentile of patient volume indicates that the actual patient volume will be less than or equal to that patient volume 85% of the time. The maximum number of patients was calculated for each time interval and day. The staffing capability represents how many patients can be handled by the staff for each time interval and day of the week, based on target ratios established by the CPU supervisors. For example, since the target patient-to-nurse ratio in the recovery area is 3.0, the staffing capability of nurses in the recovery area is three times greater than the actual number of nurses working at that time. If there are 5 nurses working in the recovery area at 9:00 am on Tuesday, then the nurses have a capability equal to 15. For the recovery area graphs (Appendix 6), the patient-to-nurse ratio line was added in addition to patient and staffing volumes. Also, the opening and closing times of the short-term recovery component of the recovery area were represented by vertical lines at 9:00 am and at 10:00 pm. FINDINGS AND CONCLUSIONS The team calculated patient and staffing volumes for all areas of the CPU, as well as patient-tonurse ratios in the recovery area. This section describes the team s findings and conclusions. Finding and Conclusion #1 The team discovered that actual staffing volumes may differ from scheduled staffing volumes. For example, the dailies may not accurately reflect staffing volumes if staff members leave work early due to low patient volume, call in sick, or work overtime. Fortunately, these events do not significantly affect the analysis due to the large sample size (1,947 instances of staff members and codes). Finding and Conclusion #2 The dailies do not have a standard format, which prevents someone from using a macro to quickly compute the number of staff working in each area of the CPU. If a macro could be used, the team believes that the likelihood of counting errors would decrease and the overall repeatability of the analysis would increase. The team proposes a staffing summary sheet that would be compatible with a macro in the recommendations section. Finding and Conclusion #3 The team determined that the EMR data is representative of patient volumes in all areas of the CPU. The team summarizes the findings of the EMR verification in Table 4 (below) for Tuesday, Thursday, Thursday, and Friday (February 17, 19, 26, and 27, 2015 respectively). 13

18 Table 4: Summary of the Verification Results for Prep, Procedure, and Recovery Sample Size: 321; Source: IOE 481 Team and EMR Data; Collection Period: Feb. 17, 2015 (9 am 1 pm, 2 pm 5 pm); Feb. 19, 2015 (9 am 1 pm, 2 pm 5 pm); Feb. 26, 2015 (9:15 am 1:15 pm, 3 pm 5 pm); Feb. 27, 2015 (2 pm 4 pm) Tuesday (2/17/2015) Thursday (2/19/2015) Thursday (2/26/2015) Friday (2/27/2015 % Match Average value for Cath and EP Prep % Within 1 Patient % Within 2 Patients % Match Average value for Cath, Cath 5, and EP Procedure % Within 1 Patient % Within 2 Patients % Match Total for Obs & Recovery % Within 1 Patient % Within 2 Patients Recall from the methodology section that the % Match column shows the percentage of time periods where the number of patients observed exactly matched the number of patients present in an area according to the EMR data. The columns titled % Within 1 Patient and % Within 2 Patients show the percentage of time periods where the number of patients observed was within one or two patients of the EMR data (respectively). Table 4, above, shows that the majority of the observed data for the prep and procedure areas was within 1 or 2 patients of the EMR data. One possible explanation for why the data does not agree 100% could be because data points with incomplete EMR data entries were deleted, which could cause observed patient volumes to be greater than EMR patient volumes. As seen in Table 4, the agreement between observed and EMR data for the recovery area was very low, with perfect matches typically around 0% to 25%. While these numbers were initially concerning, the team discovered that matching increased to approximately 70% when patient counts were given ranges of plus or minus one or two patients. The team hypothesized two 14

19 explanations for why there was very low matching of identical counts, but high matching when ranges were considered: (1) There may be a delay from when patients arrive to the recovery area to when nurses enter data into the electronic system because they are taking care of newly arrived patients. This could result in more patients being counted for the observed data compared to the EMR data. (2) Staffing supervisors indicated that recovery rooms may occasionally contain non-ep and non-cath patients. The presence of non-ep and non-cath patients would cause the team s observed numbers to be higher than the EMR numbers. Despite the variability in percentage of perfect matches, the team believes the EMR data is representative of actual patient volumes because the percent of matches plus or minus two patients ranges from 73% to 100%. The team discussed these results with the Directors of Cardiovascular Medicine and Clinical Operations, who confirmed that the data was sufficiently verified. Since the electronic data was verified, the team could reliably utilize the EMR data to compute patient volumes. Finding and Conclusion #4 Patient and staffing volumes vary based on day of the week, staff type, and area of the CPU. Appendices 2 through 6 contain 25 graphs to illustrate this finding. Administrators can utilize the graphs to investigate areas of potential over- and under- staffing. Finding and Conclusion #5 Out of the total 165 time intervals, there were only six instances where the ratio of maximum number of patients to average number of nurses exceeded 3:1. The team talked with the recovery supervisor and determined that these outliers were not indicative of actual patient-to-nurse ratios, since the supervisor will simply have a nurse work overtime hours to handle the extra patient volume. Therefore, the recovery area always maintains a 3:1 patient-to-nurse ratio. RECOMMENDATIONS First, the team recommends that administration repeat this experiment over a longer period of time to capture more data points as well as detect trends in other months and seasons that may indicate under- or over- staffing in the CPU. In addition to using the methodology used so far, the team recommends adding a qualitative component. If potential areas of under- or overstaffing are identified by administrators, the supervisors and staff in the CPU should be interviewed about their perceived workload during these time periods to determine if true underor over- staffing occurs. If the CPU continues this project, the team recommends that the supervisors fill out an additional form at the end of each workday called a daily staffing summary. For this project, the team encountered frequent variability in daily staffing schedules, whether there were unique procedure types, different numbers of staff working in the unit, or input errors. Due to this variability, the team was unable to write a macro that would analyze each daily automatically. The team believes that the staffing analysis would be easier to repeat and there would be fewer errors if a 15

