Reduction of Call Abandonment Rate of Information Services Helpdesk at United Health Services

Submitted as a requirement for SSIE 520Modelling and SimulationChun-An Chou, instructor

Reduction of Abandonment Rate of IS Helpdesk Calls at UHSKhaire Manasi, Khurjekar Neel, Mande Apurva, Yadagiri Lokesh

SSIE 520 Modelling and SimulationDepartment of Systems Science and Industrial Engineering

State University of New York at Binghamton

Abstract

In the present era, technology has made its mark in each and every industry imaginable. The healthcare industry is no exception to that. As a result, use of information technology services in this industry has become widespread. Right from monitoring a patients vitals, to storing their records, everything is manages electronically. However such extensive use of technology has its own repercussions. The problems faced by the staff while handling data digitally are solved by the organizations information service (IS) help desk. This simulation project focuses on the problem of abandoned calls faced by IS helpdesk and identifies the causes leading to the problem. Also a revised model to reduce the problem is proposed.

KeywordsInformation services, United Health Services (UHS), Simio, help desk attendants, call center.

1. IntroductionThe use of technology in the healthcare industry is rapidly gaining importance. Most of the operations carried out in the hospitals make tremendous use of computers and advanced software. Despite the machines and software being user friendly, there are certain complications which occur and are beyond the scope of the user to rectify for example screen blackout, freezing of the computer screen, errors in the software etc. These problems are solved by the information service (IS) helpdesk of the organization. The IS helpdesk aims to solve such queries faced by the UHS staff. The staff mainly comprises of physicians, nurses and other administrative officials. The helpdesk receives a number of calls and it so happens that each time every caller may not get served properly which leads to abandoned calls. The main aim of this simulation study is to reduce the rate of such abandoned calls received by the helpdesk center.

2. Literature ReviewThe study conducted in this simulation project is conducted at the United Health Services (UHS), Binghamton, New York.United Health Services (UHS) is a chain of hospitals spread across 11 counties in upstate New York. Use of technology at this hospital leads to many problems which are solved by the Information Service desk. The Information Service (IS) desk at UHS receives calls regarding issues for Information Technology by the hospital staff. The main purpose of the staff working at IS, is to solve queries of the UHS staff related to information technology. The IS staff works every day of the week for 24 hours. The number of people working at IS varies per hour. For example, at peak hours from 8 am to 12 pm, number of people working is more than people who work between 1 am to 4 am in the morning.

2.1 Problem DefinitionThe main problem Information Systems is trying to solve is of abandoned calls. Abandoned calls are such calls which enter the system but are not completed due to primary reasons like,

Caller told to hold for a longer period of time Attendant busy Improper staffing of attendants

These reasons lead to abandonment of the calls, leading to unsolved queries, ultimately leading to unsatisfied callers.

Khaire, Khurjekar, Mande, Yadagiri

3. Data Description3.1 Process flow diagramIn order to find out how many calls were abandoned we studied logic that is happening at the call center in hospital. So following is the process flow diagram for the same.

Figure1: Process flow

The above process diagram is for a particular hour during a day when only two attendants are scheduled to answer the call. So, flowing is the process that happens internally in the call center of the hospital:

Customer calls the hospital with some question As the call arrives, it checks if there is queue of calls in the system. If there is a queue, then the customer

call has to wait in the queue till next attendant attends it. Hence, the loop goes on till the call is answered and then finally the call departs whether answered or not answered.

So, if there is no queue in the system, first attendant attends the call. He solves the query of the customer and then the call departs.

But, if first attendant is busy then the call goes forward to attendant number two. The system checks if second attendant is busy or not. If he is not busy then he answers the call and then the

call departs. But, if the second attendant is busy as well, then the call goes again in the queue and the same process

repeats till call is answered. If this process takes too much time, then there are chances that the customer is not willing to or not in a

position to wait for long time and hence he cancels the call and hence the call gets abandoned.

3.2 GoalAfter knowing the problem statement, as stated upwards, the main intention is this simulation model is to develop a model that will minimize the abandonment call rate. For those two things has to be studied

Pattern of calls


For good simulation model, it is very important to find out what is the pattern of customers calling in hospitals. It will include parameters like what are the peak hours when maximum number of calls arrive in hospitals, how many calls are actually answered and then departed and how many calls departed without answering that means abandoned, what all are the general queries that are asked, how much time is needed per call.

Staffing scheduleStaffing schedule is the most crucial parameter to be studied because call center staff is the one who answers the calls. It depends upon them that how many calls gets abandoned and how many get answered. Depending upon the peak hours the number of staff should increase.

3.3 Real time data Service times

It included the total time of calls that were answered by the attendants in call center.

Abandoned call timesIt included the total time of calls that were abandoned that means were either not answered and then abandoned or else were put on wait for longer time and then abandoned.

