An interpretive structural modelling for sustainable ...

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Int. J. Qualitative Research in Services, Vol. 2, No. 1, 2015

An interpretive structural modelling for sustainable healthcare quality dimensions in hospital services Faisal Talib* Faculty of Engineering and Technology, Mechanical Engineering Section, University Polytechnic, Aligarh Muslim University, Aligarh, India Email: [email protected] *Corresponding author

Zillur Rahman Department of Management Studies, Indian Institute of Technology Roorkee, Roorkee, India Email: [email protected] Abstract: The purpose of this paper is to develop a comprehensive framework in order to identify, rank and classify key quality dimensions for healthcare establishments (HCEs) and to understand the contextual relationship between them for growth and development of Indian HCEs. The methodology adopted in this research was interpretive structural modelling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analyses to identify and classify the healthcare quality dimensions (HCQDs), typically identified by an extensive literature review and through experts opinion. The integrated model revealed quality dimensions such as ‘state of knowledge management’; services of HCE for disease burden of society’ and ‘patient expectation and perception of hospital services’ as dependent HCQDs. ‘Priority area management for critical care’ and ‘treatment chain management including referral and evacuation chain management’ were found as the linkage quality dimensions. While ‘core quality dimension’; ‘associated supportive quality dimension’ and ‘clinical governance quality’ were found to be independent HCQDs. This research will aid healthcare practitioners and decision makers in selecting right quality dimensions for the sustainable growth of Indian HCEs. Keywords: healthcare establishments; HCEs; healthcare quality dimensions; HCQDs; interpretive structural modelling; ISM; Matrice d’Impacts Croisés Multiplication Appliquée á un Classemen; MICMAC; sustainability; India. Reference to this paper should be made as follows: Talib, F. and Rahman, Z. (2015) ‘An interpretive structural modelling for sustainable healthcare quality dimensions in hospital services’, Int. J. Qualitative Research in Services, Vol. 2, No. 1, pp.28–46. Biographical notes: Faisal Talib is an Assistant Professor at Mechanical Engineering Section, University Polytechnic, Faculty of Engineering and Technology, Aligarh Muslim University, Aligarh, U.P., India. He received his PhD from IIT Roorkee and Masters in Industrial and Production Engineering Copyright © 2015 Inderscience Enterprises Ltd.

An ISM for sustainable healthcare quality dimensions in hospital services

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from AMU. He has more than 17 years of teaching experience and has more than 60 publications to his credit in national/international journals and conferences. His special interest includes quality engineering, TQM, service quality, quality concepts, industrial management, operations management, qualitative and quantitative techniques, and quality management in service industries. Zillur Rahman is an Associate Professor at Department of Management Studies, IIT Roorkee. He is a recipient of the Emerald Literati Club Highly Commended Award and one of his papers was The Science Direct Top 25 Hottest Article. His work has been published and cited in various journals including Management Decision, Managing Service Quality, International Journal of Information Management, Industrial Management and Data Systems, The TQM Magazine, Business Process Management Journal, International Journal of Service Industry Management, Information Systems Journal, Decision Support Systems, Journal of Business and Industrial Marketing, and International Journal of Computer Integrated Manufacturing, to name a few.

1

Introduction

In this new era of globalisation, business organisations all over the world are striving hard to evolve effective quality measurement parameters, tools, and strategies to survive competition (Balan et al., 2006; Talib et al., 2013a) and also to achieve differentiation (Seth et al., 2008). In this process, the hierarchical classification of key process variables leads to rich source of information (Qureshi et al., 2007; Faisal, 2010). The entire manufacturing or service process requires effective and efficient remote monitoring with web enabled platforms (Ali et al., 2007). At the same time, organisations have to integrate environmental management into their strategic plans (Kannan et al., 2008), so that flexibility is considered in respect of time and cost to mobilise the enterprise-wide resources to meet the agile need for change. A culture of quality is considered essential to maintain quality in an industry (Talib et al., 2013b; Talib, 2013). However, in real life it is not a centralised decision making model due to various practical reasons because only an optimum perceived level of trust can lead to quality culture (Saxena et al., 2009). Therefore, to develop quality culture, sustainable healthcare quality model is only the way. Healthcare industry is considered as one of the most challenging sector. It is a developing industry globally covering innumerable hospitals, clinics, nursing homes, and institutions meant to provide primary, secondary and tertiary level of patient care (Kaiser, 2006). Over 10% of gross domestic product (GDP) of most developed nations is consumed by healthcare industry. Across the world, patients, physicians, and governments are becoming increasingly concerned about the quality of health services. In the USA, China, UK, Australia, and Canada, major health reforms have been undertaken (The Economist, 2004) to improve healthcare system and results are encouraging. Healthcare spending of US is at the top and is approximately 16% of GDP which is expected to rise further steadily to 19.6% of GDP by 2017 (WHO, 2009). Switzerland

