Dynamic Staffing Model and Analysis for Pulmonary, Nephrology, and Urology
Submitted To:
Tammy Ellies; Katie Schwalm
Project Manager and Lean Coach; Industrial Engineer Associate
University of Michigan Health Systems Department of Internal Medicine
1500 E. Medical Center Dr.
Ann Arbor, MI 48109
Mary Freer; Amy Kauffman; Malissa Eversole
Division Administrator Pulmonary; Division Administrator Nephrology; Director of Operations
Urology
University of Michigan Health System
1500 E. Medical Center Dr.
Ann Arbor, MI 48109
Professor Mark Van Oyen
Associate Professor
University of Michigan Industrial and Operations Engineering Department
1205 Beal Ave.
Ann Arbor, MI 48109
Submitted By:
Team 2
Alexis Baker
Benjamin Gezon
Erik Rood
Arnold Yin
Date Submitted: April 22th, 2014
Table of Contents
1. Executive Summary……………………………………………………………………….….1
2. Introduction.…………………………………..…………………………………………...….3
3. Background……………………………………………………………………………......….3
Pulmonary and Nephrology……………………………………………………………..4
Urology……………………………………………………………………………….…4
4. Key Issues………………………………………………………………………….…………5
5. Goals and Objectives...………………………………………………………………………..5
6. Data……………………………………………………………………………………………5
Data Collection……………………………………………………………….………….6
Data Analysis and Model Development……………………………………...…………..7
7. Findings and Conclusions……………………………………………………….……............13
Pulmonary and Nephrology…………………………………………………………..…14
Urology………………………………………………………………………..………..15
8. Recommendations…………………………………………………………………...….…….17
9. Expected Impact………………………………………………………………………...…….18
10. Appendix………………………………………………………………………………………i
Figures and Tables:
Figure 1............…………………………………………………………………………....….4
Figure 2……………………………………………………………………………………….4
Figure 3……………………………………………………………………………………….8
Figure 4……………………………………………………………………………………….9
Figure 5…………………………………………………………………………………...….10
Figure 6…………………………………………………………………………………...….11
Figure 7………………………………………………………………………………...…….12
Figure 8…………………………………………………………………………………...….12
Figure 9…………………………………………………………………………………...….12
Figure 10……………………………………………………………………………….…….13
Figure 11………………………………………………………………………….………….13
Figure 12……………………………………………………………………….…………….14
Figure 13………………………………………………………………….………………….15
Figure 14………………………………………………………………………….………….16
Figure 15……………………………………………………………………….…………….17
Table 1…………………………………………………………………………....………….10
1. Executive Summary
Three clinics within the University of Michigan Health System- Pulmonary, Nephrology and
Urology- have recently undergone several changes including a UMHS-wide implementation of
the electronic health record MiChart Epicare system, a restructuring of their management system,
and renovations to their physical space. The management at each of these clinics wanted to
analyze current medical assistant (MA) and clerical staff levels in relation to patient volume. IOE
481 Team 2 was asked to develop a model that can accurately predict the number of MA and
clerical personnel necessary subject to patient volume. The clients want the team to analyze the
current process flow pertaining to the MAs and clerical staff in each clinic. This flow includes
patient check-in, intake, and patient check-out. Lastly, Urology has asked the team to determine
key metrics associated with patient satisfaction (CG-CAHPS).
Background
University of Michigan health system believes that it is valuable to have accurate staffing levels
in order to optimize time utilization and productivity. The Pulmonary, Nephrology, and Urology
clinics at UMHS have recently been introduced to the CG-CAHPS in order to measure patient
satisfaction. A primary question on the survey asks whether the patient was able to see the
physician within 15 minutes of their appointment time. This question is related to patient intake.
The MA intake process consists of taking a patient’s vitals, along with collecting patient family
history and medications. Pulmonary and Nephrology are currently staffed with eight MAs while
Urology is staffed with seven MAs (3 for patient intake and 4 for procedures). When examining
the data, it was found there was a direct correlation to patient type and intake time, not only
varying from new or returning patient, but also specific patient type, such as Cystic Fibrosis. The
intake time ranges from 3 minutes to 16 minutes, depending of the type of patient. The clerical
staff are responsible for the administrative tasks of check patients in upon arrival and checking
them out a after they have seen the provider.
Methodology
The team performed multiple tasks and analysis to evaluate and improve the current scheduling
situation at the center.
Data Collection
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
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Literature/Past Projects. A study was found that examined staffing needs in Pod D in
Internal Medicine in the University of Michigan Health System in 2013.