3.4 Distribution data Software used : Expert fit, version 8.01 For distribution data, we plotted Density histogram, Distribution function differences plot, P-P plot and

performed all goodness of fit tests that are:o KS testo Anderson Darling testo Chi square test

Author listing: 12 point Times New Roman, bold, centered Author names; department or college; university or company; city, state and zip, country. Authors with the same affiliation must be grouped together on the same line with affiliation information following in a single block. An example is shown above.

4. Methodology4.1 Distribution fitFor this, service time for the calls and abandoned call time were two input variables. Easyfit software was used for the same.

Service timesFor service times, we performed distribution test. Following was the relative evaluation of service time data.

Table1: Evaluation of service time


Pearson type VI(E) is the best fitted among all others for this distribution. It is kind of continuous probability distribution. P in Pearson distribution is defined as any valid solution to differential equation.Pearson type VI distribution is as follows:

Now as per the evaluation, Pearson type VI(E) is the best suited for service time. Following is the density-histogram plot obtained from it.

Figure2: Pearson distribution plot

Goodness of fit tests Anderson darling, KS test and Chi square test were also performed for this.


Abandoned call timeFor abandoned call times, we performed distribution test. Following was the relative evaluation of abandoned call time data.

Table2: Evaluation of service time

Johnson SB is the best fitted for this distribution among others. Johnson SB distribution is used to represent empirical distribution. Its probability density function is as follows:

Now, Johnson SB is the best fitted. Following is the density-histogram plot obtained from it.


Figure3: Johnson SB plot

We also performed goodness of fit test for the same. Following are the results that were obtained from Anderson darling test and KS test.

Goodness of fit testso Anderson Darling test

It is a kind of statistical test which is used to test if the provided sample data is drawn from the provided probability distribution. It will consider that there are no parameters that are needed to be estimated in distribution. Following are the results of the test.

Figure4: Results of Anderson-darling test

For Anderson darling test, the test statistics that is A2 is calculated. It is given as:

Where, is the CDF.

o KS testKolmogorov-Smirnov test is a kind of nonparametric test of 1D and continuous probability distribution. It will identify the distance between cumulative distribution function and empirical distribution function. Following are the results that are obtained from KS test.

Figure5: KS test results


The goodness of fit test is done with the use of critical values of K distribution. Null hypothesis is rejected at level α if:

Where Kα can be calculated as,

The power of test is 1.

For empirical distribution function, Fnfor n number of samples is given as

Where I[-∞,x](Xi) is the indicator function.

For cumulative distribution function, the function is given as

Where, supx is supremum.

4.2 Baseline ModelConsidering the distribution of the input variables including the service times for each call and the call times for abandoned calls, a simulation model was developed which would follow a logic mentioned in the process flow chart.The caller calls in to the IS help desk to get a query addressed. Based on the length of the queue the caller is diverted to other available callers. If the call is kept on hold for more than a specified length of time the caller hangs up. This hanging up of the call is called abandoned calls. In this study, from the available data for a sample of abandoned calls a Johnson DB distribution was fitted. Based on the input distribution of Pearson VI (E) for service times, the below logic is developed.

Figure 5: Logic for the baseline simulation model

Figure 1 shows the various decision nodes in the process flow of baseline model. The decision to forward the call to the available attendant is taken at decide 1 node. If the attendant is busy then the call is forwarded to Assign 2 node where it is assigned to another attendant. Based on the distribution fed into the decision mode, the calls are either


abandoned or they are answered and destroyed in the simulation system. Below is the equation for the abandoning the calls.

ModelEntity.Time_exit_station–ModelEntity.Time_enter_station>Random.JohnsonDB (1)

The baseline simulation was run for one replication which led to 2937 abandoned calls out of a total of 10866 incoming calls. In the real world system, the number of calls abandoned were 2207 and number of incoming calls is 7692. From these values we can certainly say that the percentage of abandoned calls for the real system is 0.286 % whereas the abandoned calls for the real system is 0.2706 %. For proving that the real system and baseline simulation model are statistically similar, a 2-sample proportion test was performed.

4.3Validation For performing validationof the baseline model, two samples need to be considered. The sample is taken from the real world system where number of events is 2207(number of abandoned calls) out of 7692 number of trials (number of incoming calls). The second sample considered was the output of the 7 replications performed of the baseline model. The average number of abandoned calls for seven replications is 3007 and average number of incoming calls for 7 replications is 10150. For validation of the baseline system, below hypothesis testing was performed. The null hypothesis in this situation is to establish whether the real system and the simulated baseline model are the same. Whereas the alternative hypothesis says that the real system and simulated baseline model is not same.

