Analyzing Physician Task Allocation and Patient Flow at the Radiation Oncology Clinic. Final Report

Analyzing Physician Task Allocation and Patient Flow at the Radiation Oncology Clinic Final Report Prepared for: Kathy Lash, Director of Operations University of Michigan Health System Radiation Oncology Clinic Sheri Moore, Project Coordinator Industrial Engineer Lead Program and Operations Analysis Prepared by: Megan Haubert Andrea Litscher Rebecca Rosenbaum Vincent Verdeschi Practicum in Hospital Systems Industrial and Operations Engineering University of Michigan Date of Submission: December 8, 28

Table of Contents Executive Summary... Project Goals... Background... Findings and Conclusions... Recommendations...3 Introduction...4 Background...4 Key Issues...5 Project Goals...6 Project Scope...6 Methodology...6 Preliminary Observations...6 Literature Search...7 Data Collection...7 Physician Time Studies...7 Patient Flow Data...8 On-Treatment Visit Data...8 Review of Past Hospital Data...8 Data Validation...9 Data Analysis...9 Findings from Physician Analysis... Findings from Patient Analysis...4 Follow-Up Appointments...4 ii

Value Stream Map...4 Wait Time and Touch Time Distributions...6 Medical Staff Encounter Paths...7 Consult Appointments...8 Value Stream Map...8 Wait Time and Touch Time Distributions...9 Medical Staff Encounter Paths...2 On-Treatment Visits...2 Conclusions...2 Improvement Opportunities in Physician Scheduling...2 Bottleneck Areas in Patient Flow...2 Recommendations...22 Appendix A: Physician Time Study Form... A- Appendix B: Patient Flow Data Collection Form...B- Appendix C: Value Stream Map for Follow-Up Patients...C- Appendix D: Follow-Up Medical Encounters... D- Appendix E: Wait Time and Encounter Distributions for Follow-Up Patients... E- Appendix F: Value Stream Map for Consult Patients... F- Appendix G: Consult Medical Encounters... G- Appendix H: Wait Time and Encounter Distributions for Consult Patients... H- Appendix I: Value Stream Map for On-Treatment Patients... I- iii

List of Figures and Tables Figure : Average Physician Task Allocation per Day...2 Figure 2: Task Time Box plots of Physician Activities...2 Figure 3: Average Physician Task Allocation per Day... Figure 4: Average Physician Time Spent in Each Activity per Activity Occurence... Figure 5: Task Time Box plots of Physician Activities...2 Figure 6: Consult, On-Treatment, Office, and Follow-Up Task Time Stratified by Physician...3 Figure 7: Frequency of On-Treatment Visits Stratified by Hour...4 Figure 8: Distribution of Wait Time for Follow-Up Patients...6 Figure 9: Follow-Up Patient Wait Time and Touch Time for Common Paths...7 Figure : Distribution of Wait Time for Consult Patients...9 Figure : Consult Patient Wait Time and Touch Time for Common Paths...2 Table : Touch Times and Wait Times for Follow-Ups, Consults, and On-Treatment Visits...3 Table 2: Means and Confidence Intervals for Mean Time in Clinic for Consults and Follow-ups...9 Table 3: Means and Confidence Intervals for Physician Time with Patient for Consults and Follow-Ups...9 Table 4: An Average 8-Hour Day for a Clinic Physician... Table 5: Follow-Up Appointments Touch Time and Wait Time Distribution...5 Table 6: Follow-Up Encounter Path Touch Times and Wait Times...7 Table 7: Consult Appointments Touch Time and Wait Time Distribution...8 Table 8: Consult Encounter Path Touch Times and Wait Times...2 Table 9: On-Treatment Visits Mean Time Distribution...2 iv

Executive Summary The staff at the Radiation Oncology Department at the University of Michigan have perceived long patient wait times at the clinic based on complaints expressed on patient comment cards. Data analysis from a current Radiation Oncology lean team shows physician scheduling as a major cause for these long patient wait times due to certain physician clinic obligations not being factored into the physician schedules. The Radiation Oncology Department would like to better understand the current situation of physician task allocation and patient flow in the clinic. With this information, the clinic will work towards a long-term goal of optimizing physician scheduling to reduce patient wait times, improve process efficiency, and allow physicians to devote more time to academia and research. Project Goals The student team s goal was to take the first steps toward the clinic s long-term goal by analyzing physician task allocation and patient flow to determine the causes for long patient wait times at the Radiation Oncology Clinic. The secondary goals of this project were to: Identify specific bottleneck areas in patient flow Identify improvement opportunities in physician scheduling Recommend steps for further investigation that allow the clinic to achieve its long-term goal Background Throughout the day, a clinic physician may be involved in the following tasks: consult appointments; follow-up appointments; on-treatment visits; time spent in office areas and the physician workroom; and calls to simulations, dosimetry, and the treatment room. Of those tasks, consults and follow-up appointments are scheduled ahead of time in the physicians schedules while on-treatment visits and calls to simulations, dosimetry, and treatment rooms are not. As a result, the staff and patients perceive long patient wait times as a physician works to fulfill both scheduled and unscheduled duties throughout the day. Methodology To achieve the project goal, the team conducted a literature search, observed the current processes at the clinic, performed 6 hours of physician time studies, distributed and collected 29 patient flow forms, analyzed the time studies and patient flow forms using Excel, and developed conclusions and recommendations for further investigation by future lean teams, which will help the clinic to achieve its long-term goal. Findings and Conclusions The data collected from the physician time studies indicates that the average physician task allocation per day is as shown in Figure on the following page:

Dosimetry,.5% Personal,.% Treatment Room,.8% Out-of-Department, 2.6% Simulation, 4.4% Follow-up,.2% On-Treatment, 3.% Office / Work Room, 49.9% Consult, 5.6% The result of 6 hours of team data collection from 9/29/8 to /28/8, tracking 9 clinic physicians Figure : Average Physician Task Allocation per Day Figure shows that almost half of a physician s day is spent in offices or the physician workroom. Consults, follow-ups, and on-treatment visits, activities that are considered touch time (value-added) from the patient s perspective, together occupy 39.9% of a physician s day. Additionally, the unscheduled activities (on-treatment visits, and calls to dosimetry, simulation, and treatment rooms) together occupy 22.3% of the physician s day. The team further analyzed the four tasks that occupy the largest portion of a physician s day: consults, follow-ups, on-treatment visits, and time spent in office areas and physician workroom. Figure 2 below shows box plots of activity task time per activity occurrence. 6 5 Overall Task Time 4 3 2 Consults Follow-Ups Office/Workroom On-Treatment n = 58 n = 9 n = 295 n = 4 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8, N=585 Figure 2: Task Time Box plots of Physician Activities Figure 2 shows that each of these activities has a large amount of variation with many outliers. This variation may be due to different practices among all of the physicians studied. The above findings from the physician time studies led to the following conclusions: A high proportion of a physician s day is filled with unscheduled activities, mainly ontreatment visits 2

Less than half of a physician s day is spent performing activities that involve touch time with a patient High variability in activity time exists between physicians From the patient analysis, the team created value stream maps for follow-up, consult, and ontreatment visit patients. Table below summarizes the data from these value stream maps. Table : Touch Times and Wait Times for Follow-Ups, Consults, and On-Treatment Visits Touch Time Wait Time Time in Clinic Percent Touch Time (Value Added) Percent Wait Time (Non-Value Added) Follow-Up Mean :27 :37 :4 42% 58% Consult Mean :52 :5 :43 5% 5% On-Treatment Mean :7 :5 :22 32% 68% Data collected from 29 patient flow forms and 6 hours of physician time studies, from 9/29/8 to /29/8 As shown in Table, consult, follow-up, and on-treatment visit patients spend at least half of their time waiting. Additionally, follow-up patients are scheduled for 2-3 minutes, yet a patient spends an average of hour 4 minutes in the clinic with 27 minutes of touch time. Consult patients are scheduled for -.5 hours, yet the average patient spends hour 43 minutes in the clinic with 52 minutes of touch time. The findings from the patient flow analysis led to the following conclusions: The wait times for all consult, follow-up, and on-treatment visit patients are too high The scheduled length of time for a patient appointment does not match the time that the patient is actually in the clinic Recommendations To achieve their long-term goal, the Radiation Oncology Department should: Further investigate the distribution and duration of on-treatment visits for each physician throughout the day. These visits can then be incorporated into physicians schedules by adding buffers at appropriate times, which will reduce patient wait time. Investigate the breakdown of physician time spent in the office areas and physician workroom to determine how to better distribute office tasks throughout the day to reduce patient wait time. Investigate causes for the high variability in appointment lengths within and between physicians. The clinic should develop methods to standardize these activities as much as possible to reduce variability. Even after these activities are standardized, though, activity times between physicians may still vary based on patient diagnosis and physician practice; therefore the clinic may also create schedules unique to each physician. When creating optimal physician schedules, the clinic should ensure that the length of time for which a patient is scheduled closely matches the time that the patient spends in the clinic. Additionally, the clinic should consider the physician touch time and variation for each appointment type, as well as the frequency and duration of unscheduled activities, to appropriately place each patient appointment within physicians schedules. 3

Introduction The Radiation Oncology Department at the University of Michigan Hospital utilizes ionizing radiation treatment to provide cancer care for its patients. The staff have perceived long patient wait times at the clinic based on complaints expressed on patient comment cards. Data analysis from a current Radiation Oncology lean team shows physician scheduling as a major cause for these long patient wait times due to certain physician clinic obligations not being factored into the physician schedules. The Radiation Oncology Department would like to better understand the current situation of physician task allocation and patient flow in the clinic. With this information, the clinic will work towards a long-term goal of optimizing physician scheduling to reduce patient wait times, improve process efficiency, and allow physicians to devote more time to academia and research. The student team s main objective was to take the first steps toward this long-term goal by analyzing physician task allocation and patient flow. To accomplish this, the team created physician time study sheets (See Appendix A) and Patient Flow Forms (See Appendix B) to observe physicians tasks and determine the patient flow process. The team analyzed the collected data and determined physician task allocation as well as identified improvement opportunities in physician schedules. The team also created value stream maps of the current patient flow process and then developed recommendations for further investigation by future lean teams, which will allow the clinic to achieve its long-term goal. The purpose of this report is to present data analysis, the results of this analysis, and the team s recommendations. Background The Radiation Oncology Department at the University of Michigan Hospital utilizes ionizing radiation treatment to treat patients diagnosed with cancer. Patients are referred to the clinic area of this department to learn about radiation therapy and to be assessed during and after radiation treatments. The clinic has nine exam rooms and is open from 8:3am to 5pm Monday through Thursday, and 9am to 5pm on Friday. Ten physicians work within the clinic, with two to seven physicians present at any given time during clinic hours. On days when a physician is not scheduled in the Radiation Oncology Clinic, he or she may be conducting research, teaching, or working at multidisciplinary clinics. The physicians and other clinic staff determine the physicians clinic days based on their schedules and clinic needs and cancel clinic time when necessary for meetings and other obligations. Additionally, the staff reports that physicians often add extra clinic appointments at the discretion of the clinic, leading to overbooked schedules. During their initial visit to the Radiation Oncology Department, patients are assigned a specific clinic physician based on their diagnosis. All clinic appointments for that patient are scheduled with the assigned physician. The following are activities that occur in the clinic: Consult Appointments - Consult appointments are scheduled with the physicians for hour, except for one physician who schedules for.5 hours. At these appointments, the patient learns about radiation treatment and decides whether to begin this type of treatment. 4