Interviews: The team members each shadowed MAs and documented the intake
procedure along with any extra tasks involved. In addition, questions were asked
regarding patient flow, amount of work, and suggestions to eliminate non value-added
time. Furthermore, the team examined the check-in and check-out processes with clerical
staff and asked questions regarding their daily tasks.
Work Sampling: For Pulmonary and Nephrology clinics, a data sheet was created and
distributed to all the MAs. The MAs recorded the intake start time, intake end time, and
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total patient visit time. About 50 patient intake times were recorded over 10 working
days. In Urology, MA and clerical staff task information was acquired by generating
reports from their new “tap-in” system that they are currently piloting. The reports were
generated from March 17th to March 21st of 2014, which is one working week. This data
consists of roughly 400 patient visits.
Data Analysis




Verification of Data: The team used this long term historical data to compare and verify
the data collected by the team members
MA Modeling Pulmonary and Nephrology: Using the collected and formatted MiChart
and work sampling data, the team developed a dynamic staffing model for Pulmonary
and Nephrology MAs. This model was created using Microsoft Excel and focuses on the
intake times associated with different patient visit types.
MA Modeling Urology: The team developed a dynamic staffing model for the Urology
clinic similar to the one developed for Pod C. However, for the Urology model, the team
divided the data analysis between intake times and procedure.
Clerical Staff Guideline for all clinics: Using the previously performed literature search
of a project involving Pod D analysis, the team developed a guideline for staffing clerical
personnel based on 20 working days of historic patient data collecting approximately
1400 patient appointments.
Findings and Conclusions
The team developed a dynamic staffing model based on data collected from work sampling and
MiChart that would output the staffing needs based on patient volume. From this model, it was
found that current staffing levels of Pod C MAs and clerical staff did not match the model’s
recommended staffing based on patient volume. The optimal number of MAs varied by day, but
the maximum number of MAs needed for optimal patient flow is five MAs. Results for the Pod
C clerical staff were similar, with recommended numbers ranging from four to six staff. From
the model, it was also determined that Urology MAs and clerical staff did not match the
recommended staffing levels based on patient volume. The analysis revealed that MAs and
clerical staff suffered a deficiency or surplus in staffing varying by time of day. The metrics for
the CG-CAHPS survey, including provider face time and patient appointment time until they see
the doctor, were also analyzed. From our results, the average provider face time consisted of a
range of 3 to 43 minutes with an average of 18 minutes, while the appointment time until seeing
the doctor ranged from 12 to 50 minutes with an average of 29 minutes.
Recommendations
The team recommends that the Pulmonary and Nephrology clinics use the developed model to
determine the number of MAs needed and pilot a new staffing schedule. Second, the team
recommends the clinics provide MAs with additional value added tasks and responsibilities when
not with the patients continue to cross train MAs, and adopt the “tap-in” system.
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For Urology, the team recommends that upon receiving patient volume for a given week, closer
inspection is performed into staffing needs by half hour in order to capture the aforementioned
variations in volume. Additionally, it’s recommended that future analysis occurs once the data
inputs have been further refined.
2. Introduction
This project was requested by three clinics within the University of Michigan Health System:
Pulmonary, Nephrology, and Urology. All three clinics wish to analyze current medical assistant
(MA) staff levels in relation to patient volume; additionally, Pulmonary and Nephrology wish to
examine their clerical staff levels in relation to historic patient volume. Lastly, Urology
requested a data metric to be used in their Clinical and Grouping Consumer Assessment of
Healthcare Providers and Systems (CG-CAHPS) evaluations that included the time from patient
appointment to being seen by the physician as well as the amount of time the patient spends with
the physician.
The Pulmonary and Nephrology clinics, located in the Taubman Center, Level 3, Area C is an
outpatient clinic serving patients with lung problems including asthma, emphysema and lung
disease while focusing on extensive research of the lungs. Second, the Nephrology clinic is
responsible for prevention and treatment to kidney related issues and contains several clinics and
services such as dialysis centers and stone prevention clinics to deliver optimal patient
care. Lastly, the Urology clinic provides innovative and collaborative care to cure or reduce
urological disease while also contributing to the understanding of urologic diseases and cancers
through exceptional research.
Recently, the clinics have undergone several changes and they are unsure if the clerical and MA
staffing matches the current patient volume. The first change includes a UMHS-wide
implementation of the electronic health record MiChart Epicare in August 2012. This resulted in
a new process for documenting and billing patient visits that affected all clinics at UMHS.
Second, within the Pulmonary and Nephrology clinics, there has been a restructuring of their
management system along with several renovations to their physical space that were finally
completed in August 2013. Consequently, there has been a decrease in the number of patients
seen within these clinics while MA and clerical staffing levels have remained constant.