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F. Talib and Z. Rahman

(11.5%), France (11%), Germany (10.8%), Portugal (10.2%), and Canada (10%) follow US in healthcare spending (OECD, 2008). In India there is proposal to substantially increase the health budget in ensuing 12th Plan (The Times of India, 2011). The ultimate goal is to achieve improvement of the healthcare system globally to provide quality healthcare to the patients. Improvement of quality in healthcare is a challenging task especially when ensuring to improve and also to reduce costs (Ovretveit, 2000). Natarajan (2006) has emphasised that in the last two decades, healthcare industry has witnessed increasing attention to it, covering different dimensions of healthcare comprising core values, delivery of services, quality of care, in absentia healthcare (that is non-face-to-face communication) among many other factors. Attempts are being made in a number of directions to approach the problem and seek solutions by utilising various quality assurance mechanism, practices, tools, and processes adopting them from other sectors (Miller et al., 2009; Kozak et al., 2007). Such studies justify the need to change from traditional approaches to improve hospital quality. There are likely to be many shortcomings and gaps in traditional healthcare quality parameters. These are required to be addressed by designing a quality model for healthcare establishments (HCEs) which should satisfy not only the definition of health as provided by World Health Organization (WHO) but it should also satisfy the clientele along with other related stake holders. In addition, several studied have been conducted to understand the healthcare quality dimensions (HCQDs) across the world (Karin et al., 2009; Arguedas et al., 2010; Azam et al., 2012a, 2012b; Talib et al., 2010, 2011a). But literature review suggest that extensive studies on HCEs still lags behind other service industry counterpart in terms of research studies on HCQDs in Indian context. It was also revealed that no study has been undertaken to identify, classify and analyse the relationships between HCQDs. Little is known about the direct and indirect effects of each dimension on Indian HCEs. Further, no study depicts the use of interpretive structural modelling (ISM) and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) methodologies to understand and classify HCQDs in Indian context to gain insight into the problem and formulate policies for sustainable hospital services. To overcome these research gaps, the objectives of the present study were made and are presented in Section 3. Furthermore, the proposed model will help the healthcare practitioners and decision makers to develop the awareness and knowledge on the HCQDs as well as to understand their respective interrelationships. The rest of the paper is structured as follows. The next section presents the literature review and discusses the identification of HCQDs in details followed by development of research objectives of the present study. Subsequently, ISM methodology, ISM model development and MICMAC analysis were carried out on the developed model. At the end, the discussion and conclusion of this research study were presented, which is followed by scope for future research.

2

Literature review

A thorough review of literature has been performed in the current work and is presented in this section on different aspects of healthcare quality. Based on an extensive review of literature, the critical dimensions of healthcare quality have been identified and listed as in Table 1.

An ISM for sustainable healthcare quality dimensions in hospital services Table 1

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Proposed critical dimensions of quality in healthcare and related literature

Quality dimension

Objective

Important related literature

To optimise care and cost to the society

WHO (2002), Li et al. (2010), Meltzer et al. (2009), Hazard et al. (2009), and Singh (2005)

State of knowledge management

To self correct and improve quality of care continuously

Thothathri (2003), Desouza (2002), Desouza and Evaristo (2003), and Edvardsson and Olsson (1996)

Core quality dimension

To refine quality dimensions at professional technical level to synergise quality at functional/operative level

Dijkstra and Bij (2002), van Hees (1998), and Ovretveit (2000)

Associated supportive quality dimension

To refine quality dimensions at supportive managerial level to synergise quality at functional/operative level

Dijkstra and Bij (2002), van Hees (1998), and Ovretveit (2000)

Priority area management for critical care

To achieve optimal quality for critical care of casualty (emergency), operation theatre (OT)and intensive care unit (ICU) services

Dey et al. (2006), Feeney and Zairi (1996), and Fernandes and Christenson (1996)

Clinical governance quality

To improve input, structure, process, outcome quality standard of the clinical governance

Vanu (2004), WHO (2002), Thomson (1998), Ratnavel (2009), and Galizzi and Miraldo (2011)

Treatment chain management including referral and evacuation chain management

To achieve better input on patient management internally and on referral to improve quality of healthcare (internal customer input for management internally and on referral to improve quality of healthcare management including documentation/maintenance of records)

Sureshchandar et al. (2002), Boshoff and Gray (2004), and Duggirala et al. (2008)

Patient expectation and perception of hospital services

To study the impact of quality parameters of hospital services correlated with patient perception of hospital services

Balasubramanian et al. (2003), Sureshchandar et al. (2002), Iyer and Muncy (2004), and Lim and Tang (2000)

Services of HCE for disease burden of society

A brief explanation of all these quality dimensions is presented in the following section. In Table 1, various dimensions of healthcare quality, their objectives, and some related studies to support the dimensions are shown.

2.1 Identification of HCQDs in HCEs 2.1.1 Services of HCE for disease burden of society WHO (2002) has emphasised that disease burden on society should be considered as a priority to be dealt at every level, i.e., preventive, promotive and curative level. Therefore, at these levels of healthcare quality planning, adequate, affordable and effective facilities have to be created for prevention and treatment of disease priorities to

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the satisfaction of all patients ensuring optimal utilisation of resources. This will allow reduction in costs of any HCE by providing necessary focus on diseases both at preventive as well as at promotive and curative level reducing the load of such diseases on the HCE thus, optimising the care and costs to the society.

2.1.2 State of knowledge management Edvardsson and Olsson (1996) identified a range of resources that are encompassed within the service system. They include: human resources; customers; physical/technical system; organisation and control. Thus, healthcare services should be embedded in healthcare philosophy complementing the service processes which are chain of activities which must function if service is to be produced. All these system and process are knowledge intensive, hence, knowledge management in healthcare is essential aspect of the service to enhance service quality especially core quality and clientele satisfaction to be competitive. Knowledge management in HCE like any other industry revolves around its core competency and its managerial competency both existing together complementing each other to achieve its primary aim of patient satisfaction.

2.1.3 Core quality in healthcare These are qualities required by the medical profession to manage patients using EBM for optimal patient rehabilitation. These may include ethical and professional-technical essential core competences. Clinical quality, understanding illness, professional and technical care, professional quality and technical aspects indicate HCE core quality features.

2.1.4 Associated supportive quality These are qualities required to support core quality attributes and to provide optimal patient comfort and care. These supportive attributes are meant to improve satisfaction in related interest groups; i.e., patients, family members’ payers and family doctor, which emphasise choice, cost and quality.

2.1.5 Priority area management for critical care With the explosive development of knowledge, technology and globalisation there is now an increasing requirement of high-technology medical care. Every country is striving hard to cope with this increasing need of healthcare facilities in terms of both human and material resources (Feeney and Zairi, 1996). However, it is important that these facilities are available, they perform to the required standards so as to satisfy both healthcare personnel and patients. Like any other industry, operation in healthcare industry is considered as a series of processes which are essential in order to remain competitive.

2.1.6 Clinical governance quality Unlike previous policies which concentrated more on finance, the proposals under clinical governance attempt to mesh with the strategies on human resources and information technology providing an environment for an organisation wide approach to effectively manage healthcare quality improvement systems (Thomson, 1998).


