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UNIVERSITY OF MICHIGAN HEALTH SERVICES PROGRAM AND OPERATIONS ANALYSIS

Optimizing the UMHS Interpreter Services Workload and Scheduling

Team 5

Final Report

To: Michelle Harris, Program Coordinator, Interpreter Services

Richard Coffey, PhD, Director, Program and Operations Analysis

From: IOE 481 Project Five Team, Program and Operations Analysis Barney Charles, BSE Candidate, College of Engineering Stephen Joe, BSE Candidate, College of Engineering

Shamico Tribble, BSE Candidate, College of Engineering Date: December, 12th, 2008

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UNIVERSITY OF MICHGAN HEALTH SERVICES INTERPRETER SERVICES DEPARTMENT

TABLE OF CONTENTS

Executive Summary ………………………………. 3 Introduction ………………………………. 5 Goals and Objectives ………………………………. 5 Background ………………………………. 5 Project Scope ………………………………. 7 Expected Impact ………………………………. 7 Project Approach ………………………………. 7 Data Analysis ………………………………. 10 Step 1: Obtain Data ………………………………. 10 Step 2: Validate Data ………………………………. 10 Step 3: Observe Data ………………………………. 11 Step 4: Stratify Data ………………………………. 14 Demand Distribution(Day of Week) ………………………………. 15 Demand Distribution(Time of Day) ………………………………. 18 Demand Distribution(Region) ………………………………. 19 Interpretation Time ………………………………. 20

Literature Search ………………………………. 21 Workforce Capacity vs. Outsourcing Fund Allocation

………………………………. 21

Conclusions ………………………………. 23 Recommendations ………………………………. 24 Support Given By Operating Entities ………………………………. 27 Appendices ………………………………. 28 References ………………………………. 47

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EXECUTIVE SUMMARY The language translation needs of patients within the University of Michigan Health System are met by the UMHS Interpreter Services. This department's work force is comprised of department officials, schedulers, staffed interpreters and temporary interpreters. The management of the Interpreter Services department believes that the current scheduling method is inefficient. Two of the main reasons for this inefficiency are considered to be appointments that require travel between the five regions the department provide service to and interpretation needed for emergency situations such as paternity cases. The Interpreter Services coordinator wants to increase interpreter productivity, reduce interpreter travel between appointments and optimize the response times to last minute requests for interpretation services. To achieve this goal; the team took a five phase approach which began with the interviewing of two managerial staff and two interpreters. The interpreter interviews were followed by an interpreter appointment time-study. The project team then performed a literature search to find how other health systems provide language interpretation services. With the assistance of the program coordinator and aforementioned staff interviews, the team constructed a flowchart of the current UMHS Interpreter Services appointment scheduling process. The team then determined the work force capacity of the department and calculated the potential amount of funds spent on outsourcing; using temporary interpreters to meet interpretation demand. The work force capacity and outsourcing fund allocation took into account the interpreter's current productivity rate versus the industry standard rate. Data analysis of the data within the Interpreters Service appointment scheduling database followed. This phase had four steps: obtaining the data in a suitable form, validating the data to make sure it was accurate, determine what specific languages would be optimal for analysis and stratifying the data points in order to identify trends. The completion of the data analysis allowed the team to then form general conclusions as well as conclusions by language. The general conclusions found were as follows:

• Demand for Interpreter Services was significantly greater on the weekday than the weekend

• As the total demand increased for a certain language, so did the number of appointments that could not be filled

• The demand for most appointments occurred during the regular business hours of 8am to 4pm

• The Blue (Main Hospital) Region had the largest demand and in turn, the largest number of appointments that were unable to be scheduled

Conclusions included the demand of a language by region, by day of the week and by peak time during the day. The team also determined the mean interpreting time for an appointment given a specific language within a 95% confidence interval. With the information gained through the interpreter and department staff interviews, work force capacity versus outsourcing allocation and data analysis driven conclusions,

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the team developed recommendations for the University of Michigan Health System Interpreter Services department. Some of the recommendations the team thought might be most beneficial to the University’s Interpreter Services department include the following:

• Allow the temporary interpreters limited access to the LS3 database • Install computers with LS3 access in all of the buildings that interpreters have

appointments at • Have a computer with a web camera installed at all of the clinics and the hospital. • Standardize the means for Interpreter data input • Create a new system where the temporary interpreters turn in an availability

schedule at the beginning of each month for that month. • Double-book some interpreter appointments at the smaller locations/clinics for the

same interpreter. • Increase the work force and regional allocation for Spanish, Chinese (Mandarin),

and Japanese in the blue region. • Provide incentive to interpret in the Blue (Main Hospital) region

The expected impact of implementing these recommendations is that the number of appointments that remain unfilled should decrease, travel time should be reduced between appointments, improved scheduling of the interpreters, and standardization should lead to more accurate data input and employee productivity.

