integrated lean six sigma approach for patient flow improvement in hospital emergency department

Integrated Lean Six Sigma Approach for Patient Flow Improvement in Hospital Emergency Department

Ali Al Owad1, a,M. A. Karim1, b, Lin Ma1, c

1Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

[email protected],[email protected], [email protected]

Keywords: Lean, Six Sigma, Fuzzy Logic, Patient Flow, Emergency Department

Abstract.Because of increased competition between healthcare providers, higher customer

expectations, stringent checks on insurance payments and new government regulations, it has

become vital for healthcare organisations to enhance the quality of the care they provide, to increase

efficiency, and to improve the cost effectiveness of their services. Consequently, a number of

quality management concepts and tools are employed in the healthcare domain to achieve the most

efficient ways of using time, manpower, space and other resources. Emergency departments are

designed to provide a high-quality medical service with immediate availability of resources to those

in need of emergency care. The challenge of maintaining a smooth flow of patients in emergency

departments is a global problem. This study attempts to improve the patient flow in emergency

departments by considering Lean techniques and Six Sigma methodology in a comprehensive

conceptual framework.

The proposed research will develop a systematic approach through integration of Lean techniques

with Six Sigma methodology to improve patient flow in emergency departments. The results

reported in this paper are based on a standard questionnaire survey of 350 patients in the Emergency

Department ofAseerCentral Hospital in Saudi Arabia. The results of the study led us to determine

the most significant variables affecting patient satisfaction with patient flow, including waiting time

during patient treatment in the emergency department; effectiveness of the system when dealing

with the patient’s complaints; and the layout of the emergency department. The proposed model

will be developed within a performance evaluation metric based on these critical variables, to be

evaluated in future work within fuzzy logic for continuous quality improvement.

Introduction

Manufacturing and other industries are looking carefully into quality assurance programs to

improve organizational performance and bolster the bottom line by eliminating variations in any

given process[1].One significant characteristic of successful production is on-time

delivery[2].Hence, it is natural that hospitals concerned about timely delivery of their services are

lookingfor the most effective ways to improve patient flow. Such an improvement would have the

dual benefit of increasing safety and increasing customer satisfaction.

Emergency departments (EDs) suffer more from patient delays than perhaps any other component

of the healthcare system. Delays in emergency departments remain an essential challenge,

prompting comments such as: “Access Block and ED overcrowding have created a dynamic tension

and the future of emergency medicine will be determined by the resolution of this conflict” [3].But

to understand and deal with the problems of long waiting lists and access block in any given health

care facility, specific studies and actions are required [4].

Some studies have examined theapplication of the Lean and Six Sigma principles in several areas of

healthcare, but so far none has reported in-depth statistical findings about individual projects.

Hence, these studies have had a low level of demonstration ofimprovement[5].To understand the

actual effectiveness of Lean and Six Sigma in healthcare, any analysis should be supported by clear,

measurable evaluation metrics.

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The basic Lean concepts are the elimination of waste through the standardisation of processes, and

the participation of all employees in process improvement[6]. In addition, Six Sigma helps to

quantify problems; facilitates evidence-based decisions (and so keeps time from being wasted on

anecdotal evidence); helps the company understand variation and reduce it; and identifies the root

causes of variation in order to find sustainable solutions[7].

In spite of many efforts, scientific knowledge is still limited regarding which strategies and

systematic models actually improve patient flow in EDs. It is currently unknown which strategies

provide the best solution to fix throughput in an ED, according to a statement released by the

American Academy of Emergency Medicine[8]. Currently, there is no integrated model available

for evaluating Lean and Six Sigma as a means of improving patient flow in EDs.

To develop an integrated framework that includes Lean thinking and Six Sigma methodology for

improving patient flow in an ED represents a big challenge. However, it is very important to look

for a combined approach. The integration of two methodologies should achieve better results than

either method could achieve alone[9].