Specifically, the clients asked Industrial and Operations Engineering students from the
University of Michigan to develop a staffing model that can accurately predict the number of
MA personnel necessary to perform patient intake and procedures subject to user inputted patient
volumes and time period. Additionally, the Pulmonary and Nephrology clinics asked for clerical
staffing guidelines that would be based on historic patient visit and check-in/out information; this
guideline will provide them with an understanding of peak patient visit periods and the staff
necessary to accommodate these periods.
3. Background
The following sections contain clinic-specific background information about the current state of
the Pulmonary, Nephrology, and Urology departments within UMHS.
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First, pertaining to all clinics at UMHS, CG-CAHPS has recently been introduced in order to
survey and measure patient satisfaction. One of the primary questions is whether the patient is
able to see the physician within 15 minutes of their appointment time, which is directly related to
patient intake. The time needed to complete the intake process depends on the type of patient that
the MA is seeing. As shown in our patient volume data, and accounted for in the staffing model,
new patients tend to take much longer because new paperwork consisting of patient history must
be filled out and the MA. Conversely, returning patients are generally quicker. As well, there is
variation in the patient intake time dependent on the type of patient visit, which is accounted for
in our model.
The clinics have implemented a system sometimes pairing MAs with physicians when
scheduling. This allows for the MAs to get started on patient-specific paperwork before the
patient arrives, because they will know in advance which patients they expect to see that
day. This change has implications on both staff scheduling and staffing levels.
Figure 1, below, provides a high-level overview of current patient flow through the clinics and
which staff member is responsible for each step.
Figure 1: Patient Flow Through Clinic
Figure 2, below, explains the MA intake process in detail.
Figure 2: MA Intake Process
3.1. Pulmonary and Nephrology
The Pulmonary and Nephrology clinics within UMHS Taubman Center share a newly renovated
physical space, clerical staff, and several MAs. Currently, the clinics have a team of eight MAs
and seven clerks; two of these MAs are cross-trained, meaning they are capable of performing
patient intake for both clinics. Within the clinics, the clerical staff is responsible for the
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administrative process of checking patients in on arrival and out after they’ve seen the physician.
The MAs are responsible for the patient intake process, which includes taking patient vitals,
verifying patient history with regards to medication and family illness, and any other necessary
preparation for the specific patient.
3.2. Urology
The Urology clinic currently has a team of seven MAs. The MAs are responsible for patient
intake as well as assisting with patient procedures. On a normal day, three MAs are dedicated to
patient intake while four are dedicated to patient procedures. Additionally, Urology MAs all are
assigned individual tasks to complete throughout the week that include, but are not limited to,
inventory, ordering, and administrative tasks. The Urology clerical staff is responsible for
serving Urology as well as the General Surgery and Thoracic clinics; for the sake of this project
they will not be evaluated. Lastly, the Urology clinic is currently implementing a new “tapin/out” system that allows for the collection of more detailed and accurate MiChart data. The
“tap-in/out” system is a new process that was first used in the ED and now the Urology clinic is
piloting it as well. The system is very simple to use and only requires the medical staff to “tap”
their MCards to a reader. This allows for MiChart to track when people leave or enter rooms.
4. Key Issues
The following key issues are contributing to the problems under investigation:
Pulmonary and Nephrology
 The physical space has recently been changed in the Pulmonary and Nephrology clinics
and it is unsure whether this change has impacted staffing
 The Nephrology clinic has lost patient volume to other floors
 The management of the Pulmonary and Nephrology clinics has recently changed
 MiChart Epicare module was implemented in August 2012 and resulted in a change in
workflow and workload for medical assistants and providers
Urology
 MiChart Epicare module was implemented in August 2012 and resulted in a change in
workflow and workload for medical assistants and providers
 The recent implementation of the CG-CAHPS survey has caused Urology to want a new
benchmark to measure the time from patient appointment to being seen by the provider as
well as the amount of time the patient spends with the provider.
5. Goals and Objectives
The overall goals of this project are to optimize MA staffing necessary to perform patient intake
and procedures, to provide guidance to Pulmonary and Nephrology on clerical staffing, and to
provide Urology with their desired data points. To achieve these goals, the team has performed
the following:



Develop a staffing model that can dynamically determine MA staffing needs based on
user-inputted patient volumes
Develop a guideline for clerical staffing needs based on historical patient visit and checkin/out data (Pulm/Neph Only)
Record and analyze key metrics for Urology’s CG-CAHPS
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
Develop any other recommendations for improvement
The student team’s methodology consisted of data collection, using a combination of MiChart,
observations, and interviews. Next, data analysis was conducted using Microsoft Excel while the
model development utilized both Microsoft Excel and Visual Basic for Applications (VBA).
