A study on optimization of waiting time for outpatients at ... · Cause and effect diagram Also Called:Ishikawa Diagram Variations: cause enumeration diagram, process fishbone, time

A study on optimization of waiting time for outpatients at Ganga hospital,

Coimbatore

1Dr.R.Vinayagasundaram,

2Dr.V.Muthukumaran,

1Associate Professor, Department of Management, Kumaraguru College of Engineering,

Coimbatore, Tamil Nadu, India,

2Professor, Department of Mechanical Engineering, Kumaraguru College of Technology,

Coimbatore, Tamil Nadu, India,

[email protected]

Abstract

To identify the various procedures at the outpatient clinic as well as to investigate the

possible operational problems that may lead to excessive patients’ waiting time. A patient’s

experience in waiting time will radically influence his/her perceptions on quality of the service.

Method: The study was carried out in one of the leading hospital in Coimbatore The subjects

were outpatients who came to the outpatient clinic at Ganga hospital. Data were analyzed using

the DMAIC method and supported by the statistical tools. Direct observation and oral survey

was conducted in order to study the current process and make some improvements.

Conclusion: The main cause for the outpatient waiting time and total process time is the delay in

x-ray due to higher patient arrival rate. A model is proposed based on scheduling the patients that

helps to reduce the waiting time of the patients.

Keywords: patients, scheduling, DMAIC method, outpatient waiting time.

1. Introduction

In every hospital, if a patient enters he or she is subjected to waiting time. The waiting time

differs from patient to patient based on their needs. A patient’s experiences in waiting time will

radically influence their perceptions about the quality of the service. Discharge delay is also a

biggest issue in the hospitals; it leads to the waiting time for the newly admitted patients. Ganga

International Journal of Pure and Applied MathematicsVolume 119 No. 17 2018, 2305-2317ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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hospital is one of the leading orthopedics, plastic and reconstructive surgery, neurosurgery, and

radiography hospitals in Coimbatore. It comprises of the general outpatient clinics, the specialist

clinics, X-ray department, surgical and inpatient facilities.

2. Need for the study:

Even though Ganga hospital was highly advanced in technology, they experienced

difficulties in outpatient waiting time, total process waiting time and delay in discharge of the

patient and receives many complaints from the patient regarding these issues. This has aroused

the interest of the researcher to minimize the outpatient waiting time, appointment waiting time,

total process time and delay in discharge using DIMAC method.

3. OBJECTIVES OF THE CASE STUDY

3.1 Primary objectives

To study about optimization of waiting time for outpatients and discharge for inpatients

at Ganga hospital, Coimbatore.

To study about the current process of outpatient waiting time, total process waiting time and

discharge of patients.In order to study about the outpatient waiting time, total process time and

discharge delay, primary data were collected through direct observation and oral survey. Data

collection is the process of collecting data in order to analyze and understand the current

progress level. The patient waiting time has been directly observed and measured using the stop

watch.By means of conducting small oral survey with the nurse, registration desk and the patient,

the current process and the difficulties in the process can be known.

4. Methodology

4.1 DIMAC method

I Define phase

In this phase the problem was identified as the waiting time of the patient. A patient’s experience

in waiting time will radically influence his/her perceptions on quality of the service. It also

increases the working time for the doctors and the nurses.

International Journal of Pure and Applied Mathematics Special Issue

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II Measure phase

In Measure phase, three methods were used such as Process mapping for understanding

the flow of process, direct observation and survey for the data collection.

Process Mapping

Process mapping is a workflow diagram Shennes Mathew, T. Janani (2017) ,to bring forth a

clearer understanding of a process or series of parallel processes.

OUTPATIENT PROCESS FLOW


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III Analyze phase

Cause and effect diagram

Also Called:Ishikawa Diagram

Variations: cause enumeration diagram, process fishbone, time–delay fishbone, CEDAC

(cause–and–effect diagram with the addition of cards), desired–result fishbone, reverse fishbone

diagram.

The fishbone diagram identifies many possible causes for an effect or problem. It can be used to

structure a brainstorming session. It immediately sorts ideas into useful categories.