To summarise, existing literature has not established a true empirical or theoretical foundation for

demonstrating the effectiveness of,or the goal-specific improvements achieved by, the Lean and Six

Sigma methodologies in healthcare organisations[10,11]. In addition, there are very few research

studies that empirically document how these two methods might be integrated into one approach,or

how they might achieve world-class results[12]. There is no Lean-Six Sigma integrated model

available to improve patient flow in ED, and there is no effective evaluation method to measure the

performance efficiency of such a model.

Research Questions and Research Methods

In order to achieve the aim of the study, the following main research question is proposed:

How can the patient flow in anED be continuously improved with adoption of the Lean-Six

Sigma approach?

To answer the main research question, the following sub-questions are addressed:

1. How can Lean thinking and Six Sigma methodology be integrated in one combined

approach for continuous quality improvement and improved patient flow in EDs?

2. How can a Lean strategy and Six Sigma techniques identify wastethat affects patient flow

and quality of service in EDs?

3. How can adoption of an integrated Lean-Six Sigma model improve patient flow in EDs?

4. How can an integrated Lean-Six Sigma model determine the performance measurements to

evaluate any improvement in patient flow?

5. How can the effectiveness of an integrated Lean-Six Sigma model in improving patient flow

be evaluated for continuous process improvement?

Research Design

Answering the above questions requires careful selection of an appropriate research paradigm.

The conceptual framework in this research proposes an integrated approach to improve patient flow

in the ED of one hospital (Aseer Central Hospital) in Saudi Arabia. This approach includesthe

adoption of anintegrated Lean-Six Sigma approach and evaluation of valuable performance metrics

such as (time and quality) based on fuzzy logic. This approach will be helpfulin determining some

useful performance metrics to be measured for continuous quality improvement.

1894 Research in Materials and Manufacturing Technologies

As noted above, some other researcherswho have studied the problem of patient flow through

EDs and the corresponding chain of care have attempted to explore the benefits of applying

industrial operational management concepts in healthcare[13]. However, research in these situations

requires a systematic approach. Furthermore, the adoption ofa combination of Lean thinking and

Six Sigma methodology in Saudi Arabian hospitals will require a specific approach. Evaluating the

effectiveness of this adoption, both qualitatively and quantitatively, based on fuzzy logic, will help

decision makers determine the appropriate improvement plan for continuous progress.

Conceptual Framework

This study will be conducted as‘action research,’ with the aim of solving real-world problems.

Therefore, examination of issues in the real-world context will be necessary to conduct the study.

The research will be informed by an exploratory qualitative case study to adapt Lean-Six Sigma for

patient flow in an ED. The performance category (quality) will be gathered from this case study in

different stages to be quantified in future work by fuzzy logic. Fuzzy logic will be used to evaluate

the effectiveness of integrated Lean-Six Sigma in patient flow improvement. The proposed

theoretical framework is presented in Figure 1.

Fig.1: Conceptual Framework.

Advanced Materials Research Vols. 834-836 1895

The research study will combine qualitative and quantitative methods in data collection processes. It

will begin with a case study of a real problem and will conduct its investigation through in-depth

observation of the patient flow in an ED. This observation will help to build a clear picture about

the current system for managing patient flow in the ED. It will be based on an integrated Lean-Six

Sigma approach following DMAIC and Lean thinking principles,and will categorize the different

patient flows in the ED from admission until discharge. In addition, it will build Value Stream

Mapping (VSM), which will help to identify the value- and non-value-added activities. Patient

needs will then be gathered by questionnaire to understand the patient requirements (VOC). As a

result, performance metrics that determine value-to-patient and ED staff will be identified using

Value Stream Mapping and Quality Function Deployment, based on previous investigation. These

performance metrics, which are related to time and quality, will be demonstrated for the evaluation

stage. Based on these metrics, Lean-Six Sigma improvement will be quantified and evaluated with

fuzzy logic algorithms. It is anticipated that this approach will improve the patient flow in the ED

by reducing the cycle timeand non-value-added activities, eliminating bottlenecks and improving

patient satisfaction – or, in short,by reducing variation and improving process flow in the ED.