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Healthcare organisations (WHO, 2002) and their structures, processes and management have become increasingly significant to the improvement of healthcare. Vanu (2004) has concluded emphasising that ‘clinical governance’ acknowledges these complexities and attempts to overcome some of the problems by promoting an integrated and organisation wide approach towards continuous quality improvement.

2.1.7 Treatment chain management including referral and evacuation chain management Many authors recognise that internal customers and internal suppliers within a hospital supply each other and are invisibly connected in terms of the input-output links in the value chain. These links are formed through the internal network of staff relationships that emerge from health service process design in the organisational system. It can therefore, be conceptualised that internal networks provide a framework for health service value delivery. Individually, workers may have some impact on patients, but greater value is provided through synergy of services provided by other areas. Each worker involved is part of the chain providing value to each other, which in turn impacts on patient outcomes.

2.1.8 Patient expectation and perception of hospital services There is a general agreement about the influence of patient’s expectations in overall service quality and patient satisfaction. Considerable work remains to be done regarding the exact way this process takes place. It is assumed that patients create expectations prior to their service experience against which performance is evaluated. Perception can disconfirm expectation or confirm it. A patient’s perception of value and satisfaction begins with an initial purchase and continues throughout the actual ownership and the overall service experience. Regardless of whether the perception is positive or negative, a patient’s thoughts and desires will influence what the hospital provides as it strives to maintain a healthy relationship with its patients.

3

Research objectives

Based on the above literature review, a list of eight HCQDs have been identified (Table 1). The descriptions used for the dimensions have been taken from various literature referred in Table 1 of Section 2. Hence, the objectives of the present study are: •

to identify, rank and classify the HCQDs for HCEs

•

to impose the relationships between the identified HCQDs to derive key managerial insights using ISM approach and suggest the scope for future research.

The present research on HCQDs may be useful to medical practitioners and decision makers in the HCEs to identify and quantify quality dimensions. Based on the findings, healthcare service providers formulate and implement future strategies to become a preferred global hub for providing sustainable quality services and able to overcome with the challenges faced by Indian HCEs.

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4


ISM methodology

Warfield (1973) in his research on societal systems and planning model first proposed ISM to analyse the complex socioeconomic systems in an organised way. The logic behind this methodology is to use expert’s opinion and knowledge to break a complex system into several factors (or sub-systems) and develop a multi-level structural model. It is often used to provide fundamental understanding of complex situations, and to formulate a course of action for solving a problem (Satapathy et al., 2012). Sage (1977) have described ISM as a method for developing hierarchy of factors while Nelson et al. (2000) have explained it in different way, in which it is relatively more efficient in some cases and tends itself to being replicated more effectively. Further, according to Faisal (2010), ISM provides a means by which order can be imposed on the complexity of variables and MICMAC provides a systematic analysis for complex issues (Hu et al., 2009; Satapathy et al., 2012; Diabat et al., 2013). It is a structural analysis tool which describes a system using a matrix and categorises variables based on the relationship and the extent they influence one another to find out the key factors in the system. This method identifies the main variables that are both independent and dependent. Therefore, ISM-MICMAC is a modelling technique as the specific relationships and overall structure are portrayed in a graphic model. Current literature on ISM show that numerous studies have been carried out using this approach and therefore, its application are widely spread across various disciplines. Some of the applications of ISM are presented in Table 2 as revealed from the literature. Table 2

ISM as reported in the literature

Author(s)

Area of ISM application

Mehregan et al. (2014)

Development of sustainability supplier selection criteria

Mangla et al. (2014)

Multi-objective decision modelling for green supply chains

Aich and Tripathy (2014)

Modelling of success factors for green supply chain management in Indian computer

Azevedo et al. (2013)

Evaluation of automotive supply chain performance

Singh and Sushil (2013)

Modelling enablers of TQM to improve airline performance

Jain and Banwet (2013)

Modelling the elements of selection for strategic alliance partners for network managed services

Debnath and Shankar (2012)

Improving service quality in technical education

Satapathy et al. (2012)

Service quality of e-electricity utility service

Talib et al. (2011b)

Modelling the practices of TQM in service sector

Talib et al. (2011c)

Barriers to TQM implementation in service sector

Sahney et al. (2010)

Quality framework in education for administrative staff in Indian context

Faisal (2010)

Sustainable supply chains

ISM methodology can be applied by adopting the following eight steps (Al-Zaabi et al., 2013; Satapathy et al., 2012; Talib et al., 2011b, 2011c). They are:


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Step 1 Identification of relevant factors through literature survey, brain storming sessions and experts’ opinion. Step 2 A contextual relationship establishment among factors with respect to which pairs of factors would be examined. Step 3 A structural self-interaction matrix (SSIM) is prepared based on pair-wise comparison of factors of the system under consideration. Step 4 Developing a reachability matrix from the SSIM and checking the matrix for transitivity. Transitivity of the contextual relation is a basic assumption in ISM which states that if factor ‘A’ is related to ‘B’ and ‘B’ is related to ‘C’, then ‘A’ is related to ‘C’. Step 5 Partition of the reachability matrix into different levels. Step 6 Based on the relationships given in the reachability matrix and the determined levels for each factors, a directed graph is drawn and the transitive links are removed. Step 7 The resultant digraph is converted into an ISM by replacing variable nodes with statement. Step 8 The developed ISM model is reviewed to check for conceptual inconsistency and necessary modifications are made.

