Table of Contents Executive Summary 1.. Introduction and Background 1.1 Goals and Objectives 2. Description of Current System 2.1 Intake Process erification 2.2 Appointment Analysis 2.3 Telephone Management 2.4 Medical Assistance 2.5 Model Experimentation 3. Approach and Methodology 3.1 Intake Process erification 3.2 Appointment Analysis 3.3 Telephone Management 3.4 Medical Assistance 3.5 Model Experimentation 4. Findings and Conclusions 4.1 Intake Process erification 4.2 Appointment Analysis 4.2.1 Pediatrics Continuity Clinic 4.2.2 OB/GYN Clinic 4.2.3 Family Practice Clinic 4.3 Telephone Management 4.3.1 Telephone Memo Analysis 4.3.2 Telephone Time Study 4.4 Medical Assistance 4.5 Model Experimentation. 5. Recommendations 5.1 alidation of Simulation Model 5.2 Appointment Analysis 5.3 Telephone Management System 5.4 Medical Assistance Analysis 1 1 2 2 2 2 3 3 4 4 4 4 5 6 7 7 8 9 9 1 11 11 14 16 18 21 21 21 22 22 Appendices Appendix A: SimulationModel Flow Chart Appendix B: Check-In I Paperwork Flow Chart Appendix C: Telephone Management Flow Chart Appendix D: Model Experimentation Output Appendix E: Check-In / Paperwork Raw Data Appendix F: Appointment Analysis Raw Data Appendix G: Medical Assistant Time Study Form Appendix H: Sample of CLS Scheduling Data Appendix I: Phone Call Distribution Graphs Appendix. 3: Phone Call Time Study Data Appendix K: Medical Assistance Time Study Raw Data
Executive Summary The purpose of this project is to extend, refine, and modify the current baseline simulation model for the development of clinic operations in the East Medical Campus. Kodak has worked with the Program and Operations Analysis Department at the University of Michigan Medical Center to develop the current simulation model through the use of MedModel software. The data used in the current simulation was provided by the Program and Operations Analysis Department. Data was collected from the medical center s computer information system and time studies performed on various processes. Due to time constraints on obtaining data for use in the baseline simulation, estimates were used for some processes, while other processes were not incorporated into the model at all. In our efforts to refine, extend, and modify the current model, we collected and analyzed data on the processes of patient intake, MA assistance, and telephone management. To collect data on these processes, times studies were performed by the project team and clinical staff, data was tabulated from the clinic scheduling system, and clinic memos were reviewed. In addition, interviews were conducted to confirm the results of our analysis of the data collected. Upon completion of the data collection, a detailed statistical analysis was performed for each process. From our analysis we conclude the following: the distribution of the time associated with the patient intake process is not uniform unscheduled MA assistance occurs infrequently as a percentage of the number of appointments the time a nurse spends on the phone is small in comparison to the amount of time required for phone call related paperwork and research there are substantial differences between patient no show rates and appointment type distributions by clinic a change in the distribution of the intake function noticeably affects the wait time and length of stay of a patient, while having little impact on provider utilization The need for unscheduled MA assistance varied by clinic. If the utilization data is to be folded into the model, the differences between clinics should be included. However, because of the infrequent need for unscheduled MA assistance, including this utilization in the simulation model may not be necessary. A direct transfer approach to phone calls is a potential way to improve the current system. Instead of the clerk making a memo for the nurse to return pertinentcalls, it would be feasible for the nurse to answer the call directly. Access to patient information through computerization would decrease the time spent on paperwork and research. Modification of one process distribution in the simulation model noticeably affected the performance of the clinic. Work needs to be done to continue refining the process time distributions so that the model results adequately reflect real life. Differences between clinics need to be considered when simulating each clinic s operations. Our analysis of scheduling data showed a strong distinction between the percentages of appointment types for different providers in the clinics we examined. In addition, we found patient no show rates vary by clinic. If these differences have a strong impact on clinical operations, they need to be accounted for in the simulation model.
The main issues regarding the use of simulation are the accuracy with which the model pomays actual operations and the level of complexity used in creating the model. By collecting data in great detail on every process, operations can be represented with a high level of precision. This level of detail, however, becomes exnaordinarily me consuming. For practical purposes and because of the cost associated with such data collection efforts. limitations must be placed on the level of detail incorporated into the simulation while still maintaining an accurate representation of the current system. Furthermore, the output of the simulation should be checked to ensure that it accurately estimates the current results of the system. Once the simulation model accurately reflects operations. modifications can then be made to the simulation to investigate ways to improve operations before actually altering the current system.
1. Introduction The purpose of this project is to extend and refine the base simulation model for the East Medical Campus. Upon its completion, the East Medical Campus will be home to several University of Michigan Medical Center clinics. The computer model is a tool used to simulate the operations in these clinics involving patients, employees, and paperwork before they are opened. MedModel software allows the user to visually track the flow of these entities while running the program. Once the model is run, the software can output results of key variables that serve as measures of clinical efficiency and customer service times. The Program and Operations Analysis Department will use this computer model to evaluate and improve existing and future clinic operations. 1.1 Goals and Objectives In our project, we performed analyses and experiments to help move the simulation model forward for the inclusion of other clinics and necessary employee functions. The simulation model will help the Programs and Operations Analysis Department obtain three maingoals: improve internal processes of clinical operations reduce patient wait time and length of stay increase employee utilization During the process of completing our project we have accomplished the following objectives: erification of current process distributions for the intake process Collection and analysis of telephone management and medical assistance data for incorporation into the current model Model experimentation upon modification of the current model U i
medical advice the attending patient calls. the 2. Description of Current Systems Currently, the University of Michigan Hospital is using the simulation package, MedModel, as developed by Eastman Kodak Company. The Eastman Kodak Company and the Program and Operations Analysis Department devised a flow chart, shown in Appendix A, for the clinic operations of the U of M East Campus Internal Medicine Clinic. The time intervals for the different functions were either estimated or studied in Internal Medicine with the assumption that many process rates remain constant throughout the clinics. 2.1 Intake Process erification The only area of our study that is currently included in the simulation model is the intake process. One part of this function is the escort wait time or paper work time. The time interval that defines this function is the time a clerk begins a patient s registration and chart work until the clerk places the chart in a bin as ready for a medical assistant. Currently, one.clerk works at the check-in desk to greet patients and prepare their charts and paperwork for the medical assistants. At times when the check-in desk was quite busy, another clerk would put aside her work and assist with the check-in process. A flowchart of this process is located in Appendix B. The range of times used in the current model is a uniform distribution from 3.5 to 5. minutes. This interval is an estimate established by the.internal Medicine Clinic, not an actual time study. A time study was needed to modify the current distribution. Once we completed the time study analysis, the data can be used to modify the current simulation. Upon modification, the simulation results will be compared. 2.2 Appointment Analysis Distributions of appointment lengths and provider types of the current system needed to be developed for other clinics in the East Medical Campus. Distributions of appointment times and provider types are an important piece in the simulation logic. The distribution of this data is the eighth step in the simulation flow chart (see Appendix A) and is denoted by the percentage of doctors versus nurse practitioners that see patients for 15, 3, or 45 minute appointments. 2.3 Telephone Management One area of our project that are not currently included in the simulation model is the telephone management system used by nurses in the clinics. Prior to our study, statistical data did not exist on the length and frequency of calls to and from clinics. The data collected and analyzed on these processes will be used in further modifications of the current simulation. In general, clerks answer all incoming telephone calls to the clinic. At that time, the clerk is either able to answer the patient s question easily, such as a request for appointment time, or must fill out a form for a nurse s consultation or return call. In some cases, when doctors are available, they will attend to some of the necessary calls. Normally, two nurses are scheduled for this clinic each day. The nurses number one priority is attending to patient needs and their second priority is to When a nurse is available, they will return calls from phone memos in the Nurse Triage area. Nurses return patient calls according to a specified prioritization. For instance, nurses will return a patient call for before placing a call for request of immunization forms. See Appendix C for a detailed flowchart of the telephone management process.... 2
