Developing a Care Coordination Model Using a Hybrid DEMATEL and PLS-SEM Approach

Abstract

The aim of this article is to develop and assess a model for care coordination (CC). A novel hybrid approach of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and partial least square structural equation modelling (PLS-SEM) has been used to assess the CC model. The study has been conducted in four phases: (a) literature review, (b) Delphi session, (c) development of CC model through DEMATEL and (d) validation of the model through PLS-SEM. The study involves perspectives of service providers as well as service receivers, for which data were collected from hospitals across India. The literature review and Delphi session helped in finalising the seven measures of CC. Identified measures of CC are: IT-enabled coordination, inter-professional teamwork and consistency, patient centredness, communication and information transfer, physical infrastructural facilities and requirements, delivery of quality care, and facilitating transitions and accountability. Patient-centredness was found to be the most important construct of CC. Delivery of quality care is the most influenced construct and is affected by all the other constructs. Based on the results, practitioners may develop an overarching strategy to deliver seamless care and better health outcomes. This understanding may help in designing processes which in turn would deliver health as a social good.

Keywords

Care coordination health care service delivery measures patient centricity

Introduction

With the evolving living conditions of people, expectations with regard to health care systems are also changing across the world. International bodies and national governments have started giving due consideration to the development of effective health care systems through policy reforms and effective regulation (Prakash, 2015). Governments need to realize that health is a social phenomenon and a hospital is a social institution which is affected by the socio-economic and political conditions of its environment (Wang, Wan, Clement, & Begun, 2001). Inadequate infrastructure, ineffective manpower and high load of patients are some of the major challenges of the Indian health care system (Bajpai, 2014). Achieving a high level of coordination becomes difficult, because hospitals are organized into specialized departments and this leads to strong interdependencies. In order to address this, health care systems aim to enhance patients’ outcome through care coordination (CC), which helps hospitals in improving their resource functioning. Coordination in the health care system is defined as a structural collaboration between participants who operate within distinct organizational structures using separate resources. It aims to be patient-centric, where the services are expected to be flexible so as to meet patients’ varying needs and are delivered through a minimum number of professionals following the norms of the therapeutic relationship (Rosenbach & Young, 2000). The focus of coordinated care is on orchestrating the efforts of doctors, nurses and administrative resources for enhancement in patient experience (Segal, Dunt, & Day, 2004; Solberg, 2011).

In order to meet the demands of coordination, hospitals have shifted towards standardized work processes, referred to as coordinated care pathways (Havens et al., 2010). Compared to care processes, care-coordinated pathways focus more on reorganization of care mechanisms. This raises the questions of who does what, when, where and how. Previous studies have measured CC in the specific environment of certain diseases, but their generalizability is limited (Crump et al., 2017; Young, Walsh, Butom, Solomon, & Shaw, 2011). Walsh et al. (2010) and Schultz and McDonald (2014) have developed conceptual models of CC. Moreover, much of the literature is built from studies conducted in developed economies. The existing literature attempts to conceptualize CC, but its measurement aspects have not been explored much. This article aims to explore the measurement aspects of CC. The research questions are:

RQ1. What are the relevant measures for implementing CC?

RQ2. How these are measures interrelated with each other?

RQ3. To what extent can these interrelationships be used as a basis for a generic model for coordinated care?

To address these research questions, the measures of CC have been identified and interrelationships between these constructs have been assessed. This assessment is through a novel hybrid research approach which combines Decision-Making Trial and Evaluation Laboratory (DEMATEL) and partial least square structural equation modelling (PLS-SEM). Findings reveal that CC is a polymorphous notion with various underlying concepts, aims and interventions in practice. Despite differences in patient groups, heterogeneous needs and the foci of providers, there are certain common components that are essential for the development of coordinated care services. Therefore, it is inferred that delivery of quality care and patient-centricity are central to CC. Subsequent sections of the article include the literature review, theoretical framework of the study, methodology used, data analysis, and results and discussion.

Review of Literature

CC has been recognized as an important aspect of high-quality health care delivery. There is a growing consensus that poor coordination, coupled with fragmented care, compromises the delivery of quality care and increases the chances of medication errors (Schultz, Pineda, Lonhart, Davies, & McDonald, 2013). The focus on CC originates from the increasing fragmentation and supply-oriented approach in health care, which leads to discontinuity, duplication and an absence of responsibility (Øvretveit, 2011; Yates, 2004). CC addresses these challenges and enhances quality of care and customer satisfaction. CC optimizes experiences of current patients and also improves social, psychological and economic outcomes (Kibbe, 2002; van Achterberg, Stevens, Crebolder, de Witt, & Philipsen, 1996).

CC is operationalized through mechanisms of information coordination, clinical management coordination and administrative coordination. Information coordination refers to the transfer and use of the patient’s clinical information, which is needed to coordinate activities between service providers (Øvretveit, 2011). Clinical management coordination refers to the provision of health care in a sequential and complementary manner. Administrative coordination refers to the coordination of patients’ access to different kinds of health services. Coordination is a concerted phenomenon where hospitals are configured through a single or a combination of care mechanisms. Elements of CC vary with the complexity of the disease (Gittell & Weiss, 2004; Michie, Miles, & Weinman, 2003). Issues of CC along with various units of care are elaborated in Table 1.

Table 1.

Description of Units of Care

Units of Care	Description
Mental health	Assessing medical and psychiatric conditions are difficult and results in poor coordination, thus, requiring training, education and supervision of staff (Powell et al., 2008).
Intensive care units	Technological adoption, specialized doctors and the quality of communication permeates efficiency in ICU operations and reduces variation in patient outcomes. Development of a questionnaire for measuring patient’s perception of coordinated healthcare. Questionnaire can be used for need assessment, program evaluation and development of patient-centred care (Ohlinger, Brown, Laudert, Swanson, & Fofah, 2003).
Paediatrics	Development of a metric for measurement of CC and linking it with resources, outcomes and patient satisfaction achieved (Antonelli, Stille, & Antonelli, 2008).
Stroke patients	Assessing effectiveness of bio-psychosocial interventions with coordinated delivery of care. It involved coordinating use of resources across the healthcare system, monitoring patient progress and managing psychological and social risks. Staff needs adequate level of communication, information sharing, patient-centredness and accountability, all of which enhance coordination (Claiborne, 2006).
Neonatal units	Coordination mechanisms such as shared goals and values, effective communication, effective leadership, collaboration, defined processes, competent teamwork and conflict management results in quality enhancement (Pollack & Koch, 2003).
Surgical units	Development of the operational teamwork assessment for surgery (OTAS) tool which highlights the benefits of teamwork for coordinated services (Healey, Undre, & Vincent, 2004).
Orthopaedics	Relational issues such as problem-solving, communication, interdependence, information exchange and accountability enhance CC in inter- and intra-organizational networks (Aller et al., 2015).
Breast cancer patients	Mechanisms such as standardization, monitoring, feedback, defined roles and resources, adequate infrastructure and information exchange help in treatment of early-stage breast cancer (Bickell & Young, 2001).
Surgical units	Coordination involves assembling and synchronization of various processes and combination of effectible feedback and programming results in optimal care (Zlateva et al., 2015).
Spina bifida patients	Coordination requires systems approach. Services for spina bifida patients require standards, integration, and efficiency and delivery policies (Kinsman et al., 2000).