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INTRODUCTION The translation needs of foreign language speaking in-patients and out-patients within the University of Michigan Health System are fulfilled by the Interpreter Services Department. Interpreters translate for non-English speaking patients by prearranged appointments as well as patients treated for emergencies such as child births or sudden injuries. Interpreters can be assigned to interpretation jobs at the local University Hospital or to travel to other locations affiliated with the University Health System. Due to the contributing factors of the current economic strain, inefficiency in the scheduling process, and high gas prices, the Interpreter Services department wants to optimize the method for scheduling interpreter appointments and reduce travel related expenses. The Interpreter Services department coordinator wishes to increase the productivity of the interpreters within the department, reduce interpreter travel between appointments, and meet the demand better for language interpreters (especially last minute requests), in a cost efficient and travel reduced manner. Therefore, the coordinator requested that the project team analyze the department’s LS3 (Language Service, Schedule and Support) scheduling database to determine if trends exist in requests for and scheduling of the interpreter work force by: region, language, facility, interpretation time, day, etc. The team has analyzed trends in the data and looked for ways to increase the efficiency of the department’s scheduling method and interpreter assignments. This final report presents the team’s recommendations for improving the scheduling of interpreters, reducing the travel between appointments and the optimization of the workforce allocation to meet demand. GOALS AND OBJECTIVES The goal of the project was to optimize the scheduling methods of the University of Michigan Health System Interpreter Services department. Accomplishing this goal allows for achieving the following objectives:

• Increased interpreter productivity • Optimized workforce allocation and usage • Reduced department costs • Reduced interpreter travel

BACKGROUND The University of Michigan Health System Interpreter Services department provides foreign language interpretation services to multiple facilities within five regions in the state of Michigan. The regions as illustrated by the map in Appendix A-1 are designated with the following region name and color scheme:

• Main Region, Blue

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• Ypsilanti Region, Green • East Ann Arbor Region, Yellow • Briarwood Region, Orange • Periphery Region, Purple.

The department has interpreters for 43 languages. The dominant language requests are for Spanish, Chinese (Mandarin and Cantonese), Sign Language, French, Korean, Arabic, Japanese, and Russian. Other interpretation requests include: Chaldean, Albanian, Bengali, Hindi, Romanian, Persian, Turkish, Urdu, and Vietnamese. The method for requesting an interpreter depends on the requesting facility. All out-patient operations request interpreters using the hospital-wide EWS (Enterprise Wide Scheduling) system, which links to the interpreter department’s LS3 (Language Service, System and Support) system. The facilities and in-patient operations, such as the paternity ward and emergency room also use emails, faxes, phone calls, instant messaging, and EWS to contact the Interpreter Services department and request an interpreter. These requests are then placed manually into the LS3 system by department schedulers. The Interpreter Services work force is comprised of department officials, schedulers, staffed interpreters and temporary interpreters. Schedulers process all requests not received through the LS3 system, assign appointments to staff interpreters, and offer appointments to temporary interpreters. In addition, staff interpreters have access to the LS3 system and can assign themselves appointments, assign other staff interpreter appointments and list themselves as unavailable for a particular day or time. Each staffed interpreter is responsible for two regions: a primary and secondary. Temporary interpreters do not have access to the LS3 system and are contacted to accept or decline any appointment they are offered. When an appointment can not be filled by a staff or temporary interpreter, the requesting facility / unit can use a telephone interpretation company contracted through the Interpreter Services department. The management of the Interpreter Services department is questioning the efficiency of the current scheduling methods, especially for emergency services and for services that require travel to in other regions for the following reasons:

• Interpreters who work or live in their primary region are sometimes assigned interpreting appointments in other regions.

• Fuel costs, magnified by increasing gas prices, are becoming a growing concern. • Time spent traveling from one appointment to the next is detrimental to

interpreter productivity. • The realization that interpreters want to work close to their residences may bias

the appointments they accept. This bias contributes to the difficulty providing emergency services for some languages.

• Interpreters are sometimes unaware that they have been scheduled to fill an appointment and miss it.

• Some interpreters are underutilized within some regions.

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PROJECT SCOPE

The project scope included:

• Analyzing the data given in the LS3 database • Determining work force allocation by region • Analyzing trends in interpreter workload data • Formulating and presenting recommendations that will help achieve the project’s

goal and objectives • Estimating financial impacts of recommendations

The project scope excluded:

• Developing a computer interface or scheduling system • Implementing the recommendations

EXPECTED IMPACT Implementing the recommendations developed from this project will result in:

Improved daily, regional, and facility scheduling of interpreters • Increased interpreter productivity • Optimized interpreter assigned region to case locations • Optimized temporary interpreter usage • Reduced interpreter travel between appointments

PROJECT APPROACH

This project involved several parties which included: • Individuals who work within the University of Michigan Health System

Interpreter Service • Clinics and hospitals in the five regions that schedule interpreters • Doctors working with the interpreters • Patients who need interpretation

Collaborating with many of these individuals, the team conducted this project in six phases: Interpreter Interviews, Interpreters Department Appointment Scheduling Flowchart, Interpreters Department Work Force Capacity / Productivity vs. Outs, Literature Research, Data Analysis and Recommendation Development.