Null Hypothesis:H 0:P1=P2 (1) Alternative Hypothesis:H a:P1≠ P2 (2)

The 2-sample proportion test is performed using Minitab software. The p-value for the critical threshold is 0.174, whereas the Fisher’s exact test p-value is 0.178. As this p-value is greater than threshold we can certainly conclude that we fail to reject the null hypothesis that the two proportions are same. Hence we can infer that the real system and the baseline model are same. The baseline model has a certain combination of attendants at every hour. This combination of attendants changes as the shift changes. Throughout the day different number of attendants answer the calls. Hence for the given baseline model 74 attendants are present throughout the day to attend the calls. But given the situation that the abandonment rate is around 27%, we propose an alternative solution for the model with a different set of attendants which changes as the shifts of the day change.

4.4 Alternate ModelThe alternative model is proposed as an improvement to the existing baseline model. The alternative model has the same logic as the baseline model. The distribution of the input services times remains the same as the baseline models along with the Johnson DB distribution which represents the abandonment data. Figure 2 shows the logic chart for alternative simulation model.


Figure 6: Logic for the alternative simulation model

The simulation model has been developed in SIMIO 7.0 software. The alterative model is multiple server system where these servers change according to the shifts of the day. After running the simulation model for one replication the number of abandoned calls reduced to 878, whereas the total incoming calls was 11010. If compared to the baseline model which had 2937 abandoned calls for a total of 10866 incoming calls.

Hence it can be claimed that the abandonment call rate for the alternative solution is 7.97 % as compared to the abandonment rate of 27 % for the baseline model.

4.5 Comparison of Baseline and Alternate ModelsThe primary analysis of the results suggests that there is a significant change in the abandonment rate of the alternative model and the baseline model. The study performs the 2- sample proportion test for the two samples in order to statistically prove that there is a significant change in the two samples.

For comparison of the alternative system with the baseline model, below hypothesis testing was performed. The null hypothesis was defined as the real system was equal to the simulated baseline model. Whereas the alternative hypothesis was defined as real system and simulated baseline model is not being the same.

Null Hypothesis:H 0:P1=P2 (1)

Alternative Hypothesis:H a:P1≠ P2 (2)

The study used the Minitab software for performing the 2-sample proportion test. The p-value for the critical threshold is 0.000, whereas the Fisher’s exact test p-value is 0.000. As this p-value is not greater than threshold we can certainly conclude that the null hypothesis is rejected for the two proportions. Hence we can infer that the baseline system model and the alternative model system are not same. The alternative model was run for a maximum of 7 replications. The average of these replications was 878 abandoned calls. A new experiment was performed with 7 replications which include a range of replications performed for each trial from 1 to 7. The average of each trial was taken and then average of these 7 averages was calculated for performing the 2-sample proportion test.

The alternative simulation model was developed with an improvement in the work schedule that was used for the baseline model. The details of the work schedule and its difference will be discussed in the later sections of the paper.


Figure 7: Simulation Diagram of the Process flow in SIMIO

Figure 3 shows the incoming calls entering the simulation system through the source1. The calls that are completed or get destroyed go to the answered tab. While the calls those are not completed or hanged up got to the abandoned tab. The call center server is representation of entire IS helpdesk center where there are different number of servers available based on the shift of the day.

5. ResultsAfter simulating an alternative model and validating the model, we can see significant changes in the number of calls going abandoned. The results of the model are seen below –

Scenario Abandoned Calls Total Calls Abandonment Rate

Baseline Model 2937 10866 27.029 %

Alternative Model 878 11010 7.97 %

Figure8: Comparing the modelsHere, the baseline model shows an abandonment rate of 27.029% with 2937 calls going abandoned and on the contrary the alternative model shows a decrease in rate of abandoned calls from 2937 to 878. The abandoned call percentage is now 7.97%.

So it is evident that the models are almost similar which has been proven in the validation and the new proposed model has a reduced abandoned call rate with a new proposed list of help desk attendants serving.


Above mentioned are the comparison between the models, having different number of help desk attendants serving. The new prosposed model has a higher number of attendants working per hour as compared to the baseline model. Also, abandoned rate has come down because of the increase in the number of attendants.

6. Conclusion and Future WorkAfter validating the models we can conclude that the new model has a reduced rate of abandoned calls with an increase in the number of attendants. The following are the conclusions drawn –

The abandonment rate was decreased from 27.029% to 7.97%. Reduction of approximately 70% in abandoned call rate. Number of helpdesk attendants has increased from 37 to 104. The help desk attendants may get repeated from multiple shifts, indicating the number 104 as the number of

paying hours.

For future work, the abandonment rate can be further reduced by implementing a different work schedule and having a combination for the attendants working per hour. Also we would suggest change in shift windows instead of varying per hour so that the number of attendants remain constant.

References:1. Averill M Law., 2014 , “Simulation Modeling and Analysis-Fourth edition”, Mc Graw Hill publications.


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Reduction of Call Abandonment Rate of Information Services Helpdesk at United Health Services