Source: The authors.

The term ‘coordination’ refers to interventions intended to improve care, as well as to describe the systemic requirements such as assessment of needs, planning and review. Organizational studies literature defines coordination as a process for managing dependencies and the integration of interdependent tasks (Prectarius, 2016). Increasing professional specialization among health care professionals gives rise to fragmentation, which necessitates integration (Minkman, 2012). CC is identified as a key strategy that has the potential to improve the effectiveness and efficiency of the health care system. It refers to clinical aspects and focuses on real-time interactions between providers. This explicates the patients’ needs and ensures that these are communicated at the right time to the right provider (Harrison & Verhoef, 2002). Irrespective of the technology-intensive nature of modern health care delivery, it is the human service provider who uses the technology to provide the services. The effect of deficiency in or absence of health personnel is not restricted to a particular service delivery, but it rather impacts the complete coordination of a hospital (Wang et al., 2001). There are various activities which encompass the functioning of hospitals and they are structured along the systemic frame of input–process– output. This classification will help in assessing the level of coordination required for each activity. Various activities of CC along this frame are depicted in Table 2.

Table 2.

Activities of CC

Level	CC Activities
Input level	Reception and enquiry, admission procedures, doctor appointments, queue management, patient record management, staff record management, emergency services, ambulance services, query and grievance handling, receipts and form management, infrastructural requirements, visitors management and so on.
Process level	Doctor consultation, assessment and planning of care, medication management counselling by system representative, safety and security of patients, hygiene and sanitation execution, caring and nursing, testing and report dissemination, patient follow-up, interpersonal communication between staff, IT coordination and so on.
Output level	Delivery of quality care, discharge formalities, easy payment modes, bills and receipts sharing, patient experience, visitors’ experience, medical record saving for future follow-ups, proper guidance after discharge about medicines and diets, patient satisfaction, employee satisfaction and so on.

Source: Gittell (2009); Gorbenko, Fraze, and Lewis (2016); Hull, Arora, Kassab, Kneebone, and Sevdalis (2011); Walsh et al. (2010) and Yates (2004).

It so emerges that there is no standard definition of CC. Its literature is in its nascent stage. Further research is needed to understand the effect of poor coordination on health care service delivery.

Methodology

The primary aim of this article is to develop and validate a model for CC. For this, a novel hybrid approach of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and structural equation modelling (SEM) has been used. This hybrid approach has been highlighted in the literature (Meng, 2014; Wei, Huang, Tzeng, & Wu, 2010). This study has been carried in four phases:

Phase I—Literature Review: An extensive literature review has been conducted based on the articles published in Scopus, SCI (Science Citation Index) and SSCI (Social Sciences Citation Index) indexed journals. Insights derived from the scrutiny and analysis of such articles have been used to develop a theoretical framework of the study.

Phase II—Delphi Interview: Fourteen measures of CC have been identified from the literature. In order to refine these measures, a regressive Delphi session was conducted with well-informed and well- cultivated doctors and academicians. This resulted in seven measures of IT-enabled coordination (C1), inter-professional teamwork and consistency (C2), patient-centredness (C3), communication and information transfer (C4), physical infrastructural facilities and requirements (C5), delivery of quality care (C6) and facilitating transitions and accountability (C7). Table 3 gives a brief description of each of these measures. Measures such as disease management, case management, in-house medication management, role of international affiliations, handling fragmentation along units of care, health care financing and health care governance were excluded. This Delphi session was conducted during November 2016.

Phase III—Development of the Model: DEMATEL was used to identify interrelationships among measures of CC. Towards this, service providers were asked to indicate the direct influence that they believe each element exerts on each of the other elements on an integer scale ranging from 0 to 3. A higher score means that an element exerts a strong influence on the other element (Tzeng, Chinag, & Li, 2007). Data were collected from service providers such as doctors, nurses and administrators of public and private hospitals, from December 2016 to January 2017, using a snowball sampling approach. Perceptions of service providers were used, because they are the actors in the coordinated care pathways and would be able to judge the importance of these measures. Data were collected from 23 service providers. This sample size is adequate, as sample size for the use of DEMATEL is prescribed to be in the range 8–45 (Ranjan, Chatterjee, & Chakraborty, 2016; Sumrit & Anuntavoranich, 2013; Tzeng et al., 2007). The questionnaire used for data collection is depicted in Annexure 1. Data analysis was performed using software package MATLAB v13.

Phase IV—Validation of the Model: The identified relationships were assessed using a PLS-SEM-based reflective model. The choice of the PLS-SEM was due to its ability to estimate a model with a large number of latent variables and indicators even with a small sample size (Sarstedt et al., 2016). PLS-SEM eliminates ambiguous incorrect solutions and ensures factor determinacy by directly estimating latent variable scores, factor identification by introducing flexible residual covariance structure and robust prediction in the context of small sample size, asymmetric distribution and interdependent observations (Hair, Hult, Ringle, & Sarstedt, 2017). PLS-SEM approach achieves high levels of statistical power in comparison to covariance-based SEM with relatively small and moderate samples and more complex models (Reinartz, Haenlein, & Henseler, 2009). Patients who received medical treatment from a hospital in the last one year were asked to indicate their perceptions about the provider organization. The perceptions of patients were considered as they are the end receivers of the service, and their experience would reveal the actual status of the service delivery. Data were collected on a five-point Likert scale from patients across India using a random sampling approach from January to July 2017. The questionnaire used for data collection is depicted in Annexure 2. Three hundred and fifty patients were approached and 137 valid questionnaires were received. The response rate was 39 per cent. This sample size is adequate as it is more than 10 times the maximum number of seven paths aiming at any construct in the outer model as well as in the inner model (Hair et al., 2017). The profile of these patient respondents is depicted in Table 4. Data analysis was performed using software package SmartPLS v3. Use of perceptions of service providers in conjunction with perceptions of patients enhances the quality of triangulation.

