MedSport Staffing & Scheduling Analysis

Transcription

1 University of Michigan Health System MedSport Staffing & Scheduling Analysis Final Report Client: Coordinator: From: Julie Agbabian, Interim Operations Management, MedSport Richard Coffey, Director, Programs and Operations Analysis IOE 481 Project Team, Programs and Operations Analysis Bradley Belsky Mike Grezlik Anne Piontek Reid Tatoris Date: April 19, 2004

2 Table of Contents Executive Summary 1 Problem.1 Findings.1 Recommendations.2 Introduction and Background..3 Key Issues. 3 Scope. 3 Approach and Methodology...3 Preparatory Work.4 Data Collection.4 Current Situation 4 Results 5 Clinician Analysis 5 Outpatient Office Assistant Analysis...6 Scheduling Analysis.6 Current State and Sensitivity Analysis 7 Recommendations..8 Appendix A: Data Collection Forms.9 Appendix B: Linear Cominations.11 Appendix C: Raw Data. 12

3 Executive Summary Problem MedSport, the University of Michigan s sports medicine therapy clinics, recently relocated to a larger facility and, at the same time, experienced a decrease in patient volume. These circumstances may require changes in staffing of MedSport s clinicians. These clinicians include physical therapists (PT s), athletic trainers (ATC s), and personal trainer assistants (aides). The purpose of this project was to determine an optimal staffing level for MedSport clinicians, and to implement a model to schedule MedSport clinicians that will account for future variations in demand. Methodology Our team flowcharted the patient process and developed a data collection strategy after four meetings with MedSport s Interim Operations Manager, Julie Agbabian. Our team created data collection forms and distributed them to the members of Team 4 and the OPOA with written data collection instructions. A sample data collection form is attached in Appendix A. Team 4 collected patient data for two weeks, from February 10 through 23. An additional week of data collection was performed from March 8 through 12. A total of 88 patient samples were collected over both data collection periods. Our team was present at the MedSport facility to observe Team 4 for two of the five days of this collection period, and recorded the data from twelve of the patients ourselves. This observation ensured there was no bias in the data collected by Team 4. To verify that the data collected by Team 4 was representative of all MedSport teams, patient data was collected from another MedSport Team (Team 1). This data was collected on March 26 and was observed by the student team to validate the recorded data. Findings Data from 88 patients were collected between February 10 and March 12, 2004, by MedSport Team 4. MedSport has five teams consisting of PT s and ATC s. Of these five teams, it was determined that Team 4 would be the most responsive to data collection and therefore were chosen as the data collection team. During this period, the mean time spent by PT s or ATC s for revisit patients was 34.4 minutes with a standard deviation of 18.7 minutes. The mean time spent by PT s on new patients was 57 minutes with a standard deviation of 7.6 minutes. Finally, the mean time spent by PT s on Medicare patients was 40 minutes with a standard deviation of 8.7 minutes. Also, the clinicians spent an average of 3.7 minutes of additional time on documentation after the patient left the MedSport facility during this period. Through the use of linear combinations, the expected total time and variance spent with patients by PT s and ATCs was determined, forming the basis for our model. Based on MedSport s patient database, it was determined that their current patient volume was 100 visits per day, re- 1

4 visit to new patient ratio was 7.5:1, and Medicare patients made up 5% of total patients. Based on this data, the model we constructed shows that there should be 238 PT hours and 217 ATC hours per week. To maintain the current staffing ratio, MedSport should have 145 patients per day. Recommendations Currently, MedSport has 342 PT hours and 314 AT hours per week in comparison to the 238 Physical Therapist hours and 217 Athletic Trainer hours per week suggested by the model. This staffing schedule indicates that MedSport is overstaffed by 104 PT hours and 97 AT hours per week (assuming there are zero sick and vacation hours during the week). Given this, we suggest that MedSport either reduce the total number of Physical Therapist and Athletic Trainer hours through the expansion of the high school outreach program or increase their patient volume to 145 patients per day if MedSport wishes to continue at their current staffing level. 2