the MAs tasks. The frequency and duration of these instances needed to be collected. needed. A doctor may request an MA for the entire length or part of an exam for Currently, the doctors at the Northeast Ann Arbor can request an MA s assistance as model Times when an MA is requested for unscheduled assistance is not incorporated in.3 predicted, and actual performance. As stated previously, some of the current intervals are As the model is currently defined, a gap in clinic operations exists between desired, estimated rather than studied and two essential functions of the clinic, telephone management and MA assistance, are not included in the current model. After collecting data for the current processes described above, modification of the simulation and experimentation with the model will provide the Program and Operations Analysis Department with the ability to determine the value of applying simulation to existing and future clinic operations. 2.5 Model Experimentation assistance, as a chaperone, or to finish any procedure. Before our study there was no data that determined the length, frequency, or probability of such a request. The objective is to incorporate the utilization of MAs for assistance in the current simulation. A full description of medical assistants utilization is not included in the current simulation 2.4 Medical Assistance
(, 3. Approach and Methodology 3.1 Intake Process erification Teresza Poe, Patient Representative at Northeast Ann Arbor Clinic, gave our group a tour of the clinic and explained the check-in process in the registration are& Teresza clarified the steps a clerk takes to prepare a chart once the patient has arrived to the clinic. A data collection form was developed to measure the time a patient is greeted by a registration clerk and asked for insurance verification until the time the patient s chart is prepared and placed on a rack to be picked up by a medical assistant or nurse. According to Lisa Cayen, it is expected that the time interval being measured should be less than the 3.5 to 5 minutes designated in the current model. We decided that the time interval should be measured with an accuracy of plus or minus 5 seconds. Since patients continually arrive to the clinic, a continuous time study performed by team members was determined to be the most accurate way to collect data. We collected data for approximately 12 hours during which data was obtained for about 12 patients. The intake process data was analyzed by entering data into an Excel spreadsheet and calculating statistical measures such as the mean and standard deviation. All data was pooled together in the statistical analysis. Since team members collected data in 4 hour time blocks, statistical hypothesis tests were performed to verify that the means associated with each time block were not statistically different. Lisa Cayen and Teresza Poe both indicated that appointments are scheduled constantly through the day on Monday through Friday. Since patients are continually arriving in the clinic, we did not analyze the data by day of the week or time of the day. A histogram of the data was plotted to study the distribution of patient wait time during the check-in process. The cumulative distribution function of the check-in process was modified in the current simulation. Model experimentation was then performed and discussed in further detail later in this report. 3.2 Appointment Analysis Data regarding appointment length and provider type data was collected from the Medical Center s CLS scheduling system for the Northeast Ann Arbor Clinic. After meeting with Lisa Cayen and obtaining access to the CLS system, we printed reports for appointments occurring between July 17, 1995 to July 31, 1995 at Briarwood Family Practice, Pediatric Continuity Clinic, and OB/GYN. Appointments where patients arrived and appointments in which the patient did not show up (no shows) were tracked. The sum of these appointments equals the number of appointments scheduled over this time period. Reschedules and cancellations were not included because the appointment slots could be filled which would result in an appointment. The distribution of appointments lengths by provider type was determined after calculating the percentage of appointment intervals in each clinic. An example of the CLS system output is included in Appendix H. 3.3 Telephone Management Telephone memo data sheets were obtained from the Pediatric Continuity Clinic. If a clerk answered a telephone call in which assistance was needed by a doctor or nurse, then the clerk completed a telephone memo sheet so that the call could be returned later by the appropriate employee (an RN or MD). The data sheets were collected from June 1, 1995 to October 4, 1995, and Sue Nebring, Nurse Manager, estimated that the data sheets capture 9% of the total calls that resulted in a written memo. We met with Sue Nehring, Nurse Manager, to discuss appropriate categories by which to analyze the phone calls. From our observations and her knowledge of the telephone process, we chose the following categories for the phone call subject matter: 4
Medical Advice General medical advice Schedule an appointment Advice which resulted in sending patient to ER Referrals Prescriptions Forms (sent, faxed, or picked up by patient) Immunization forms Crippled Children s forms Other forms Calls from people other than parents (e.g. Homecare, schools) Calls on how to access telephone system Test Results With Ms. Nehring, we also discussed the procedure to be used to obtain data on the length and category of return telephone calls. A time study was determined to be appropriate. Data from approximately 2, telephone memo data sheets was entered into an Excel Spreadsheet. The data was analyzed by frequency and statistics in the following subgroups: Day of the week (e.g. Monday, Tuesday...) Time of the day by 1 hour intervals (e.g. 9: am to 1: am) Time of day by day of the week in 1 hour intervals Category of call (e.g. prescriptions, medical advice, referrals...) Number of return calls needed (e.g. phone busy, no answer Not all memos contained complete information so the sample sizes for each subgroup used in the analysis vary slightly. Additionally, unless written on the memo, it was assumed that only one call was need to reach the patient. A time study was performed in the Pediatric Continuity Clinic under the supervision of Sue Nehring to capture times involved with the return of patient calls. Because it would be very inconvenient for staff to collect phone call lengths, we collected the data ourselves. Phone calls are typically only a few minutes long, therefore accuracy is important. Due to the complexity of the process surrounding the response to a call, a time study did not provide all the necessary information. More importantly, the only time captured by this study was the actual time spent on the phone. After further investigation and suggestions from Sue Nehring, we learned that the response time of returning calls should also include the paperwork and research done both before and after a call is made and any additional calls necessary to respond to the initial call. A nurse may work on multiple memos at once or leave the reception area to work on memos, so it is very difficult to collect accurate measures of each activity associated with a single patient. Knowing how many memos were responded to and including all the time spent to complete the response process, we calculated the average total response time per memo. 3.4 Medical Assistance A time study of the MA assistance process at the North East Ann Arbor Clinic was conducted under the supervision of Nancy Eggerer, Nurse Manager. The data needed to analyze the time for medical assistance was collected by the medical assistants (MAs) themselves. We decided data could be collected more efficiently if the MAs complete a daily assistance form. TI we collected data by monitoring the MAs, then only one MA could be monitored at a time because they assist throughout all of the clinic rooms. The 5