5

Results and discussion

The identification of HCQDs and their contextual relationship was undertaken through brainstorming sessions consisting of number of experts from healthcare industry and academia as well as personal interviews/meetings with the hard core experts in the field who are involved in the implementation of quality practices in their organisations. A total of ten experts form various facets of the industry like super-specialty hospitals, nursing homes, healthcare institutions, and medical colleges were consulted for the development of contextual relationship of different HCQDs using ISM approach. These experts were chosen based on their experience generally doctors having more than five years of experience in healthcare quality aspects as well as their willingness to participate in the study. After conducting three brainstorming sessions and several personal interviews/ meetings individually in which HCQDs identified from literature review were discussed. Finally, eight dimensions were compiled for the modelling. After the above process and as per the view of experts, the dimensions were arranged in a hierarchy to show their contextual relationship which leads to the development of initial relationship matrix and final relationship matrix. Further, the same responses were also used in the MICMAC analysis. ISM methodology suggests the use of expert opinion in developing the contextual relationship among HCQDs (Talib et al., 2011b, 2011c). Based on the opinion of experts, SSIM as shown in Table 3 was developed. Four symbols were used to understand the direction of relationship between quality dimensions (i and j): V

quality dimension i will help to achieve dimension j

A

quality dimension j will be achieved by dimension i

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X

quality dimension i and j will help to achieve each other

O

quality dimension i and j are unrelated.

Using the above four symbols, the interrelationship among all the eight HCQDs (services of HCE for disease burden of society; state of knowledge management; core quality dimension; associated supportive quality dimension; priority area management for critical care; clinical governance quality; treatment chain management including referral and evacuation chain management; and patient expectation and perception of hospital services) is shown in Table 3, distinguishing between strong and weak relations. Table 3 Dimension no. 1

Structural self-interaction matrix HCQD

8

7

6

5

4

3

2


V

A

O

A

O

O

X

O

2


V

A

O

A

A

3


O

V

A

V

X

4


O

V

A

V

5


V

V

A

6


V

O

7


V

8


1

The SSIM is transformed into a binary matrix called the initial reachability matrix by substituting V, A, X, and O by 1 and 0 as per the case. The rules for the substitution of 1 s and 0 s are: 1

if the (i, j) entry in Table 3 is V, then the (i, j) entry in the reachability matrix becomes 1 and the (j,i) entry becomes 0

2

if the (i, j) entry in Table 3 is A, then (i, j) entry in the reachability matrix becomes 0 and the (j,i) entry becomes 1

3

if the (i, j) entry in Table 3 is X, then the (i, j) entry in the reachability matrix becomes 1 and the (j, i) entry also becomes 1

4

if the (i, j) entry in Table 3 is O, then the (i, j) entry in the reachability matrix becomes 0 and the (j, i) entry also becomes 0.

Thus, by abiding the above rules, the relationship of all the eight quality dimensions is converted to binary matrix as depicted in Table 4, where ‘i’ explains the entry of eight quality dimensions in first column side one after another in one direction, and ‘j’ shows the eight quality dimensions in reverse direction in the first row. Table 4 represents the initial reachability matrix. As described in the step 4 in Section 4, the transitivity of reachability matrix is then checked. The final reachability matrix is obtained by


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incorporating the transitivity as explained in earlier section. Table 5 shows the final reachability matrix with the transitivity, driven power and dependence which are calculated and presented in the same table. Based on the driving power and dependence sum the ranking of each HCQDs were also obtained and depicted in Table 5. Table 4

Initial reachability matrix

Dimension HCQD no.

1

2

3

4

5

6

7

8

1


1

1

0

0

0

0

0

1

2


1

1

0

0

0

0

0

1

3


0

0

1

1

1

0

1

0

4


0

1

1

1

1

0

1

0

5


1

1

0

0

1

0

1

1

6


0

0

1

1

1

1

0

1

7


1

1

0

0

0

0

1

1

8


0

0

0

0

0

0

0

1

Table 5 Dimension no.

Final reachability matrix Driving Rank power

HCQD

1

2

3

4

5

6

7

8

1


1

1

0

0

0

0

0

1

3

V

2


1

1

0

0

0

0

0

1

3

V

3


1#

1#

1

1

1

0

1

1#

7

II

#

7

II

#

4


1

1

1

1

1

0

1

1

5


1

1

0

0

1

0

1

1

5

III

6


1#

1#

1

1

1

1

1#

1

8

I

7


1

1

0

0

0

0

1

1

4

IV

8


0

0

0

0

0

0

0

1

1

VI

Dependence

7

7

3

3

4

1

5

8

Rank

II

II

V

V

IV

VI

III

I

#

Note: 1 entries are included to incorporate transitivity.

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Next step is the development of partition level. A series of partitions can be made on the final reachability matrix. These partitions are made to determine the hierarchy of the elements (Chander et al., 2013). The reachability and antecedent sets for each quality dimensions are found from final reachability matrix. The reachability set consists of the HCQD itself and the other dimensions on which it may impact whereas the antecedent set consists of the HCQD itself and the other dimension which may impact on it. Therefore, the reachability set of the first HCQD ‘services of HCE for disease burden of society’ is the set of HCQDs defined in the columns that contains ‘1’ in the first row (1, 2, and 8). Similarly, the antecedent set of the first HCQD ‘services of HCE for disease burden of society’ is the set of HCQDs defined in the rows which contain ‘1’ in the column (1, 2, 3, 4, 5, 6, and 7). Therefore, the interaction of these sets is derived for all the HCQDs. The HCQDs for which the reachability and the intersection sets are the same occupy the top level in the ISM hierarchy. If the membership in reachability and the interaction completely agree then the top priority is obtained and the criterion is removed form the subsequent interaction, so this procedure leads to final interaction leading to the lowest level. This procedure leads to final iteration leading to the lowest level. Table 6 shows the first iteration of partition of reachability matrix where in ‘patient expectation and perception of hospital services’ is found at level I, therefore, it would be positioned at the top of the ISM hierarchy. Once the top level HCQD is identified, it is discarded from the other remaining quality dimensions. Then the same process is repeated to find out the next level of HCQD. This process is continued until the level of each HCQD is found. Table 7 shows the second iteration where in ‘state of knowledge management’ and ‘services of HCE for disease burden of society’ are found at level II. Table 8 describes the third iteration where in ‘treatment chain management including referral and evacuation chain management’ is found at level III. The fourth iteration where in ‘priority area management for critical care’ is found at level IV is shown in Table 9. ‘core quality dimension’ and ‘associated supportive quality dimension’ are positioned in fifth iteration and are found in level V (Table 10). Similarly, Table 11 shows the sixth iteration where in ‘clinical governance quality’ is found in level VI. Results of the iteration process are summarised in Table 12. The resulting levels helps in building the digraph and the final model. Table 6

