Dynamic Staffing Model and Analysis for Pulmonary, Nephrology, and UrologySubmitted To:

Tammy Ellies; Katie SchwalmProject Manager and Lean Coach; Industrial Engineer Associate

University of Michigan Health Systems Department of Internal Medicine1500 E. Medical Center Dr.

Ann Arbor, MI 48109

Mary Freer; Amy Kauffman; Malissa EversoleDivision Administrator Pulmonary; Division Administrator Nephrology; Director of Operations

UrologyUniversity of Michigan Health System

1500 E. Medical Center Dr.Ann Arbor, MI 48109

Professor Mark Van OyenAssociate Professor

University of Michigan Industrial and Operations Engineering Department1205 Beal Ave.

Ann Arbor, MI 48109

Submitted By:

Team 2Alexis Baker

Benjamin GezonErik RoodArnold 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

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 NephrologyThe 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. UrologyThe 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 “tap-in/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 IssuesThe 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 providersUrology

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 ObjectivesThe 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 check-in/out data (Pulm/Neph Only)

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Record and analyze key metrics for Urology’s CG-CAHPS 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. MethodsThe 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 SearchThe 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 InterviewsIn 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

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estimates of the time required by the MAs to perform various procedures were collected from two of the lead MAs in the Urology clinic.

Work SamplingThe 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 DataThe 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 sheetsThe 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 VerificationThe 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

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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 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 TimeThe 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 GuidelinesUpon 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 TrainingIt 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 ResponsibilitiesThe 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” SystemThe “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 ScheduleThe team recommends piloting the recommended staffing numbers from the model for a short period of time and analyzing the impact on staff time utilization.

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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 WorkFuture 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.  

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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

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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

Thursday Morning (4/20/14; 7:30a.m – 12:00p.m)

Time Window (Min)

Time Window (Hours)

Total Intake Time

Total Intake Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (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

Friday Morning (4/21/14; 7:30a.m – 12:00p.m)

vi

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 Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

270 4.5 532.53 8.8755 3 13.5 3

Monday Afternoon (4/17/14; 12:00p.m – 5:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

300 5 867.83 14.46383333 4 20 4

ix

Tuesday Morning (3/18/21; 7:30a.m – 12:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

270 4.5 166.35 2.7725 1 4.5 1

Tuesday Afternoon (4/18/14; 12:00p.m – 5:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

300 5 195.33 3.2555 1 5 1

x

Wednesday Morning (4/19/14; 7:30a.m – 12:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

270 4.5 1260.98 21.01633333 6 27 6

Wednesday Afternoon (4/19/14; 12:00p.m – 5:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

300 5 389.97 6.4995 2 10 2

xi

Thursday Morning (4/20/14; 7:30a.m – 12:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

270 4.5 390.54 6.509 2 9 2

Thursday Afternoon (4/20/14; 12:00a.m – 5:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

300 5 1201.31 20.02183333 6 30 6

xii

Friday Morning (4/21/14; 7:30a.m – 12:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

270 4.5 593.37 9.8895 3 13.5 3

Friday Afternoon (4/21/14; 12:00p.m – 5:00p.m) Time Window (Min)

Time Window (Hours)

Total Procedure Time (Min)

Total Procedure Time (Hours)

Number of MAs

Total MA Time (Hours)

Downtime (Hours)

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 Window (Min)

Time Window (Hours)

Total Intake Time (Min)

Total Intake Time (Hours)

Number of MAs

Total MA Time (Hours)

Lunch/Break Time

Additional Task Time

270 4.5 342.814

5.713566667

3 13.5 2.25 3

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)

Lunch/Break Time

Additional Task Time

300 5 958.083 15.96805 5 25 3.75 5

xv

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)

Lunch/Break Time

Additional Task Time

270 4.5 308.869

5.147816667

2 9 1.5 2

Tuesday Afternoon (4/18/14; 12:00p.m – 5:00p.m)

xvi

Time Window (Min)

Time Window (Hours)

Total Intake Time (Min)

Total Intake Time (Hours)

Number of MAs

Total MA Time (Hours)

Lunch/Break Time

Additional Task Time

300 5 400.889

6.681483333

3 15 2.25 3

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)

Lunch/Break Time

Additional Task Time

270 4.5 410.249

6.837483333

3 13.5 2.25 3

xvii

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)

Lunch/Break Time

Additional Task Time

300 5 842.247

14.03745

4 20 3 4

Thursday Morning (4/20/14; 7:30a.m – 12:00p.m)

Time Windo

Time Windo

Total Intake

Total Intake

Number of

Total MA

Lunch/Break Time

Additional Task

xviii

w (Min) w (Hours)

Time (Min)

Time (Hours)

MAs Time (Hours)

Time

270 4.5 709.766

11.82943333

4 18 3 4

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)

Lunch/Break Time

Additional Task Time

300 5 987.514

16.45856667

5 25 3.75 5

xix

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)

Lunch/Break Time

Additional Task Time

270 4.5 362.344

6.039066667

3 13.5 2.25 3

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)

Lunch/Break Time

Additional Task Time

300 5 420.067

7.001116667

3 15 2.25 3

xx

xxi

A4. Pulmonary/ Nephrology Daily Clerical Staffing Guidelines

Mondays (March 2014)

0

50

100

150

200

250

Check In/Out CapacityAverage of all Monday's in March 2014

n= 345 patients

Check-Out (min)

Check-In (min)

Capacity (7 stations)

Minimum

MaximumTota

l Min

utes

Tuesdays (March 2014)

0

50

100

150

200

250

Check In/Out CapacityAverage of all Tuesday's in March 2014

n= 209 patients

Check-Out (min)

Check-In (min)

Capacity (7 stations)

Minimum

MaximumTota

l Min

utes

xxii

Wednesdays (March 2014)

0

20

40

60

80

100

120

140

160

180

200

Check In/Out CapacityAverage of all Wednesday's in March 2014

n= 227 patients

Check-Out (min)

Check-In (min)

Capacity (6 stations)

Minimum

Maximum

Tota

l Min

utes

Thursdays (March 2014)

0

50

100

150

200

250

Check In/Out CapacityAverage of all Thursday's in March 2014

n= 379 patients

Check-Out (min)Check-In (min)Capacity (7 stations)MinimumMaximumTo

tal M

inut

es

xxiii

Fridays (March 2014)

0

50

100

150

200

250

Check In/Out CapacityAverage of all Friday's in March 2014

n= 250 patients

Check-Out (min)

Check-In (min)

Capacity (7 stations)

Minimum

MaximumTota

l Min

utes

xxiv