Table 3.

Description of CC Constructs

Constructs	Description
IT-enabled CC (C1)	The extent of providing better-quality care devices such as electronic medical records and patient databases to enhance the level of coordination among the stakeholders.
Interprofessional teamwork and consistency (C2)	Interprofessional teamwork and consistency can be infused through enhanced teamwork, effective leadership and consistent efforts of care providers.
Patient-centredness (C3)	Patient-centricity is defined as the extent to which patients judge the overall hospital experience. It is believed that empowering patients as consumers is the key to achieving positive outcomes across all functional domains of the healthcare.
Communication and information transfer (C4)	The extent of information sharing among all the participants in the healthcare system. Communication may occur through a wide variety of channels. It includes interpersonal communication as well as formal information transfer.
Physical infrastructure facilities (C5)	The levels of facilities provided to patients and infrastructural as well as medicinal aspects.
Delivery of quality care (C6)	The extent of determining the specific needs for care and the physical, emotional, psychological and health history of the patient. A care plan anticipates day-to-day needs and tracks the progress of the patient’s recovery.
Facilitate transitions and accountability (C7)	The extent of defining goals and objectives in designing an effective coordination system. The responsibility of providers should be apparent and properly specified.

Source: The authors.

Table 4.

Patient Respondents’ Profile (N = 137)

Criteria	Composition
Gender	Male – 99 (72%), Female 38 (28%)
Care unit	Cardiology – 10 (7%), Dermatology – 19 (13%), Gastroenterology – 32 (24%), Orthopaedics – 23 (17%), Paediatrics – 24 (18%), Psychiatry – 8 (6%), Oncology – 11 (8%), Surgery – 10 (7%).
Age	0–25 years – 14 (10%), 25–35 years – 19 (13%), 35–45 years – 47 (35%), 45 years and above – 57 (42%).
Hospitals	Public – 52 (38%), Private – 85 (62%).
Region	Northern India – 57 (42%), Central India – 37 (27%), Eastern India – 7 (5%), Western India –22 (16%), Southern India – 14 (10%).

Source: The authors.

Data Analysis and Results

Identifying Interrelationships and Development of the Model

Calculation of the average matrix (A): Average matrix A is achieved initially using 23 matrices. Each element of this average matrix is the arithmetic mean of the same items in the different direct matrices of the respondents. Average matrix (A) is calculated as follows, and the results are depicted in Table 5:

Table 5.

Average Matrix (A)

	Measures of CC
	C1	C2	C3	C4	C5	C6	C7
C1	0.000	1.5789	2.0526	1.7895	2.2632	2.000	1.3158
C2	1.5263	0.000	2.0526	2.3684	2.3684	2.0526	1.6316
C3	1.9474	1.9474	0.000	2.0526	2.4211	2.5263	1.9474
C4	1.3684	2.2632	2.1053	0.000	2.2105	2.2632	1.5789
C5	1.8421	2.0000	2.2105	1.7895	0.000	2.2105	1.4737
C6	2.0526	1.8421	2.1579	1.8421	2.2105	0.000	1.6842
C7	1.0526	1.7368	1.8421	1.6316	1.5263	1.7368	0.000

Source: The authors.

Z_{i j} = \frac{1}{m} \sum^{​} ​ x_{i j}^{k}

Calculation of the initial direct influence matrix (D): The initial direct influence matrix (D) can be obtained by normalizing the average matrix (A), in which all principal diagonal elements are equal to zero. Based on the matrix (D) the initial influence that an element exerts and receives from another is shown. The calculation of the matrix (D) is as follows, and the results are shown in Table 6.

Table 6.

Initial Direct Influence Matrix (D)

	Measures of CC
	C1	C2	C3	C4	C5	C6	C7
C1	0.0000	0.1230	0.1598	0.1393	0.1762	0.1557	0.1025
C2	0.1189	0.0000	0.1598	0.1844	0.1844	0.1598	0.1270
C3	0.1516	0.1516	0.0000	0.1598	0.1885	0.1967	0.1516
C4	0.1066	0.1762	0.1639	0.0000	0.1721	0.1762	0.1230
C5	0.1434	0.1557	0.1721	0.1393	0.0000	0.1721	0.1148
C6	0.1598	0.1434	0.1680	0.1434	0.1721	0.0000	0.1311
C7	0.0820	0.1352	0.1434	0.1270	0.1189	0.1352	0.0000

Source: The authors.

m = \min [\frac{1}{\max \sum^{​} ​ d_{i j},}, \frac{1}{\max \sum^{​} ​ d_{i j}}]

D = \frac{A}{m}

Derivation of the total relation matrix (T): The total relation matrix (T) is obtained after the direct relation matrix. This matrix elucidates the final structure of elements. The calculation of T matrix is as follows, and the results are highlighted in Table 7.

Table 7.

Total Relation Matrix (T)

	Measures of CC
	C1	C2	C3	C4	C5	C6	C7
C1	0.9927	1.2297	1.3460	1.2484	1.4098	1.3778	1.0605
C2	1.1747	1.2081	1.4398	1.3705	1.5132	1.4773	1.1551
C3	1.2602	1.4063	1.3750	1.4194	1.5918	1.5790	1.2322
C4	1.1513	1.3414	1.4250	1.1983	1.4858	1.4711	1.1382
C5	1.1593	1.3015	1.4057	1.2966	1.3128	1.4420	1.1112
C6	1.1878	1.3108	1.4227	1.3178	1.4801	1.3155	1.1395
C7	0.9477	1.1035	1.1863	1.1029	1.2124	1.2107	0.08507

Source: The authors.

Calculation of row and column sum and difference (r+c, r–c): In the total relation matrix, the sum of rows and the sum of columns are calculated and represented by r+c and r–c. These r+c and r–c values are used to interpret the results. The values for r+c represent the total effects both given and received by the corresponding indicators, whereas r–c values indicate the net contribution of the corresponding indicator on the whole system [51]. Table 8 depicts row and column sum and difference values.