Simulations Before the actual treatment begins, simulations occur at the clinic to set up for radiation and locate the area to be treated. Simulations are performed by therapists; physicians may be called in when their assistance is needed. Treatment Room Patients undergoing radiation treatment come to the Radiation Oncology Department once or twice a day to receive treatment. Sometimes a physician may be called to the treatment room if a treatment therapist has questions or concerns about the patient s treatment. Dosimetry At dosimetry, a dosimetrist determines a patient s care plan, including frequency and dosage of radiation. Physicians may be called to dosimetry when the dosimetrist has questions about the patient s care plan. On-Treatment Visits Patients receive radiation treatments in the treatment area of the Radiation Oncology Department. Every fifth treatment, the patient comes to the clinic area immediately after receiving treatment to be assessed by a physician. These assessment visits are not scheduled ahead of time and require about six minutes of the physician s time. Each physician designates one day of the week for on-treatment visits. On the designated day, the physician knows to expect on-treatment patients in addition to scheduled patients. Follow-Up Appointments Follow-up appointments with a physician are scheduled for 2-3 minutes. After a patient has completed all scheduled radiation treatments, the patient is assessed by a physician at a follow-up appointment. Office Areas and Physician Workroom In the office and physician workroom areas, physicians review their patients history, treatment plans, and progress to prepare for appointments with the patients. Consults and follow-up appointments are factored into the physicians schedules while ontreatment visits and calls to simulations, dosimetry, and the treatment room are not. As a result, the staff reports that physicians often have to leave appointments with scheduled patients to fulfill unscheduled clinic obligations. A current Radiation Oncology lean team has found these interruptions in physician scheduling to be a major cause for long patient wait times. Therefore, the Radiation Oncology Department has requested help to analyze physician task allocation and patient flow to determine how all physician activities and appointments can be incorporated into physician schedules to reduce patient wait time. Key Issues The following factors at the Radiation Oncology Clinic necessitated this study: The Radiation Oncology lean team reports that physicians are often called away from scheduled activities to fulfill unscheduled obligations, resulting in inefficient process flow The staff perceives long patient wait time based on complaints expressed on patient comment cards 5

The staff reports that physicians often add extra clinic appointments at the discretion of the clinic, leading to overbooked schedules The staff perceives that bottlenecks occur in the patient flow process Project Goals The long-term goal of this project is to optimize physician scheduling to reduce patient wait times, improve process efficiency, and allow physicians to devote more time to academia and research at the Radiation Oncology Clinic. This project took the first steps towards this longterm goal; the primary goal of this project was to analyze physician task allocation and patient flow to determine the causes for long patient wait times at the Radiation Oncology Clinic. The secondary goals of this project were to: Identify specific bottleneck areas in patient flow Identify improvement opportunities in physician scheduling Recommend steps for further investigation that allow the clinic to achieve its long-term goal Project Scope To quantify physician task allocation, the project: Included the following clinic appointments and activities: consults, simulations, dosimetry, on-treatment visits, follow-ups, treatment room, office areas, personal time, and out-of-department time Excluded a breakdown of personal time and out-of-department time Excluded walking time within clinic areas To determine the patient flow, the project: Included the patient process from the time a patient checks in with the clerk to the time the patient leaves the exam room Included scheduled physician appointments at the clinic: consults and follow-ups Excluded simulations, treatment appointments, and on-treatment visits Excluded scheduled appointments that occurred outside of the clinic Methodology The University of Michigan Radiation Oncology Clinic was the main department involved in this project. Persons involved in this clinic include patients, the Director of Operations, physicians (MD), physician assistants (PA), medical assistants (MA), dosimetrists, simulation therapists, treatment therapists, medical students, residents (RES), nurses, and clerks. Preliminary Observations The team observed the current state of the Radiation Oncology Clinic to understand the general flow of patients and physicians. Each team member spent two hours touring the clinic and 6