MAs have other tasks to complete, so the majority of their day consists of activities other than providing assistance. Since the assistance time is expected to be greater than five minutes, accuracy down to the second is not necessary, so a MA recording the time to the nearest minute will be acceptable. Each MA attached a daily data form attached to a clipboard equipped with a stop watch. An example of the data collection form can be found in Appendix G. The following information was collected from the form: Time assistance begins Completion time of the assistance The clinic in which the assist was needed Whether a doctor was present during the assist When the doctor left if present A data sheet was given to every medical assistant each day on which they recorded this information daily for one week from November 15, 1995 to November 22, 1995. To determine the frequency of MA requests, the number of appointments per clinic during this time period was derived from the CLS scheduling system and then divided into the number of MA requests per clinic to give the frequency of MA requests by clinic. 3.5 Model Experimentation The simulation model is run on an IBM computer in the Program and Operations Analysis department. To familiarize ourselves with the MedModel software, we met with Roy More who walked us through the current simulation and suggested that we work with the tutorials available in MedModel. The MedModel tutorials introduced us to the details needed to build a model and output results. After completing the MedModel tutorials, we began working to understand the current model s objects and code. Joseph Kent, the programmer from Kodak who developed the model, was made available over the phone to answer any detailed questions we had about the model. As we began to understand how the model worked, we ran initial experiments to introduce ourselves to the various results the model can track and output. Working with the manuals to determine the location of defined distributions, we altered the intake process time distribution to reflect the data we found and saved the program as a new model. Now we could run the old model and the revised model and compare the results of each. After defining the variables that we wanted to track, the appropriate changes were made to both models so that statistics would be maintained for these values. We ran fifty replications of each model and tracked fifteen different variables related to patient wait time and employee utilization rates. The results of this experimentation are included in the Findings and Conclusions section, and the raw data can be found in Appendix D..6
for each data set are summarized in Table 1. The mean and standard deviation are shown for each individual trial and then for the data grouped as a whole. The overall mean was determined as 3. minutes. The standard deviation was quite large at +1-1.5 minutes. The relatively large variance can be attributed to the variety of forms that needed to be processed for different appointment types. Table 1. Statistics of Escort Wait Time Study 4.1 Intake Process erification The escort wait time study determined the distribution of the length of time from a patient handing the clerk his insurance card until the patient s chart was completed for the MA. The raw data sheets for the escort wait time study are available in Appendix E. The results found that the means of the three sets of data were not significantly different. Once the data appeared to be statistically consistent, the histogram for the length of wait Table 2. Results of Mean Hypothesis Test was graphed as shown in Figure 1. This figure shows the distribution of paperwork process times. The distribution is skewed to the right with the average patient waiting 3. minutes. The minimum patient wait time was 4 seconds; while the maximum was 7 minutes and 45 seconds. The cumulative distribution function is also plotted in Figure 1. Table 3 outputs this cumulative distribution numerically. In the simulation model, this cumulative distribution function is how we defined the modified process time. 2&3 1.5 Accept I 2 1.68 Accept I 8 3.425 Accept, Data Sets to Result: of collection, a hypothesis test was conducted on the means. In performing the test, we did In order to assume that the data from the three trials were not dependent on the day or time a t-test using an alpha of.5. For the hypothesis that the means are equal to be rejected, the t-value must be greater than 2.. As shown by the summary of the test in Table 2, we 2 41 3.4 1.8 7 ( total 118 3 1.5 3 42 2.9 1.1 1 35 2.7 1.5 Data Set Count Mean(min) Std Deu(min) 4. Findings and Conclusions
Figure 1. Distribution Function of Paperwork Times The CLS data was tabulated and the results are displayed in the following tables. The 4.2 Appointment Analysis results are displayed in the following sections according to individual clinics. The raw number data can be found.in Appendix F. 4/os + t. q Aol -j- 8% +3% 18 t7 C nli I 16%. 9% T.5 % 1. 2% 1.5 15% 2. 31% 2.5 46% 3..6% 3.5 69% 4. 77% 4.5 83% 5. 88% 5.5 92% 6. 96% 6.5 98% 7. 98% 7.5 99% 8. 1% Table 3. Cumulative Distribution Function for Escort Wait Times Escort Wait Time (mm) Cumulative % o 6t Time (min:sec) o - - CM J C ) C ) IC) U) CO CD t- r. o a a a a a a o o a a o o..- CM CM C ) C ) IC) U) CO CO I- t. o C ) C ) C ) C ) C ) C ) C ) C ) % -I % 2%1-11j +1% t2 () E 8%t ±Ano, 4.. 1 :I LI I -I- rio! I 1 T /e I I t5% Cu fr I r iml C) C) C) C) C) C) C) ) C) ) C) C) C) C) C) C) CM L() CM if) CM In CM if) CM IC) CM if) CM U) CM IC) 8
4.2.1 Pediatrics Continuity Clinic Results from the Pediatrics Continuity Clinic are based on 374 appointments for residents and 189 appointments for faculty. Table 4. Arrivals and No-Shows by Provider Type in Peds Continuity Clinic Provider Type Appointment Residents Faculty Length % Arrivals % No Shows % Arrivals % No Shows 15 mm 77% 23% 87% 13% 3 mm 8% 2% 85% 15% Table 4 suggests that residents and faculty experience similar arrival and no show rates for both 15 minute and 3 minute appointments. In all cases, the average percent of arrivals is about 82 percent. Table 5. Appointment Lengths by Provider Type in Peds Continuity Clinic Provider Type Residents Faculty %ofl5min appointments 86% 16% %of3min appointments 14% 84% Table 5 shows that residents have a much higher proportion of 15 minute appointments than faculty providers in the Pediatrics Continuity Clinic. Residents are over 5 times more likely to have a 15 minute appointment than faculty. 4.2.2 OB/GYN Clinic Results from the OB/GYN clinic are based on 132 appointments for nurse practitioners and 35 appointments for MDs. Table 6. Arrivals and No-Shows by Provider Type in OB/GYN Clinic Provider Type Appointment Nurse Prnctitioner MD Length % Arrivals % No Shows % Arrivals %No Shows 15 mm 93% 7% 98% 3% 3mm 1% % 93% 7% 45 mm 1% % 1% % The data for the OB/GYN clinic suggests that nurse practitioners and MDs experience similar arrival and no show rates for 15 minute, 3 minute, and 45 minute appointments. Table 6 shows a high percentage of arrivals for all appointment types. 9
Provider Type appointments 77% 69% 1 : %oflsmin %ofómin. % % % % of 45 mm %of3min 1% %of45min % of 3 mm %of2min NP MD Table 7. Appointment Lengths by Provider Type in OB/GYN Clinic C.. : appointments % 2% % appointments % 6% % appointments 51% % 22% appointments 49% 64% 78% % of 15 mm NP MD DO Table 9. Appointment Lengths by Provider Type in Family Practice Clinic Provider Type minute, and 6 minute appointments. percent of the time. MDs are the only provider type to offer 2 minute, 4 minute, 45 Table 8 shows a high rate of arrivals for all appointments lengths and provider types in the Family Practice clinic. In all cases, patients arrived for scheduled appointments over 97 6 mm 1% % 3 mm 98% 2% 97% 3% 1% % 2mm 98% 2% 45 mm 1% % 15 mm 1% % 98% 2% 1% % Length % Arrivals % No Show % Arrivals % No Show % Arrivals % No Show 4 mm 1% % Appointment Nurse Pmcthioner MD DO Table 8. Arrivals and No-Shows by Provider Type in Family Practice Clinic Provider Type 721 appointments for MDs, and 9 appointments for DOs. 4.2.3 Family Practice Clinic Results for the Family Practice clinic are based on 78 appointments for nurse practitioners, %of4min.... 1% % and MDs in the OB/GYN clinic. Table 7 suggests that all appointment types are divided evenly between nurse practitioners appointments 21% 28% appointments 2% 3%
It appears from Table 9 that there is an even distribution of 15 minute and 3 minute appointments for nurse practitioners. Both types of doctors, MDs and DOs, have a higher percentage of 15 minute appointments than 3 minute appointments. 4.3 Telephone Management 4.3.1 Telephone Memo Analysis Figure 2 shows the distribution of telephone calls by day of the week. Incoming calls are most frequent on Mondays where the number of phone calls is about 5% higher than the rest of the weekdays. The number of incoming calls is quite constant from Tuesday to Friday. There are very few incoming calls on Saturday, since the clinic does not have regular hours on the weekend. (I) Cu C-) II 25% 28% 15% 18% 5% % S Ion Tue Wed Thu Fri Sat count= 1768 Figure 2. Incoming Calls by Day of the Week Day of the Week 18% 98% 8% 78% -6% -5% -48% -3% -2% 18% -% a) > 4-. E C.) The following table displays the average and standard deviation of the number of calls per weekday. About 18 weeks of data was available for analysis. In order to compensate for the estimated 1 percent of missing memo sheets, the last column in the table represents the approximate average number of calls per day assuming 1 percent of the data was available. Table 1. Summary of Weekda Statistics of Incoming Calls Day AG Calls/Day Standard Deviation Count Max Mm AG 1% Monday 22.9 7.9 17 35 6 25.2 Tuesday 19.6 5.3 17 28 13 21.5 Wednesday 18.8 5. 18 31 1 2.7 Thursday 18.4 5.9 18 32 1 2.3 Friday 18.4 5.9 18 29 1 2.3 11
