Determining Patient Volumes, Staffing Volumes, and Patient-to...
Transcript of Determining Patient Volumes, Staffing Volumes, and Patient-to...
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
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 #1 ...................................................................................................... 3
Finding and Conclusion #2 ...................................................................................................... 3
Finding and Conclusion #3 ...................................................................................................... 3
Finding and Conclusion #4 ...................................................................................................... 3
Finding and Conclusion #5 ...................................................................................................... 3
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 ............................. 11
Calculating Patient-to-Nurse Ratios for the Recovery Area ..................................................... 12
Creating Graphs......................................................................................................................... 13
FINDINGS AND CONCLUSIONS ............................................................................................. 13
Finding and Conclusion #1 ....................................................................................................... 13
Finding and Conclusion #2 ....................................................................................................... 13
Finding and Conclusion #3 ....................................................................................................... 13
Finding and Conclusion #4 ....................................................................................................... 15
Finding and Conclusion #5 ....................................................................................................... 15
RECOMMENDATIONS .............................................................................................................. 15
EXPECTED IMPACT .................................................................................................................. 16
APPENDIX 1: Sample of a Daily ................................................................................................. 17
APPENDIX 2: Graphs for EP Prep and Procedure Areas with Patient and Staffing Volumes .... 18
APPENDIX 3: Graphs for EP Procedure Area with Patient Volumes and the Technician
Capabilities ................................................................................................................................... 23
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 ................................................................................................................................... 33
APPENDIX 6: Graphs for Recovery Area with Patient Volumes, Nursing Capabilities, and
maximum number of patient-to-Nurse Ratio ................................................................................ 38
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
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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
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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 2015.
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 February 16, 2015.
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, 2015. 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-to-
nurse ratios in the recovery area. This section describes the team’s findings and conclusions.
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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.
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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
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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 2015.
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
February 16, 2015. Each daily contains the names of the nurses and techs working in each area,
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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 9:00am - 7:30pm
Q 6:30am - 5:00pm
D 7:00am - 2:30pm
EP Nurses A 6:30am - 7:00pm
a 9:00am - 7:30pm
Q 6:30am - 5:00pm
D 6:00am - 2:30pm
G 9:00am - 9:30pm
Cath Techs A 7:00am - 7:30pm
a 9:00am - 7:30pm
Q 7:00am - 5:30pm
D 8:00am - 4:30pm
Cath Nurses A 7:00am - 7:30pm
a 9:00am - 7:30pm
Q 7:00am - 5:30pm
D 8:00am - 4:30pm
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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
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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.
Table 6: Patient Volume Data Collection Times
Date Data Collection Times
Tuesday, February 17 9 am – 1 pm, 2 pm – 5 pm
Thursday, February 19 9 am – 1 pm, 2 pm – 5 pm
Thursday, February 26 9:15 am – 1:15 pm, 3 pm – 5 pm
Friday, February 27 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
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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 February 16, 2015 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 February 16, 2015. 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
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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
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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 11/3/2014
10. 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
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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 11/3/2014
6. 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.
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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-to-
nurse 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).
14
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
Average value for Cath
and EP Prep
% Match 71.88 56.55 78.57 83.33
% Within 1
Patient
93.75 93.75 97.62 100.00
% Within 2
Patients
93.75 97.92 100.00 100.00
Average value for
Cath, Cath 5, and EP
Procedure
% Match 73.33 68.75 80.95 100.00
% Within 1
Patient
91.11 97.92 100.00 100.00
% Within 2
Patients
97.78 100.00 100.00 100.00
Total for Obs &
Recovery
% Match 6.67 25.00 7.14 0.00
% Within 1
Patient
60.00 81.25 35.71 100.00
% Within 2
Patients
73.33 100.00 78.57 100.00
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
15
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 over-
staffing 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 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”. 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
16
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 P1 0630 1700 Nurse 1/16/2015 Fri EP Lab EP Prep P2 0630 1900 Nurse 1/16/2015 Fri EP 1 EP RN P3 0630 1900 Nurse 1/16/2015 Fri EP 1 EP Tech P4 0630 1700 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.
17
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
18
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
19
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
20
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
21
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
22
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
23
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
24
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
25
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
26
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
27
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
28
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
29
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
30
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
31
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
32
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
33
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
34
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
35
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
36
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
37
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
38
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
39
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
40
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
41
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015
42
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;
Source: EMR Data; Collection Period: November 3, 2014 through November 26, 2014, and January 1, 2015 through
February 16, 2015