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Phase I – Interpreter Interviews and Time Studies Two interpreter appointment time studies were conducted to determine how the interpreter recorded information relevant to the appointment and LS3 database. The team observed two interpreters during actual appointments and recorded the following:

• The time the interpreter arrived at the facility where the appointment was held • The time the patient arrived • The length of time the interpreter and the patient waited to see the physician • The length of the interpretation appointment

The team noted the appointment waiting, start and end time to see if it corroborated with the data point values similar to the means found within the database for that language, and it was similar. The team interviewed the same two interpreters to collect interpreter suggestions on ways to optimize the scheduling procedure and improve interpreter productivity. The interview occurred immediately after the time study and involved questions pertaining to:

• Travel time and distance between appointments • The Interpreter Services scheduling procedure • Suggestions on making the department run more efficiently

Phase II – Department Scheduling Procedure Flowchart The team mapped out the scheduling process with the assistance of the Interpreter Services program coordinator. The flowchart that developed showed how appointments arrive to the department, the process by which the appointments are assigned or filled and how the appointment details such as interpretation time are recorded. The team also consulted with two interpreters, and other department staff members to corroborate the department’s process. The flowchart of the Interpreters Services Department appointment scheduling procedure is shown in Appendix A-2. Phase III – Data Analysis The team conducted data analysis in a four step process. The steps were:

Step 1: Obtain Data - Retrieving the data found within the Interpreters Services LS3 scheduling database in a format (Microsoft Excel) that would allow for easy data interpretation and stratification.

• Step 2: Validate Data – Determine if the data given is accurate enough to properly reflect the scheduling activities of the Interpreters Services department and calculate the margin of error given the number of invalid data points.

• Step 3: Determine Optimal Data Observation – Decide the optimal languages to analyze so that they would provide the greatest benefit to the department’s scheduling.

• Step 4: Stratify the optimal data using pivot tables to show:

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o Total Interpreter Demand from (5/10/2008 – 10/5/2008) o Interpreter Demand by Day of the Week o Interpreter Demand by Time of the Day o Interpreter Demand by Region o Interpreter Appointment Time by Language o Unable to Schedule Jobs from (5/10/2008 – 10/5/2008) o Unable to Schedule Jobs by Day of the Week o Unable to Schedule by Region

Phase IV – Department Work Force Capacity vs. Outsourcing Fund Allocation Using an employee roster supplied by the Interpreter Services coordinator, the team determined the department’s work force capacity. Work force capacity is the amount of interpretation hours that can be provided given the number of full time interpreters, which is referred to in this sense as in-house resources. Work force capacity is dependent on the productivity rate of the employees, so it does not correspond with the total number of hours worked by an employee unless they are 100% productive. Data from the LS3 database allowed the team to calculate the demand for interpretation during May 2008 to October 2008. The demand that was not covered by the in-house resources would need to be outsourced; assigned to temporary interpreters. With all the aforementioned information and conducted analyses, the team was able to determine how much the department was potentially spending on outsourcing given their current productivity level versus the industry standard for productivity. Phase V – Literature Research A literature research was conducted on interpreters, interpreter scheduling and other foreign language translation tools in relation to hospital and health system operation. This phase occurred in conjunction with the data analysis part of the project. There was repository research conducted from the National Council on Interpreting in Health Care, NCIHC’s website. The team attempted to find the best practices used currently in other occupational scheduling and interpreter jobs. Trends found through the data analysis would be compared to the issues found in the literature research. The team searched for information related to the goals and objectives of the project by examining research articles and publications found the University of Michigan’s e-journal database. The resources utilized by the team are cited in the References. Phase VI – Recommendation Development Finally, observation from the data analysis and conclusions were used to formulate recommendations that could be used to meet the goals of the Interpreter Services department. The expected impact of the recommendations formulate are to increase the productivity of the department’s interpreters, reduce interpreter travel between appointments, and meet the demand better for interpreters in each of the regions in a cost efficient manner.

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DATA ANALYSIS Data Analysis was conducted to identify trends within the department’s interpreter scheduling and within the UMHS interpreter demand distribution. Identifying these trends would allow the team to make the department aware of them and develop recommendation to suit them. The team completed the Data Analysis phase through a four step process which included: Obtaining the Data, Validating the Data, Determining the Optimal Data to Observe and finally, Stratifying the Optimal Data. Step 1 - Obtain Data The data was given to the team in the form of a Microsoft Excel file and had fields including: Interpreter Name, Language Requested, Location of interpretation, Interpreter position (staff interpreter, temporary interpreter, contractor, and telephone interpreter services), Day / Time of the appointment, and the Length of the interpretation appointment. The database of LS3 system was received from the University of Michigan Interpreter Services department. The database contained over 21,200 data points and provided appointment scheduling information for the department's operations from May 2008 to October 2008. Step 2 – Validate Data The team validated the data by determining the number of unaccounted appointments that existed, see Table 1 below. Unaccounted appointments are appointments which were not show up in the following categories when stratified in Excel:

• Filled – Having a staffed interpreter or temporary interpreter assigned to the appointment

• Unable to Fill – Having the requesting facility use the telephone interpretation company for the appointment because no staff or temporary interpreters are available.