:: Figure 3 shows the distribution of incoming telephone calls by the time of the day. The clinic is open from 8: am to 6: pm. Incoming telephone calls are the most frequent between 8: and 9: am, and then tend to decrease in frequency as the day progresses. 25% -- 1% 9% 2% 8% - ioio 15% 6% 5% 1% 4% %E 3 Time (hr:min) count= 1571 Figure 3. Distribution of Incoming Calls by Time of Day Table 11 summarizes the statistics associated with the average number of calls per day for one hour intervals. Again, in the last column of the table we estimated the average number of calls per day if a complete set of data was available. Since the data is estimated to include 9 percent of all telephone memos passed on to doctors or nurses, we added 1 percent extra to the calculated average to account for the missing data.. Table 11. Phone Calls Per Day By One Hour Intervals Time of Day AG Calls/Day Standard Devianon Count Max Mm AG + 1 7:-7:59..2 87 2 8:-8:59 3.6 2.3 87 11 4. 9:-9:59 2.8 1.9 87 8 3.1 1:-1:59 2.4 1.8 87 6 2.7 11:-11:59 2. 1.6 87 8 2.2 12:-12:59 1.6 1.6 87 6 1.7 13:-13:59 1.6 1.4 87 6 1.7 14:-14:59 1.4 1. 87 4 1.5 15:-15:59 1.3 1.2 87 5 1.5 16:-16:59.9.9 87 3 1. 17:-17:59.1.3 87 1.1 12 -.-
13 C of the following graphs shows a similar distribution, we can generalize the distribution of physician. count= 1852 - ensure that the separate daily distributions are similar to the overall distribution. Since each calls by time of day for all weekdays by the overall distribution. Because this distribution distribution to reduce complexity in the model. Although the above five graphs are not time of day into each weekday. It is necessary to compare the disthbution of each day to the assumption that the distribution of telephone calls throughout the day does not depend will be added into the simulation at a later date, it is helpful to have an accurate general on the day of the week. identical, they each show a pattern of a high frequency of incoming calls in the early morning with a gradual decrease of calls throughout the rest of the day. Because of this Figure 4 shows the distribution of the number of return calls needed to get in contact with first return call. However, many times the doctor or nurse returning the calls must make multiple phone calls in order to reach the patient. In these cases, either an answering machine picked up, no one answered the telephone, the telephone was busy, or the patient similarity, we feel comfortable recommending that the simulation model be modified with was not available to speak. Figure 4. Distribution of the Number of Return Calls Needed to Reach the Patient Figure 5 shows the distribution of the content of the incoming calls. The most common reasons for an incoming call are general medical advise and making an appointment to see a # of Return Calls 1 2 3 4 5 6 % 2%.1- the patient. About 9 percent of the time, the patient is home and answers the phone on the In Appendix I, there are figures which break down the distribution of incoming calls by 1% 1% 4% 2% C.) 3%.1-I 7%. I I I - I % 4 o 3% - 4% - C 5% - C a) 6% -.2! 7% 8% - 9% - 6%.? 5% ca T 1% + 9%
75th %ile 4:25 99th %ile 11: 25th%ile 1: max 11: mm :15 standard deviation 2:55 mean 3:1 Table 12. Statistics for Nurse Return Call Length (min:sec) 5% % E 15% 1% 3 25% 2% 35% 4% 4% 1% 2% 14 Figure 5. Distribution of Reasons for Incoming Calls variance is a result of the wide variety of phone calls the nurse returns. Some of the calls count = 1793 minutes). 4.3.2 Telephone Time Study The time study at the Pediatric Continuity Clinic resulted in several sets of data. The first Table 12 lists the notable results for the time study. The mean was found to be 3 minutes set of data collected was the length of time the nurse spent actually talking on the phone. and 1 seconds, but the standard deviation is large at 2 minutes 55 seconds. This large were simply setting up an appointment for a patient (as shown by a minimum length of one minute). Other calls, however, were more involved such as giving advice for a sick child or explaining a diagnosis to a worried parent (as shown by the maximum length of 11 C...=, C C. C C C S % 1% 9% 8% 7% 6% 5% 3% c C. 3 E C.., S S E 3- o 3- w Q 3.- w 3- e5 (3 3-1 C Reason for Call -3 o C =.3.3. w S
The distribution of this data is shown in Figure 6. Over 5% of the calls lasted under two minutes, however, the disthbution has a long tail accounting for the large variance. The each memo (phone time plus preparation and/or ap up time). To find this data we cumulative distribution function is also shown in Figure J. This distribution follows the estimated the length of actual time the nurse spent on phone related activities and divided it data by rising quickly and then leveling off for the majority of the intervals. The second value evaluated by this time study was the length of time the nurse spends on Table 13. Results of Time Study of Phone Related Work 11 the four data sets are shown in Table 13. Data sets 1, 3, and 4 have times per memo that by the total number of memos completed during the period studied. The results for each of are extremely close in range. Data set 2 may be different due to differences in the rate of 1% 3% 25% 2% 4 4: 2:11 11 11:54 Total 14: 8:6 47 1:18 15 3 3: 2:1 11 11:48 2 3:3 2:15 18 7:3 = N = = = I( In I5 a a a %.8% 1.% 5% 15% 1.8% 9.% 8.8% 7.% 6.% 5.8% 3.% 2.% Figure 6. Length of Nurse Return Calls N a a i i i i I i 1 3:3 1:3 7 12:54 (Iu:min) Phone (hr:min). (min:sec) Data Set Length of Time Study Time Spent on # of Memos Avg. Time per Memo Time (min:sec). á a t4 Lfl Lfl In In In In In a a a a a a a a a a average length of time a nurse takes to work on a memo is around 1 - minutes. incoming calls or employee variation. Overall, in 14 hours of study, we found that the _ -. N I I I 4.% =
5% 15% 1% 35% 3% 4% 16 25% 2% C, C, C, WE 45%- Pediatric Clinic or Med-Peds Clinic. We were concerned about the small sample size of the Figure 7 shows the distribution of MA assists by clinic. Raw data for the MA time study is located in Appendix K. For the week data was collected, no assists were performed in the data collection because we expected a higher number of assists. After meeting with Nancy themselves. Eggerer, she felt confident that the MAs collected accurate data and recorded all assists during the week. She was not alarmed by the small sample set and confirmed that the data Figure 7. Distribution of Medical Assists by Clinic count = 18 Clinic O8/GN Family 1% 2% 3% = 9% 8% 4.4 Medical Assistance Practice Peds Med P eds 4% - represented actual needs for the week. During the week, providers were present with the MAs during 72 percent of the assists, while the MAs performed 28 percent of the assists by 5% g
Date - Practice - Family OB/GYN Total 17 Time for flssist (min:sec) C = I 1W 3% 1 2 1 Assistance 15% 1%.% 5% 25% 2% 5.% 1.% 35%. 9.% 6.% 3.% 2.% E = 4.% I 4% IM 1.% 8.% 7.% mean =9 standard deviation =7 Figure 8. Distribution of Medical Assist Times count=18 %- H C, C, C, Figure 8 shows the disthbution of MA assist times. All of the assists are under 2 minutes except for the one assist time of 35 minutes. % of Appts. Requiring 2% 4% 6% 4% need for unscheduled MA assistance was low. utilized MAs for unscheduled assistance more than the other clinics. However, the, overall In Table 14 the daily frequency of each clinic s MA assists is shown. The OBIGYN clinic 2-Nov 2 3 5 21-Nov 4 4 22-Nov 1 Total Appts. for Week 129 227 111 467 * LI, C. LI, La C LI, 17-Nov 2 2 16-Nov 1 5 6 Table 14. Freouency of Daily Medical Assists by Clinic C. LI, C LI, 1
Internal Medicine 14 9 8 35 5 OB/GYN 8 2 7 12 5 18 any number in the specified range had an equal likelihood of being picked as the process time. After gathering data and preparing a cumulative distribution function of intake program is to defined data distributions. The old model used a uniform distribution 4.5 Model Experimentation Overall 9 7 18 35 3 Family Practice 3 3 5 3 Clinic AG Time (mm) St. Dev. (mm) Count Max (miii) Mm (mit Table 15. Statistical Analysis of Assist Time by Clinic Experimenting with the modified simulation model allowed us to examine how sensitive the between 3.5 and 5 minutes to define the intake process function. With this disthbution, process times, we defined a new distribution and used it in the modified simulation model. Figure 9 shows the differences in average patient length of stay depending on appointment Figure 9. Comparison of Average Lengths of Stay by Appointment Types The new distribution had a higher likelihood of lower process time, so we would expect results of 5 replications (of 12 hour days) are shown below in Figures 9, 1, and 11. This new distribution was defined as a continuous function with a mean 3. minutes. The length times. Detailed results of the simulation dat4 can be found in Appendix D. lower wait times in the new model. The results of the model experimentation verify this Table 15 summarizes the average assist time for each clinic and the associated variance. hypothesis. Every appointment type experienced a shorter length of stay in the modified DOId Distribution Modified Distribution a ci) c Cl) 4 6 C) 2 -J 3 - Appointment Type 15 mm 3 mm 45 mm 4.1 39.4 379