Integrated Lean-Six Sigma Approach

As noted above, previous studies of the implementation of Lean and Six Sigma in several areas

of healthcare have not reported in-depth statistical findings about individual projects and hence

have a low level of evidence of improvement[14]. To understand the actual effectiveness of Lean

and Six Sigma in healthcare, any analysis should be supported by clear, measurable evaluation

metrics.

Also as noted above, to develop an integrated framework that includes Lean thinking and Six

Sigma methodology for improving patient flow in an ED remains a significant challenge. It is very

important to look for a combined approach (see Fig. 2). The integration of two methodologies

should be able to achieve better results than either method could achieve alone[15], although there

Fig.2: Integrated Lean-Six Sigma Tools for investigation of Patient Flow in ED.


are very few research studiesthat empirically document how these two methods might be integrated

into one approach, or how they might achieve world-class results[16].

The following process shows the procedure for usingLean and Six Sigma tools in collecting data

from an ED:

1. Initially, define waste, identify measurement value, and investigate and analyse the current

patient flow by using Voice of the Customer (VOC) +A3 Problem Sheet +Value Stream

Mapping (VSM).

2. Use thetools shown in Fig.2 to obtainthe perspectives of patientsand hospital staff and to

observe and investigate current patient flow in the ED.

3. Based on VOC, A3 and VSM, investigate the current ED patient flow to obtain patient and

staff perspectives for measuring the quality of services in six dimensions, as shown in Table 1:

Table 1: Dimensions of quality in healthcare services Dimension Definition

Tangibles Physical facilities, equipment and

appearance of contact personnel

Responsiveness Willingness to help customers and

provide prompt service

Reliability Ability to perform the promised service

reliably and accurately

Assurance Knowledge and courtesy of employees

and their ability to inspire trust

Empathy Provision of caring, individualised

attention to consumers

Professionalism Experience, skills and innovation of

hospital staff

4. Determine performance metrics in terms of Lean-Six Sigma attributes and six dimensions of

healthcare quality services.

Results and Discussion

This paper will discuss one part of my proposed research method, which is the collection of the

Voice of the main Customer (VOC). The analysis and discussion will focus mainly on patient

satisfaction and healthcare service quality. The questionnaire was used to get the voice of the main

EDcustomer, the patient. The survey was analysed in terms of six dimensions of healthcare quality

services. As a result of the VOC descriptive analysis, performance metrics for the current patient

flow in the ED was investigated and identified,in order to be demonstrated with fuzzy logic

evaluation in future work to continuously improve the quality.

One of the research methods adopted in this study is a questionnaire designed for the study. The

questionnaire was used to collect data from the EDofAseer Central Hospital in Saudi Arabia.

Itcontained five main questions. The first of these concerned background information, and the

otherswere related to the patient’s reasons for attending ED; quality concepts; patient satisfaction

with the current healthcare services system; and the main problems, seen from the patient’s

perspective. The questionnaire was distributed to about 350 participants who attendedthe ED

between November 2012 and February 2013. 120 patients responded, resulting in a response rate of

34.29 %.


After receipt of the completed questionnaires, a coding frame was developed. Data were then

transferred to a database and the aggregate data were analysed. Statistical analysis was primarily of

a descriptive nature,where a SPSS package is used. The following section shows the initial analysis,

which discusses patient satisfaction and the quality of healthcare services, based on the survey’s

descriptive analysis. The performance metrics based on six dimensions of the ED’s healthcare

service quality are then determined.