Long

waiting

time

Time schedule

Patients Overcrowding

Unawareness Token system

X-ray

overcrowdi

ng

Token of Op is not

useful for X-ray

patients

High patient

arrival rate

Issue delay

Uneducated

people

Scattered

arrangements

No scheduling

system for OP

Lag of control

over crowd

Not arrived on

time

Improper

scheduling

E mail

interference

Patient

perception


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IV Improve Phase

ASSUMPTIONS:

1. As per the model suggested the timings for consulting the doctor is classified into seven

batches.

BATCH TIMING Number of patients

Batch I 8.30a.m – 9.30a.m 12

Batch II 9.30a.m – 10.30a.m 12

Batch III 10.30a.m – 11.30a.m 12

Batch IV 11.30a.m – 12.30p.m 12

Batch V 12.30p.m – 1.30p.m 12

Batch VI 2.30p.m – 3.30p.m 12

Batch VII 3.30p.m – 4.30p.m 12

Total 84

For every OP clinic = (12*7) = 84 patients

Total Number of patients = 84*5 = 420

Total number of OP clinic= 5

Total number of X-ray unit = 4

2. Assume that arrival rate between patients in each batch is 2minutes and all the patients were

going for the same OP clinic.

3. From the study, among every 10 patients, 7 will the X-ray patient. (70% are X-ray patients).

50% of the patients getting the consultation in 6 minutes, so assume that the consultation time for

every patient is 6minutes.

4. From the study,

Average waiting time (with in X-ray room) = 4.5 minutes

Average waiting time between two consecutive X-ray patients = 14 minutes


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Comparing before and after implementation of the suggested model

Testing of Hypothesis using Paired Sample T-test:

Step 1

Null Hypothesis

H0: The waiting time of the patients not reduced after the implementation of the model.

Ha: The waiting time of the patients reduced after implementing the model.

Step 2

Statistical Test: Paired Sample T-test

Justification

The study is focusing on waiting time before and after implementing the model. In order

to compare before and after impact of the suggested model paired sample t-test has been used to

analyze the data.

Since the data were available in parametric scale, paired sample t-test is more likely

preferred to get the accurate output.

Step 3

Level of Significance

For this study the alpha value is equal to 5% i.e. level of significance = 0.05.

Step 4

Process in SPSS

Analyze Compare means Paired Sample T-test


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Step 5

Analyze and Interpretation

Paired Samples Statistics

Mean N Std. Deviation Std. Error

Mean

Pair 1 Before Reg.time 4.6000 30 .93218 .17019

After Reg.time 4.3333 30 .47946 .08754

Pair 2

Before

wait.timebeforeXray 16.6333 30 12.67222 2.31362

After wait.timebeforeXray 10.9333 30 11.97968 2.18718

Pair 3 Before Xraywaiting.time 80.9667 30 42.81837 7.81753

After Xraywaiting.time 16.1667 30 12.14093 2.21662

Pair 4 Before wait.timeafterXray 9.7000 30 8.62294 1.57433

After wait.timeafterXray 9.4000 30 9.05291 1.65283

Pair 5 Before Total.waiting.time 111.7333 30 50.96039 9.30405

After Total.waiting.time 40.5333 30 15.57348 2.84332

From the table pair 3 has the maximum t value.

Decision Table for pair 3, t=7.820

P value Relation Significance Result

0.000 Less than 0.050 Reject the null.

From this interpretation the P value (0.000) is less than the significance value (0.050).

Therefore reject the null. Which implies that alternate hypotheses is accepted.

i.e. Ha is accepted

Step 6

Result

Therefore the waiting time of the patients will be reduced by implementing the model. It

helps to reduce both the X-ray waiting time and the total process waiting time.

Descriptive Statistics


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Table Average time after and before implementing the model

Registration time

(minutes)

Waiting time before

X-ray (minutes)

X-ray waiting

time

(minutes)

Waiting time after

X-ray (minutes)

Total waiting time

(minutes)

Before After Before After Before After Before After Before After

4.6 4.3

16.6 10.9 80.9 24.25 9.7 9.4 111.7 40.5

Fig.Average time after and before implementing the model

Interpretation:

The outpatient waiting time is mainly occurred due to the X-ray waiting time. X-ray waiting time

is the root causes for the outpatient waiting time and total process time.