• Pending – No attempts have been made to assign the appointment (usually due to the appointment having a date well into the future)

Table 1 - Validity Check of Data from LS3 Database

Day of the Week

Appointments Remaining

after Cancellation

Filled Appointments

Unable to Fill Appointments

Pending Appointments

Unaccounted Appointments

Monday 3575 2928 71 216 360 Tuesday 3681 3326 67 287 1 Wednesday 3601 3214 68 319 0 Thursday 3382 3066 54 262 0 Friday 2844 2606 53 185 0 Saturday 311 276 11 23 1 Sunday 123 116 1 6 0

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(Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202) Unaccounted appointments result from a number of causes ranging from managerial or interpreter forgetfulness in recording details into the database to simple human error. The small number of unaccounted appointment occurrences, 362, when compared to the total number of appointments after cancelation, 17,517 proves the database to be valid, with a mere 2% of error. A second data validity check was done through the time study of the interpreter. During the appointment, the team noted the appointment waiting, start and end time to see if it corroborated with the data point values similar to the means found within the database for that language. Step 3 – Observe Data After obtaining and observing the validity of the data, the team began to look at the total demand for interpreters. Figure 1 shows a graph of the total demand for the interpreters in each language from 5/10/2008 to 10/5/2008. This confirms the information that we were given about the eight languages (Spanish, Chinese, Japanese, Arabic, Russian, Sign, Korean, and French) being dominant languages having a significantly greater demand than the other languages.

Figure 1 – Graph Representing the Total Demand for Interpreter Services Jobs from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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Since there were so many languages, a further analysis was taken to determine which languages were having difficulty meeting the demand for the interpretations services. This would help identify which languages actually needed to be focused on for the further data stratifications. For each requested language, Table 2 shows the Total Demand compared to the Total Filled Appointments. It also shows the percentage of the demand that was met and the percentage of the demand that was not met. The percentages of met demand give the perception that most departments are doing well with meeting interpretation needs. However, the languages with some of the highest percentage of met demand have the greatest number of unmet appointments. For example, Table 2 shows that Chinese met 99.3% of its demand, yet it had the second highest number of appointments that were unable to be scheduled. Using the actual number of unmet appointments, the team decided which languages needed the most improvement. The most problematic languages would be identified as languages with more than five unmet appointments, since there were five months (5/10/2008 to 10/5/2008) of data collection. These most problematic languages were highlighted in Table 2. Table 2 – Table Identifying Languages with the Most Unmet Demand

REQUESTED LANGUAGE

Total Demand

Total Filled %met % unmet #unmet

Spanish 4425 4374 0.988 0.012 51 Chinese 3048 3026 0.993 0.007 22 Japanese 2033 1988 0.978 0.022 45 Arabic 1558 1542 0.990 0.010 16 Russian 1470 1461 0.994 0.006 9 Sign 855 846 0.989 0.011 9 Korean 576 525 0.911 0.089 51 French 416 397 0.954 0.046 19 Chaldean 160 160 1.000 0.000 0 Vietnamese 143 120 0.839 0.161 23 Romanian 141 139 0.986 0.014 2 Cantonese 138 126 0.913 0.087 12 Hindi 85 77 0.906 0.094 8 Farsi/Persian 155 153 0.987 0.013 2 Somali 60 44 0.733 0.267 16 Portuguese 54 51 0.944 0.056 3 Urdu 42 41 0.976 0.024 1 Albanian 35 19 0.543 0.457 16 Punjabi 34 32 0.941 0.059 2 Greek 28 28 1.000 0.000 0 Italian 22 21 0.955 0.045 1 Croatian 21 20 0.952 0.048 1

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Turkish 9 9 1.000 0.000 0 Gujarati 9 9 1.000 0.000 0 Polish 9 7 0.778 0.222 2 Bengali 9 7 0.778 0.222 2 Kurdish 4 3 0.750 0.250 1 Serbian 4 3 0.750 0.250 1 Hmong 3 2 0.667 0.333 1 Bosnian 3 3 1.000 0.000 0 Macedonian 2 2 1.000 0.000 0 Ukrainian 2 2 1.000 0.000 0 Bulgarian 2 0 0.000 1.000 2 Amharic 2 2 1.000 0.000 0 Thai 1 0 0.000 1.000 1 Laos 1 1 1.000 0.000 0 Armenian 1 0 0.000 1.000 1 Swedish 1 0 0.000 1.000 1 Telugu 1 0 0.000 1.000 1 Grand Total 15562 322

(Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202) Once the most problematic languages were identified, the total demand distribution for these languages was compared to the Unmet Demand for each language. Figure 2 shows that there was still a significant number of appointments that were unable to be schedule for some patients even though there was a large demand that was met for each language.

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Figure 2 – Graph Representing the Total Demand for the Most Problematic Languages vs. the Unmet Demand for Those Languages in the Interpreter Services Department from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202) The thirteen languages that were deemed most problematic and will be used for the duration of this analysis are thus Spanish, Chinese, Japanese, Arabic, Russian, Sign, Korean, French, Vietnamese, Cantonese, Hindi, Somali, and Albanian and are considered the optimal languages to improve. Step 4 – Stratify Data The demand for interpreters was then observed by the day of the week for the most problematic languages. Figure 3 shows the demand distribution for Mondays during the 22 weeks (from 5/10/08 to 10/5/08). The demand for interpreters was relatively high for most of the languages on Monday. The demand distribution shown in Appendix (A-3) to Appendix (A-6) for the other weekdays (Tuesday to Friday) had a similar distribution to that shown for Monday in Figure 3.

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Demand Distribution by Day of the Week

Figure 3 – Graph Representing the Monday Demand vs. Unable-to-Schedule Appointments for Interpreter Services from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

The demand for interpreters on the weekend, however, was significantly lower than the demand for interpreters during the week. Figure 4 shows that the demand for Spanish interpreters was 90.3% less on Saturdays than it was on Mondays. Figure 5 shows that the demand for Spanish interpreters was 99.7% lower on Sundays than it was on Mondays. This disparity in interpreter demand between the Saturdays and Sundays was similar to the disparity in demand for the other days of the week shown in the appendix as well.