19 The results of patient length of stay are very close regardless of appointment type. This concerned us and needs to be examined. After reviewing the simulation flow chart (see Appendix A) we found some possible reasons for the similarities of results. In the model, second encounter, respectively. These second encounters average.1 minutes for 15 minute appointments, and 28.1 minutes for 3 minute appointments. This likelihood and appointments that doesn t occur in 45 minute appointments. Also, while the mean of duration of second encounters adds additional time to 15 minute and 3 minute necessary provider time in 45 minute appointments is higher than the other appointments, likelihood for the provider process time to be very low. Figure 1 shows the statistical results for other variables concerning patient wait time. This figure shows a comparison of values for Average Wait for Provider, Average Waiting Room Time, Max Wait Time for Provider, and Max Waiting Room Time. distribution than in the old distribution. With the modified model, the Average Walt Time Figuie 1. Có pnof Average Waiting Times for Provider decreased 11.7 percent, the Average Waiting Room Time decreased 14.1 percent, the Max Wait Time for Provider decreased 1.8 percent, and the Max Waiting Room Time decreased 6.6 percent. Provider for Waiting Time Avg Wait Max. - 1 E3 C) 4 9 8 5.8 percent. 7 6 2 model. The average decrease in length of stay for the three appointment types was about 45 minute appointment types never require a second encounter during their stay. 15 minute and 3 minute appointment types have an 8 percent and 6 percent likelihood of requiring a the distribution is skewed further to the left than the other distributions. This results in a Again, the average values of these variables are consistently lower in the modified c Old Distribution Modified Distribution Time 5 IRoom Avg Room Provider Waiting for Max Wait 8.5 7.3 I I 77.5 761
same. The confidence intervals for these variables (see Appendix D) are quite similar for the two models providing evidence that the differences in results are not very significant. 1 L This figure shows the average percent utilization for each provider in the two models. The affect of the modified disiribution on employee utilization rates are displayed in Figure Percent utilization is defined as the amount of time that workers are attending to work. Figure 11. Comparison of Utilization of Providers utilization of Providers 1, 2, and 7 rose slightly, the percent utilization of Provider 4 decreased slightly, and those of the nurse practitioner and Provider 6 basically remained the It appears that the modified disthbution doesn t have as significant an effect on employee utilization rates as it does on variables considering patient wait times. While the percent z I. 2 [OId Distribution Modified Distribution % 4%- 2%- 6%- 7% 9% 86% 88% 1 % t/ I / = 3%-.2 5%- 8% as Provider 5, and Provider 3 is not scheduled for the day of the week we examined. Providers 1,2, 5, 6, and 7 are doctors. In the simulation, the nurse practitioner is defined > I.-. ci) > > c. J L. ci) > C Co ci) > I. ci)
21 Experiments with the MedModel software showed that the simulation was quite sensitive to data distributions in the model. We found that small changes in a single process time distribution had significant effects on several key variables. To validate the use of the simulation model, the model must first reflect the results experienced in real life. Then the model can be used to correctly estimate the impact of different changes. In order for the distributions are based simply on estimations. While these estimations may provide general adequately reflect real life. One reason we feel that the current results inadequately estimate real life was the small and disadvantages of overbooking must first be considered before implementing such a Once the model is an accurate simulation of actual clinical processes, it will be a useful tool to determine actual patient lengths of stay to compare real life values with the simulation model to approach the actual results experienced in different clinics, close attention must be paid to the data distributions used in the model. In the current model, some key guidelines for process times, they may result in output statistics that are unrealistic. In order for the simulation to be a valuable tool, it needs to adequately represent real life. Process time distributions need to be continuously refined so that the model results difference in patient length of stay for varying appointment types. Studies should be done estimations. If there is a large disparity between the sets of numbers, the model needs to be modified to account for the differences. utilization rates. Then steps could be taken in real life to adjust the process distribution that was changed in the model. Also, the model could be used to reflect the results of changes will only be a reliable and valuable tool if it can show estimates that currently are indicative The results of the scheduling data showed differences in scheduling policies and arrival sensitive to data distributions, assumptions that different clinics experience the same of real life results. More work is needed to get the model to that point. in staffing plans such as replacing a doctor with a nurse practitioner. However, the model 5.1 alidation of Simulation Model for improving current systems. For instance, if a current problem in a clinic was low 5.2 Appointment Analysis provider utilization, relevant distributions could be changed in the model to increase the rates in different clinics. TI these clinics are to be modeled with the simulation, it is with the implications of the assumptions that are made. important that these differences are reflected in the model. Since the model is quite process time distributions may lead to inaccurate result estimations. If the same distributions are to be used in different clinics, the simulation user must be comfortable The scheduling procedure for residents and faculty needs to be examined in the Pediatrics residents need to understand how they are being utilized. provider according to the results we tabulated from the scheduling data. The advantages Continuity Clinic. The results of this study should be compared with scheduling guidelines used in the clinic. There should be strong reasons for the disparity of appointment lengths by provider type if such a difference in scheduling rates is acceptable. Faculty and The rates of no shows that we determined could be incorporated in the scheduling system. For instance, the Pediatrics Continuity Clinic could overbook their appointment types by change. Other clinics, such as OB/GYN and Family Practice, should be careful in 5. Recommendations
The telephone management study at the Pediatrics Continuity Clinic showed that the and other functions before calling the next patient. A direct transfer system would require A direct transfer approach to telephone management may lead to some possible problems, the nurse to put aside what they are doing to respond to the patient s call immediately. This the calls they are required to return. Some current categories of calls don t require a in the Northeast Ann Arbor Clinic. Because of the infrequent need for unscheduled MA to make an informed decision. Another problem with this system would be the problem of 5.3 Telephone Management System incorporated in the simulation model arrival rates for the individual clinics. One way to potentially improve the current system would be to implement more of a direct overbooking because of the low rate of no shows. These rates of no shows should also be amount of time nurses spend on the phone is small compared to the paperwork time associated with a call. If the paperwork time could be reduced, the nurses could respond to patient s needs much more effectively. transfer approach to phone calls. Instead of the clerk making a memo for the nurse to return pertinent calls, it would be quite feasible for the nurse to answer the call directly. This is possible because the nurse s phone line is rarely in use. A direct transfer approach might be successful if the time required for nurses to document necessary information could be decreased. A possible solution is keeping patient information on-line (i.e., computerizing patient information)/ If records and forms were kept in a computer database, incoming phone call. Computerized forms would also decrease the time needed to record information, and would reduce excessive paperwork throughout the clinic. prior task. Once they have called the patient, the nurse completes the necessary paperwork nurses could quickly access patient information and respond to their needs during the initial however. It might be more difficult for nurses to respond to a patient s question since they have not studied the patient s medical history prior to the phone call. The nurse would have to quickly access the computer records to find and read medical histories if necessary could cause the nurse to lose concentration on tasks and potentially forget important The results of the MA utilization time study showed that unscheduled MA assistance is rare interruptions. Currently, the nurse responds to a patient call when they have completed a information to record on the patient s computer record. Hopefully, the advantages in transfer approach. Another way to reduce demands on the nurses in the clinic would be to reassign some of nurse s knowledge and could be handled by clerks. For instance, calls regarding immunizations forms, referrals, and other documents reassigned to the clerks. This would 5.4 Medical Assistance Analysis efficiency and utilization would outweigh the potential problems associated with the direct increase the amount of time nurses are able to attend to patient calls. assistance, including this utilization in the simulation model may not be necessary. If the data is to be folded into the model, the differences between clinics should be included. Different clinics in the Northeast Ann Arbor Clinic experienced different assistance needs according to our data, so different distributions are needed. While there is a need for unscheduled assistance from MAs, it does not appear that these tasks create an excessive demand on the MAs time. We would suggest that the model does not need to require the integration of these employee roles that occur on average less than 4% of the time in the 5 clinics we studied. Further study may be needed to verify this recommendation. 22