6. Methods
The following sections contain an analysis of the key issues faced by the departments along with
the team’s methodology for solving the problems. This includes data collection, data analysis,
modeling, and recommendations.
6.1 Data Collection
Below follows the methods that the team used to obtain the data necessary to build the staffing
models and guidelines for each clinic as well as the methods used to analyze the model and
develop recommendations.
Literature Search
The team conducted a literature search and found a similar project done at UMHS pertaining to
staffing needs in Pod D in Internal Medicine in the University of Michigan Health System in
2013. This project was undertaken and provided to the team by Internal Medicine Industrial
Engineering Associate and project coordinator, Katie Schwalm. We used this literature to
develop similar data collection methods such as work sampling, observations, and
interviews. Additionally, this project formed the foundation for the team’s analysis of the
clerical staffing and the development of its guidelines.
Observations and Interviews
In order to finish observations and interviews in a timely manner, the team was divided into two
observations teams. Two members of the team conducted all observations and interviews within
the Pulmonary and Nephrology clinics and the remaining two members conducted all of the
observations and interviews within the Urology clinic.
The team spent 30 total hours in Pulmonary and Nephrology clinics observing and interviewing
both MA and clerical staff. The team members each shadowed MAs and documented the intake
procedure along with any extra tasks involved. In addition, questions were asked regarding
patient flow, amount of work, and suggestions to eliminate non value-added time. Furthermore,
the team examined the check-in and check-out processes with clerical staff and asked questions
regarding their daily tasks.
The team also spent 20 total hours in the Urology clinic. Only the MAs were observed in the
Urology clinic. The team members shadowed an MA and documented the intake procedure
along with any extra tasks involved. In addition, the team documented the numerous tasks
performed by the MAs and did an informal time study to see how long these tasks took to
complete. These tasks included individual tasks (e.g. inventory ordering and stocking) as well as
tasks associated with performing various procedures exclusive to the Urology clinic. Best-guess
estimates of the time required by the MAs to perform various procedures were collected from
two of the lead MAs in the Urology clinic.
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Work Sampling
The team conducted work sampling on MAs performing intake in both clinics; all intake times
were recorded.
For Pulmonary and Nephrology clinics, a data sheet was created and distributed to all the MAs.
The MAs recorded the intake start time, intake end time, and total patient visit time. These data
sheets were then collected at the end of the day in a central location. As a result, we collected
data on approximately 50 patient visits. These sheets were collected for 10 working days from
March 3rd to March 14th.
For the Urology clinic, the MA and clerical staff task information was acquired by generating
reports from their new “tap-in” system that they are currently implementing. The reports were
generated from March 17th to March 21st, or one working week. This data consists of roughly
400 patient visits. Additionally, for the Urology clinic only, a data sheet was distributed to all
incoming patients in order to determine the time it takes to be seen by the physician after
entering the exam room and the total time spent with the physician; this sheet was completed by
approximately 400 patients. Lastly, informal interviews with lead MAs and the director of the
Urology clinic allowed the team to determine an average time to perform each of the procedures
associated with MAs.
Historical Data
The team collected historical data by retrieving MiChart data from Pulmonary, Nephrology, and
Urology. This data consisted of patient volume, arrival times, and patient type. The Pulmonary
and Nephrology clinics provided four weeks of historical data from February 3rd to February
28th. In addition, the team has collected historical MiChart data dating from 2013 that shows the
number of patients depending on the day and the type of patient, for both clinics.
CG-CAHPS - Patient data sheets
The team collected patient data in the Urology clinic by using patient data sheets. The clerical
staff at the clinic handed out the sheets to each patient for the span of a week in March (March
17th, 2014 to March 21st, 2014). The data sheet asks the clerical staff to fill out the patient MRN
and the patients to record the time the physician enters the room and the time the physician exits
the room.
6.2 Data Analysis + Model Development
To develop the model, the data collected had to be organized in a way that could be used for the
model. The data was used to perform calculations to predict the number of required MAs based
on the inputs of the user.
Data Analysis and Verification
The team has thoroughly analyzed the collected data from the clinics and the historical data
provided by the clients. Pivot tables in Microsoft Excel were used to give the team a better
understanding of the trends seen in the data. The team used this long term historical data to
compare and verify the data collected by the team members. Additionally, historical patient
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volume data dating from March 17th, 2014 to March 21st, 2014 was used to generate results in
the model and allow the team to compare current staffing levels to model-recommended staffing
levels; this process allowed the team to better understand the expected impact of the model.