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Figure 4 – Graph Representing the Saturday Demand for Interpreter Services from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

Another observation made was that the higher the demand was for a certain language, the more likely it is that there would be significant unmet demand for that language. Figure 4 showed that only the two languages with the highest demand (Japanese and Spanish) had some appointments that were unable to be scheduled. The other languages that had fewer request for interpreter services was able to meet all of its demand. Similarly, Figure 5 shows that the greatest number of requests for interpreter services on Sunday was seven total and the demand for each language request made on Sundays was completely met.

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Figure 5 – Graph Representing the Sunday Demand for Interpreter Services from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

The trend for interpreter demand for the days of the week shows that there is a significantly higher demand for interpreters during the weekdays than the weekends.

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Demand Distribution by Time of the Day

After finding that the greatest demand for interpreter services was during the weekday, further stratifications were conducted. The demand for interpreter services was observed across the different times of the day. Figure 6 shows that the greatest demand for Spanish, Chinese, Japanese, and Arabic occurred between 8am to 4pm. The demand for interpretations was 92.6% for Spanish, 93.0% for Chinese, 92.3% for Japanese, and 91.9% for Arabic during the 8am to 4pm hours. Appendix (A-7) to Appendix (A-8) shows a similar trend for the nine languages analyzed. This trend shows that most appointments occurred during regular business hours.

Figure 6 – Graph Representing the Demand for Interpreter Services by Time of Day from 5/10/2008 to 10/5/2008 for Spanish, Chinese, Japanese, and Arabic (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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Interpreter Demand Distribution by Region Next, an analysis was conducted to determine if there was a demand disparity by region. Figure 7 showed that the Blue Region (the Main Hospital) had the greatest demand for interpreters. Appendix (A-9) to Appendix (A-12) shows the demand distribution for the other regions. When the demand for Spanish interpreters in Figure 6 is compared to the total demand for interpreters in Figure 2, it is observed that 85.9% of the demand was found in the Blue Region alone. This is comparable to the second greatest demand for Spanish interpreters at 9.1% in the Green (Ypsilanti) Region. Similarly, 86.1% of the demand for Chinese interpreters was found in the Blue Region alone. This is comparable to the Yellow (East Ann Arbor) Region which had the second highest demand for Chinese interpreters at 11.6%. The greatest demand for Interpreters occurred at the Main Hospital (Blue) Region.

Figure 7– Graph Representing the Demand for Interpreter Services in the Blue (Main Hospital) Region from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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Interpretation Times To determine the distribution of time the interpreters spent on average at their interpreter appointments, box plots were created. Figure 8 shows the box plot distributions of the average number of minutes interpreters spent interpreting during their assigned appointment. The average time spent interpreting is the number shown next to the box plot display. Even though some languages had interpretation times as low as 5 minutes and as high as 480 minutes, the average interpretation time for most languages appeared to be about an hour long. Appendix (A-13) to Appendix (A-14) shows the interpretation times of the other eight of the 13 languages observed. The Albanian language was omitted from this observation due to error with the interpreters putting their data into the LS3 database for interpreting times.

FrenchKoreanSignRussian

700

600

500

400

300

200

100

0

Requested Language

Tim

e S

pent

Int

erpr

etin

g (i

n m

inut

es)

67.0529

111.148

52.427978.4505

Time Spent Interpreting for Russian, Sign, Korean, French

Figure 8 – Graph Representing the Time Interpreters Spent Interpreting 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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LITERATURE SEARCH Through the duration of our literature search, the team has found that there are other hospitals that are working on improving their interpreter services. However, from the information, it seems like these hospitals are a step below the improvements that the UMHS is working towards (Reference, [1]). Because the UMHS Interpreter Services is already a step above other hospitals, the team looked at new technologies that could possibly help the process improvement. One consideration was a speech translation device where, the need for interpreters could be eliminated completely. The team did research to find out if there is any similar device currently on the market. From this search, two useful articles were found that could be the most helpful to the project. However, these articles were from 2005 and 2003, so they are not very up to date. The article from 2005 details a speech translation technology that was introduced, but this technology is only for one way translation and not for a conversation (Reference, [4]). The article from 2003 discusses translation software that IBM is working on. This software would allow conversation between two people of different languages. It will translate the conversation out loud along with typing the conversation up on the device that is being used. IBM plans on making this software compatible with personal hand held items that people use in their everyday lives (Reference, [3]). One problem with these items was the lack of information available on these items. The second more important problem was the similarity of the products to the phone systems used by the hospitals to interpret. When most doctors reach the last resort of providing interpretation services to patients via phone, they generally choose not to provide an interpretation service at all. Most doctors either don’t have time to set up the phones, don’t like the level of service of not having a person there to interpret, or feel uncomfortable having to use the phone services for severe diagnosis such as cancer or other terminal illnesses. Speech translation devices and translation technology have been eliminated from the recommendation considerations. Finally research was conducted on web cam systems. The doctors would be able to bring a computer into the room that has a web cam on it. This way an interpreter could interact with the patient and the doctor while being at a completely different location. This would eliminate the need for the interpreter to travel to the site and possibly handle multiple appointments at the same time. Since the university already has computers that can accommodate webcam, the only expense that would come across is the cost of the web cam (Reference, [5]). The only thing that might pose a problem is the need for a secured network for the web cam to be used on so the patient’s information can’t be hacked into. WORKFORCE CAPACITY VS. OUTSOURCING FUND ALLOCATION The team calculated the work force capacity of the Interpreters Services Department. Work force capacity is the numbers of hours of interpretation by language that are potentially available given the number of staffed interpreters. The first steps in this