20 macro were used. A sample Excel form, shown in Figure 3, is structured to be compatible with a macro. Date (M/D/Y) Day Location Area Role Name Start Time End Time Staff Type (Nurse, Tech, Other) 1/16/2015 Fri EP Lab EP Charge P Nurse 1/16/2015 Fri EP Lab EP Prep P Nurse 1/16/2015 Fri EP 1 EP RN P Nurse 1/16/2015 Fri EP 1 EP Tech P Tech Figure 3. Sample Daily Staffing Summary This form will take minimal time to fill out, and will greatly increase the accuracy of staffing volume analyses in the future. It will not be a replacement for the current dailies, since the daily is a visual tool that ensures all roles in the CPU are staffed. EXPECTED IMPACT Patient and/or staffing volumes may change in the future due to changes in operational efficiency, technology, procedure demand, or other factors. To better understand the implications of these changes, administrators can work with industrial engineers and supervisors to repeat this project and obtain an updated view of patient volumes, staffing volumes, and patient-to-staff ratios. With this ability, the CPU will have opportunities to improve their operations: operating costs can be reduced, patient wait times can be reduced, and supervisors can better predict expected number of staff needed throughout the day. 16

21 APPENDIX 1: Sample of a Daily Figure 4, below, is a sample of a daily currently utilized by the supervisors. All names and personally identifiable information (PII) have been removed but the scheduling codes are still displayed. All cells that have a name without a scheduling code are blacked-out. Figure 4: Sample Daily Staffing Schedule 17

22 APPENDIX 2: Graphs for EP Prep and Procedure Areas with Patient and Staffing Volumes This appendix contains the graphs for EP prep and procedure with patient and staffing volumes for each of the five days (Monday through Friday). The EP prep and procedure areas aim for a 1:1 patient-to-nurse ratio. Figure 5: EP Prep and Procedure with Patient Volume and Number of Nurses for Monday Patient Volume Sample Size: 224; Patient Volume Data Points Deleted: 12; Staffing Volume Sample Size: 73; 18

23 Figure 6: EP Prep and Procedure with Patient Volume and Number of Nurses for Tuesday Patient Volume Sample Size: 289; Patient Volume Data Points Deleted: 21; Staffing Volume Sample Size: 76; 19

24 Figure 7: EP Prep and Procedure with Patient Volume and Number of Nurses for Wednesday Patient Volume Sample Size: 231; Patient Volume Data Points Deleted: 14; Staffing Volume Sample Size: 73; 20

25 Figure 8: EP Prep and Procedure with Patient Volume and Number of Nurses for Thursday Patient Volume Sample Size: 209; Patient Volume Data Points Deleted: 7; Staffing Volume Sample Size: 65; 21

26 Figure 9: EP Prep and Procedure with Patient Volume and Number of Nurses for Friday Patient Volume Sample Size: 282; Patient Volume Data Points Deleted: 19; Staffing Volume Sample Size: 77; 22

27 APPENDIX 3: Graphs for EP Procedure Area with Patient Volumes and the Technician Capabilities This section contains the graphs for EP procedure with the patient volumes and the technician capabilities for each of the five days (Monday through Friday). The EP procedure labs aim for a 1:2 patient-to-technician ratio. The reason that it appears that there are no staff working while there are patients in the unit from 8:00 pm to 11:00 pm is that EP procedures have variable times, and can last up to 8-12 hours. Sometimes procedures that begin late in the day (~5:00 pm) may take longer than expected and run late into the evening, but the daily staffing schedules do not reflect the exact hours of overtime work. Figure 10: EP Procedure with Patient Volume and Number of Techs for Monday Patient Volume Sample Size: 112; Patient Volume Data Points Deleted: 6; Staffing Volume Sample Size: 93; 23

28 Figure 11: EP Procedure with Patient Volume and Number of Techs for Tuesday Patient Volume Sample Size: 147; Patient Volume Data Points Deleted: 8; Staffing Volume Sample Size: 90; 24