MA Modeling -- Pulmonary and Nephrology
Using the collected and formatted MiChart and work sampling data, the team developed a
dynamic staffing model for Pulmonary and Nephrology MAs. This model was created using
Microsoft Excel and focuses on the intake times associated with different patient visit types. The
data analysis showed that there were significant differences between the average intake times of
the different patient visit types. As a result, the team decided to keep each patient visit type as a
separate input to ensure that the model is as accurate as possible. Figure 3 below shows the
intake times of the different patient visit types seen in Pod C using a sample of 580 intake times.
Figure 3: Average intake times for each patient visit type, n=580
As seen in Figure 3, the intake times varied from 16 minutes for a new patient Sarcoidosis visit
to 2.5 minutes for a new patient GD visit.
Aggregate intake time is based on the number of different patient visit volume multiplied by
their corresponding intake time average. The model is also designed to allow the user to choose
the time period that they wish to evaluate and allows the user to input the amount of additional
task time and break-time that each MA should have. Figure 4 below is a screenshot of the user
interface of the model that prompts the user to input the required information.
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Figure 4: The user interface for the Pod C intake model.
As seen in the figure above, the user must input the time period they wish to evaluate, the
number of patients of each type scheduled, and the amount of time required for additional tasks
and breaks for the time period. The model also allows the user to update the average intake
times of each visit type for future use. On a second excel worksheet, there is a table that contains
the current average intake times for each visit type. Table 1 below shows the table the user can
modify to change the aggregate intake times.
Table 1: Average intake time table for Pod C
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When the user updates the intake time values in the table, the model will respond accordingly by
using the new inputted values to calculate the aggregate intake time.
Clerical Staff Guidelines -- Pulmonary and Nephrology
Using the previously performed literature search of a project involving Pod D analysis, the team
developed a guideline for staffing clerical personnel based on 20 working days of historic patient
data dating March 4th to March 25th of 2014. This data consisted of over 1,400 patient
appointments. The patient volume is broken down by each day of the week and then by half-hour
increments and the aggregate clerical time needed for that half hour is presented. This aggregate
time includes the number of patient check-outs performed multiplied by an average check-out
time of 10 minutes. Additionally, it includes the number of patient check-ins performed
multiplied by an average check-in time of 2 minutes. Figure 5, below, illustrates the clerical
staff utilization for the average Monday in March, 2014.
Figure 5: Check in/Out capacity of Pod C of Mondays in March 2014
As can be seen, the output presents the user with the average utilization as well as the minimum
and maximum utilization that can be expected. It compares this to the utilization capacity of all
7 clerical staff.
MA Modeling -- Urology
Using the collected and formatted MiChart and work sampling data, the team developed a
dynamic staffing model for the Urology clinic similar to the one developed for Pod C. However,
for the Urology model, the team divided the data analysis between intake times and procedure
times. Similar to the data analysis done for Pod C, the intake times were categorized by patient
visit type while the procedure times were categorized by procedure type.
To model intake, the different patient visit types were broken down into four different inputs:
second opinion return visits, all other return visits, new patient vasectomy, and all other new
patients. Each of these inputs has an associated volume-weighted average intake time calculated
using one week’s worth of patient volume information from March 17th to March 21st. Figure 6
below, presents the users inputs and their associated averages.
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Figure 6: Average intake time of patient visit types seen in Urology, n=279
For the procedure model, the inputs are broken down into the various procedure types. As seen in
Figure 7 below, the procedure times range from 25.32 minutes for Cystoscopy procedures to
65.07 minutes for Fluorourodynamic procedures. These average procedure times were
calculated using patient-volume weighted averages over the course of a one-year period from
March 2013, to March, 2014. The team averaged the scheduled appointment times for each
patient type and added an additional 5 minutes to each to account for MA room setup/clean-up
time.
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Figure 7: The average times for each procedure type seen in Urology
The user of the model is expected to input expected patient volume information for a particular
time period and the model calculates the aggregate MA time needed. Figures 8 and 9 below
show the interface the user is presented with when opening the intake and procedure models
respectively. Additionally, the Urology model also requires the user to input the number of each
type of procedure to be performed in the given time period. The model will then take this input
and calculate the aggregate MA time needed to meet procedure-patient demand.
Figure 8: User interface for the Urology intake model
Figure 9: User interface for the Urology procedure model
As seen in both figures above, the user has to input the patient volume for the different patient
visit types and procedure types, Historical patient volume was then found ranging from March
17th to March 21st and inputted into the model. This allowed a comparison of our model’s
recommended staffing versus current staffing levels.
CG-CAHPS - Provider Facetime and Arrival Time
The team collected a total of 204 patient data sheets from the Urology clinic. Using the MRN
numbers, the team was able to look up which patient corresponded to which provider. The MRN
number also provided information on when each patient’s appointment time was, which allowed
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the team to calculate the difference between the provider arrival time and the appointment
time. Figure 10 below shows the average time for each provider.