HCQDs level iteration I Reachability set R (Di)

Antecedent set A (Di)

Intersection set R(Di) ∩ A(Di)

1

1, 2, 8

1, 2, 3, 4, 5, 6, 7

1, 2

2

1, 2, 8

1, 2, 3, 4, 5, 6, 7

1, 2

3

1, 2, 3, 4, 5, 7, 8

3, 4, 6

3, 4

4

1, 2, 3, 4, 5, 7, 8

3, 4, 6

3, 4

5

1, 2, 5, 7, 8

3, 4, 5, 6

5

6

1, 2, 3, 4, 5, 6, 7, 8

6

6

7

1, 2, 7, 8

3, 4, 5, 6, 7

7

8

8

1, 2, 3, 4, 5, 6, 7, 8

8

Dimension (Di)

Level

I

An ISM for sustainable healthcare quality dimensions in hospital services Table 7

HCQDs level iteration II Reachability set R(Di)

Antecedent set A(Di)


Level

1, 2

1, 2, 3, 4, 5, 6, 7

1, 2

II

2

1, 2

1, 2, 3, 4, 5, 6, 7

1, 2

II

3

1, 2, 3, 4, 5, 7

3, 4, 6

3, 4

4

1, 2, 3, 4, 5, 7

3, 4, 6

3, 4

5

1, 2, 5, 7

3, 4, 5, 6

5

6

1, 2, 3, 4, 5, 6, 7

6

6

7

1, 2, 7

3, 4, 5, 6, 7

7

Reachability set R(Di)



3

3, 4, 5, 7

3, 4, 6

3, 4

4

3, 4, 5, 7

3, 4, 6

3, 4

5

5, 7

3, 4, 5, 6

5

6

3, 4, 5, 6, 7

6

6

7

7

3, 4, 5, 6, 7

7

III




Level

3

3, 4, 5

3,4,6

3, 4

4

3, 4, 5

3,4,6

3, 4

5

5

3,4,5,6

5

6

3, 4, 5, 6

6

6




Level

3

3, 4

3, 4, 6

3, 4

V

4

3, 4

3, 4, 6

3, 4

V

6

3, 4, 6

6

6




Level

6

6

6

VI

Dimension (Di) 1

Table 8

HCQDs level iteration III

Dimension (Di)

Table 9

HCQDs level iteration IV

Dimension (Di)

Table 10

HCQDs level iteration VI

Dimension (Di) 6

IV

HCQDs level iteration V

Dimension (Di)

Table 11

Level

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Table 12 Iteration

Levels of HCQDs iteration I–VI Dimension (Di)

Reachability set R(Pi)

Antecedent set A(Pi)

Intersection set R(Pi) ∩ A(Pi)

Level

I

8

8

1, 2, 3, 4, 5, 6, 7, 8

8

I

II

1

1, 2

1, 2, 3, 4, 5, 6, 7

1,2

II

II

2

1, 2

1, 2, 3, 4, 5, 6, 7

1,2

II

III

7

7

3, 4, 5, 6, 7

7

III

IV

5

5

3, 4, 5, 6

5

IV

V

3

3, 4

3, 4, 6

3,4

V

V

4

3, 4

3, 4, 6

3,4

V

VI

6

6

6

6

VI

As described in step 7 of Section 4, the next step of ISM methodology is to develop a digraph based on partition of levels. The levels of the HCQDs, identified above along with the final reachability matrix are used to draw up the digraph ISM model. The links between HCQDs are shown with arrows indicates the direction of each impact as shown in Figure 1. The digraph ISM model for HCQDs show that the ‘clinical governance quality’ is at the bottom implying that this dimension can influence other factors like ‘core quality dimension’ and ‘Associated supportive quality dimension’ directly and must other dimensions indirectly while it cannot be influenced by other quality dimensions. Hence, it can be concluded that ‘clinical governance quality’ dimension within the Indian HCE will certainly enhance the current hospital services and encourage the sustainability in the system. Further, it can be inferred that by following major quality policy initiatives like clinical governance, would facilitate multi-disciplinary teamwork, partnerships, and cooperative working practices that will have far reaching implications for clinical relationships, the behaviour of healthcare professionals and finally the delivery of care. Figure 1

Interpretive structural model (ISM) of quality dimensions in HCEs


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The next level consists of two quality dimensions: ‘core quality dimension’ and ‘associated supportive quality dimension’. These quality dimensions too exert influence on other factors and aggravate them while they themselves can be influenced by ‘clinical governance quality’. Further, these two quality dimensions are also interrelated and lead to ‘priority area management for critical care’. The quality dimension ‘priority area management for critical care’ is the next level of the hierarchy. It can be influenced by the previously discussed quality dimensions and at the same time it directly affects ‘treatment chain management including referral and evacuation chain management’ and other quality dimensions indirectly. ‘Priority area management for critical care’ and ‘treatment chain management including referral and evacuation chain management’ forms the hub of the healthcare system. They directly influences number of quality dimensions and is amenable to be influenced by other quality dimensions. This means that the priority areas which should be carefully managed are operation theatre, emergency unit, and intensive care unit in the hospital. An effort should me made to evolve suitable parameters for priority areas of HCEs are called for as unavoidable necessity. This will propagate in the treatment chain management in strengthening the referral and evacuation chain management. As there are a number of disciplines that interact to meet the needs of patients, it is appropriate to use a value chain to describe internal service channels in hospitals. Thus, it can be argued that relationships among channel members who provide the links in the service value chain impact on the creation of value and overall service quality. Hence, it can be concluded that these two quality dimensions are extremely important dimensions requiring major management involvement. The next level quality dimensions are ‘state of knowledge management’ and ‘services of HCE for disease burden of society’ is the penultimate level of quality dimensions and is having two-way relationship. Finally, the top-level quality dimension amenable to be influenced and hence, controlled by other quality dimensions is ‘patient expectation and perception of hospital services’. A patient’s perception of value and satisfaction begins with clinical governance and continuing through priority area management, treatment chain management, state of knowledge management, and services for disease burden of society and overall service experience. This relationship can be built on trust, confidence, and patient loyalty towards the healthcare organisations, provided that the hospital continues to meet or exceed the patient’s expectations. The driving power and dependence diagram as shown in Figure 2 helps to classify various HCQDs into four clusters viz. autonomous, dependent, linkage and independent dimensions. The first cluster is located in the south-west quadrant. It consists of the ‘autonomous quality dimensions’ that have weak driving power and weak dependence. These quality dimensions are relatively disconnected from the model with which they have only few links which may be strong (Ahuja et al., 2009; Singh and Sushil, 2013). No HCQD comes under this category. The second cluster lies in the south-east quadrant and has weak driving power but strong dependence. These quality dimensions primilarily come at the top of the ISM model. Top level quality dimensions such as ‘state of knowledge management’, ‘services of HCE for disease burden of society’ and ‘patient expectation and perception of hospital services’ comes under this category. Such a result forces the healthcare organisations, policy makers and management to give greater consideration and carry out a deeper analysis on these HCQDs. Third cluster lie in the north-east quadrant. The linkage factors have strong driving power and also strong