observing the patient check-in and exam room assignment processes. During these observations, the clerks and MA s also explained the clinic s patient tracking system, Varis (Varian s Information System), to the team. This system is used to track how long each patient has been waiting in the lobby, which patient is in each exam room, how long the patient has been in that exam room, what type of appointment the patient is scheduled for, and if a medical staff member is currently in the exam room. These observations allowed the team to gain a basic understanding of the clinic flow and develop a plan for data collection. Literature Search The team reviewed previous Practicum in the Hospital projects including: Analysis of Waste in the Radiation Oncology Clinic Patient Flow Process by Katie Mickley, Greta Schaltenbrand, and Sara Swenson Analysis of Treatment Process and Start Times During Radiation Therapy, by Nitin Gupta, Emily Servinsky, and Kelly Wendling Utilization of Linear Accelerators in the Radiation Oncology Department, by Sepehr Mowlavi, Zach Shoup, and Alex Wang. This information was provided to the team by the Industrial Engineer Lead, and helped the team understand the format and content of the reports as well as learn more about the Radiation Oncology Department. The Industrial Engineer Lead also provided the team with a patient flow form template from a cancer center clinic analysis performed by a University of Michigan Health Service team: Robert Beasley, Bradley Hoath, Peter Li, and Zach Shoup. Additionally, the team has reviewed the following electronic articles regarding patient wait times, physician productivity, and overall patient flow: Analysis of Patient Flow in the Emergency Department and the Effect of an Extensive Reorganization by Ò Miró, M Sánchez, G Espinosa, B Coll-Vinent, E Bragulat, J Millá Patient Bottlenecks: Find Them and Fix Them, by Tammy Worth, from Medical Economics Magazine Patient Waiting Times in a Physician s Office by James P. Meza, MD, MSA The team used the information in the above articles to develop the project methodology and help identify possible problem areas and improvement opportunities within the Radiation Oncology Clinic. Data Collection The team collected data to analyze physician task allocation and patient flow in three parts: performing physician time studies, collecting patient flow data, and collecting on-treatment visit data. This section details the type of data that was collected, the dates it was collected, and the methods used. Physician Time Studies To understand physician task allocation, the team developed physician time study forms (see Appendix A). The team directed four department staff members on how to use these forms; to 7

collect data, one team or staff member followed one physician in two- to six-hour blocks of time and recorded the times that the physician began and completed different activities throughout the day. Physicians were tracked on their busy days, which were defined as days when a physician saw on-treatment visit patients in addition to those previously scheduled such as consult and follow-up patients. The team and staff members tracked nine physicians from September 29, 28 to October 28, 28, resulting in 6 hours of data collection. Patient Flow Data To understand patient flow, the team created Patient Flow Collection Forms (see Appendix B). These forms were based on forms used for the cancer center clinic analysis (see Literature Search section). The team explained the project goal and the importance of these forms to the office clerks at the Radiation Oncology Clinic; the clerks were responsible for filling out a portion of the forms and then distributing these to the patients. For each patient, the clerk would record the time at patient check-in, the appointment time and type, and the physician name, and then give the form to the patients. The patients then would record the start and end times for vitals to be taken, the time upon entering an exam room, and the start and end times for each medical staff encounter. Finally, the clerks would collect these forms from the patients at checkout. The office clerks were able to distribute and collect 29 completed Patient Flow Collection Forms from September 3, 28 to October 29, 28. On-Treatment Visit Data As on-treatment visit patients are not scheduled beforehand, they were not included in the patient flow analysis. To determine mean in-room wait time for these patients and evaluate the effect of these patients on the wait times of scheduled patients (consults and follow-up patients), the team collected additional data. This data was collected concurrently with the physician time study. Each time the physician being tracked went into an on-treatment visit appointment, the team or staff member tracking that physician would check the Varis system (See Preliminary Observations) to find how long the patient had been in the room waiting for the physician. While only the team members recorded the wait times for on-treatment visit patients, as part of the physician time study, the team plus staff members recorded the start and end times for the physician encounter with these patients. Therefore, from September 29, 28 to October 28, 28, the team collected wait times for 7 on-treatment visit patients, and the team and staff members collected 38 physician encounter times with on-treatment visit patients. Review of Past Hospital Data The Director of Operations provided the team with the following data, all in Excel format, which was used to learn more about the Radiation Oncology Department, understand the current situation, and develop the project methodology: A report from the current lean team in the Radiation Oncology Clinic, which identifies physician scheduling as a major cause for long patient wait time Consult and follow-up scheduling data, which shows the total number of consults and follow-ups scheduled each year from 2 to 28 Patient comment card reports from 27 and 28 Finally, the Director of Operations provided an Excel document with existing patient wait time 8

data from August 28; this data shows the average time patients spent in the lobby and in the exam room, separated by appointment type and physician. This data was used to validate the data collected for this project. Data Validation The team validated the patient flow data by comparing it to the existing Excel data from August 28. For each data set, the team looked at the mean time in the clinic for each appointment type (consult and follow-up) from the time a patient checked in to the time a patient left the exam room. The team created 95% confidence intervals for these mean times. The results are shown in Table 2: Table 2: Means and Confidence Intervals for Mean Time in Clinic for Consults and Follow-ups Mean 95% Confidence Interval Consults August 28 Data 2.4 (9.7, 5.) Patient Flow Data 2. (95., 9.) Follow-Ups August 28 Data 53.8 (47., 6.6) Patient Flow Data 63. (58.4, 67.6) Table 2 shows the 95% confidence intervals overlap between both data sets for consults and follow-ups. Thus, with 95% confidence, the mean time in the clinic for both appointment types is not significantly different between the data sets. Once the team ensured validation of the patient flow data, the data was used to validate the physician time study data. For each data set, the team found the mean time that the doctor spent with the patient for each appointment type (consult and follow-up) and created 95% confidence intervals for these mean times. The results are shown in Table 3: Table 3: Means and Confidence Intervals for Physician Time with Patient for Consults and Follow-ups Mean 95% Confidence Interval Consults Patient Flow Data 24. (9.6, 28.4) Physician Time Studies 23.7 (2.5, 26.8) Follow-Ups Patient Flow Data. (8.7, 3.3) Physician Time Studies 8.9 (7.8, 9.9) As shown in Table 3, the 95% confidence intervals overlap between both data sets for both consults and follow-ups. Thus, the team can conclude with 95% confidence that the mean time that a doctor spent with a patient is not significantly different between data sets. Data Analysis The team used the collected data to analyze the physician time studies and patient flow to determine the causes for long patient wait times at the Radiation Oncology Clinic. This data was also analyzed to determine physician task allocation, identify specific bottleneck areas in patient flow, identify improvement opportunities in physician scheduling, and recommend steps for 9