Table 8.

r+c and r–c Values

Measures of CC	r+c Values	r–c Values
IT-enabled coordination (C1)	16.5386	0.7913
Interprofessional teamwork and consistency(C2)	18.2399	0.4373
Patient-centredness (C3)	19.4643	0.2635
Communication and information transfer (C4)	18.1651	0.2573
Physical infrastructural facilities and requirements (C5)	19.0350	–0.9767
Delivery of quality care (C6)	19.0475	–0.6994
Facilitating transitions and accountability (C7)	15.3015	–0.0733

Source: The authors.

Calculation of the threshold value (p): A threshold value (p) is set to filter the apparent effects denoted by the elements of the matrix (T), and it is necessary to explain the structure of the elements. If all the information from the matrix (T) converts to the impact–diagraph map, the map will be too complicated to show the necessary information for decision-making. The threshold value for this analysis was 1.2836. Only those elements, whose influence level in the matrix (T) were higher than the threshold value, can be chosen and converted into the impact–digraph map. Figure 1 depicts the impact–digraph map. The values on this map are plotted on the basis of r+c and r–c values and the relationships between them are established by the threshold values. The values on this map are plotted on the basis of r+c and r–c values and the relationships between them are established by the threshold values.

Based on the r+c values the seven measures in order of decreasing importance are as follows: patient-centredness (C3), delivery of quality care (C6), infrastructural facilities and requirements (C5), inter-professional teamwork and consistency (C2), communication and information transfer (C4), IT-enabled coordination (C1), facilitating transitions and accountability (C7). Measure C3 is the most important measure for CC with a value of 19.4643, while C7 is the least important measure with a value of 15.3015. In contrast, the importance of C1, C2, C3 and C4 are net causes, and C5, C6 and C7 are net effects based on r–c values. Figure 1 shows that measure C1 affects C3 and C6 and gets affected by C5 and C7, and the other measures are mutually influenced by each other.

Figure 1.

Source: The author.

Figure 2.

Source: The author.

Various researchers argue that coordination is an activity that involves many stakeholders and effective communication among them will enhance their accountability. Such communication relies on a robust physical infrastructure (Hilligoss, Song, & McAlearney, 2016; Young et al., 2011). The interrelationship among the constructs constitutes a model of CC. This model of CC is depicted in Figure 2.

Conceptual Model of CC and Research Hypotheses

Based on the derived model, the following hypotheses are proposed:

Relationships between Information Technology and CC

Information technology (IT) infrastructure enables CC and improves information, communication and dissemination across functions, and it results in an enhanced level of service delivery and customer satisfaction. Thus, there is a linkage between IT-enabled coordination and CC measures (Gittell & Weiss, 2004; Williams, Asi, Raffenaud, Bagwell, & Zeini, 2016). Formally stated:

H₁ IT-enabled coordination positively influences delivery of quality care.

H₂ IT-enabled coordination positively influences communication and information transfer.

Relationships between Inter-professional Teamwork and CC

CC depends upon the way the service providers function. Teamwork among service providers enables an effective information transfer and enhances delivery of quality care to the patients (Gittell, 2009; Kibbe, 2002; Solberg, 2011). Thus, there is a linkage between inter-professional teamwork and quality of CC. Formally stated:

H₃ Inter-professional teamwork and consistency positively influences delivery of quality care.

H₄ Inter-professional teamwork and consistency positively influences communication and information transfer.

Relationships between Patient Centricity and CC

The patient-centric approach is at the core of CC. Assessing patients’ needs and delivering quality care boosts coordinated care outcomes. Patient-centredness bolsters collaborative problem-solving and increases the satisfaction of patients as well as health professionals (Michie et al., 2003; Okhuysen & Bechky, 2009). Formally stated:

H₅ Patient-centredness positively influences the delivery of quality care.

H₆ Patient-centredness positively influences inter- professional teamwork and consistency.

Relationship between Communication and Information Transfer and CC

CC is a patient-centred activity geared to assess and meet the needs of patients along the health care value chain. Communication and information sharing along the health care value chain enhances the delivery of care (Lillrank, 2012; McGuiness & Sibthorpe, 2003). Thus, there is a linkage between communication and information transfer and the delivery of quality care. Formally stated:

H₇ Communication and information transfer positively influences the delivery of quality care.

Relationships between Physical Infrastructure and CC

A hospital’s state-of-the-art physical infrastructure supports IT enablement and aids in patient-centredness. Effective IT infrastructure enables sharing of information about patient needs, thereby resulting in the delivery of quality care (Wang et al., 2001; Yates, 2004). Formally stated:

H₈ Physical infrastructure positively influences IT-enabled coordination.

H₉ Physical infrastructure positively influences the delivery of quality care.

H₁₀ Physical infrastructure positively influences patient-centredness.

Relationships between Accountability and CC

Facilitating transitions and accountability-related processes of CC meet a patient’s needs through effective information sharing and teamwork along the health care value chain. An accountability-linked service delivery system facilitates the delivery of quality care (Walsh et al., 2010, Young et al., 2011). Thus, there is a linkage between accountability and other CC measures. Formally stated:

H₁₁ Facilitating transitions and accountability positively influence communication and information transfer.

H₁₂ Facilitating transitions and accountability positively influence IT-enabled coordination.

H₁₃ Facilitating transitions and accountability positively influence inter-professional teamwork and consistency.

Assessment of Measurement Model

Reliability was assessed by Cronbach’s alpha, which was greater than 0.8 for all the constructs. The overall composite reliability was 0.921; both results confirm the consistency and reliability of the data. Further, all the scales were examined for composite reliability (CR) as well as for convergent and discriminant validity. As presented in Table 1, the average variance extracted (AVE) values for all construct measures were higher than 0.5, suggesting adequate convergent validity. The CR values ranged from 0.916 to 0.958, higher than the suggested classical cut-off value of 0.7 (Nunnally & Bernstein, 1994) and reflect high internal consistency. Taken together, the analyses provide a robust support for convergent validity of the model. All AVE values in Table 9 (range 0.689– 0.785) exceeded the 0.5 cut-off limit suggested by Anderson and Gerbing (1988). Additionally, the diagonal elements in Table 9 (square root of latent variable AVEs) are higher than the respective construct correlations, suggesting discriminant validity. The Heterotrait–Monotrait (HTMT) values for the indicators have been calculated using the complete bootstrapping procedure of the SMARTPLS v3 software. All the values (ranging from 0.529 to 0.794) were below the acceptable range of 0.9, indicating the discriminant validity (Hair et al., 2017).