5 Introduction and Background MedSport is the home of the University of Michigan sports medicine clinic. They are a physical therapy clinic whose objective is to provide complete orthopedic care to athletes of all interests, levels and ages. Currently, MedSport employs 28.5 Full-Time Equivalences (FTE s), 9.6 of which are PT s. MedSport has five teams at the Domino s Farms facility. Each team consists of one to two PT s and one to three ATC s. MedSport also employs aides as support staff to provide extra assistance to teams. Only a PT can see new patients or those with Medicare insurance. ATC s can perform all other tasks of a PT under direct supervision by a PT. On average, MedSport sees about 100 patients per day, ten to eleven of which are new patients. Patients seen by PT s are billed in 15-minute increments (billed by units). Recently, MedSport expanded from a 10,000-square-foot facility to a 45,000-square-foot facility at Domino s Farms on Plymouth Road at the north side of Ann Arbor. At the same time, the clinic experienced a decrease in patients, largely due to new insurance company restrictions. For example, beginning in January 2003, M-Care requires patients to pay a $15 co-pay for each visit. This additional cost to patients is partly responsible for the decrease in patient volume. Decreased patient volume has led to financial concerns for MedSport, which have forced them to evaluate their patient-to-employee ratios and their employee scheduling process. These circumstances require changes in the utilization of MedSport s physical therapists (PT s), athletic trainers (ATC s), and personal trainer assistants (aides). MedSport has identified a need to evaluate their scheduling practices for their employees due to the transition to a new facility and the decrease in operating margin. Thus, the Interim Operations Manager is concerned that MedSport may be overstaffed. Therefore, the purpose of this project was to determine an optimal staffing level for MedSport clinicians and to implement a scheduling model that will account for future variations in demand. The purpose of the report is to discuss the findings of the student team and review the methodology used to obtain their results. Key Issues The following key issues drove the need for this project. A decrease in the number of patients A decrease in operating margin MedSport s perception that the facility may be overstaffed Scope Project Included: MedSport facility at Domino s Farms only 3

6 Physical Therapist (PT), Athletic Trainer (ATC), aide, and Out Patient Office Assistant (OPOA) functions Project Excluded: MedSport facilities at the Ice Cube, Brighton, and Livonia locations Medical Doctor (MD) functions Golf Fit clinic Approach and Methodology Preparatory Work Following four meetings with Julie Agbabian, the Interim Operations Manager at MedSport, our team flowcharted the patient process and developed a data collection strategy. The client chose to focus data collection on Team 4 (comprised of three physical therapists and two athletic trainers), and the Out Patient Office Assistants (OPOA) for MedSport s staffing and scheduling project. Ms. Agbabian perceived Team 4 to be most representative of the teams at MedSport. Also Ms. Agbabian briefly discussed their role in the patient data collection process. Our team created data collection forms and distributed them to the members of Team 4 and the OPOA with written data collection instructions.. Data Collection Data was entered by each member of Team 4 to log the amount of time PT s and ATC s spent with each type of patient. A sample data collection form is attached in Appendix A. Team 4 and the OPOA collected patient data for two weeks, between February 10 and 23. Team 4 recorded data from 51 patients and the OPOA recorded data from 91 patients during that time. An additional data collection period was held for one week, between March 8 and 12, where Team 4 patient data was again collected. This period yielded data from 37 patients. Our team was present at the MedSport facility to observe Team 4 for two of the five days of this collection period, and recorded the data from twelve of the patients ourselves. The purpose of this observation was to verify there was no bias in the data self-collected by Team 4. To verify that the data collected by Team 4 was representative of all MedSport teams, patient data was collected from another MedSport Team. The team chosen to verify this data was MedSport Team 1. This data was collected on March 26 and was observed by the student team to validate the recorded data. 4