29 Figure 12: EP Procedure with Patient Volume and Number of Techs for Wednesday Patient Volume Sample Size: 117; Patient Volume Data Points Deleted: 6; Staffing Volume Sample Size: 86; 25

30 Figure 13: EP Procedure with Patient Volume and Number of Techs for Thursday Patient Volume Sample Size: 106; Patient Volume Data Points Deleted: 2; Staffing Volume Sample Size: 77; 26

31 Figure 14: EP Procedure with Patient Volume and Number of Techs for Friday Patient Volume Sample Size: 143; Patient Volume Data Points Deleted: 8; Staffing Volume Sample Size: 85; 27

32 APPENDIX 4: Graphs for Cath Prep and Procedure Areas with Patient and Staffing Volumes This section contains the graphs for Cath prep and procedure areas with patient volume and the number of nurses for each of the five days (Monday through Friday). The Cath prep and procedure areas aim to have a 1:1 patient-to-nurse ratio. Figure 15: Cath Prep and Procedure with Patient Volume and Number of Nurses for Monday Patient Volume Sample Size: 411; Patient Volume Data Points Deleted: 35; Staffing Volume Sample Size: 62; 28

33 Figure 16: Cath Prep and Procedure with Patient Volume and Number of Nurses for Tuesday Patient Volume Sample Size: 329; Patient Volume Data Points Deleted: 21; Staffing Volume Sample Size: 56; 29

34 Figure 17: Cath Prep and Procedure with Patient Volume and Number of Nurses for Wednesday Patient Volume Sample Size: 327; Patient Volume Data Points Deleted: 30; Staffing Volume Sample Size: 58; 30

35 Figure 18: Cath Prep and Procedure with Patient Volume and Number of Nurses for Thursday Patient Volume Sample Size: 261; Patient Volume Data Points Deleted: 29; Staffing Volume Sample Size: 47; 31

36 Figure 19: Cath Prep and Procedure with Patient Volume and Number of Nurses for Friday Patient Volume Sample Size: 244; Patient Volume Data Points Deleted: 17; Staffing Volume Sample Size: 57; 32

37 APPENDIX 5: Graphs for Cath Procedure Area with Patient Volumes and Technician Capabilities This section contains the graphs for Cath procedure area with patient volumes and technician capabilities for each of the five days (Monday through Friday). The Cath procedure labs aim to have a 1:3 patient-to-technician ratio. Figure 20: Cath Procedure with Patient Volume and Number of Techs for Monday Patient Volume Sample Size: 209; Patient Volume Data Points Deleted: 14; Staffing Volume Sample Size: 91; 33

38 Figure 21: Cath Procedure with Patient Volume and Number of Techs for Tuesday Patient Volume Sample Size: 166; Patient Volume Data Points Deleted: 21; Staffing Volume Sample Size: 87; 34

39 Figure 22: Cath Procedure with Patient Volume and Number of Techs for Wednesday Patient Volume Sample Size: 165; Patient Volume Data Points Deleted: 14; Staffing Volume Sample Size: 88; 35

40 Figure 23: Cath Procedure with Patient Volume and Number of Techs for Thursday Patient Volume Sample Size: 135; Patient Volume Data Points Deleted: 10; Staffing Volume Sample Size: 82; 36

41 Figure 24: Cath Procedure with Patient Volume and Number of Techs for Friday Patient Volume Sample Size: 138; Patient Volume Data Points Deleted: 9; Staffing Volume Sample Size: 116; 37

42 APPENDIX 6: Graphs for Recovery Area with Patient Volumes, Nursing Capabilities, and maximum number of patient-to-nurse Ratio This section contains the graphs for recovery area with patient volume and the number of nurses for each of the five days (Monday through Friday). The recovery area is required to be at or below a 3:1 patient-to-nurse ratio, even under the heaviest patient volumes. The vertical lines on the recovery graphs indicate the times that the recovery area opens and closes. Figure 25: Recovery with Patient Volume and Number of Nurses for Monday Patient Volume Sample Size: 213; Patient Volume Data Points Deleted: 149; Staffing Volume Sample Size: 90; 38

43 Figure 26: Recovery with Patient Volume and Number of Nurses for Tuesday Patient Volume Sample Size: 287; Patient Volume Data Points Deleted: 83; Staffing Volume Sample Size: 82; 39

44 Figure 27: Recovery with Patient Volume and Number of Nurses for Wednesday Patient Volume Sample Size: 257; Patient Volume Data Points Deleted: 99; Staffing Volume Sample Size: 81; 40

45 Figure 28: Recovery with Patient Volume and Number of nurses for Thursday Patient Volume Sample Size: 226; Patient Volume Data Points Deleted: 66; Staffing Volume Sample Size: 71; 41

46 Figure 29: Recovery with Patient Volume and Number of nurses for Friday Patient Volume Sample Size: 270; Patient Volume Data Points Deleted: 41; Staffing Volume Sample Size: 84; 42

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