R² and Q² Values

The coefficient of determination (R²) measures the predictive accuracy of the constructs. These values represent the amount of variability in the endogenous constructs of C1, C2, C3, C4 and C6, which were 38.8 per cent, 57.6 per cent, 41.8 per cent, 51.1 per cent and 70.2 per cent, respectively; these were explained by the respective exogenous factors. These values are moderate and reflect predictive relevance. Values of R² in conjunction with Stone–Geisser’s Q² value (Geisser, 1975; Stone, 1974) were used to assess predictive relevance. The Q² value of latent variables is obtained by using the blindfolding technique (Hair et al., 2017) with an omission distance of 7 which yielded cross-validated redundancy Q² values, all of which were above 0, thus indicating predictive relevance. The values are presented in Table 9.

Table 9.

Measurement Properties and Discriminant Validity

Factors	Factor Loadings	t-values	AVE	CR	R² Values	Q² Values
IT-Enabled Coordination (C1)			0.731	0.916	0.388	0.197
Provision of comprehensive medical record	0.844	8.213
Admission/discharge process facilitated through IT	0.889	8.570
Database management of patients medical history	0.817	7.649
Ease of online payments	0.868	8.695
Interprofessional Teamwork and Consistency (C2)			0.785	0.936	0.576	0.252
Staff works with sense of collaboration and trust	0.812	8.966
Level of professionalism among the staff	0.921	9.621
Empathy towards patients	0.858	9.320
Consistent performance for the patient’s needs	0.947	9.773
Patient-Centredness (C3)			0.768	0.958	0.418	0.159
Clarity of guidance for admission and discharge process	0.926	10.120
Query and grievance handling	0.843	9.441
Waiting time	0.860	9.725
Hygiene and sanitation	0.851	9.686
Caring and courteous nature of staff	0.964	11.104
Safety and security	0.789	8.744
Providing client-centred information	0.890	9.856
Communication and Information Transfer (C4)			0.750	0.923	0.511	0.273
Clarity of information flow at patient level	0.954	10.998
Interrelationships among the staff	0.747	8.323
Immediate communication in times of emergency	0.866	9.354
Establishing clear goals and tasks of the staff	0.884	9.478
Physical Infrastructure Facilities (C5)			0.689	0.917	*	*
Availability of testing labs and medical stores	0.875	9.117
Ambulatory services	0.744	8.398
Adequate number of rooms and beds in the hospital	0.813	8.998
Facilitative location and layout of the hospital	0.821	9.017
Visitors management	0.889	9.134
Delivery of Quality Care (C6)			0.744	0.945	0.702	0.254
Availability of doctors on time	0.759	8.417
Proper diagnosis and consultation by the doctors	0.911	10.784
Good care provided by the nurses	0.893	9.985
Regular patient follow-up	0.735	8.269
Extent to which staff respected privacy of patients	0.932	11.243
Doctors’ expertise and professional skill for providing treatment	0.852	9.563
Timely availability of reports/records to the doctors	0.837	9.154
Facilitating Transitions and Accountability (C7)			0.746	0.921	*	*
Responsible and accountable staff	0.786	8.635
Ease of transfer from one ward to other	0.911	10.784
Staff share responsibilities to manage patient health as a priority	0.855	9.572
Response to changed requirements of patients	0.897	10.112
Correlation among Constructs
	C1	C2	C3	C4	C5	C6	C7
C1	0.731
C2	0.565	0.785
C3	0.623	0.725	0.768
C4	0.640	0.598	0.751	0.750
C5	0.698	0.513	0.647	0.683	0.689
C6	0.564	0.651	0.710	0.717	0.639	0.744
C7	0.540	0.657	0.678	0.571	0.682	0.673	0.746

Source: The authors.

Note: * C5 and C7 are exogenous constructs.

Multicollinearity

Before analysing the structural model, in addition to reliability and validity, the variance inflation factor (VIF) must be assessed to compute multicollinearity. Hair et al. (2017) recommend a cut-off value of 5.0 for multicollinearity. The VIF results for each construct were below the threshold value of 5.0 and indicate the absence of multicollinearity issues.

Assessment of Structural Relationships

The hypothesized relationships were tested using the PLS algorithm to generate the standardized path coefficients. As a follow-up analysis, a bootstrapping method was used to determine the significance of path coefficients. The results are summarized in Table 10.

Table 10.

Path Analysis

Structural Relationships	Path Coefficient	t-statistics*	Significance**	f² Values	Inference
IT-enabled coordination positively influences delivery of quality care.	0.395	3.761	0.000	0.211	Supported
IT-enabled coordination positively influences communication and information transfer.	0.268	2.909	0.000	0.163	Supported
Interprofessional teamwork and consistency positively influences delivery of quality care.	0.431	5.176	0.000	0.303	Supported
Interprofessional teamwork and consistency positively influences communication and information transfer.	0.277	3.101	0.000	0.184	Supported
Patient-centredness positively influences the delivery of quality care.	0.517	5.810	0.000	0.425	Supported
Patient-centredness positively influences interprofessional teamwork and consistency.	0.404	4.862	0.000	0.398	Supported
Communication and information transfer positively influences the delivery of quality care	0.304	3.101	0.000	0.177	Supported
Physical infrastructure positively influences IT-enabled coordination	0.368	2.833	0.000	0.191	Supported
Physical infrastructure positively influences the delivery of quality care	0.647	11.186	0.000	0.720	Supported
Physical infrastructure positively influences patient-centredness.	0.299	2.950	0.000	0.194	Supported
Facilitating transitions and accountability positively influence communication and information transfer.	0.342	2.633	0.000	0.263	Supported
Facilitating transitions and accountability positively influence IT-enabled coordination.	0.307	3.060	0.000	0.242	Supported
Facilitating transitions and accountability positively influence interprofessional teamwork and consistency.	0.495	6.120	0.000	0.407	Supported

Source: The authors.

Notes: *Significant at 0.05; **Significant at 0.001.

f2 Values

The effective size of each path in the model is assessed by f ² values (Cohen, 1988). These f ² values are depicted in Table 10. These values are in the range 0.177–0.425 and, as a norm, values above 0.15 show moderate effect and those above 0.3 indicate high effect.

Assessment of Structural Model

The model fit indices used were standardized root mean square residual (SRMR), normed fit index (NFI) and rms_theta. The chi-square value came out to be 1205.189 and 1268.268 for the saturated and estimated models, respectively. These indices are depicted in Table 11.

Table 11.