7 Results Clinician Analysis Data was collected from February 10 through 23 and from March 8 through 12 by MedSport Team 4. Over this period, 88 samples were collected, of which 74 were re-visits. The proportion or re-visits to total visits is consistent with patient data provided from the client s patient database. Figure 1 displays the total time spent with each re-visit patient by Physical Therapists and Athletic Trainers. Time spent with the patient by aides is being ignored as they only spent time with four patients. Figure 1: Total time spent (min) per re-visit patient by Physical Therapists and Athletic Trainers This data displayed in Figure 1 is approximately normal as determined by the Anderson-Darling normality test (A-Squared is.800) and will be analyzed using the normal distribution. Point estimates for the mean and standard deviation are 34.4 minutes and 18.7 minutes respectively. The point estimates were validated by both samples from Team 1 as well as samples taken by the student team. Neither of these additional samples have statistically significant deviation from the main data set. Of the 74 patients, Athletic Trainers saw 51 while 32 were seen by Physical Therapists (some were seen by both). Table 1 shows the time spent per patient by clinician type. 5

8 Table 1: Time spent with patient by clinician type Seen by Sample Size (min) Average PT Time (min) Standard Deviation (min) Average AT Time (min) Standard Deviation (min) PT Only AT Only PT and AT The time spent per patient is distributed approximately normally in all three cases for time spent by both Physical Therapists as well as Athletic Trainers (verified by Anderson-Darling). The analytical tools for the normal distribution are well-developed, making the analysis much simpler. The remaining patients were either Medicare patients or new patients. These differ from re-visits in that only Physical Therapists can work with the patient. Table 2 shows the amount of time spent with each patient by the Physical Therapist. Table 2: Average time spent PT s by patient type Standard Patient Type Average Time Spent (min) Deviation (min) New Visit Medicare Although the sample size for each is small relative to re-visits, the standard deviation is small. Thus, confidence in the average time spent is high. Additionally, time spent per patient is distributed approximately normally (verified by Anderson-Darling). Both PT s and ATC s spend additional time on documentation after the patient leaves MedSport. The average time spent on documentation is 3.7 minutes. Analysis was done by patient type, and differences were found to not be statistically significant from the overall mean. Outpatient Office Assistant Analysis From the 88 patients sampled, 44 met with an outpatient office assistant (OPOA) after their visit. The total time spent with all 88 patients by the OPOA s over the data collection period was 28 minutes. Given this, the number of patients seen by an OPOA in a given day has a negligible effect on their workload. Scheduling Analysis The average time spent per patient can be determined through the use of linear combinations. The mathematical theory for linear combination is presented in Appendix B. Using this, the expected total time and variance spent with patients by Physical Therapists and Athletic Trainers can be determined. 6

9 From factory physics, variability will degrade the performance of the system 1. Therefore, MedSport must include a buffer over what is required from the expected total time. The data collected suggests that MedSport has a low coefficient of variation (defined as the ratio of the variance of a system to the expected time squared). Looking at simulations with low coefficients of variation and other parameters similar to MedSport, a buffer of 15% above the expected total time will make up for degradation of the system due to variability. Additionally, break time and meetings must be included to determine the optimal number of Physical Therapist and Athletic Trainer FTE s. The total time (which includes two 15-minute breaks and one half hour lunch) must be increased by an additional 15% to determine the total number of FTE s needed on-site. This additional buffer yields a total of a 30% buffer to compensate for variability in the system. Current State and Sensitivity Analysis Using MedSport s current patient volume of 100 patients per day, re-visit to new patient ratio of 7.5:1, and Medicare patient ratio of 5%, the constructed model shows that there should be 238 Physical Therapist hours and 217 Athletic Trainer hours per week. For a detailed description of equations contained in the model, see Appendix C. The student team examined three additional scenarios to determine the sensitivity of the model to various inputs. The first scenario is a change in the re-visit to new patient ratio. If the total number of visits were to remain the same, changing this ratio has a negligible effect on the total number of hours needed as shown in Figure 2. Staffing Requirement (hrs/week) Revisits/New Patient ratio vs Staffing Requirements (Current Ratio) 7 6 Revisits/New Patient visit Figure 2: Effect of Revisit/New Patient ratio on PT and ATC hours/week 5 PT hours/week ATC hours/week 1 Factory Physics, Hopp and Spearman, 2 nd Edition, Irwin-McGraw Hill, 2000; pg