Patient Satisfaction and Healthcare Service Quality

The mean of the 33 elements (variables) is 2.99, which is the average of the overall quality

dimensions showing satisfactory results on the Likert five-point scale, with Cronbach’s Alpha value

.929 for reliability analysis (see Appendix). However, the categorisation of critical dimensions can

be ranked as follows, based on the mean of each element in each dimension:

1. Responsiveness with mean = 2.61

2. Tangibles with mean = 2.75

3. Assurance with mean = 2.95

4. Empathy with mean = 3.1

5. Professionalism with mean = 3.36

6. Reliability with mean = 3.41

The results of the study show that in the view of the patients who responded, the most important

quality factors which impact on patient flow in Aseer Central Hospital’s ED are: “waiting time

during patients treatment in the ED”; “comfort of the waiting area”; “waiting time before being

diagnosed by the doctor”; “effectiveness of the system when dealing with the patient’s complaints”;

“layout of the ED”; “waiting time before examination by the nurse”; and “quiet location”.

On the other hand, the least important quality factors, in the view of the patients who

responded,are: “pharmacist’s explanation of how to use the medicine”; “doctor’s ability in

prescribing the proper treatment”; “skills of radiology technicians in required radiology”;

“Reception’s treatment of you and your family”; “nurses have done the required medical check-up”;

and “ accuracy of the test report”.

Descriptive statistics for patient satisfaction and healthcare service quality are presented in Table 2.


Table 2: Healthcare Service Quality Dimensions and Degree of Satisfaction Dimensions Elements Mean of Satisfaction

Tangibles

---------------------

Responsiveness

---------------------

Reliability

---------------------

Assurance

---------------------

Empathy

---------------------

Professionalism

1. ED accessibility 3.22

2. Quiet location 2.47

3. Comfort of the waiting area 2.04

4. Layout of the ED 2.25

5. Clarity of signboards 3.12

6. Cleanliness of the ED 3.31

7. Availability of prescribed medicine in the ED

pharmacy 2.81

8. Supplying thecorrect health service the first time 3.14

9. Effectiveness of the system when dealing with

the patient’s complaints 2.13

10. Waiting time before examination by the nurse 2.46

11. Waiting time before being diagnosed by the doctor 2.14

12. Waiting time during your treatment in the ED 1.94

13. Co-operation in health services provided to you 2.84

14. Speed of locating your medical file 3.04

15. Obtaining laboratory results at the promised time. 2.83

16. Obtaining radiology results at the promised time 2.94

17. Nurses have done the required medical check-up 3.46

18. Accuracy of the doctor’s diagnosis 3.56

19. Accuracy of the test report 3.43

20. Accuracy of the radiology report 3.38

21. Doctor’s explanation ofhow to use the medication 3.35

22. Explanation of necessary procedures before the

laboratory examination 3.12

23. Explanation of necessary procedures prior to radiology 3.14

24. Pharmacist’s explanation of how to use the medicine 3.81

25. Doctors spent adequate time to discuss with you 2.58

26. Doctors’ use of comprehesible terms when discussing

your case with you 3.32

27. Reception’s treatment of you and your family 3.47

28. Communication among employees in order to facilitate

obtaining health services 2.73

29. Skills of the laboratory technicians

in taking the required specimen 3.39

30. Ability of nurses to do the right thing 3.22

31. Doctor’s ability in prescribing the proper treatment 3.69

32. Skills of radiology technicians in required radiology 3.52

33. Integrated services in ED 2.97


As a main objective of this study is to determine the performance metrics for improved patient

flow, Table 3 shows the main critical metrics based, on the previous descriptive analysis:

Table 3: Main critical metrics based on Voice of Customer in ED LSS

Attribute

Performance

Category

Dimension Metrics Index

Waste

Elimination

JIT

Reduce

Variation

Quality

Tangibles

Responsiveness

M1 Waiting time during patients treatment in ED

M2 Comfort of the waiting area

M3 Waiting time before being diagnosed by physicians

M4 Effectiveness of the system when dealing with the

patient’s complaints

M5 Layout of the ED

M6 Waiting time before examination by nurse

M7 Quietness of ED location

It is not easy to measure patient satisfaction and get a clear decision for quality improvement