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process are breaking down the work force, and then calculating the department’s in-house resources. The department has a total work force (administrative staff, staff interpreters and temporary interpreters) of approximately 90 people. Table 3 below, gives an in-depth look at the workforce. It describes the staff positions in the Interpreter Services department, the description of their role, and the number of people assigned to that role. Table 3 - Interpreter Services Workforce Break Down

Position Description Number of People Administrative Staff Individuals who strictly complete

administrative work and provide no interpretation services. Example: Department officials, Schedulers, etc.

3 people (providing a total of 100 hours weekly)

Interpreter / Admin. Staff

Interpreters who serve as a Lead for a certain region, language or set of languages. They have the role similar to that of a manager; serving directly below the department coordinator and also having administrative duties.

5 people

Staff Interpreters Interpreters that are employed full-time by the department, receiving fringe benefits and are paid a salary not based on hours of interpretation rendered. Staff interpreters are considered in-house resources.

20 people

(Source: UMHS Department Coordinator, October 28, 2008) Not all of the employees work the same number of hours per week. Table 4 shows the total number of hours available per week for each of the languages that has full time interpreters. This number represent the time that the department would be able to fill based on the number of hours per week that the interpreters work altogether. The chart is read according to the following key scheme: [Language (# of staffed interpreters): Hours available per week]. Table 4 - Hours of Staff Interpretation Available Per Week by Language [Chinese (2) : 60 hours weekly]

[Sign (3) : 78 hours weekly] [Korean (1) : 32 hours weekly]

[Arabic (2) : 72 hours weekly]

[Russian (2) : 62 hours weekly]

[Spanish (6) : 192 hours weekly]

[French (1) : 40 hours weekly]

[Japanese (3) : 86 hours weekly]

(Source: UMHS Department Coordinator, October 28, 2008)

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Using the data from the LS3 database the team then determined the cost of using outside resources (temporary interpreters) to fill the demand for interpreters within the University of Michigan Health System. Temporary Interpreters cost $17 dollars per hour and the phone-based interpretation company cost $1.12 per minute. Telephone-based interpretation is only financially efficient if used for 15 minutes or less. With appointments generally ranging from 45 minutes to 90 minutes, the team did not consider the telephone-based interpretation when determining the outsourcing fund allocation. According to the department coordinator, staff interpreters are currently operating at 30% productivity and the industry standard is approximately 60% – 70%. Appendix XX shows the Interpreter Services In-house Work Capacity, Interpreter Demand and resulting Outsourcing expense for their current 30% productivity in comparison to a 65% industry standard. Table 5 shows the approximate amount of money being spent on temporary interpretation at the two levels of productivity. The use of the specific 13 languages in Figure 3 will be explained in the Data Analysis – Optimal Data Observation portion of the report. Table 5- Funds Spent Potentially on Outsourcing Operating at 30% or 65% productivity

Language

Temporary Interpreter Cost 30% Productivity

Temporary Interpreter Cost 65% Productivity

Spanish $56,190.10 $31,057.30 Chinese $36,448.00 $28,594.00 Japanese $22,031.72 $10,774.32 Russian $18,125.40 $10,116.70 Arabic $15,574.27 $6,149.47 Sign $20,318.40 $10,108.20 Korean $3,753.60 -$435.20 French $2,584.00 -$2,652.00 Cantonese $2,932.50 $2,932.50 Vietnamese $2,633.58 $2,633.58 Hindi $1,685.83 $1,685.83 Somali $1,020.00 $1,020.00 Albanian $446.25 $446.25 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202) If the Interpreter Services department was operating at the industry standard productivity rate they stand to potentially save over $20,000 over the 22 weeks given the demand for Spanish interpretation alone. The in-house resources for French and Korean interpretation would outweigh the demand and thus leave those interpreters with spare time to provide administrative aide, especially with scheduling. CONCLUSIONS The team determined conclusions via the data stratification process in the data analysis phase of the project approach. Some of the main conclusions are as follows:

• The demand for interpreters is greater during the week than on the weekend.

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• Most appointments were scheduled around the regular business hours. • The higher the demand for a language, the greater number of unmet demand it

had. • The Blue (Main Hospital) Region had the largest unmet demand of all the regions

served and it also had the greatest unmet demand.

The other conclusions are represented by language and show the demand of a language by: Region, Day of the Week, and Peak Time during the Day. The 95% confident interval mean interpreting time is also given in the conclusion. Figure 9 shows the conclusions for the Spanish language. The conclusions for the remaining 12 languages are in Appendix (A-15).