further investigation that will allow the Radiation Oncology to achieve its long-term goal of optimizing physician scheduling. Findings from Physician Analysis The data collected from the physician time studies indicates that the average physician task allocation per day is as seen in Figure 3 below: Dosimetry,.5% Personal,.% Treatment Room,.8% Out-of-Department, 2.6% Simulation, 4.4% Follow-up,.2% On-Treatment, 3.% Office / Work Room, 49.9% Consult, 5.6% The result of 6 hours of team data collection from 9/29/8 to /28/8, tracking 9 clinic physicians Figure 3: Average Physician Task Allocation per Day Figure 3 represents the percentage of time that is allocated to specific tasks during each day. As the figure shows, almost half of a physician s day is spent in offices or the physician workroom. In terms of time spent with patients, physicians spend the majority of their time on consults followed by on-treatment visits and follow-ups. These three activities, which are all considered touch time (value-added) from the patient s perspective, together occupy 39.9% of a physician s day. Calls to simulation, dosimetry, and treatment rooms occupy a very small portion of a physician s day. Additionally, the unscheduled activities (on-treatment visits, and calls to dosimetry, simulation, and treatment rooms) together occupy 22.3% of the physician s day. The physician time study data also showed the average time that a physician spends on each activity, per activity occurrence. This data is presented in Figure 4 on the next page.

25 2 Minutes 5 5 Consult Out-of-Department Office / Work Room Follow-up On-Treatment Dosimetry Simulation Mean Standard Deviation Treatment Room Personal The result of 6 hours of team data collection from 9/29/8 to /28/8, tracking 9 clinic physicians Figure 4: Average Physician Time Spent in Each Activity per Activity Occurence Figure 4 shows that consults are the most time consuming per occurrence. Calls to dosimetry, simulation, and treatment rooms are the least time consuming per occurrence. Using the above information of task allocation (Figure 3) and activity duration (Figure 4), the team created a layout of the average eight-hour day for a clinic physician, which shows the total time dedicated to each activity, the activity duration per occurrence, and the frequency of each activity. Refer to Table 4. Table 4: An Average 8-Hour Day for a Clinic Physician Total Hours per Activity Average Activity Duration per Occurrence (minutes) Frequency of Activity Office / Work Room 4..8 2.3 Consult.2 23.7 3.2 On-Treatment. 6.6 9.5 Follow-up.9 8.9 6. Simulation.3 5.2 4. Out-of-Department.2 2.9. Dosimetry. 5.4.3 Personal. 2.7.7 Treatment Room. 3.9. The result of 6 hours of team data collection from 9/29/8 to /28/8, tracking 9 clinic physicians

As shown in Table 4 on the previous page, physician time spent in office areas and the physician workroom, as well as physician time spent in on-treatment visits, occur frequently, for shorter durations per occurrence. Consult appointments and trips out-of-department occur infrequently for longer durations per occurrence. From the physician time studies, the team also created box plots for the four clinic appointments and activities that occupy the largest percentage of a physician s day: consults, follow-ups, office and physician workroom time, and on-treatment visits. Figure 5 below shows box plots of activity task time per activity occurrence. 6 5 Overall Task Time 4 3 2 Consults Follow-Ups Office/Workroom On-Treatment n = 58 n = 9 n = 295 n = 4 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8, N=585 Figure 5: Task Time Box plots of Physician Activities The office / work room box plot in Figure 5 shows that the time spent in these areas is highly variable with many outliers. This variability can be explained by the variation in tasks performed in the office. Physicians may go into the office areas for short periods of time in between patients to use the computer or obtain patient information. In contrast, when a physician does not have any waiting patients, the physician may be working in the office area for an extended period of time. Figure 5 also shows that consults, follow-ups, and on-treatment visits are relatively variable. This variability results from differences between physicians. To further understand the differences among physicians, the data for the four main tasks were stratified by physician. Refer to Figure 6 on the next page. 2

5 Consult Task Time by Physician (min) n = 58 4 On-Treatment Task Time by Physician n=4 Consult Task Time 4 3 2 On-Treatment Task Time 3 2 BenJosef Eisbruch Hamstra Hayman Jagsi Lawrence Pan Pierce Tsien n=3 n= n=4 n= n=4 n=5 n=6 n=3 n=2 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8 BenJosef Eisbruch Hamstra Hayman Jagsi Lawrence Pan Pierce Tsien n=4 n=53 n=4 n=28 n=7 n=8 n=5 n=8 n=4 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8 Office Task Time by Physician (min) n = 295 Follow-Up Task Time by Physician (min) n = 9 6 3 5 25 Office Task Time 4 3 2 Follow Up Task Time 2 5 5 BenJosef Eisbruch Hamstra Hayman Jagsi Lawrence Pan Pierce Tsien BenJosef Eisbruch Hamstra Hayman Jagsi Lawrence Pan Tsien n = 22 n =9 n = 5 n = 49 n = 3 n = 9 n = 29 n = 6 n = 23 n = n = 47 n = 4 n = 8 n = n = 8 n = 5 n = 8 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8 Data collected for 9 physicians over 6 hours total from 9/29/8 to /28/8 Figure 6: Consult, On-Treatment, Office, and Follow-Up Task Time Stratified by Physician Figure 6 shows that consults, follow-ups, on-treatment visits, and office times vary depending on the physician performing the activity. Differences in sample size, type of patient diagnosis, and physician practice may account for these differences among physicians. The box plots not only show that physicians time distributions vary among different physicians, but that the time that one physician spends on each activity is quite variable as well. The on-treatment panel of Figure 6 above shows that on-treatment visits frequently take up to minutes, with a maximum duration of 38 minutes. To understand where on-treatment visits are occurring in a physician s day, the team generated a graph that shows the frequency of ontreatment visits based on the time of day. The graph is standardized by the number of observations that were available for each hour. Refer to Figure 7 on the next page. 3