Figure 10: The average time between the scheduled appointment time and the time the provider
sees the patient
As seen in the figure above the times range between roughly 50 minutes to 12 minutes depending
on the provider. The patient data collection sheets also allowed the team to easily calculate the
difference between the time the provider arrived and the time the provider exited the room,
which results in the provider face time. Figure 11 below shows the average face time for each
provider.
Figure 11: Average face time by provider
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Each provider also spends a different amount of time with his or her patients. As seen in the
figure above, the average face times range from 45 minutes to 3 minutes depending on the
provider.
7. Findings + Conclusion
The following section contains our findings and conclusions separated by clinic and staff type.
The analysis period for all of the MA findings was March 17th-March 21st, 2014. In addition,
the analysis period for the Pulmonary and Nephrology clerical staffing was March 3rd to March
28th, 2014.
7.1 Pulmonary and Nephrology
MA Modeling
Upon analyzing the current MA staffing levels, the team found that patient volume does not
match current staffing levels. Below, Figure 12 compares current MA staffing levels to the
model’s output’s recommended levels. To generate the recommended MA levels, the team
assumed that for each half day, the MAs had an hour of additional tasks and 45 minutes for lunch
and breaks.
Figure 12: The current number of MAs compared to the recommended number of MAs in Pod C
intake
From this figure, we can see that MA staffing is higher in every case than what the patient
volume calls for.
Clerical Staff Guidelines
Upon analyzing the current clerical staffing in relation to patient volume, the team found that
clerical staffing does not perfectly match patient volume. Our findings, showing aggregate
check-in and check-out time by half-hour for each day may be found in the appendix.
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With these times, the team determined the necessary staffing based on the day of week. These
results may be found in Figure 13 below:
Figure 13: The estimated staffing needs for Pod C clerical staff
From this we can see that clerical staffing is higher for all days and times than what patient
volume requires to perform check-in and check-out. Additionally, we can see that spikes in
patient volume occur, resulting in significant variations in staffing needs by half hour.
7.2 Urology
MA Modeling
Upon analyzing the current MA staffing levels for intake, the team found that the levels do not
match the model’s recommended staffing levels based on patient volume. Figure 14, below,
compares current MA staffing levels (for intake) to the model recommended levels for the week
of March 17th to March 21st, 2014. The team assumed that for each half-day period, the MAs
have 60 minutes of non-intake time (downtime).
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Figure 14: The current number of MAs compared to the recommended number of MAs in
Urology intake
From this figure, we can see that increases in patient volume are causing either a deficiency or
surplus in MA staffing levels. These same trends holds true for procedural MA staffing; Figure
15, below, compare current MA staffing levels (for procedures) to the model recommended
levels for the week of March 17th, 2014 to March 21st, 2014. The team assumed that for each
half day period, the MAs have 60 minutes of non-intake time (downtime).
Figure 15: The current number of MAs compared to the recommended number of MAs in
Urology procedures
The figure shows that there are spikes in patient volume that to either a deficiency or surplus of
MAs. For a more detailed breakdown of patient time and non-patient time for the Urology
intake and procedure process, please refer to the appendix.
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8. Recommendations
After the data analysis and the observations in Pod C and the Urology clinic, there are many
potential changes that can be made. The recommendations that follow will help increase
efficiency and utilization of the MAs and the clerical staff.
8.1 Pulmonary and Nephrology
From the team’s finding, it can be seen that current staffing levels within the Pulmonary and
Nephrology clinics, for both MAs and clerical staff, is higher than the model recommended
levels that are based on patient volume. In order to increase efficiency and better utilize staff
time, the team recommends the following:
Cross Training
It is recommended that the Pulmonary and Nephrology continue to cross train MAs between the
Pulmonary and Nephrology clinics. At the moment, both sets of MAs in the Pulmonary and
Nephrology clinics have some non-patient flow time. If more MAs were cross trained, this
would lead to a more continuous patient flow through both clinics. For example, instead of
staffing two MAs to specifically oversee Nephrology patient flow and another 4 MAs for
Pulmonary patient flow, there can be a total of 4 cross trained MAs that can constantly flow
patients through the intake process.
Increased Responsibilities
The team recommends an increase in responsibilities of the clerical staff within the Pulmonary
and Nephrology clinics to better utilize non-patient flow time. The clerical staff is currently
responsible for checking in, checking out, and scheduling patients for future appointments. If
they had more responsibilities, they could occupy their time with other, productive, non-patient
time tasks. Specifically, the team recommends these tasks for clerical staff members that have
lower patient volume based on the location of their desks.