Appendices C
Iosf,, sopl, P. Ku 1 kndq G4)lIia,,y C, C C 2% to5m) IgoestoRmR oomp. after ecam Dr: 9% Nota 1: (mm; a1; 99% rnaic) S I Ii )WA )$ ç iwi
212mm Ea.tinan Pontts Joseph P. Kent p1 iuiip1l Internal MCdICI atlent Flow. 15 nm Appointment 3 mm Appointment 45 mm Appointment lime between is? limo botweon 1st limo between 1st and 2nd encounter and 2nd encounter and 2nd encounter (,1 7.8,4 ijmin (1 4.6,51?)mln 15 mm pporntm] 3 mm Appomnlm] 45 mm Appointment Provider 2nd Provider2nd Provider 2nd oncounterw/palient oncounterw!patient oncountorwpatient (j,8321?)min (4,l4.3,22?min N rpounenvsic. rcqulrod? Y9% Nursin Service RN: treatment and oducation pl 28.74 mm N SocialWo,IcI lioniequt Ycl% Social Work/Nutxition Note: Since <1% Social Work! Social Worker or Nublliorr will not be Nutritiondoneth inckzdod inthe model. in MP counsel rm. PiChecksout Note t:frntn;avg;99%mac) Paqo2 I ll I O )(l 11 Ill IfljtI Ko1ik Comins
Appendix B. Patient Check-In Process Flow Chart Patient Check-In / Paperwork Process: Northeast Ann Arbor Health Center I Patient hands clerk insurance and hospital cards Clerk processes necessary paperwork
and signs memo notes on memo - RN writes and places call -. memo to RN memo and chart problem patient and finds No Clerk writes RN talks chart with memo RN resolves when not busy with RN returns to desk and attaches Clerk pull chart Yes to caller on RN s desk Clerk places Is an RN available? Yes needed to answer Clerk anwers End IsanRNorMD No phone call question? question transfers call Incoming Clerk memo Pediatric Continuity Clinic Start Telephone Call Flow: Appendix C. Telephone Management Flow Chart
rovider7 % In Use vider6 - % In Use wider 4 % In Use Provider 1 % In Use Provider 2 % In Use Provider 3 % In Use Pct_Pts_Seen_w_in_lojnin_ Avg alue NP % In Use Max_Wait_Time_ Avg alue Avg_Wait_Time_ - Avg alue Avg_Tinie_to_See_Prvd_ - Avg alue Max_Time_to_See_Prvd_ Avg alue Avg_LOS_3_ - Avg alue Avg_LS_15_ Avg alue IPLE. REPLICATION SUMMARY Avg_LOS_45_ - Avg alue C:medmodeIoutputum91 3b.mrs - 3b.mrs c:medmodei\output\um91 82.3994 61.5124 4.4274 22.129 37.58 7.72839 71.9447 37.2982 38.6569 73.5734 8.19592 38.995 7.8647 82.8716 79.2742 78.5259 35.912 36.644 36.2162 36.3678 36.4124 36.4947 36.586 36.5755 36.9888 37.1871 37.557 37.713 39.76 39.9258 4.598 4.6989 4.768 41.7 36.895 38.1844 38.595 39.122 39.3317 4.15 41.4253 42.221 42.849 43.434 43.5867 43.6379 51.3429 51.3515 5.73213 5.75682 5.84867 5.9542 6.7781 6.288 6.42584 6.4573 6.98525. 7.15366 7.19749.214 39.6353 38.1931 3658. 22.7 36.3534 5.7984 25.123 5.69376 37.5518 41.3131 74.124 8.3785 62.3953 81.3577 Provider 1 % In Use 5 85.1848 86.454 9.61524 1.3598.59344 Provider 2 % In Use 5 66.725 65.7861 17.9786 2.54256 1.88 Provider 3 $ In Use 5 Provider 4 $ In Use 5 83.4429 83.724 8.66753 1.22577.145343 Provider 6 % In Use 5 81.4897 8.4222 9.2931 1.3239.1568 Provider 7 % In Use 5 8.7982 79.4368 9.44538 1.33578.93682 Pct_Pts_Seen_w_in_1O_min_ Avg alue 5 9.22782 9.46693 3.56216.53765.211776 NP % In Use 5 76.6942 74.4546 1.773 1.52353.118132 Max_Wait_Ti!ne_ Avg alue 5 45.6519 41.8223 18.352 2.5556.446386 Max_Time_to_See_Prvd_ Avg alue 5 77.493 79.4534 19.153 2.7865.25589 Avg_Wait_Time_ Avg alue 5 8.5752 8.39446 2.68959.38366.636258 Avg_Time_to_See_Prvd Avg alue 5 23.185 22.5411 3.78218.53488.343368 37.8842 22.1986 72.8853 Avg_LOS_3_ Avg alue 5 4.147 4.3323 5.14379.727442.256548 Avg_LOS_45_ Avg alue 28.579 29.6834 3.236 3.3632 3.9636 31.323 32.257 33.5478 34.6528 35.175 35.6194 35.9273 38.523 38.6116 38.9513 39.12 39.447 44.817 44.89 45.3566 46.98 46.695 46.9932 5.7931 51.686 33.7897 35.996 35.3861 35.461 35.5463 36.3927 36.4218 36.4365 36.4772 36.5135 36.5857 36.8443 4.97 4.3949 4.8528 4.9164 41.2912 41.3636 45.138 45.9819 46.6599 47.1795 47.7891 5.251 41.41 41.5952 42.31 42.321 44.999 44.62 43.5397 43.7678 43.8798 43.9329 43.9884 44.719 Avg_LS_3_ Avg alue 31.57 31.4654 32.915 32.5441 33.5594 33.5784 Avg_LOS_iS_ Avg alue 3. 3.5217 3.6714 3.84 3.83 3.8461 Data for: Sorted Data Avg_LOS_15_ Avg alue 5 38.7943 38.358 5.16459.73383.37181 Avg_LOS_45_ Avg alue 5 39.3614 39.9596 6.1458.86841. Statistics for: Reps Mean Median Std ev Std rr Skewness Appendix D.. MedModel Output Using Current Distribution Avg_Wait_Time_ Avg alue 4.66 4.51129 4.76449 4.7913 5.6658 5.6716 31.94 27.9725 28.853 29.4241 29.689 3.283 25.2583 25.4346 25.63 25.6725 25.9698 24.3315 24.546 24.542 24.6292 24.829 24.8354 22.1536 22.2753 22.293 22.3251 22.3754 22.5122 22.571 23.819 24.44 24.16 24.255 19.8855 2.6328 2.8779 2.9931 21.677 21.1177 Avg_Time_to_See_Prvd_ Avg alue 16.5385 17.1991 17.4541 17.4743 18.1224 18.6114 18.794 18.7954 19.795 19.988 19.7436 19.8666 55229 44.6854 44.871 45.4742 46.6616 49.9328 5.493 36.7283 37.954 38.3911 39.2358 39.38 41.6321 42.4927 42.5512 42.8453 43.2746 43.337 32.546 32.8578 32.89 33.856 33.2787 33.9214 34.872 34.7155 34.82 35.5223 35.6642 35.969 4.2838 4.3653 4.4252 41.668 41.422 43.3699 43.547 43.7172 43.9535 43.999 44.6376 38.644 52.6994 7.87 84.9773 72.4845 8.939 81.4275 88.9421 8.559 86.8298 38.137 42.157 36.9619 41.769 21.636 24.5865 7.45653 9.55852 7.83585 1.6198 59.6951 73.7459 77.173 84.4891 36.7761 4.8124 77.891 85.884 4.367 41.3844 4.8387 24.184 9.15457 82.17 49.997 79.2859 1.848 87.498 71.457 85.5281 83.752 83.75 78.8219 84.1575 8.932 85.9537 78.62 83.5344 4.294 41.6371 41.143 24.242 9.28665 83.413 5.8764 79.8149 1.2597 87.972 71.9286 Confidence Intervals for: 9% 95% 99%
84.48 84.828 85.3458 85.957 86.3843 86.9757 47.619 47.852 53.772 54.3688 56.6352 6.5736 7.9843 72.251 72.3582 73.1784 74.128 74.1444 74.665 76.311 76.8465 76.8965 76.9881 79.3258 32.951 35.3984 35.463 36.2877 37.946 38.7194 8.16712 74.3 84.2993 15.548 NP % In Use 52.721 55.69 62.8654 63.61 65.7549 65.988 Max_Wait_Time_ Avg alue 16.3149 17.4537 18.162 19.4232 23.4525 24.617 9.68435 1.1218 1.75 1.44 1.5945 1.656 74.5125 77.111 77.3238 77.4958 78.1897 78.9276 24.9783 25.5728.549.635 29.8775 3.8198 77.4711 77.9166 79.8734 8.483 83.752 84.752 9.985 91.334 91.4564 91. 913 94.4376 94.517 6.88486 7.483 7.3857 7.4385 7.6167 8.15744 87.1178 88.1214 88.5982 89.358 9.9783 8.9642 9.16948 9.25512 9.34958 9.55147 9.62573 1.9283 11.324 12.737 12.8584 13.3358 13.7661 65.992 68.1192 68.8554 71.5822 71.7967 73.7885 79.5811 82.815 82.5921 82.615 83.764 88.4564 88.5525 9.1166 94.51 95.257 95.4356 97.2729 97.5959 97.8939 1.1 11.23 16.396 114.986 117.493 39.15 39.4846 4.561 4.3717 4.7555 4.8365 41.4914 42.1533 43.143 43.6273 44.85 46.8311 61.1745 62.3922 62.6941 62.7383 62.9382 63.1889 64.45 66.986 67.478 67.153 67.7317 69.1137 9.812 9.9759 66.879 66.496 67.122 67.289 67.7867 67.9157 68.6943 69.4479 69.6145 69.96 7.4555 7.5346 94.7275 95.39 Pct_Pts_Seen_w_in_1_min_ Avg alue 1.34676 2.4713 2.99718 3.6157 3.7878 4.4789 4.77114 5.4598 5.2519 6.2171 6.3839 6.38563 78.435 79.9386 8.4562 8.5989 8.9256 82.1222 83.9847 84.658 85.6146 85.893 85.9928 93.37 93.3777 93.7466 94.898 94.113 94.7769 95.2199 95.549 95.5341 95.7985 95.9914 96.3735 97.212 97.431 54.5745 54.6145 54.721 56.1859 57.984 58.77 6.1278 6.823 61.4796 63.1975 63.63 63.643 63.7335 63.752 63.821 63.8638 64.5725 65.5347 65.6312 65.949 68.916 71.835 73.322 73.3786 Provider 2 % In Use 45.9582 49.3574 51.7246 53.6456 77.3734 8.2914 8.49878 8.68692 8.77716 8.82512 8.9149 14.1561 15.732 86.394 86.568 9.1562 9.211 9.8124 9.8915 91.2411 91.4456 91.4867 91.7428 91.845 92.5771 C:\medmodefloutputum91 3b..mrs - c:\medmodeioutputum91 3b..mrs : 83.7148 77.7134 77.9198 78.33 78.8687 79.518 8.8763 82.1581 82.1811 85.428 86.7892 86.9523 88.3437 73.4552 73.6183 74.8435 76.1353 76.3617 77.671 Provider 1 % In Use 62.4194 65.8114 69.36 7.2959 7.7163 7.8224 48.8451 54.2856 56.9918 59.8632 6.6724 6.7939 71.8837 72.151 72.1834 75.9897 76.81 77.3294 Max_Time to See Prvd. Avg alue 4.8216 42.278 42.986 42.9498 45.95 46.4723 Page2 97.2358 97.2718 84.288 84.384 84.173 84.3334 84.921 89.75 95.6396 96.3388 96.3568 96.9285 96.9488 97.498 89.19 91.3196 93.998 93.597 94.6279 95.428 79.951 8.4982 8.9648 81.3333 81.4284 81.9828 82.8868 83.258 83.2794 83.4174 83.697 73.8818 74.651 75.5234 75.6329 77.124 77.8 78.144 78.1524 78.3736 78.3927 78.6163 79.2879 Provider_4 % In Use 65.5429 69.3424 7.8 7.8175 7.8294 72.584 Provider 3 % In Use 91.3171 94.3215 13.2 13.18 13.359 13.5524 14.744 14.964 8.19789 8.5183 8.8495 9.83 9.317 9.4688 9.47298 9.5143 9.5661 1.2357 1.3664 1.4817 1.5411 1.7334 1.8418 11.4924 11.5635 11.8517 11.8695 12.1817 12.4485 12.63 12.8725 15.7147