Frequency of On-Treatments per day.6.4.2..8.6.4.2..6.3.8.4...4.9. Time of Day The result of 6 hours of team data collection from 9/29/8 to /28/8, tracking 9 clinic physicians Figure 7: Frequency of On-Treatment Visits Statified by Hour Figure 7 shows that 8:am to 8:59am has less on-treatment visits than other hours. From 9:am to 4:59pm, there is about an equal number of on-treatment visits per hour, showing that on-treatment patients come consistently throughout the day after 9:am. Findings from Patient Analysis The Patient Flow Forms revealed findings for follow-up and consult appointments, and the physician time studies revealed findings for on-treatment visits. Follow-Up Appointments The team analyzed the follow-up patient flow data by creating a value stream map and analyzing the wait time and touch time distributions as well as the medical encounter paths. Value Stream Map The value stream map for follow-up appointments is in Appendix C. As shown in the map, first a patient arrives and checks in with the clerk. Next, the patient waits in the lobby until the patient is called to the clinic area by the nurse. The nurse then obtains the patient s vitals and places the patient in an exam room. Next, the patient waits in the exam room for the medical staff encounters. Thirty-seven percent of the patients have only one medical staff encounter, 58% of patients have two medical staff encounters, and 5% of patients have three medical staff encounters. Upon completing the medical staff encounter(s), a patient leaves the exam room, checks out, and exits the department. The value stream map displays the wait time in the lobby and the wait time between all encounters, as well as the process time for each medical encounter, using the following summary statistics: mean, standard deviation, median, minimum, and maximum. The map further breaks 4

down each medical staff encounter by showing the most frequently occurring medical providers who performed each encounter. The complete percentage breakdowns of medical encounters are shown in Table D. of Appendix D. Additionally, the map displays the mean process time for those medical providers at each encounter. Additional summary statistics for these providers are shown in Tables D.2 and D.3 of Appendix D. The total process time and wait time statistics for all staff encounters are shown in Table 5 below: Table 5: Follow-up Appointments Process Time and Wait Time Distribution Touch Time Wait Time Time in Clinic Percent Touch Time (Value Added) Percent Wait Time (Non- Value Added) Best Case Mean :2 :3 :5 4% 6% Worst Case Mean :37 :46 :23 45% 55% Weighted Mean :27 :37 :4 42% 58% Data Collected from 94 Patient Flow Forms, distributed from 9/3/8 to /29/8 Best Case Mean is the case where a patient has only one medical encounter before leaving the clinic; Worst Case Mean is the case where a patient has three medical encounters before leaving the clinic; Weighted Mean is the overall mean touch time for all patients. As shown from the table, Best Case patients spend 6% of their time waiting, Worst Case patients spend 55% of their time waiting, and all patients spend about 58% of their time waiting. In all cases, patients spend over half of their time waiting. Additionally, the table shows that the average Best Case patient spends 5 minutes in the clinic and the average Worst Case patient spends hour 23 minutes in the clinic; currently, follow-up appointments are only scheduled for 2-3 minutes. The touch time for Best Case and Weighted Case is, however, within this scheduled appointment time. The average Worst Case touch time, where patients have three medical staff encounters, exceeds the scheduled appointment time. Analysis of the follow-up wait time data revealed the distribution of wait time in the lobby and between each medical staff encounter. Since only 63% of patients stay after the first encounter for a second encounter, and only 8% of those patients stay after the second encounter for the third encounter, the pie chart was generated using a weighted average of mean wait times. Refer to Figure 8 on the following page. 5

Encounter 2 Wait Time, 7.2% Encounter 3 Wait Time,.8% Lobby Wait Time, 38.2% Encounter Wait Time, 43.7% Data Collected from 94 Patient Flow Forms, distributed from 9/3/8 to /29/8 Figure 8: Distribution of Wait Time for Follow-Up Patients Figure 8 shows that the largest percentage of wait time occurs while a follow-up patient is in the exam room waiting for the first medical encounter. The second largest percentage of wait time occurs while the patient is waiting in the lobby. Wait Time and Touch Time Distributions Analysis of the follow-up Patient Flow Forms revealed the distributions of wait times between encounters and the touch times of each encounter. The data for follow-up encounter durations and wait time distributions are shown in Appendix E. Distributions are shown only up to the second medical staff encounter due to insufficient data for third encounters. Additionally, lobby wait time was calculated using the minimum wait time between appointment time to time called to room and arrival time to time called to room. Furthermore, this lobby wait time includes the time the patient spent filling out paperwork in the lobby. The 7 th percentile for each distribution in Appendix E revealed the following key points. 7% of follow-up patients: Waited in the lobby for under 5 minutes, with a large range of wait times extending to a maximum of 6 minutes Waited in the exam room for under 5 minutes for the first medical encounter, with a maximum of 52 minutes Had a first medical encounter duration of up to 2 minutes, with a maximum encounter duration of 4 minutes Waited under minutes for the second medical encounter, with maximum of 57 minutes 6