Additionally, it is recommended that Pod C increase MA responsibilities (e.g. inventory,
administrative tasks, projects, etc...). This will lower the amount of down time available to the
MAs and result in a better utilization of their time.
“Tap-In/Tap-Out” System
The “tap-in/tap-out” system currently being piloted by the Urology clinic can also help with
future projects in the Pulmonary and Nephrology clinic as well, allowing the clinics to better
track the time utilization of its staff. As seen in the Urology clinic, the system allows for a more
precise way to collect data. It can be implemented to record the duration of doctor visits, intake,
or any other patient care process. The “tap-in/tap-out” system is a great way to allow for more
accurate data collection.
Pilot New Staffing Schedule
The team recommends piloting the recommended staffing numbers from the model for a short
period of time and analyzing the impact on staff time utilization.
17
8.2 Urology
From our findings and conclusions section, we can see that spikes in patient volume are causing
both deficiencies and surpluses in staffing levels recommended by the model for both intake and
procedures. The team recommends that upon receiving patient volume for a given week, Urology
perform closer inspection into staffing needs by half hour subject to this volume. This inspection
will allow for increased staffing accuracy in relation to patient volume.
Recommended Future Work
Future analysis may be conducted regarding the inputs for both the procedural and intake model.
It is a known issue that the data currently has several missing elements, and there is a ticket in to
resolve this. Thus, upon receiving a larger sample size of data, the times used across both models
may be further refined to improve the model’s overall utility.
9. Expected Impact
The model will allow the Pulmonary, Nephrology and Urology clinics to better determine their
staffing needs subject to patient volume. Also, the team’s recommendations will increase overall
clinic productivity and improve resource allocation. Ultimately, this will aid the clinics in
improving their patient satisfaction levels, as well. Lastly, the data collected by the team will be
available to our clients for any future use so that they may continue to improve their clinics.
18
Appendix
A1. Urology Half-day Staffing (Intake)
A2. Urology Half-day Staffing (Procedures)
A3. Pulmonary/Nephrology Half-day Staffing (Intake)
A4. Pulmonary/ Nephrology Daily Clerical Staffing Guidelines
i
A1. Urology Half-day Staffing (Intake)
Monday Morning (4/17/14; 7:30a.m – 12:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time (Min)
Total Intake Time
(Hours)
Number of
MAs
Total MA Time
(Hours)
Downtime
(Hours)
270
4.5
389.53
6.492166667
2
9
2
Monday Afternoon (4/17/14; 12:00p.m – 5:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time (Min)
Total Intake
Time (Hours)
Number of
MAs
Total MA
Time (Hours)
Downtime
(Hours)
300
5
479.2
7.986666667
2
10
2
ii
Tuesday Morning (3/18/21; 7:30a.m – 12:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
270
4.5
566.85
9.4475
3
13.5
3
iii
Tuesday Afternoon (4/18/14; 12:00p.m – 5:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
300
5
358.4
5.973333333
2
10
2
Wednesday Morning (4/19/14; 7:30a.m – 12:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
270
4.5
731.28
12.188
4
18
4
iv
Wednesday Afternoon (4/19/14; 12:00p.m – 5:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
300
5
459.85
7.664166667
2
10
2
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
Thursday Morning (4/20/14; 7:30a.m – 12:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
Total Intake
Time (Hours)
v
(Min)
270
4.5
493.65
8.2275
3
13.5
3
Thursday Afternoon (4/20/14; 12:00a.m – 5:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
270
4.5
484.43
8.073833333
3
13.5
3
vi
Friday Morning (4/21/14; 7:30a.m – 12:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
270
4.5
399.13
6.652166667
2
9
2
Friday Afternoon (4/21/14; 12:00p.m – 5:00p.m)
Time
Window
(Min)
Time Window
(Hours)
Total
Intake
Time
(Min)
Total Intake
Time (Hours)
Number
of MAs
Total MA Time
(Hours)
Downtime
(Hours)
300
5
264.82
4.413666667
2
10
2
vii
viii
A2. Urology Half-day Staffing (Procedures)
Monday Morning (4/17/14; 7:30a.m – 12:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
270
4.5
532.53
8.8755
3
13.5
3
Monday Afternoon (4/17/14; 12:00p.m – 5:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