Page3 - C 92.694 92.77 92.7959-93.7844 94.1423 95.74 95.375 79.849 81.395 81.5918 83.5966 83.879 87.4382 82.1796 82.8694 84.5382 84.9662 86.515 87.387 92.9192 93.5845 92.863 73.2969 74.4438 75.333 75.6137 75.9848 76.513 79.3636 79.51 79.7439 79.8559 8.8356 82.912 Provider 7 In Use 61.3835 61.5524 63.185 63.9827 68.2274 7.848 7.123 71.27 71.7317 71.9838 72.393 72.5951 76.8363 76.9458 77.1194 77.1931 78.6679 788725 87.4453 87.5585 87.7569 88.7439 89.6463 89.6955 9.7183 9.9399 91.5319 91.5922 91.8253 92.237 72.4151 72.7385 72.7647 73.6873 73.9893 74.457 74.5482 74.8859 75.143 75.283 77.1973 77.372 77.34 77.663 77.6387 77.8194 78.843 78.4862 87.9578 89.29 89.834 91.325 92.2754 93.88 93.67 93.732 94.1639 94.4331 94.8956 95.25 Provider_6 % In Use 63.4 67.3739 67.481 67.676 69.1984 7.642 C:medmodeIoutputum91 3b.mrs - c:medmodeioutputum91 3b.mrs :
95% 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 37.2947 7.31378 76.6568 13.737 87.798 68.5492 81.962 42.7466 82.16 76.1416 81.446 36.2314 37.94 2.434 6.9764 77.6165 37.5171 89.7281 69.8828 82.3885 37.914 37.24 35.5568 7.11345 76.956 Statistics for: Reps Median Std ev Mean C IPLE REPLICATION SARY C:medmode1outputum481.mrs - c:\medmode1outputum481.mrs 2.6781 14.758 8.6984 7.6839 1.8667.8858 7.866 81.4765 69.7176 82.5656 67.4761 84.871 79.1499 83.7422 78.6812 84.219 77.7164 85.1757 32.4516 32.6811 32.8112 32.8797 33.41 33.2996 33.4215 34.6739 34.728 35.1329 35.3987 35.4159 39.1359 39.4157 39.4463 4.936 4.62 41.2349 47.1497 48.9736 31.8992 32.56 32.497 32.573 32.5978 22.8573 33.2719 33.3876 32.4174 33.4325 33.4864 34.739 25.568 36.132 36.442 36.8667 36.964 43.1196 43.2377 42.4673 43.518 43.8522 44.384 44.2547 45.3791 45.5584 46.33 46.8351 46.8974 47.8392 52.796 16.1759 16.296 16.9822 17.3571 17.5398 17.9115 21.5215 21.733 22.1245 22.729 22.7457 22.7613 22.521 23.547 23.7784 23.922 24.121 24.5923 24.7628 24.9115 25.23.13 27.7725 4.7788 4.89281 5.5757 5.9286 5.2922 5.ji18 5.4792 5.5534 5.61857 5.74822 5.795 2.7777 35.339 27.118 24.2325 99% 3.9646.56679.51963 2.6659 2.694 2.771 2.7116 2.7414 2.7661 4.96342.71934.315912 5.5282.714576.251796 3.4897.493518.19765 2.43942.344987.3384 22.1758 3.13614.141181 Std Err Skewness 5.8444.8523.386168 19.739 2.7938.898161 12.2787 1.73647.19795 17.311 2.4857 2.41737 9.54451 1.3498.448324 9.14438 1.29321.55185 7.7571 1.972.31873 35.374 37.4255 34.7936 37.6693 34.2919 38.171 36.751 38.513 35.831 38.7584 35.322 39.91 36.534 39.346 36.2469 39.533 35.6562 4.2238 19.5639 21.2429 19.3925 21.4143 19.397 21.767 6.792 7.964 6.6712 8.244 6.3654 8.72 37.9998 47.4933 37.38 48.4623 35.364 5.4567 78.7764 83.1763 78.3274 83.6253 77.431 84.5497 35.5524 35.5613 36.25 36.993 36.9336 37.141 33.424 33.877 33.997 34.18 34.36 34.681 34.7184 34.8249 35.749 35.6366 35.7259 36.466 38.9151 39.4382 39.5582 39.625 39.7114 4.4557 4.8544 4.9529 41.28 41.46 41.45 42.1577 37.111 37.369 37.548 37.3441 37.3492 38.1566 18.5981 18.6698 19.962 19.715 2.796 2.5395 73.729 79.618 73.999 8.2138 71.8588 81.4549 12.7769 14.6845 12.5822 14.8792 12.1815 15.2799 85.9423 89.6394 85.5649 9.167 84.7882 9.7934 64.452 72.6465 63.6156 73.4829 61.8941 75.244 8.941 83.84 79.7131 84.273 78.929 84.9914 31.311 31.5291 31.7285 32.577 32.1863 32.243 38.3628 38.6232 38.7392 38.887 38.8948 38.967 41.2358 41.4595 41.6228 42.9956 44.1621 45.2136 31.7569 31.8792 32.727 32.7894 32.931 32.9479 36.8737 37.391 37.9287 38.7841 38.7963 38.856 42.613 44.231 44.4348 44.763 44.7915 45.2281 17.9427 18.935 18.976 18.2531 18.575 18.5123 38.2131 39.6662 4.2937 4.8214 42.619 43.27 45.894 49.363 Sorted Data 9% Max_Time_to_See_Prvd_. Avg alue Avg_Wait_Time_ Avg alue Avg_Time_to_See_Prvd_ Avg alue Avg_LOS_4 5_ - Avg alue Avg_LOS_3_ - Avg alue Avg_LOS_15_ - Avg alue Avg Wait Time Avg alue 3.2994 3.53655 3.66656 3.84111 4.2549 4. 4 Avg_Time_to_See Prvd_ Avg alue 14.5221 24.7777 14.8183 14.8791 14.892 15.596 Avg_LOS_45_ Avg alue 28.2981 28.6568 29.177 3.7758 21.966 31.5634 Avg_LOS_b_ Avg alue 28.968 29.2421 29.4469 29.687 3.1923 31.2869 Avg_LOS_15_ Avg alue 27.252 27.5472 28.4345 28.597 29.5513 3.91 Data for: Provide r_7 $ In Use )vider6 $ In Use ( vjder4 % In Use ovider_3 % In Use Provider 1 $ In Use Provider 2 % In Use?ct_Pts_Seen_w_in_1_min_ Avg alue NP % In Use Max_Wait_Time Avg alue Avg_Wait_Time_ - Avg alue Avg_Tizne_to_See_Prvd_ Avg alue Max_Tirne_to_See_Prvd_ Avg alue Avg_LOS_3D_ Avg alue Avg_LOS_15_ - Avg alue Avg_LS_4 S - Avg alue Confidence Intervals for: Previder_7 % In Use Provider_4 % In Use Provider_S % In Use Provider_2 % In Use Provider 1 $ In Use Provider 3 $ In Use?ct_?ts_Seen_w_in_1_min_ Avg alue N? % In Use Max_Wait_Time_ - Avg alue Appendix D. MedModel Output Using Revised Distribution 5.4167
Max_Wait_Time_ Avg alue 13.7716 16.733 18.733 2.774 2.8947 22.975 55.193 56.2271 56.5274 59.47 59.882 68.7285 69.7598 69.9883 112.123 12.212 45.29 47.6738 51.366 54 9393 Max me_to_see_prvd_ Avg alue 36.3547 37.3824 38.3228 39.8614 42.2512 45.1257 1.1962 1.6538 1.9128 1.9324 11.2449 11.6383 11.8535 12.9492 94.2497 94.71 95.1569 95.24 96.816 97.81 98.2858 11.65 14.753 15.39 16.297 19.58 8.8635 8.87259 8.969 9.14972 9.23174 9.66812 71.3 71.545 73.2494 73.7679 74.9613 76.447 7.9755 7.12936 7.15934 7.18496 7.21951 7.85372 8.11942 8.2476 8.62314 8.7215 8.8292 8.82715 77.7572 77.8758 78.589 78.7213 79.672 79.2848 81.3878 85.6916 86.6446 89.3214 92.9588 93.8919 23.8389 23.8912 27.2119 27.7991 28.2762 28.332 28.4138 28.9518 29.2493 3.5443 31.197 32.6671 32.979 35.227 35.1845 35.6416 35.6987 36.8119 37.581 37.51 38.1364 38.2758 39.334 39.4486 4.746 4.3296 42.2776 43.1212 45.383 46.8452 51.3689 51.4219 65.1869 66.1394 66.5369 66.9913 84.3452 99.1375 72.4332 72.7371 74.486 74.3151 74.5566 74.8813 76.4676 77.4336 77.7958 78.5119 79.116 79.692 79.7111 8.899 81.9344 82.28 82.4881 83.752 84.73 86.1645 88.1814 89.222 9.8947 91.115 8.83999 9.2517 9.48295 9.95974 1.2943 1.311 1.5576 1.5856 1.8316 11.158 11.314 11.4629 11.7396 11.9 12.3437 12.62 12.9769 13.3458 13.4565 14.6951 14.6996 14.934 15.2351 15.2857 83.4519 83.8619 83.966 84.3642 84.8421 85.2166 86.2113 86.2352 87.4977 89.3847 9.716 9.166 9.1595 91.7822 92.3732 92.616 92.628 92.782 93.996 93.4494 93.5822 93.6386 93.7612 94.68 94.5561 94.9137 95.251 6.359 61.52 61.8258 62.4484 63.3373 63.445 63.9799 64.631 64.5425 64.6424 64.749 66.7699 67.3159 67.3259 67.3791 68.483 68.358 68.689 73.4385 73.5684 73.7648 74.1344 74.1543 74.4939 75.1565 76.4949 77.8133 78.2161 78.4 8.56 81.4323 83.36 85.646 88.2241 93.5844 94.55 95.8548 87.4716 87.8355 88.35 88.1975 88.769 89.558 97.8496 97.9754 82.86 92.59 92.7143 93.115 94.461 94.16 95.571 62.5781 62.728 63.4524 65.2927 67.122 67.9116 Pct_Pts_Seen_w_in_1_min_ Avg alue 5.67876 6.94899 7.89972 8.42967 8.52547 8.59928 16.71 16.8541 16.8764 17.1927 17.4332 17.4388 17.9134 18.1 18.423 18.8748 19.639 19.7789 NP % In Use 47.3145 52.721 56.435 58.76 59.7635 61.9161 67.3353 71.278 71.5156 71.9663 75.2481 76.3457 68.791 69.1523 69.4895 7.5721 71.4881 71.792 96.2842 96.4181 15.31 15.431 15.445 15.937 16.2945 16.5862 73.357 Provider 2 In Use 49.141 57.9471 6.446 6.1944 Provider 3 % In Use 87.592 88.3256 95.428 96.336 96.1647 96.3149 96.4646 96.6632 97.163 98.4975 69.223 7.5453 71.681 71.2745 71.7173 73.157 7.9459 71.5621 72.4948 73.285 73.877 73.996 Provider 4 % In Use 56.2 58.277 65.7594 69.1354 69.4233 7.1598 C:medmodeIoutputum481.mrs - c:medmodei\outputum481.mrs Page2.. O 8.37. 8.3198 8.6572 81.435 81.5962 81.8517 Provider 1 % In Use 65.534 71.2781 72.3395 75.31 75.411 79.6229 21.345 22.6844 92.879 92.8459 93.4153 93.854 95.8199 96.1659 82.2469 82.53 83.962 83.1628 83.465 83.8575 85.3278 85.5539 85.7799 85.8965 86.67 86.8219 74.3862 74.9144 75.3164 76.2229 76.6345 79.281 8.1152 8.1746 8.6485 8.6576 81.9994. 82.1898
Page 3 72.3168 72.43 73.7797 73.9153 75.85 75.3639 96.2892 96.4798 95.1675 97.197 78.7964 78.8416 79.343 79.4767 79.8977 8.5152 91.6548 91.8435 93.7797 94.227 94.5922 94.595 74.4 74.3556 74.4697 75.391 76.8929 77.9131 87.874 87.2182 87.2539 87.6415 87.8793 88.1349 88.3644 9.9255 92.2182 92.8991 93.9453 94.8421 87.255 87.2579 87.5167 87.9774 89.4291 89.5861 75.5852 75.9747 76.434 76.5314 78.67 78.613 8.5447 8.8521 81.2131 81.383 82.1645 82.7881 82.966 83.1232 83.4324 84.3678 85.84 87.2383 77.92 78.19 78.1128 78.5694 78.6835 78.8952 79.956 79.4421 8.3933 8.498 81.377 81.8273 82.817 82.2434 82.31 83.332 84.1197 84.249 84.6523 85.617 85.723 86.63 86.2932 86.3359 Provider 6 % In Use 53.9248 57.852 64.2793 69.9167 71.9672 72.552 Provider 7 % In Use 61.4335 67.218 67.8541 68.371 7.8551 72.4769 C:medmodeIoutput\um481.mrs - c:medmodefloutputum48 1. mrs