Model Fit Indices

Model Fit Assessment		Fit Values of the Structural Model	Inference
Fit Indices	Norms	Fit Values of the Structural Model	Inference
Normed-fit-index	Should lie between 0 and 1 and value should be close to 1	0.894	Data shows a reasonable fit with the underlying model.
Standardized root mean square residual	Should be less than 0.10	0.086	Data shows a reasonable fit with the underlying model.
rms_theta	Should be less than 0.12	0.032

Source: The authors.

CC Response Index (CCRI)

Importance-performance map analysis (IPMA) considers the performance of each construct on a target construct. This analysis extends the basic results of PLS-SEM by applying the total effects of the structural model (importance) and the average of latent variable scores (performance; Ringle & Sarsedt, 2016; Völckner, Sattler, Hennig-Thurau, & Ringle, 2010). Insights from IPMA may help service providers to prioritize their alternatives. Delivery of quality care was taken as target construct because it is the outcome of CC. IPMA results are depicted in Table 12. Physical infrastructure facilities and patient-centredness emerge to have the highest importance (0.56 and 0.47, respectively). In addition, inter-professional teamwork and consistency, along with communication and information transfer, have the highest performance (66.554 and 64.538, respectively).

Table 12.

Results of IPMA Analysis

Constructs	Total Effects (Importance)	CCRI (Performance)
C1	0.216	63.672
C2	0.239	66.554
C3	0.478	61.665
C4	0.559	64.538
C5	0.213	60.945
C7	0.330	61.682

Source: The authors.

Discussion

The aim of this article is to improve the understanding of measurement and implementation of coordinated care. Results demonstrate that effective coordination mechanism can be achieved when processes are specifically designed to respond to the needs of patients. Moreover, strong channels of communication as well as teamwork, which are facilitated by adequate infrastructure and technological support, help in delivering an enhanced level of care.

CC operates along a continuum varying from generic care to specialized care. It emerges that most of the studies in CC are disease-specific and represent specialized care. The disease-oriented approach to CC aims for high responsiveness and is more suitable for developed economies. The generic approach to care is more prevalent in emerging economies and aims to achieve high efficiency in their health systems. CC integrates activities along the value chain and nurtures efforts in meeting a patient’s needs (Walters & Jones, 2001). This helps in enhancing responsiveness, reducing unnecessary workload, improving resource planning and utilization, eliminating wasted time, strengthening synchronization, standardizing processes and enhancing patient satisfaction (van Achterberg et al., 1996).

CC is a process in which all components are orchestrated to enhance service delivery. CC requires a multi-dimensional approach in which each construct builds upon the other to create superior value (Rosenbach & Young, 2009). The constructs of infrastructure facilities and requirements, as also facilitating transitions and accountability, emerge as independent variables. Thus, CC is a process which is governed by the efforts of human resources. While tangible resources like infrastructure are important, it is the competence of professionals that drives the service processes (Michie et al., 2003). CC is needed to transform increasingly fragmented health care systems into patient-centric service delivery systems (Øvretveit, 2011). Ageing populations, increasing chronic diseases and variable health care settings necessitate coordination. Coordination takes time, which is typically not reimbursed. Infrastructure and resources are needed to respond to and meet the patient’s requirements.

Although managing transitions through CC in a health care system is in a nascent stage, this could be strengthened by infusing accountability-driven mechanisms. Accountability fosters patient-centric approaches and provides continuity in care-coordinated pathways. Best-in-class health care is patient-centred and outcome-oriented (Solberg, 2011). It aids in autonomous and informed decisions and should facilitate CC along bidirectional interaction between patients and providers. The process redesigns facilitate patients’ choices and streamline interaction points to improve outcomes and reduce costs (Okhuysen & Bechky, 2009).

Effective CC improves service delivery and outcomes of the health system. Various stakeholders such as policy makers aim for effective and efficient care, while doctors, nurses and staff aim to deliver high-quality and high-value care to the patients (Walsh et al., 2010). The proposed framework may be used to enhance system-wide performance. A hospital works on a set of activities, and when these activities are carried out in a well-coordinated system, the value proposition of service delivery is improved. Coordinated health care activities lead to an improved value chain in the system (Walter & Jones, 2001). A hospital may use value chain mapping to identify areas for improvement in quality or reduction in costs by delivering or connecting patients to the right services, so that they benefit from the entire set of activities required for effective care (Kibbe, 2002; Schultz et al., 2013). While private hospitals across the world are rapidly adopting these approaches, public hospitals are still struggling with poor infrastructure, lack of manpower and higher patient loads.

Meeting a patient’s need requires developing a culture of quality where individuals, departments, patient care teams and administrators are held accountable. Moving towards this kind of performance evaluation, measures such as procedures, clinical treatment protocols, critical pathways and statements of expected health care outcomes have been conceptualised. CC, through effective listening, communicating, information and knowledge sharing, clinical collaboration and enhanced patient experience, contribute to health care quality (Crump et al., 2017; Hilligoss et al., 2016). Hence, the aim of coordinated care is to facilitate the appropriate and efficient delivery of health care services within and across systems.

Concluding remarks

The aim of this article is to develop and validate a generic model for CC. Towards this, perceptions of service providers and receivers have been analysed. The service provider’s perceptions explicate that CC measures are interrelated with each other and their synchronization enhances the level of coordination. On the other hand, service receiver’s perceptions depict interrelationships between CC measures and may act as a tool to assess the levels of service delivery and customer satisfaction.

Results of DEMATEL-based analysis highlight that a patient-centric approach improves the CC pathways. Results of PLS-SEM analysis highlight that the delivery of care matters most to patients. The patients’ perspective suggests that CC pathways should be implemented to upgrade the service delivery channel of health care system. Implicitly, this framework is a measure of coordination at the system level. Organizational coordination is achieved at the system level and it needs the support of various arrangements, but its ultimate measure of success is how well it contributes to health outcomes. A potential limitation of this approach is loss of details about the experiences of coordination at the provider level. A second potential limitation is that patients may find it difficult to make an overall assessment.

Declaration of Conflicting Interests

The authors declare no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Annexure 1.

Measurement of Service Provider’s Perception of Interdependency among Elements of Coordination

Please rate following factors on a scale of 0–3 (0: no dependency; 1: low dependency; 2: medium dependency; 3: high dependency) in order to evaluate their interdependency on each other.

Measures of Care Coordination
	IT enabled coordination	Inter-professional teamwork and consistency	Patient - centredness	Communication and information transfer	Physical Infrastructure	Delivery of quality care	Facilitating transitions and accountability
IT enabled coordination
Inter-professional teamwork and consistency
Patient-centredness
Communication and information transfer
Physical infrastructure
Delivery of quality care
Facilitating transitions and accountability

Annexure 2.