Figure 9 - Conclusion: Spanish Language (Source: UMHS Interpreter Services 4/2008 – 8/2008. Sample Size: 21,202) RECOMMENDATIONS The recommendations have been divided into three sections: general recommendations, recommendations for the thirteen most problematic languages, and recommendations for the remaining low-demand languages. The general recommendations suggestions a few changes that could be made across the department as a whole. The recommendations for the thirteen most problematic languages are suggestions for the languages with the greatest number of unmet demand that could drastically improve the allocation of interpreter resources. The recommendations for the remaining languages are minor changes that could be made to marginally improve the other languages.

Language - Spanish Demand – By Region

Greatest Least Blue Region Green Region Orange Region Yellow Region Purple Region

2607 275 78 62 13 Demand – By Day of the Week

Greatest Least Tuesday Wednesday Monday Thursday Friday Saturday Sunday

668 651 596 533 490 35 2 Demand – Peak Time during the Day

Peak 1 Peak 2 Peak 3 9 am 10 am 2 pm 95% Confident Interval Mean Interpreting Time: 63.98 minutes

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1. Allow the temporary interpreters limited access into LS3. Temps would only be able to access the system at certain times of the day, resulting in:

• A decrease on the number of appointments that remain unfilled at the last minute

• Reduced workload for the schedulers. However, the temporary interpreters will not have full access to LS3. They will only be able to:

• See appointments that are coming up within a specific amount of time that is selected by management

• Schedule themselves only and not other interpreters. 2. Install computers with LS3 access in all of the buildings that interpreters have

appointments at. These computers will

• Allow the interpreters to be productive during the times that they are waiting on their next appointments

• Reduce the need to travel to another building to use a computer It is understood that many but not all of the buildings have computers that potentially can be used for this purpose.

3. Have a computer with a web camera installed at all of the clinics and the hospital.

• This computer could be secured to a cart that could be moved into different rooms and could access the wireless network.

• The nurse or medical assistant would set up the computer just the same as if they prepared the patient to take their blood pressure or weight.

• The interpreter would be at a remote location. Using this system, the interpreter might be able to handle multiple appointments at multiple locations all at the same time.

• This would significantly reduce travel time all together.

4. Standardize the means for interpreter data input. This would allow for more accurate data in the data base to be able to see the trends of which regions, languages, and clinics need improvement upon. This could be done by:

• Making the use of an official form to fill out during the appointment mandatory, instead of “remembering” the data and filling it out later

• Install some sort of data recording system for interpreter data input.

5. Create a new system where the temporary interpreters turn in an availability

schedule at the beginning of each month for that month. This should improve the scheduling process as it:

26

• Allows the schedulers to know which temporary interpreters are potentially available for appointments that need to be scheduled without having to call around first.

• allow the secretaries at the appointments to see what time interpreters with the needed language are available

• makes the patient aware of times an interpreter would and would not be available

• Reduces ambiguity in the temporary interpreter scheduling process • Eliminates the over booking of certain languages which will also help

eliminate access temporary interpreter usage

6. Double book some interpreter appointments at the smaller locations/clinics for the same interpreter. This would allow an interpreter to do more than one appointment at a time. Most doctors can see more than one patient at a time and similarly, an interpreter can interpreter for more than one patient at a time since the doctor is usually only in a room with the patient for minutes at a time. One of the interpreters mentioned how successful it was for her to do multiple interpretations in the same hour. Also, if the interpreter has a computer at the location, as previously recommended, the interpreter will be able to have maximum productivity and very little down time. This will increase the interpreter productivity by reducing the travel time that interpreters have when going to different appointments.

7. Increase the work force and regional allocation for Spanish, Chinese (Mandarin), and Japanese in the blue region. These are the three languages and regions that have the highest percentages of unmet appointments over the period of the sample data. After looking into this, the team recommends looking into the other languages and regions as needed.

8. Provide incentive to interpret in the Blue (Main Hospital) region: One of the main reasons the disparities between the demand for Interpreter Service at the main hospital and the appointments that go unmet is the difficulty finding parking in the area. Difficulty parking has been identified as by numerous hospital affiliates as a burden for working that University’s hospital. Reducing the difficulty with parking might reduce the number of full time and temporary employees who avoid appointments at the hospital in an effort to avoid the hassle of parking.

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SUPPORT GIVEN BY OPERATING ENTITIES In closing the team will like to acknowledge and thank the following individuals for the support they have give us through the duration of the project. Michelle Harris, the Program Coordinator for Interpreter Services who is also the project client, has provided support by:

• Scheduling interviews with interpreters, schedulers, and doctors • Checking staff availability for interviews • Providing a map of the five regions • Providing interpreter schedule data for LS3 in Excel format for April 2008 –

August 2008 • Introducing the team to department staff • Acting as a liaison between the team, department staff, and other operating

entities Dr. Richard Coffey, the Project Coordinator, has provided support by:

• Keeping project within scope • Suggesting analytical methods for data stratification • Assisting in determining trends within data • Providing guidance through project completion

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Appendices Appendix (A-1)

30

Appendix (A-2)

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Appendix (A-3) - Graph Representing the Tuesday Demand for the Most Problematic Languages in the Interpreter Services Department from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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Appendix (A-4) - Graph Representing the Wednesday Demand for the Most Problematic Languages in the Interpreter Services Department from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

33

Appendix (A-5) - Graph Representing the Thursday Demand for the Most Problematic Languages in the Interpreter Services Department from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

Appendix (A-6) - Graph Representing the Tuesday Demand for the Most Problematic Languages in the Interpreter Services Department from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