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dependence. These quality dimensions are unstable because of the fact that any action on these dimensions will have an effect on other quality dimensions and also a feedback on themselves. According to Jha and Devaya (2008), these are the most important quality dimensions and would require maximum management attention. Figure 2

Driver power dependence matrix (see online version for colours)

In the present study, there are two quality dimensions falling under this quadrant that is ‘priority area management for critical care’ and ‘treatment chain management including referral and evacuation chain management’. Fourth cluster includes the independent quality dimensions having strong driving power but weak dependence. Independent dimensions are the HCQDs that are located in the north-west quadrant. Bottom level quality dimensions such as ‘core quality dimension’, Associated supportive quality dimension’ and ‘clinical governance quality’ comes under this category. These are the most important quality dimensions and need maximum management attention since they can influence other dimensions to the maximum extent.

6

Conclusions

This paper has identified all the important quality dimensions of HCEs in India by reviewing several research papers. A total of eight HCQDs were finally identified in Indian context and ISM-MICMAC approach has been applied to analyse the contextual relationship between them. In this research, an integrated model for sustainable hospital services have been developed using ISM and MICMAC approach, which may be helpful to healthcare organisations, medical colleges, nursing homes, super-specialty hospitals, and healthcare practitioners and decision makers to employ this model in order to identify and classify the important quality dimensions and to reveal the direct and indirect effects of each quality dimension on HCEs in India. The HCQDs identified in this model are very recent and useful for the growth of HCEs in India. This finding provides important guidelines to the decision makers and healthcare practitioners that they should evaluate various HCQDs in India by implementing them in their organisations. Accordingly, they may also strategically plan long-term growth of their organisations to meet the global


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HCE requirements. The Indian healthcare industry is facing new challenges due to deregulation and market changes, customer requirements, while at the same time, current healthcare services are advancing. New, optimised healthcare models are required to support the development of sustainable healthcare processes to know the direction into which the input resources and hospital capabilities be developed further. The future scope also offers to test and validate this model using structural equation modelling (SEM) approach which has the ability to test the hypothetical model statistically. The methodology adopted in this study can be transported to other industries to capture perception of customers in regard to services offered to them.

References Ahuja, V., Yang, J. and Shankar, R. (2009) ‘Benefits of collaborative ICT adoption for building project management’, Construction Innovation, Vol. 9, No. 3, pp.323–340. Aich, S. and Tripathy, S. (2014) ‘An interpretive structural model of green supply chain management in Indian computer and its peripheral industries’, International Journal of Procurement Management, Vol. 7, No. 3, pp.239–256. Ali, A., Chen, Z. and Lee, J. (2007) ‘Web-enabled platform for distributed and dynamic decision making systems’, International Journal of Advanced Manufacturing Technology, Vol. 38, No. 11, pp.1260–1270. Al-Zaabi, S., Al-Dhaheri, N. and Diabat, A. (2013) ‘Analysis of interaction between the barriers for the implementation of sustainable supply chain management’, International Journal of Advanced Manufacturing Technology, Vol. 68, Nos. 1–4, pp.895–905. Arguedas, A., Kvaerner, K., Liese, J., Schilder, A.G.M. and Pelton, S.I. (2010) ‘Otitis media across nine countries: disease burden and management’, International Journal of Pediatric Otorhinolaryngology, Vol. 74, No. 12, pp.1419–1424. Azam, M., Rahman, Z. and Talib, F. (2012a) ‘A critical study of quality parameters in health care establishment: developing an integrated quality model’, International Journal of Health Care Quality Assurance, Vol. 25, No. 5, pp.387–402. Azam, M., Rahman, Z. and Talib, F. (2012b) ‘Core quality and associated supportive quality parameters/dimensions: a conceptual quality frame work in health care establishment’, International Journal of Business Excellence, Vol. 5, No. 3, pp.238–277. Azevedo, S., Carvalho, H. and Cruz-Machado, V. (2013) ‘Using interpretive structural modelling to identify and rank performance measures: an application in the automotive supply chain’, Baltic Journal of Management, Vol. 8, No. 2, pp.208–230. Balan, S., Vrat, P. and Kumar, P. (2006) ‘Assessing the challenges and opportunities of global supply chain management’, International Journal of Value Chain Management, Vol. 1, No. 2, pp.105–116. Balasubramanian, S., Konana, P. and Menon, N.M. (2003) ‘Customer satisfaction in virtual environments: a study of online investigating’, Management Science, Vol. 49, No. 7, pp.871–889. Boshoff, C. and Gray, B. (2004) ‘The relationships between service quality, customer satisfaction and buying intentions in the private hospital industry’, South African Journal Business Management, Vol. 35, No. 4, pp.27–38. Chander, M., Jain, S.K. and Shankar, R. (2013) ‘Modeling of information security management parameters in Indian organizations using ISM and MICMAC approach’, Journal of Modelling in Management, Vol. 8, No. 2, pp.171–189. Debnath, R.M. and Shankar, R. (2012) ‘Improving service quality in technical education: use of ISM’, Quality Assurance in Education, Vol. 20, No. 4, pp.387–407.