Had a second medical encounter duration of less than minutes, with a maximum of 4 minutes Medical Staff Encounter Paths The analysis from the follow-up Patient Flow Forms also revealed various medical staff encounter paths that follow-up patients experienced. For example, a follow-up patient may first see a Resident (RES), then a physician (MD), then leave the clinic, while another patient may only see a Physician s Assistant (PA) and then leave the clinic. Percentage occurrences for the most common paths and the touch time versus wait time of each path are displayed in Table 6. Table 6: Follow-Up Encounter Path Touch Times and Wait Times Follow-Up Paths Only PA PA to MD MD to MA/none RES to MD Path Frequency (%) 28% 33% 4% 2% Touch Time (min) 2 27 26 36 Wait Time (min) 24 4 36 42 Total Time (min) 45 67 62 78 Data Collected from 94 Patient Flow Forms, distributed from 9/3/8 to /29/8 Table 6 indicates that PAs are the most common first encounters for follow-up visits. Twentyeight percent of follow-up patients will see only the PA and exit, and 33% of patients will see a PA and then an MD. Less common paths include seeing an MD first then an MA, and seeing a RES first and then an MD. The path where a patient only sees a PA has a shorter average clinic time (45 minutes) than the other paths. The relationship between touch times and wait times for different follow-up encounter paths is as shown in Figure 9 below: Percentage.9.8.7.6.5.4.3.2. Wait Time (Non-Value Added) Touch Time (Value Added) Sample size of 94 patients, collected from 9/3/8 to /29/8 Figure 9: Follow-Up Patient Wait Time and Touch Time for Common Paths 7

Figure 9 shows that over 5% of the patient s time is spent waiting instead of seeing a physician. Additionally, the percentage of touch time versus the percentage of wait time is about the same regardless of the medical encounter path that patient follows. Consult Appointments The team analyzed the consult patient flow data by creating a value stream map and analyzing the wait time and touch time distributions as well as the medical encounter paths. Value Stream Map The value stream map for consult appointments is shown in Appendix F. Consult patients follow a process similar to follow-up patients (see Follow-Up Appointments: Value Stream Map on page 4). As shown in the map, 74% of the patients have two medical staff encounters and 25% of patients have three medical staff encounters. Like the follow-up value stream map, this value stream map shows the wait time in the lobby and the wait time between all encounters, as well as the overall process time mean for each medical encounter. The map further breaks down each medical staff encounter by showing the most frequently occurring medical providers that performed each encounter. The complete percentage breakdowns of medical encounters are shown in Table G. of Appendix G. The map also displays the mean process time for those medical providers at each encounter. Additional summary statistics for these providers are shown in Tables G. and G.2 of Appendix G. The total process time and wait time statistics for all staff encounters are shown in Table 7 below: Table 7: Consult Appointments Touch Time and Wait Time Distribution Touch Time Wait Time Time in Clinic Percent Touch Time (Value Added) Percent Wait Time (Non- Value Added) Best Case Mean :47 :48 :35 49% 5% Worst Case Mean :5 :57 2:2 53% 47% Weighted Mean :52 :5 :43 5% 5% Data Collected from 35 Patient Flow Forms, distributed from 9/3/8 to /29/8 Table 7 shows that Best Case patients, who have two medical encounters before leaving the clinic, spend 5% of their time waiting, Worst Case patients, who have three medical encounters before leaving the clinic, spend 47% of their time waiting, and all patients spend about 5% of their time waiting. In all cases, patients spend about half of their time waiting, regardless of the number of medical staff encounters they have. Additionally, the average Best Case patient spends hour 35 minutes in the clinic and the average Worst Case patient spends 2 hours 2 minutes in the clinic, yet consult appointments are only scheduled for one hour (except for one physician who schedules for.5 hours). The average touch time, however, is within the scheduled appointment length. Analysis of the consult wait time data revealed the distribution of wait time in the lobby and between each medical staff encounter. Since only 26% of patients stayed after the second 8

encounter for a third encounter, the pie chart was generated using a weighted average of mean wait times. Refer to Figure below. Encounter 3 Wait Time, 4.6% Lobby Wait Time, 25.8% Encounter 2 Wait Time, 39.7% Encounter Wait Time, 29.8% Data Collected from 35 Patient Flow Forms, distributed from 9/3/8 to /29/8 Figure : Distribution of Wait Time for Consult Patients Figure shows that the largest percentage of wait time occurs while a consult patient is waiting for the second medical encounter. The second largest percentage of wait time occurs while the patient is waiting for the first medical encounter. Wait Time and Touch Time Distributions Analysis of the consult Patient Flow Forms revealed the distributions of wait times between encounters and the touch times of each encounter. The data of consult encounter durations and wait time distributions are shown in Appendix H. Like the follow-up distributions, the consult distributions display times only up to the second medical staff encounter due to insufficient data for third encounters. Additionally, lobby wait is calculated using the minimum wait time between appointment time to time called to room and arrival time to time called to room. Furthermore, this lobby wait time includes the time the patient spent filling out the paperwork in the lobby. The 7 th percentile for each distribution in Appendix H revealed the following key points. 7% of consult patients: Waited in the lobby for under 5 minutes, with a maximum lobby wait time of 33 minutes Waited under 5 minutes in the exam room before the first medical encounter, with a large range of times extending to 5 minutes Had a first medical encounter duration of up to 25 minutes, with a maximum of 5 minutes 9