with a questionnaire approach, but applying qualitative research beside quantitative technique in a

future study would grant a better perception of the complex problem of quality in the healthcare

area[17]. In addition, fuzzy logic is a brilliant technique for evaluating the overall performance and

service quality of every dimension, in that way helping hospital decision makers in choosing and

applying accurate approaches[18]. Actually, ambiguous data such as human judgments can be

measured simply and applied to established results when using fuzzy logic[19]. Accordingly, these

metrics will be used in a fuzzy logic model to evaluate ED patient-flow performance for continuous

quality improvement. However, this study is still in progress with the fuzzy logic model, and the

evaluation will take place in future work.

Conclusions and Future Work

The study discusses the proposed framework for an integrated Lean-Six Sigma approach to improve

the flow of patients in the Emergency Department ofAseer Central Hospital in Saudi Arabia. To

integrate Lean with Six Sigma methods, some Lean and Six Sigma techniques were used to

investigate and determine the patient flow problems in the ED. Performance metrics were then

determined from the viewpoint of patients to be demonstrated and evaluatedin fuzzy logic in future

work for continuous quality improvement. In this paper, the Voice of the Customer (ED patients)

was gathered by using a questionnaire. Descriptive analysis by SPSS was conducted to categorise

the most significant factors that affect ED patient flow. As a result, the seven metrics shown in

Table 3 were identified for evaluation in fuzzy logic for continuous quality improvement.

Assessments of customer satisfaction depend on human judgment, which differs from one person to

another. Fuzzy logic has been proven to be an excellent choice for many control system

applications, due to its ‘human’ control logic. In addition, fuzzy logic evaluates both qualitative and

quantitative measures by a single score, usually from 0.0 to 1. Because this research is still in

progress with a developing fuzzy logic evaluation model, it is clear that the study has a limitation

and will require further work to demonstrate and evaluate these metrics within fuzzy logic. It will

then be possible to evaluate accurately the effectiveness of the integrated Lean-Six Sigma approach

in improvingED patient flow, by measuring a Lean-Sigma score between zero and one, based on the

proposed fuzzy evaluation model.


Appendix:

Descriptive Statistics

Elements N Min. Max. Mean

Std.

Deviation

Cronbach's

Alpha

E1 119 1 5 3.22 1.208 .927

E2 119 1 5 2.47 1.199 .928

E3 119 1 5 2.04 1.123 .927

E4 119 1 5 2.25 1.159 .926

E5 119 1 5 3.12 1.243 .930

E6 119 1 5 3.31 1.056 .927

E7 119 1 5 2.97 1.204 .927

E8 117 1 5 3.14 1.129 .925

E9 118 1 5 1.94 1.119 .927

E10 119 1 5 3.47 1.199 .924

E11 119 1 5 3.04 1.203 .927

E12 119 1 5 2.13 1.070 .926

E13 119 1 5 2.84 1.179 .925

E14 119 1 5 2.73 1.170 .925

E15 118 1 5 3.56 .992 .926

E16 118 1 5 3.69 1.002 .925

E17 117 1 5 3.32 1.209 .926

E18 118 1 5 2.58 1.277 .927

E19 118 1 5 2.14 1.183 .926

E20 118 1 5 3.35 1.157 .928

E21 118 1 5 3.22 1.185 .925

E22 118 1 5 3.46 1.174 .927

E23 116 1 5 2.46 1.347 .928

E24 117 1 5 3.12 1.044 .927

E25 118 1 5 3.39 .970 .927

E26 118 1 5 2.83 1.172 .929

E27 118 1 5 3.43 .901 .926

E28 117 1 5 3.14 1.074 .925

E29 118 1 5 3.52 .748 .928

E30 118 1 5 2.94 1.149 .926

E31 118 1 5 3.38 .886 .927

E32 118 1 5 2.81 1.341 .929

E33 118 1 5 3.81 1.006 .930

Valid N

(listwise)

111


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