34

Appendix (A-7) - Graph Representing the Demand for Interpreter Services by Time of Day from 5/10/2008 to 10/5/2008 for Russian, Sign, Korean, and French (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

35

Appendix (A-8) - Graph Representing the Demand for Interpreter Services by Time of Day from 5/10/2008 to 10/5/2008 for Vietnamese, Cantonese, Hindi, Somali, and Albanian (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

36

Appendix (A-9) - Graph Representing the Demand for Interpreter Services in the Green (Ypsilanti) Region from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

37

Appendix (A-10) - Graph Representing the Demand for Interpreter Services in the Orange (Briarwood) Region from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

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Appendix (A-11) - Graph Representing the Demand for Interpreter Services in the Purple (Periphery) Region from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

39

Appendix (A-12) - Graph Representing the Demand for Interpreter Services in the Yellow (East Ann Arbor) Region from 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

40

ArabicJapaneseChineseSpanish

900

800

700

600

500

400

300

200

100

0

Requested Language

Tim

e S

pent

Int

erpr

etin

g (i

n m

inut

es)

63.9843 57.0865 61.259776.8479

Time Spent Interpreting for Spanish, Chinese, Japanese, Arabic

Appendix (A-13) - Graph Representing the Time Interpreters Spent Interpreting 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

41

SomaliHindiCantoneseVietnamese

400

300

200

100

0

Requested Language

Tim

e S

pent

Int

erpr

etin

g (i

n m

inut

es)

79.825103.176 98.8298

83.1579

Time Spent Interpreting for Vietnamese, Cantonese, Hindi, Somali

Appendix (A-14) - Graph Representing the Time Interpreters Spent Interpreting 5/10/2008 to 10/5/2008 (Source: UMHS Interpreter Services 5/2008 – 10/2008. Sample Size: 21,202)

42

APPENDIX (A-15) – Conclusions

Language - Chinese Demand – By Region

Greatest Least Blue Yellow Orange Purple Green 2161 291 51 8 0 Demand – By Day of the Week

Greatest Least Wednesday Tuesday Thursday Monday Friday Saturday Sunday


Peak 1 Peak 2 Peak 3 8 am 9am 10 am / 1pm

95% Confident Interval Mean Interpreting Time: 57.09 minutes

Language - Japanese Demand – By Region


Greatest Least Tuesday Wednesday Monday Thursday Friday Saturday Sunday


Peak 1 Peak 2 Peak 3 10 am 1 pm 2 pm

95% Confident Interval Mean Interpreting Time: 61.26 minutes

43

Language - Russian Demand – By Region

Greatest Least Blue Orange Yellow Green Purple 961 118 34 6 6 Demand – By Day of the Week

Greatest Least Tuesday Wednesday Monday Thursday Friday Sunday Saturday



Language - Arabic Demand – By Region

Greatest Least Blue Yellow Orange Green Purple 900 53 42 27 11 Demand – By Day of the Week

Greatest Least Thursday Tuesday Wednesday Monday Friday Saturday Sunday



44

Language - Sign Demand – By Region

Greatest Least Blue Orange Purple Yellow Green 503 74 39 32 5 Demand – By Day of the Week

Greatest Least Tuesday Thursday Monday Wednesday Friday Saturday Sunday



Language - Korean Demand – By Region


Greatest Least Wednesday Tuesday Monday Thursday Friday Saturday Sunday


Peak 1 Peak 2 Peak 3 9 am 10 am 11 am 95% Confident Interval Mean Interpreting Time: 52.43 minutes

45

Language - French Demand – By Region

Greatest Least Blue Green Orange Purple Yellow 233 138 13 2 0 Demand – By Day of the Week

Greatest Least Thursday Tuesday Monday Wednesday Friday Saturday Sunday



Language - Cantonese Demand – By Region

Greatest Least Blue Orange Yellow Purple Green 115 7 5 4 2 Demand – By Day of the Week

Greatest Least Monday Tuesday Thursday Wednesday Friday Saturday Sunday



46

Language - Vietnamese Demand – By Region

Greatest Least Blue Yellow Orange Green Purple 84 7 6 5 2 Demand – By Day of the Week

Greatest Least Monday Tuesday Wednesday Thursday Friday Sunday Saturday



Language - Hindi Demand – By Region

Greatest Least Blue Purple Yellow Green Orange 62 3 3 0 0 Demand – By Day of the Week

Greatest Least Thursday Monday Wednesday Friday Tuesday Saturday Sunday



47

Language - Somali Demand – By Region

Greatest Least Blue Green Orange Yellow Purple 35 12 7 2 0 Demand – By Day of the Week

Greatest Least Friday Wednesday Thursday Monday Tuesday Saturday Sunday


Peak 1 Peak 2 Peak 3 10 am 11 am 8 am / 1 pm / 2 pm 95% Confident Interval Mean Interpreting Time: 83.16 minutes

Language - Albanian Demand – By Region

Greatest Least Blue Region Purple Region Yellow Region Green Region Orange Region

27 1 1 0 0 Demand – By Day of the Week

Greatest Least Tuesday Thursday Monday Friday Wednesday Saturday Sunday


Peak 1 Peak 2 Peak 3 12 pm 8 am 9 am 95% Confident Interval Mean Interpreting Time: N/A * * Human Error

48

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