Measurement of Patient’s Perception Level of Coordination

You are requested to provide your perception about level of coordination on the five point Likert scale ranging from 1 to 5.

1: Strongly Agree; 2: Agree; 3: Neutral; 4: Disagree; 5: Strongly Disagree

Elements of Coordination	Measurement Scale
Elements of Coordination	1	2	3	4	5
IT Enabled Coordination
Provision of comprehensive medical record
Admission/discharge process facilitated through IT
Database management of patients’ medical history
Ease of online payments
Interprofessional Teamwork and Consistency
Staff works with sense of collaboration and trust
Level of professionalism among the staff
Empathy towards patients
Consistent performance for the patients’ needs
Patientcentredness
Clarity of guidance for admission and discharge process
Query and grievance handling
Waiting time
Hygiene and sanitation
Caring and courteous nature of staff
Safety and security
Providing clientcentred information regarding care process
Communication and Information Transfer
Clarity of information flow at the patient level
Interrelationships among the staff
Immediate communication in times of emergency
Establishing clear goals and tasks of the staff
Physical Infrastructure Facilities
Availability of testing labs and medical stores
Ambulatory services
Adequate number of rooms and beds in the hospital
Facilitative location and layout of the hospital
Visitors management
Delivery of Quality Care
Availability of doctors on time
Proper diagnosis and consultation by the doctors
Good care provided by the nurses
Regular patient follow-up
Extent to which staff respected privacy of patients
Doctors expertize and professional skill for providing treatment
Timely availability of reports/records to the doctors.
Facilitating Transitions and Accountability
Responsible and accountable staff
Ease of transfer from one ward to other
Staff share responsibilities to manage patient health as a priority
Response to changed requirements of patients

References

Aller

M-.B.

, Vargas

, Coderch

, Calero

, Cots

, Abizanda

, (2015). Development and testing of indicators to measure coordination of clinical information and management across levels of care. BMC Health Services Research, 15, 323–339. doi: 10.1186/s12913-015-0968-z

Anderson

J. C.

, & Gerbing

D. W.

(1988). Structural Equation Modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.

Antonelli

R. C.

, Stille

C. J.

, & Antonelli

D. M.

(2008). Care coordination for children and youth with special healthcare needs: A descriptive, multisite study of activities, personnel costs, and outcomes. Pediatrics, 122(1), 209–216.

Bajpai

(2014). The challenges confronting public hospitals in India: Their origins and possible solutions. Advances in Public Health. Retrieved 4 December 2018, from https://dx-doi-org.web.bisu.edu.cn/10.1155/2014/898502.

Bickell

M. D.

, & Young

J. D.

(2001). Coordination of care for early stage breast cancer patients. Journal of Internal Medicine, 16(11), 737–742.

Claiborne

(2006). Effectiveness of a care coordination model for stroke survivors: A randomized study. Health and Social Work, 31(2), 87–96.

Cohen

(1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

Crump

, King

, Graham

, Thorlby

, Raleigh

, Redding

, & Goodwin

(2017). Developing a user reported measure of care coordination. International Journal of Integrated Care, 17(1), 1–10.

Geisser

(1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(3), 320–328.

10.

Gittell

J. H.

(2009). Relational coordination: Guidelines for theory, measurement and analysis. Waltham, MA: Brandeis University.

11.

Gittell

J. H.

, & Weiss

S. J.

(2004). Coordination networks within and across organizations: A multi-level framework. Journal of Management Studies, 41(1), 127–153.

12.

Gorbenko

K. O.

, Fraze

, & Lewis

V. A.

(2016). Redesigning care delivery with patient support personnel: Learning from accountable care organizations. International Journal of Care Coordination, 19(3/4), 73–83.

13.

Hair

J. F.

, Hult

G. T. M.

, Ringle

C. M.

, & Sarstedt

(2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks, CA: SAGE Publications.

14.

Harrison

, & Verhoef

(2002). Understanding coordination from the consumer’s perspective in a regional health system. Health Services Research, 37(4), 1031–1054.

15.

Havens

D. S.

, Vasey

, Gittell

J. H.

, & Lin

W. T.

(2010). Relational coordination among nurses and other providers: Impact on the quality of patient care. Journal of Nursing Management, 18(8), 926–937.

16.

Healey

A. N.

, Undre

, & Vincent

C. A.

(2004). Developing observational measures of performance in surgical teams. Quality and Safety in Healthcare, 13(Suppl. 1), 33–40.

17.

Hilligoss

, Song

P. H.

, & McAlearney

A. S.

(2016). Coordination mechanisms in four accountable care organizations. International Journal of Organization Theory & Behavior, 19(2), 207–232.

18.

Hull

, Arora

, Kassab

, Kneebone

, & Sevdalis

(2011). Observational teamwork assessment for surgery: Content validation and tool refinement. Journal of American College of Surgeons, 212(2), 234–243.

19.

Kibbe

D. C.

(2002). Care coordination: Helping physicians improve care for the chronically ill. Medical Group Management Association, 2(4), 44–47.

20.

Kinsman

, Harrison

, Kengeya-Kayondo

, Kanyesigye

, Musoke

, & Whitworth

(2000). Implementation of a comprehensive care coordination related to AIDS education programme for schools in Masaka district, Uganda. AIDS Care, 11(1), 591–601.

21.

Lillrank

(2012). Integration and coordination in healthcare: An operations management view. Journal of Integrated Care, 20(1), 6–12.

22.

McGuiness

, & Sibthorpe

(2003). Development and initial validation of a measure of coordination of healthcare. International Journal of Quality in Healthcare, 15(4), 309–318.

23.

Meng

(2014). Study on integrated method of SEM and DEMATEL. Advanced Materials Research, 926–930, 3722–3727.

24.

Michie

, Miles

, & Weinman

(2003). Patient centeredness in chronic illness: What is it and does it matter? Patient Education and Counseling, 51(3), 197–206.

25.

Minkman

M. M. N.

(2012). The current state of integrated care: An overview. Journal of Integrated Care, 20(6), 346–358.

26.

Nunnally

J. C.

, & Bernstein

I. H.

(1994). Psychometric theory (3rd ed.). New York, NY: McGraw-Hill.

27.

Ohlinger

, Brown

M. S.

, Laudert

, Swanson

, & Fofah

(2003). Development of potentially better practices for the neonatal intensive care unit as a culture of collaboration: Communication, accountability, respect, and empowerment. Pediatrics, 111(2), 471–481.