44


Desouza, K.C. (2002) Managing Knowledge with Artificial Intelligence, Quorum Books, Westport, CT. Desouza, K.C. and Evaristo, J.R. (2003) ‘Global knowledge management strategies’, European Management Journal, Vol. 21, No. 1, pp.62–67. Dey, P.K., Seetharam, H. and Naomi, B. (2006) ‘Managing healthcare quality using logical framework analysis’, Managing Service Quality, Vol. 16, No. 2, pp.203–222. Diabat, A., Khreishah, A., Kannan, G., Panikar, V. and Gunasekaran, A. (2013) ‘Benchmarking the interactions among barriers in third-party logistics implementation: an ISM approach’, Benchmarking: An International Journal, Vol. 20, No. 6, pp.805–824. Dijkstra, L. and Bij, H. (2002) ‘Quality function deployment (QFD) in healthcare: methods for meeting customer requirements in redesign and renewal’, International Journal of Quality Reliability Management, Vol. 19, No. 1, pp.6–89. Duggirala, M., Rajendran, C. and Anantharaman, R.N. (2008) ‘Patient-perceived dimensions of total quality service in healthcare’, Benchmarking: An International Journal, Vol. 15, No. 5, pp.560–583. Edvardsson, B. and Olsson, J (1996) ‘Key concepts for new service development’, The Service Industries Journal, Vol. 16, No. 2, pp.140–164. Faisal, M.N. (2010) ‘Sustainable supply chains: a study of interaction among the enablers’, Business Process Management Journal, Vol. 16, No. 3, pp.508–529. Feeney, A. and Zairi, M. (1996) ‘TQM in healthcare’, Journal of General Management, Vol. 22, No. 1, pp.35–47. Fernandes, C.M.B. and Christenson, J.M. (1996) ‘Continuous quality improvement reduces length of stay for fast track patients in an emergency department’, Academic Emergency Medicine, Vol. 3, No. 3, pp.258–263. Galizzi, M.M. and Miraldo, M. (2011) ‘The effects of hospitals’ governance on optimal contracts: bargaining vs. contracting’, Journal of Health Economics, Vol. 30, No. 2, pp.408–424. Hazard, E., Munaka, J., Bigal, M.E., Rupnow, M.F.T. and Lipton, R.B. (2009) ‘The burden of migraine in the United States: current and emerging perspectives on disease management and economic analysis’, Value in Health, Vol. 12, No. 1, pp.55–64. Hu, H.Y., Chiu, S.I. and Yen, T.M. (2009) ‘Modified IPA for order-winner criteria improvement: a MICMAC approach’, Journal of Applied Sciences, Vol. 9, No. 21, pp.3792–3803. Iyer, R. and Muncy, J.A. (2004) ‘Who do you trust?’, Marketing Health Services, Vol. 24, No. 2, pp.26–31. Jain, D. and Banwet, D.K. (2013) ‘Modeling critical elements of selection for strategic alliance partner for network managed services using interpretive structural modelling’, Journal of Modelling in Management, Vol. 8, No. 3, pp.290–304. Jha, N.K. and Devaya, M.N. (2008) ‘Modelling the risks faced by Indian construction companies assessing international projects’, Construction Management and Economics, Vol. 26, No. 4, pp.337–348. Kaiser, H.J. (2006) ‘Healthcare costs’, From the Henry J. Kaiser Family Foundation, http://www.kff.org/insurance/snapshot/chcm050206oth2.cfm (accessed 6 February 2010). Kannan, G., Haq, N.A., Sasikumar, P. and Arunachalam, S. (2008) ‘Analysis and selection of green suppliers using interpretative structural modelling and analytic hierarchy process’, International Journal of Management and Decision Making, Vol. 9, No. 2, pp.163–182. Karin, M.M., Lemmens, A.P.N. and Robbert, H. (2009) ‘A systematic review of integrated use of disease management Interventions in asthma and COPD’, Respiratory Medicine, Vol. 103, No. 5, pp.670–691. Kozak, M., Asunakutlu, T. and Safran, B. (2007) ‘TQM implementation at public hospitals: a study in Turkey’, International Journal of Production and Quality Management, Vol. 25, No. 11, pp.193–207.