Waited under 25 minutes in the exam room for the second encounter, with a maximum wait time of 53 minutes Had a second medical encounter duration of up to 25 minutes, with a maximum of 43 minutes Medical Staff Encounter Paths The analysis from the consult Patient Flow Forms also revealed various medical staff encounter paths that consult patients experienced. Percentage occurrences for the most common paths and the touch time versus wait time of each path are displayed in Table 8. Table 8: Consult Encounter Path Touch Times and Wait Times Consult Paths RES to MD Other Path Frequency (%) 66% 34% Touch Time (min) 5 54 Wait Time (min) 54 39 Total Time (min) 5 93 Data Collected from 35 Patient Flow Forms, distributed from 9/3/8 to /29/8 Table 8 shows that 66% of consult patients see a RES and then see an MD before exiting the clinic. The other 34% include less common paths, such as seeing an MD or PA first. Table 8 also shows the values for touch time and wait time for both paths. It can be seen that the average total clinic time, wait time, and touch time is similar for both paths. The relationship between touch times and wait times for both consult encounter paths is as seen in Figure below: Percentage.9.8.7.6.5.4.3.2. Res --> MD (66%) Other Paths (34%) Sample size of 35 patients, collected from 9/3/8 to /29/8 Wait Time (Non-Value Added) Touch Time (Value Added) Figure : Consult Patient Wait Time and Touch Time for Common Paths 2

As shown in Figure, for the most common consult path (RES to MD), the patient wait time comprises over 5% of the patient s time in the clinic. The other paths also have a large wait time as well, where over 4% of the patient s time in the clinic is spent waiting. On-treatment Visits The value stream map for on-treatment visits is shown in Appendix I. For on-treatment visits, a patient arrives to the clinic from the treatment room area. Since these patients are already in the clinic, they do not check in with the clerk or wait in the lobby but instead immediately check in with the nurse and enter into an exam room. The patient then waits in the exam room, sees the physician, and exits the clinic. The touch time and wait time statistics are shown in Table 9 below: Table 9: On-Treatment Visits Mean Time Distribution Touch Time Wait Time Time in Clinic Percent Touch Time (Value Added) Percent Wait Time (Non- Value Added) :7 :5 :22 32% 68% Data Collected from physician time studies from 9/29/8 to /28/8, with 38 touch time data points and 7 wait time data points As shown in Table 9, 68% of a patient s time in the clinic is spent waiting. Patients wait on average 5 minutes to see a doctor for 7 minutes. Conclusions The above findings from the physician time studies and patient flow collection led to several conclusions about the current process in the Radiation Oncology Clinic. Improvement Opportunities in Physician Scheduling The findings from the physician time studies showed that less than half of a physician s day is spent on activities that involve touch time with a patient. Additionally, a high proportion of a physician s day is filled with unscheduled activities, mainly on-treatment visits. On-treatment visit patients, though unscheduled, arrive consistently throughout the day. Finally, the stratification by physician of the four main physician activities (on-treatment visits, office areas and physician workroom, consult appointments, and follow-up appointments) showed that a great deal of variability exists in activity time between physicians and within each physician. Bottleneck Areas in Patient Flow The patient flow findings showed that the wait time for consults, follow-ups, and on-treatment visits is too high relative to the touch time. The data also revealed that regardless of the medical encounter path taken by consult and follow-up patients, the percentage of patient wait time relative to total time in the clinic was about equal. Thus, long wait times exist for all patients regardless of medical encounter paths. 2

Additionally, the data revealed that both consult and follow-up patients are in the clinic much longer than the scheduled appointment length. The average touch time for consult and follow-up patients, however, is within the scheduled appointment length. Recommendations The team has generated the following recommendations for the Radiation Oncology Clinic to achieve the long-term goal of optimizing physician scheduling to reduce patient wait times, improve process efficiency, and allow physicians to devote more time to academia and research: Further investigate the distribution and duration of on-treatment visits throughout the day. To account for these unscheduled visits, physicians schedules need to be adjusted to allot the appropriate amount of time to these visits, without increasing the wait time of scheduled patients. The clinic should collect additional data to determine the frequency and length of on-treatment visits for each physician. Using this data, the clinic can add buffers to each physician s schedule throughout the day for these visits. Investigate how time is spent in the office areas and the physician workroom. While the physician time studies showed that almost half of the physicians day is spent in office areas and the physician workroom, the specific tasks that are occurring in these areas is unknown. By further investigating these tasks, the clinic can determine which of these office tasks can potentially be eliminated, and the clinic can determine how to better distribute the necessary tasks throughout the day to reduce patient wait time. Collect additional data to investigate the causes for high variability in appointment lengths within each physician as well as between the physicians. The team hypothesizes that the variability is most likely due to differing physician specialties as well as each physician s personal practice. After further investigating the causes for physician variation, the clinic can develop methods to standardize physician activity times and reduce variation within physicians. Even after these activities are standardized, though, activity times between physicians may still vary based on patient diagnosis and physician practice; therefore the clinic may also create schedules unique to each physician. When creating optimal physician schedules, the clinic should ensure that the length of time for which a patient is scheduled closely matches the time that the patient spends in the clinic. Additionally, the clinic should consider the physician touch time and variation for each appointment type, as well as the frequency and duration of unscheduled activities to appropriately place each patient appointment within physicians schedules. Prior to making changes in physicians schedules, and after the changes are in place, the clinic should distribute satisfaction surveys to the physicians to have a metric by which to measure the impact of the changes in physicians schedules. 22