300
5
867.83 14.46383333
4
20
4
ix
Tuesday Morning (3/18/21; 7:30a.m – 12:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
270
4.5
166.35
2.7725
1
4.5
1
Tuesday Afternoon (4/18/14; 12:00p.m – 5:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
300
5
195.33
3.2555
1
5
1
x
Wednesday Morning (4/19/14; 7:30a.m – 12:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
270
4.5 1260.98 21.01633333
6
27
6
Wednesday Afternoon (4/19/14; 12:00p.m – 5:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
300
5
389.97
6.4995
2
10
2
xi
Thursday Morning (4/20/14; 7:30a.m – 12:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
270
4.5
390.54
6.509
2
9
2
Thursday Afternoon (4/20/14; 12:00a.m – 5:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
300
5 1201.31 20.02183333
6
30
6
xii
Friday Morning (4/21/14; 7:30a.m – 12:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
270
4.5
593.37
9.8895
3
13.5
3
Friday Afternoon (4/21/14; 12:00p.m – 5:00p.m)
Time
Time Window Total
Total
Number Total MA
Downtime
Window (Hours)
Procedur Procedure
of MAs Time (Hours) (Hours)
(Min)
e Time
Time (Hours)
(Min)
300
5
206.07
3.4345
1
5
1
xiii
xiv
A3. Pulmonary/Nephrology Half-day Staffing (Intake)
Monday Morning (4/17/14; 7:30a.m – 12:00p.m)
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
270
4.5 342.814
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
5.713566667
3
13.5
2.25
3
Monday Afternoon (4/17/14; 12:00p.m – 5:00p.m)
Time
Window
(Min)
300
Time
Window
(Hours)
Total
Intake
Time
(Min)
5 958.083
Total
Number Total
Lunch/Break Additional
Intake
of MAs MA
Time
Task
Time
Time
Time
(Hours)
(Hours)
15.96805
5
25
3.75
5
xv
Tuesday Morning (3/18/21; 7:30a.m – 12:00p.m)
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
270
4.5 308.869
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
5.147816667
2
9
1.5
2
Tuesday Afternoon (4/18/14; 12:00p.m – 5:00p.m)
xvi
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
300
5 400.889
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
6.681483333
3
15
2.25
3
Wednesday Morning (4/19/14; 7:30a.m – 12:00p.m)
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
270
4.5 410.249
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
6.837483333
3
13.5
2.25
3
Wednesday Afternoon (4/19/14; 12:00p.m – 5:00p.m)
xvii
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
300
5 842.247
Total
Number Total
Lunch/Break Additional
Intake
of MAs MA
Time
Task
Time
Time
Time
(Hours)
(Hours)
14.03745
4
20
3
4
Thursday Morning (4/20/14; 7:30a.m – 12:00p.m)
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
270
4.5 709.766
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
11.82943333
4
18
3
4
Thursday Afternoon (4/20/14; 12:00a.m – 5:00p.m)
xviii
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
300
5 987.514
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
16.45856667
5
25
3.75
5
Friday Morning (4/21/14; 7:30a.m – 12:00p.m)
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
270
4.5 362.344
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
6.039066667
3
13.5
2.25
3
Friday Afternoon (4/21/14; 12:00p.m – 5:00p.m)
xix
Time
Time
Total
Window Window Intake
(Min)
(Hours) Time
(Min)
300
5 420.067
Total
Number Total
Lunch/Break Additional
Intake Time of MAs MA
Time
Task
(Hours)
Time
Time
(Hours)
7.001116667
3
15
2.25
3
xx
A4. Pulmonary/ Nephrology Daily Clerical Staffing Guidelines
Mondays (March 2014)
250
Check In/Out Capacity
Average of all Monday's in March 2014
n= 345 patients
Total Minutes
200
Check-Out
(min)
Check-In
(min)
Capacity
(7 stations)
Minimum
150
100
50
Maximum
0
Tuesdays (March 2014)
250
Total Minutes
200
150
100
50
Check In/Out Capacity
Average of all Tuesday's in March 2014
n= 209 patients
Check-Out
(min)
Check-In
(min)
Capacity
(7 stations)
Minimum
0
xxi
Wednesdays (March 2014)
200
Check In/Out Capacity
Average of all Wednesday's in March 2014
n= 227 patients
180
Total Minutes
160
Check-Out
(min)
Check-In
(min)
Capacity
(6 stations)
Minimum
140
120
100
80
60
40
Maximum
20
0
Thursdays (March 2014)
250
Total Minutes
200
150
100
Check In/Out Capacity
Average of all Thursday's in March 2014
n= 379 patients
Check-Out
(min)
Check-In
(min)
Capacity
(7 stations)
Minimum
50
0
xxii
Fridays (March 2014)
250
Total Minutes
200
150
100
50
Check In/Out Capacity
Average of all Friday's in March 2014
n= 250 patients
Check-Out
(min)
Check-In
(min)
Capacity
(7 stations)
Minimum
Maximum
0
xxiii