0

20

40

60

80

100

120

Reg.time Waiting timebefore X-ray

X-ray waitingtime

Waiting timeafter X-ray

Total waitingtime

Before

After


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Benefits from the model

Benefits from implementing the suggested model

Better service to the people.

FIFO concept can be achieved.

X-ray waiting time gets reduced.

Better utilization of resources.

Avoid overcrowding.

Equal allocation of patients in every OP clinic.

Patients were able to know when they are going to get the service.

Before implementing the model


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After implementing the model

V Control phase

The improvements in the process were frequently inspected. The inspection has been done by

weekly or monthly monitoring process. A feedback system has been developed for the further

improvements.

Findings

X-ray waiting time is the root cause for both the outpatient waiting time and total process

waiting time.

There is an imbalance in the existing process were non X-ray patients getting service in a

short duration whereas the duration for the X-ray patient is very high.

Every patients had a perception of early visit to the hospital can helps them to get the

faster service. But their perception leads to the overcrowding.

In discharge, doctor’s instruction delay plays a major role in determining the delay in

discharge.


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In billing section there is an idle time before billing and after billing. The price fixation

by chief takes the major time in discharge delay-billing section.

During discharge, in ward secretary section the more time consuming process is getting

the signature of the consultant.

Before implementing the model

Average registration waiting time = 4.6 minutes.

Average waiting time before x-ray (in front of OP) = 16.6 minutes.

Average waiting time after x-ray (in front of OP) = 9.7 minutes.

Average X-ray waiting time = 80.9 minutes.

Average total waiting time = 111.7 minutes.

After implementing the model

Average registration waiting time = 4.3 minutes.

Average waiting time before x-ray (in front of OP) = 10.9 minutes.

Average waiting time after x-ray (in front of OP) = 9.4 minutes.

Average X-ray waiting time = 24.25 minutes.

Average total waiting time = 40.5 minutes.

5. Suggestions

The hospital should ensure that the FIFO (First in First out) is followed while providing

the service to the patients.

In spite of giving token in the X-ray reception, the token should be given

Allocate the X-ray units to the OP clinic. It leads to the equivalent distribution of the

patients to the OP clinic when leaving from X-ray unit.

The discharge delay is mainly due to the doctor’s instruction delay about the

6. Conclusion

In the service sector the time plays a major role in determining the quality of the service.

From this study it has been understood and analyzed that lot of variables involved in the waiting

time as well in the discharge delay of the patients. Even though many variables affects the OP

waiting time and total process waiting time, the X-ray waiting time is the root cause. On the

other side the root cause for the discharge delay is the doctor’s instruction delay.


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7. References

[1] Study on outpatients’ waiting time in Hospital University Kebangsaan Malaysia (HUKM)

through six sigma approach” by MohamadHanaffi Abdullah in 2005

[2] “Waiting patiently”- An analysis of the performance aspects of outpatients scheduling in

health care institutes (AnkeHutzschenreute,VrijeUniversitiet Amsterdam, BMI-paper).

[3] “Outpatient appointment scheduling” by JochemWesteneug in july 2007

[4] “Hospital discharge: A descriptive study of the patient journey for frail older people with

complex needs” (by Fraser Mitchell, Mhairi Gilmour, Gordon Mclaren)

[5] Shennes Mathew, T. Janani,” A Comparative Analysis Of Various Control Strategies On

Heat Exchanger System”, International Journal Of Pure And Applied Mathematics , Vol

117 ,No. 22, pp no. 59-63, 2017.

[6] Referred websites

http://www.reportlinker.com/ci02241/Healthcare.html

www.ibef.org/industry/healthcare-india.aspx‎


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A study on optimization of waiting time for outpatients at ... · Cause and effect diagram Also Called:Ishikawa Diagram Variations: cause enumeration diagram, process fishbone, time