10 Even large changes in the revisit/new patient ratio have no more than a 10% effect on the number of Physical Therapist and Athletic Trainer hours. On the other hand, changing the percentage on Medicare patients has a significant effect on clinician staffing. Two scenarios were discussed that could occur. M-Care (the University of Michigan s health insurance provider) may require patients to only be seen by Physical Therapists as Medicare currently does. M-Care currently represents 20% of MedSport s visits. In this scenario, these patients would be analyzed as Medicare patients. In the second scenario, all health care providers require patients to only be seen by Physical Therapists. The effects of these scenarios are shown in Table 3. Table 3: Effects of changes to Medicare patient percentage on staffing requirements ATC Scenario PT hours/week hours/week Current State M-Care allows only PT All providers allow only PT As can be seen, staffing requirements are quite sensitive to changes in the Medicare patient percentage. Recommendations Currently, MedSport has 342 Physical Therapist hours and 314 Athletic Trainer hours per week compared to the 238 Physical Therapist hours and 217 Athletic Trainer hours per week suggested by the model. The model indicates that MedSport is overstaffed by 104 Physical Therapist hours and 97 Athletic Trainer hours per week (provided that no employee is taking vacation time on a given week). Given this, we suggest that MedSport either reduce the total number of Physical Therapist and Athletic Trainer hours through the expansion of the high school visit program or increase their patient volume if MedSport wishes to continue at their current staffing level. As part of the project deliverables, the student team created a Visual Basic model to calculate necessary clinician time based on various future patient demand scenarios. Given the number of patients in a time period (as specified by the user), and the percentage of Medicare and newpatient visits, the application will calculate the required number of Physical Therapists and Athletic Trainers to service the given patient load with the same utilization level as the current state. Figures 3 and 4 give screenshots of the application. The student team recommends that MedSport begin using this application to optimize future scheduling. 8

11 Appendix A: Data Collection Forms OPOA Patient Time Sheet Date: ENTERING PATIENT EXITING PATIENT Time meet with patient Time patient left Time meet with patient Time patient left When a patient meets with you as the enter or leave, please record the time you meet with them and the time that they leave.

12 Appendix A: Data Collection Forms Patient Log Sheet Date: Total Service Time Total Documentation Patient Type NP RV MED AIC RC AQ WO Job Title PT AT Aide time in time out Documentation Time For OPOA's Please check the appropriate box for type of visit and record the time you meet with the patient and the time you finish with the patient. NP RV MED AIC RC AQ WO New Patient Revisit Medicare Acute Injury Clinic Runners Clinic Aquatic Walk Over For PT's, AT's, and Aides Please check the appropriate box for your job description and record the time you meet with the patient and the time you leave the patient. Also include time spent on documentation.

13 Appendix B: Linear Combinations A linear combination Y is defined as: n n + 1 Y = (1) i= 1 aixi + a such that a i is a constant for all values of i. The theory of linear combinations is well developed for situations where each variable x i is normally distributed. For MedSport, the variables x i represent: i = 1 i = 2 i = 3 i = 4 i = 5 Non-Medicare new patients Medicare patients Non-Medicare re-visits seen only by Physical Therapists Non-Medicare re-visits seen only by Athletic Trainers Non-Medicare re-visits seen by both Athletic Trainers and Physical Therapists As each of these variables is normally distributed, linear combination theory can be used. Since MedSport is interested in the total time spent with patients so that they can schedule based on this, the appropriate linear combination has the a i represent the number of patients for type i and a n+1 represents the total time spent on documentation. From this theory, the expected total time spent with the patient is: 5 E ( To) = aixi + an + 1 (2) i= 1 and the standard deviation of total time spent with the patient is: 2 σto = aiσi 5 i= 1 (3) Using this theory, the expected time needed for Physical Therapists and Athletic Trainers can be determined: PT Time Needed (minutes): Raw Time: 44 * # new patients + 61 * # medicare patients * # re-visits (4) AT Time Needed (minutes): Raw Time: 23.4 * # re-visits (5)

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15 Appendix C: Data Analysis Type PT AT Aide Documentation Sum Documentation/Total PT+AT AQ MDV MDV MDV MED MED MED NP NP NP NP NP RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV

16 Appendix C: Data Analysis Type PT AT Aide Documentation Sum Documentation/Total PT+AT RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV RV WO WO Team 1 Data RV RV RV RV RV RV RV RV NP MDV