28.

Okhuysen

G. A.

, & Bechky

B. A.

(2009). Coordination in organizations: An integrative perspective. The Academy of Management Annals, 3(1), 463–502.

29.

Øvretveit

(2011). Does clinical coordination improve quality and save money: A detailed review of evidence, 2. London: The Health Foundation.

30.

Pollack

M. M.

, & Koch

M. A.

(2003). Association of outcomes with organizational characteristics of neonatal intensive care units. Critical Care Medicine, 31(6), 1620–1629.

31.

Powell

D. G.

, Williams

A. M.

, Larsen

, Perkins

, Roland

, & Harris

M. F.

(2008). Coordinating primary healthcare: An analysis of the outcomes of a systematic review. Medical Journal of Australia, 188, 565–568.

32.

Prakash

(2015). Steering healthcare service delivery: A regulatory perspective. International Journal of Health Care Quality Assurance, 28(2), 173–192.

33.

Prectarius

(2016). Improving care coordination using organizational routines: Care pathways as a coordination mechanism. Journal of Health Organization and Management, 30(1), 185–208.

34.

Ranjan

, Chatterjee

, & Chakraborty

(2016). Performance evaluation of Indian Railway zones using DEMATEL and VIKOR methods. Benchmarking: An International Journal, 23(1), 78–95.

35.

Ringle

C. M.

, & Sarstedt

(2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886.

36.

Reinartz

, Haenlein

, & Henseler

(2009). An empirical comparison of the efficacy of covariance-based and variance-based SEM. International Journal of Research in Marketing, 26(4), 332–344.

37.

Rosenbach

, & Young

(2000). Care coordination and Medicaid managed care: Emerging issues for states and managed care organizations. Princeton, NJ: Mathematica Policy Research.

38.

Sarstedt

, Hair

, Ringle

, Thiele

, & Gudergan

S. P.

(2016). Estimation issues with PLS and CBSEM: Where the bias lies! Journal of Business Research, 69(10), 3998–4010.

39.

Schultz

E. M.

, Pineda

, Lonhart

, Davies

S. M.

, & McDonald

K. M.

(2013). A systematic review of the care coordination measurement landscape. BMC Health Services Research, 13, 119–132. doi: https://doi.org/10.1186/1472-6963-13-119

40.

Schultz

E. M.

, & McDonald

K. M.

(2014). What is care coordination? International Journal of Care Coordination, 17(1/2), 5–24.

41.

Segal

, Dunt

, & Day

S. E.

(2004). Introducing coordinated care (2): Evaluation of design features and implementation processes implications for a preferred health system reform model. Health Policy, 69(2), 215–228.

42.

Solberg

L. I.

(2011). Care coordination: What is it, what are its effects and can it be sustained? Family Practice, 28(5), 469–470.

43.

Stone

(1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, Series B, 36(2), 111–147.

44.

Sumrit

, & Anuntavoranich

(2013). Using DEMATEL method to analyze the causal relations on technological innovation capability evaluation factors in Thai technology-based firms. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 4(2), 81–103.

45.

Tzeng

G. H.

, Chinag

C. H.

, & Li

C. W.

(2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert System Application, 32(4), 1028–1044.

46.

Van Achterberg

, Stevens

F. J.

, Crebolder

H. F.

, de Witt

L. P.

, & Philipsen

(1996). Coordination of care: Effects on the continuity and quality of care. International Journal of Nursing Studies, 33(6), 638–650.

47.

Völckner

, Sattler

, Hennig-Thurau

, & Ringle

C. M.

(2010). The role of parent brand quality for service brand extension success. Journal of Service Research, 13(4), 379–396.

48.

Walsh

, Young

J. M.

, Harrison

J. D.

, Butow

P. N.

, Solomon

M. J.

, Masya

, & White

(2010). What is important in care coordination? A qualitative investigation. European Journal of Cancer Care, 20(2), 220–227.

49.

Walters

, & Jones

(2001). Value and value chains in healthcare: A quality management perspective. The TQM Magazine, 13(5), 319–335.

50.

Wang

B. B.

, Wan

T. T. H.

, Clement

, & Begun

(2001). Managed care, vertical integration strategies and hospital performance. Healthcare Management Science, 4(3), 181–191.

51.

Wei

L. P.

, Huang

H. J.

, Tzeng

H. G.

, & Wu

I. S.

(2010). Casual modeling of web-advertising effects by improving SEM based on DEMATEL technique. International Journal of Information Technology & Decision Making, 9(5), 799–829.

52.

Williams

, Asi

, Raffenaud

, Bagwell

, & Zeini

(2016). The effect of information technology on hospital performance. Healthcare Management Science, 19(4), 338–346.

53.

Yates

(2004). Cancer care coordinators: Realizing the potential for improving the patient journey. Cancer Forum, 28(3), 128–132.

54.

Young

J. M.

, Walsh

, Butow

P. N.

, Solomon

M. J.

, & Shaw

(2011). Measuring cancer care coordination: Development and validation of a questionnaire for patients. BMC Cancer, 11, 298–305. doi: 10.1186/1471-2407-11-298

55.

Zlateva

, Anderson

, Coman

, Khatri

, Tian

, & Fifield

(2015). Development and assessment of the medical home care coordination survey for assessing care coordination in the primary care setting from the patient and provider perspectives. BMC Health Service Research, 15, 226–237. doi: 10.1186/s12913-015-0893-1

Developing a Care Coordination Model Using a Hybrid DEMATEL and PLS-SEM Approach

Abstract

Keywords

Introduction

Review of Literature

Methodology

Data Analysis and Results

Identifying Interrelationships and Development of the Model

Conceptual Model of CC and Research Hypotheses

Relationships between Information Technology and CC

Relationships between Inter-professional Teamwork and CC

Relationships between Patient Centricity and CC

Relationship between Communication and Information Transfer and CC

Relationships between Physical Infrastructure and CC

Relationships between Accountability and CC

Assessment of Measurement Model

R2 and Q2 Values

Multicollinearity

Assessment of Structural Relationships

f2 Values

Assessment of Structural Model

CC Response Index (CCRI)

Discussion

Concluding remarks

Declaration of Conflicting Interests

Funding

Annexure 1.

Measurement of Service Provider’s Perception of Interdependency among Elements of Coordination

Annexure 2.

Measurement of Patient’s Perception Level of Coordination

References

R² and Q² Values