45

Li, Gibson, J.M., Jat, P., Puggioni, G., Hasan, M.J., West, J., Vizuete, W., Sexton, K. and Serre, M. (2010) ‘Burden of disease attributed to anthropogenic air pollution in the United Arab Emirates: estimates based on observed air quality data’, Science of the Total Environment, Vol. 408, pp.5784–5793. Lim, P.C. and Tang, N.K.H. (2000) ‘The development of a model for total quality healthcare’, Managing Service Quality, Vol. 10, No. 2, pp.103–111. Mangla, S., Madaan, J., Sarma, P.R.S. and Gupta, M.P. (2014) ‘Multi-objective decision modelling using interpretive structural modelling for green supply chains’, International Journal of Logistics Systems and Management, Vol. 17, No. 2, pp.125–142. Mehregan, M.R., Hashemi, S.H., Karimi, A. and Merikhi, B. (2014) ‘Analysis of interactions among sustainability supplier selection criteria using ISM and fuzzy DEMATEL’, International Journal of Applied Decision Sciences, Vol. 7, No. 3, pp.270–294. Meltzer, E.O., Blaiss, M.S., Derebery, J., Mahr, T.A., Gordon, B.R., Sheth, K.K., Simmons, L., Wingertzahn, M.A. and Boyle, J.M. (2009) ‘Burden of allergic rhinitis: results from the pediatric allergies in America survey’, Journal of Allergy Clinical Immunology, Vol. 124, No. 3, pp.43–70. Miller, W.J., Sumner, A.T. and Deane, R.H. (2009) ‘Assessment of quality management practices within the healthcare industry’, American Journal of Economics and Business Administration, Vol. 1, No. 2, pp.105–113. Natarajan, R.N. (2006) ‘Transferring best practices to healthcare: opportunities and challenges’, The TQM Magazine, Vol. 18, No. 6, pp.572–582. Nelson, K., Nadkarni, S., Narayanan, V.K. and Ghods, M. (2000) ‘Understanding software operations support expertise: a revealed causal mapping approach’, Management Information System, Vol. 24, No. 3, pp.475–507. OECD (2008) OECD Health Data, Organization for Economic Cooperation and Development, Paris. Ovretveit, J. (2000) ‘Total quality management in European healthcare’, International Journal of Health Care Quality Assurance, Vol. 13, No. 2, pp.74–79. Qureshi, M.N., Kumar, D. and Kumar, P. (2007) ‘Modelling the logistics outsourcing relationship variables to enhance shippers, productivity and competitiveness in logistical supply chain’, International Journal of Productivity and Performance Management, Vol. 56, No. 8, pp.689–714. Ratnavel, N. (2009) ‘Safety and governance issues for neonatal transport services’, Early Human Development, Vol. 85, No. 8, pp.483–486. Sage, A.P. (1977) Interpretive Structural Modeling: Methodology for Large-Scale Systems, pp.91–164, McGraw-Hill, New York. Sahney, S., Banwet, D.K. and Karunes, S. (2010) ‘Quality framework in education through application of interpretive structural modeling: an administrative staff perspective in the Indian context’, The TQM Journal, Vol. 22, No. 1, pp.56–71. Satapathy, S., Patel, S.K., Biswas, A. and Mishra, P. (2012) ‘Interpretive structural modeling for e-electricity utility service’, Service Business, Vol. 6, No. 23, pp.349–367. Saxena, A., Ganesh, K., Saxena, A. and Page, T. (2009) ‘Partially centralized decisions and information synchronization in supply chain’, International Journal of Enterprise Network Management, Vol. 3, No. 3, pp.246–267. Seth, A., Momaya, K. and Gupta, H.M. (2008) ‘Managing customer perceived service quality for cellular mobile telephony: an empirical investigation’, Vikalpa, Vol. 33, No. 1, pp.19–34. Singh, A.K. and Sushil (2013) ‘Modeling enablers of TQM to improve airline performance’, International Journal of Productivity and Performance Management, Vol. 62, No. 3, pp.250–275. Singh, M. (2005) ‘The burden of asthma in children: an Asian perspective’, Paediatric Respiratory Reviews, Vol. 6, No. 1, pp.14–19.

46


Sureshchandar, G.S., Rajendran, C. and Anantharaman, R.N. (2002) ‘The relationship between service quality and customer satisfaction: a factor specific approach’, Journal of Services Marketing, Vol. 16, No. 4, pp.363–379. Talib, F. (2013) ‘An overview of total quality management: understanding the fundamentals in service organization’, International Journal of Advanced Quality Management, Vol. 1, No. 1, pp.1–20. Talib, F., Rahman, Z. and Azam, M (2010) ‘Total quality management implementation in the healthcare industry: a proposed framework’, Proceedings of Second International Conference on Production and Industrial Engineering (CPIE-2010), organized by Department of Industrial and Production Engineering, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India, pp.1361–1368. Talib, F., Rahman, Z. and Azam, M. (2011a) ‘Best practices of total quality management implementation in healthcare setting’, Health Marketing Quarterly, Vol. 28, No. 3, pp.232–252. Talib, F., Rahman, Z. and Qureshi, M.N. (2011b) ‘An interpretive structural modeling approach for modeling the practices of total quality management in service sector’, International Journal of Modeling in Operations Management, Vol. 1, No. 3, pp.223–250. Talib, F., Rahman, Z. and Qureshi, M.N. (2011c) ‘Analysis of interaction among the barriers to total quality management implementation using interpretive structural modeling approach’, Benchmarking: An International Journal, Vol. 18, No. 4, pp.563–587. Talib, F., Rahman, Z. and Qureshi, M.N. (2013a) ‘Survey on the usage of total quality management tools and techniques in Indian service industries: an empirical analysis’, International Journal of Quality and Innovation (IJQI), Vol. 2, No. 2, pp.105–119. Talib, F., Rahman, Z. and Qureshi, M.N. (2013b) ‘An empirical investigation of relationship between total quality management practices and quality performance in Indian service companies’, International Journal of Quality and Reliability Management, Vol. 30, No. 3, pp.280–318. The Economist (2004) ‘Survey on health-care finance: the health of nations’ [online] http://www.economist.com/ (assessed 15 July 2013). The Times of India (2011) ‘Increase in health budget 12th plan’, 8 May, p.9, Bennett Coleman and Co. Ltd, publication, N. Delhi, India. Thomson, R. (1998) ‘Quality to the fore in health policy at last’, British Medical Journal, Vol. 317, No. 7151, pp.95–96. Thothathri, R.A., (2003) Knowledge Management, a Resource Book, 1st ed., pp.41–46, Excel Books, New Delhi. van Hees, M. (1998) Verslaving nuchter bekeken, Master thesis, Faculty of Technology Management, Eindhoven University of Technology, in Dutch. Vanu, S.C. (2004) ‘Clinical governance: a fresh look at its definition’, Clinical Governance: an International Journal, Vol. 9, No. 2, pp.87–90. Warfield, J.N. (1973) ‘An assault on complexity’, Battelle Monograph, No. 3, Battelle Memorial Institute, Columbus. WHO (2002) Reducing Risks, Promoting Healthy Life, The World Health Report 2002, pp.52–93, World Health Organization, Geneva. WHO (2009) World Health Statistics 2009, World Health Organization [online] http://www.who.int/whosis/whostat/EN_WHS09_Full.pd (accessed 6 February 2010).