Tijjie Patient Time Patient 8:14: 8:15:55 :1:4 -Oct 8:3: 8:33: :3: -Oct 8:31:55 8:34:4 :2:45 2SOct 8:39:5 8:39:5 ::45 8:51:45 :2:15 -O 8:53:35 8:54:45 :1:1 28 -Oct 8:54:15 8:55:5 :1:35 -Qct 8:55:45 9::5 :4:2 -Oct 8:57:15.9:2:2 :5:5 Qct 8:58:25 9:3:5 :4:4 -Oct 9:14:5 9:16:45 :1:55 -Oct 9:16:2 9:18:2 :2: 9:23:45 :3:5 2S-Qt 9:3: 9:31:1 :1:1 9:45:25 9:46:3 9:58:15 1::5 :2:35 1 :6:25 1:8:5 :1:4 -Oct 1:14:45 1:16:1 :1:25 Oct 1:25:5 1::2 :1:15 -Oct 1::1 1:3:4 :4:3 1:3:45 1:32:15 :1:3.O 1:4:5 1:44:1 :3:2 -Qct 1:44:45 1:47: :2:15 1:55: 1:58:35 :3:35 11:7:5 11:13:3 :6:25 -Qct 11:6:1 11:11:2 :5:1 11:7:5 11:13:3 :625 -Oct 11:15: 11:17:3 :2:3 Oct 11:19:5 11:21:45 :2:4 11:21:3 11:24: :2:3 11:22:15 11:23:5 :1:35 -Oct 11:3: 11:33:5 :3:5 -Oat 11:32:3 11:36:45 :4:15 -Oct 11:34:35 11:37:45 :3:1 -Oct 12:23:15 12:25:5 :1:5 12:39:4 12:41:5 :1:25 -O 12:46:5 12:47:55 :1:5 -Oct 12:55: 12:56:4 :1:4 25 -Oct 12:59:5 13:1 : :1:55 -Oct 13:7: 13:1: :3: 13:11:2 13:14:2 :3: 13:19:45 :3:25 13:16:45 13:21:2 :4:35 13:21:15 13:22:3 :1:15 13::4 13:29:4 :3: -Oct 13:27:5 13:33: O:O5:io 13:31:55 13:37:3 :5:35 13:3: 13:41:5 :6: -Qct 13:36:4 13:44:25 :7:45 -Oct 13:41:5 13:44:55 :3:5 13:45:15 13:5o.1 :5:Oo -Qct 13:47:2 13:53: :5:4 13:52:35 13:59:3 O:O5: Oct PP 13:53:15 13:59:2 :6:5 PP.13:54:55 14:2:1 :O7:i Appendix E. Raw Data of C1leck4 Process Time Study
16:68:35.... 15:28:5 -Oct 14:39:3 14:42:25 :2:55 -Oct 14:54:2 14:58:46 :4:25 -Oct 15:4:25 15:6: :1:35 -Oct 14:32:35 14:35:3 :2:55 -Oct 15:2:25 16:3:4 :1:15 -Oct 15:2:45 15:4:15 :1:3 -Oct 15:5:3 15:7:2 :1:5 -Oct FP 14:8:15 14:12: :3:45 31-Oct 3W. 16:2:41 :4:6. 31-Oct JW 14:2:49 14:6:37 :3:48 31-Oct 3W 14:6:2 14:8:45 :2:25 31-Oct 3W 14:8:58 14:1:15 :1:17 31-Oct JW 14:23:15 14:25:45 :2:3 31-Oct 3W 14:25:56 14:28:54 :2:58 31-Oct JW 14:29:8 14:31:16 :2:8 31-Oct JW 14:4:12 14:41: :1:14 31-Oct 3W 14:56:16 14:57:57 :1:41 31-Oct JW 14:58:25 15::19 :1:54 31-Oct 3W 15:5:54 15:8: :2:6 31-Oct.JW 15:6:58 15:1: :3:28 31-Oct JW 15:22:5 15::12 :3:22 31-Oct JW 15:2:1 15:22:14 :2:4 31-Oct 3W 15:32:16 15:34:38 :2:22 31-Oct 3W 15:39:45 15:42:48 :3:3 31-Oct 3W 15:53: 15:56:1 :2:35. 31-Oct.1W 15:56:34 15:58:23 o:oi: 31-Oct 3W 13:44:2 13:46:38 :2:18 31-Oct JW 13:46:32 13:49:58 :3: 31-Oct JW 14:37:47 14:4: :2:13 31-Oct JW 14:44:5 14:47:46 :3:41 31-Oct Jv 15:11:2 15:13:39 :2:37 31-Oct JW 15:27:11 15:29:2 :2:9 31-Oct 3W 15:29:4 15:31:2 :1:22 31-Oct 3W 15:44:34 15:47:9 :2:35 31-Oct 3W 13:23:58 13:27:24 :3: 31-Oct JW 13:28:35 13:33:52 :6:17 31-Oct JW 13:32:3 13:37:2 :4:32 31-Oct JW 13:4:35 13:44:14 :3:39 31-Oct 3W 13:19:2 13:2:56 :1:36 31-Oct 3W 13:14:21 13:18:16 :3:55 31-Oct JW 13:15:17 13:18:5 :2:48 31-Oct 3W 13:12:2 13:16:48 :4:28 31-Oct.JW 12:49:44 12:54:2 :4:36 31-Oct Jv 12:55:45 12:58:27 :2:42 31-Oct f 12:59:55 13:1:55 :2: 31-Oct JW 13:1:1 13:3:15 :2:14 31-Oct JiN 13:2:1 13:1)7:35 :5:25 31-Oct JW 12:48:16 12:52:16 :4: -Oct. 16:32:36 :4:3 31-Oct JW 12:42:2 12:43:25 :1:23 -Oct 15:23:45 15::35 :2:5 -Oct 15:27:5 15:29:15 :2:1 -Oct R 15:16:5 16:18:45 :1:55 -Oct 15:7:35 15:8:55 :1:2 -Oct 15:12:3 15:14:15 :1:45 -Oct 15:15:15 16:18:45 :3:3 -Oct 15:2: 16:24:1 :4:1 -Oct 14:18:45 14:21:2 :2:36 -Oct 14:17:3 14:19:45 :2:15 -Oct FP 14:25:5 14:27:1 :1:2 -Oct 14:29:4 14:33:35 :3:55
n
C Prouider Tgpe OB/GYN Appendix F. Raw CLS Data 3mm 28 91 7 45mm 2 12 15mm 95 7 234 6 Length Arrival No Show Arrival No Show Appointment Nurse Practitioner MD 3 mm 41 I 1 I 135 I 23 15 mm 249 74 27 4 Length Arrival No Show Arrival No Show Appointment Residents Faculty Pro uider_type Pediatric Continuitg Clinic 6mm 6 45mm 5 Appointment 4 mm 1 7 Length of 3 mm 39 1 1 79 5 2 2mm 43 1 15mm 38 456 9 7 Appointment Nurse Practitioner MD Lenath Arrival No Show Arrival No Show Arrival No Show Provider Type FamEig Practice
24 27 29 3 9 2 22 23 25 28 21 12 13 5 8 7 3 11 2 4 6 Date: MA Name: Program and Operations Analysis 14 15 1 16 17 18 19 Assistance Completed o (YIN) present Time Room Cilnic (Check One) Begin Assistance E Present? Left, if Entered to Time Was Provider Time Provider Shift: University of Michigan Medical Centers Medical Assistant Reluest Data Collection Appendix G. Medical Assistance Time Study Data Form
. By :. Keg an X next to selected appointments then PF9: T R S fl 11/16/95. ye tu CD (Ti CD O (Ti (Ti CD CD : 1JJS 1 girioiiiieehi CLI[NC SCHED 116 SYSTEM 11//95 89 Reso.,ce HEIICIIB DR. CIIRISTIIHI II BUYE Date 111595 Time Status X Date FromTo CPI 8 Uisit Patient Mame Contact Phone p f a t 11/16/95 1338148 H 256925 532 SAREIIULLAH, 11 3136654621 RU SF II 11/16/95 141115 H 21992331 532 IITAL, BhllIHINtIE 313763236 flu SF II 11/16/95 15316 H 21627756 5319 MILLER, KYLE A 4195371785 RU SF A 168163 H 2274699 532 HAIR, IIE1II.T RU SF A 11/16/95 1638178 H 17874246 532 DONHAIl, JIISOH 31376971 RU SF II 11/17/95 1231388 H 25136246 5319 SCOTT, KUIIME 11/17/95 139133 N 18993961 5319 IIELIEHS, TREUOII 313913471 RU SF II NP SF A 11/17/95 1381338 11 2895631 5319 THORNTON, STEP 11/17/95 131315 H 2193142 5321 KIll, YEONIIIEE 313764191 RU SF II HP SF II 11/17/95 1315133 N 223915 5321 BICKLEY, flue 313936986 RU SF II 11/17/95 13314 H 4631 5321 KIRK, JEHHIFER 11/17/95 14113 11 16665 5321 KIRK, ADIGAIL HP HP SF II SF A 11/17/95 1431115 H 25757278 531 ESTIICID. MARIE 31343536 RU SF 11 11/17/95 144515 N 258128 5321 RICHISSIH, CAL 3135722594 RU SF A 11/17/95 15153 H 179399 5318 CHrn1OELL, CAHD 3139365931 RU SF 11 IIACK=PF7 HEXT=PFO CLSRSDSP User Id 19? Termid 1114 Function CLDS
N C) 1 ( ( r. o ( R 1 F 5% 7% 25% 8% 3T 2%+ I 9% I 6% c l I 1 666 6 6 1.. ( I 1 o o a, O ) ) 16% 14% 6% 4% % % 2%. J :. 18% 12% 1% 2% 4% bl IIj ii i: i 6/o I%1 1% Time of Day 4% 1% Monday Incoming Calls by Time of Day Taubman Center- Pediatric Continuity Clinic Telephone Call Distribution T7% Time of Day 666 o q q c if.- c1 c,. Ifl ( o o o o o / i; 1% :x 4 i 1: 2% A 3% E1.. 4 C.) t 1 I,.l AI E I:cl Il tl j 1/ 5% : ri : : fl :4 J ll 7 9% Taubman Center - Pediatric Continuity Clinic Tuesday Incoming Calls by Time of Day Telephone Call Distribution O%Lr h H 2% 1/.L 1 \ fl I -.1 ci 1111 8% I.-. 6 3% o ti E4... 15% -- Appendix I. lncoming Calls by Day of Week and Time of Day
:: 3% Ci 1%.6 4% 2 o 15% 6% 7% 4/ 7% 25% 2% 2% Fl 8% 9% -x 1% 9% 8% 6% 4% 3% 2% 1% 5% C., E C.) a 15% Taubman Center - Continuity Clinic a a Pediatric Telephone Call Distribution Wednesday Incoming Calls by Time of Day Time of Day i I 5% Taubman Center- Pediatric Continuity Clinic -IfTlOO% Telephone Call Distribution Thursday Incoming Calls by Time of Day Time of Day %ITrij1I[:iI[ I 1 1. a > a
25% 1% Time of Day (hr:min) 1% A 2%,/% 8%. 9% Friday Incoming Calls by Time of Day Taubman Center- Pediatric Continuity C inic Telephone Call Distribution
: Call 1 1114/95 12:13:15 12:13:5 ::35 1 4.1 2 referrals 1 1/1 4195 1 2:39:55 12:41:4 :1:45 1 4.1 3 perscnptions 1 1/1 4/95 13:44:15 13:55: :1:45 1 2 4.1 immunization forms 1 1(1 4/95 1 4:4:5 14:8:5 :3:15 1 7 4.2 Crippled childrens forms 1 111 4/95 14:36:4 14:42: :5:2 1 5 5 other people (homecsre, Schools) 11/14/95 12:9:3 12:14:4 :5:1 1 1.1 1.3 11/14/95 14:2:2 14:27:45 :7:25 1 1.1 4.3 Other forms Date Began Ended Tim. Elapsed. of Call I.1 general advice Tim. Call Tim. Call Content/Nature Categories. 11/14195 12:3:4 12:9:1 :5:3 1 1.1 1.2 appointment 11/21/95 9:24:3 9:25: ::3 1 11/21/95 1:1 2:3 1:19: :6:3 1 2 11/21/95 1:32: 1:34: :2: 1 1.2 11/21/95 11:15:45 11:21: :5:15 1 1.2 11/21/95 11:23: 11:34: :11: 1 1.2 11/21/95 11:36: 11:37:3 :1:3 1 3 11/27/95 8:56:1 8:58:1 :2: 1 1.2 11/27/95 8:59:25 9::24 ::59 1 1.2 11/27/95 9:18:2 9:21:6 :2:46 1 1.2 11/3/95 8:57: 8:58:5 :1:5 1 2 11/3/95 9:9:25 9:1:2 ::55 1 3 11/3/95 9:35:55 9:37:5 :1:1 1 2 11/3/95 9:47:1 9:48:5 ::55 1 1.2 11/3/95 9:55:5 1:4:1 :9:5 1 1.2 11/3/95 1:24:15 1:3:15 :6: 1 1.1 11/3/95 1:59:55 11:2:2 :2:25 1 1.2 11/3/95 11:14: 11:17:1 :3:1 1 2 11/3/95 11:28:5 11:29:1 :1:5 1 3 11/3/95 11:31:3 11:34:35 :3:5 1 5 11/3195 1:6: 1:13:5 :7:5 1 5 1113/95 11:5:4 1 1:1 :1 :4:3 1 1.2 1113195 1:56:25 1:57:2 ::55 1 1.1 1 1/27/95 9:13:31 9:14:5 ::34 1 1.2 \ 11(21/95 11:41:15 11:51: :9:45 1 1.2 ( 11/21/95 11:39: 11:41: :2: 1 1.2 11121/95 9:25: 9:27:15 :2:15 1 2 11121/95 9:44: 9:44:15 ::15 1 11/21195 9:59: 1:1: :11: 1 2 11/21195 11:12:45 11:14:3 :1:45 1 3 11127/95 11:14:58 11:16:21 :3:23 1. 1.1 1 1/27/95 1:34:4 1:38:45 :4:5 1 1.2 11/27(95 9:52:34 9:56:57 :4:23 1 1.2 11/14/95 14:43:15 14:44:4 :1:25 1 3 6 How to access system 11/21/95 9:13:3 9:17:3 :4: 1 3 7 test results 11/21/95 9:42:3 9:44: :1 :3 1 3 11/21/95 9:45: 9:46: :1: 1 2 11/21/95 1:19: 1:27:3 :5:3 1 4.3 11/21/95 1:37:15 1:4: :2:45 1 4.1 11/21/95 1:41:15 1:42:3 :1:15 1 11/21/95 1:43:3 1:44: ::3 1 7 11/21/95 1:44:15 1:46: :1:45 1 7 11/21/95 1:46:45 1:47:45 :1 : 1 2 11/21/95 1:48: 1:51: :3: 1 3 11/21/95 11:14:45 11:1 5:3 ::45 1 4.3 11/21/95 11:37:45 11:38:3 ::45 1 3 11/21/95 11:51:15 11:51:45 ::3 1 11/27/95 :1: 1 1.1 11/27/95 :1: 1 3 11/27/95 9:8:39 9:1:8 :1:29 1 1.2 11/27/95 9:11:48 9:13:25 :1:37 1 1.2 11/27/95 9:17: 9:23:15 :6:15 1 1.1 11/27/95 :3: 1 1.2 11/27/95 9:43:45 9:45: :1:41 1 1.2 11(27/95 1:28:17 1:3:4 :2:23 1 1.2 11/3/95 9:4:15 9:6:5 :1:5 1 1.1 11/3/95 9:37:4 9:38:35 ::55 1 2 11/3/95 1:22:1 1:22:35 ::25 1 1.1 11/3/95 1:31:2 1:4:3 :9:1 1 1.1 11/3/95 1:53:5 1:54:2 :1:15 1 1.1 11/3/95 11:18:1 11:19: ::5 1 5 Appendix J. Peds Continuity Clinic Phone Time Study
2-Nov 2:35 2:4 :5 4 A,C Y 2-Nov 2:5 2:55 :5 4 A,C Y 21-Nov 14: 14:8 :8 4 C Y 2-Nov 3:1 3:18 :8 4 A,C Y 21-Nov 1: 1:1 :1 4 C Y 2-Nov 16:45 16:55 :1 2 C 21-Nov 11:15 11:27 :12 4 C 2-Nov 15:25 16: :35 2 SIGNOID 16-Nov 15: 15:3 :3 1 PEAKFLQW N 18-Nov 1: 1:3 :3 1 PEAKFLOW N 18-Nov 1:8 1:11 :3 1 PEAKFLOW N 16-Nov 1:42 1:47 :5 2 C Y Date Begin Assist End Assist Elapsed time 5=Peds) C=chaperone) Present? Left 4OB/GNY, (A=assist, Provider Time Prov. 21-Nov 11: 11:8 :8 4 C 22-Nov 2:4 2:58 :18 2 A Y 16-Nov 14: 14:1 :1 2 PFT N 16-Nov 2:3 2:4 :1 2 SPIRCMECTY N 16-Nov 16: 16:1 :1 2 C Y 16-Nov 3:32 3:46 :14 2 A 3:45 3..Med-Ped, Reason Prac, 2.IM, Clinic (1=Fam Appendix K. Medical Assistant Raw Data