Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

Abstract

The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions.

Keywords

Service quality medical caring and nursing combined institutions for the aged interval Mahalanobis-Taguchi system grey cloud clustering

1 Introduction

Currently, China is facing severe population problems. The aging of the population is accompanied by disability [1]. It is predicted that by 2030, China’s elderly population will reach 371 million, of which 43 million are senior citizens and 68 million are disabled. The daily diagnosis, treatment, and nursing problems of the elderly and disabled elderly have become prominent, and the demand for medical services for the elderly has surged [2]. It is difficult for traditional elderly care institutions to meet the needs of the elderly in terms of medical services, and the elderly and their families are paying more attention to the quality of services. Therefore, it has become an inevitable move to advance China’s elderly care industry to develop high-quality healthcare institutions that integrate elderly care. A Medical Caring and Nursing Combined Institution for the Aged, refers to an elderly care institution that can integrate elderly care and medical resources, as well as provide elderly residents with basic elderly care functions and certain medical functions [3]. Since the development of Medical Caring and Nursing Combined Institutions for the Aged proposed first in 2013, China’s Medical Caring and Nursing Combined Institutions for the Aged have developed rapidly under its vigorous promotion in the country. As of 2020, the number of Medical Caring and Nursing Combined Institutions for the Aged nationwide has reached 5,857, an increase of 59.4% compared to 2017. However, although the number of Medical Caring and Nursing Combined Institutions for the Aged is increasing, the poor quality of their services has become increasingly prominent, with issues such as “illegal diagnosis and treatment in the Nanjing Zijin nursing home,” “bundling of elderly in the Taizhou Langgao nursing and medical care home,” and “lack of medical services and medicines in the Chongqing Yixinyuan elderly apartment.”

Recently, China has vigorously carried out actions to improve the quality of services in the Medical Caring and Nursing Combined Institutions for the Aged. Many regions have issued relevant policies and achieved certain results; however, the Medical Caring and Nursing Combined Institutions for the Aged started late in China, and historical data is insufficient. At present, a scientific and unified service quality evaluation index system has not yet been established. Existing evaluation methods cannot solve the ambiguity, randomness, grey characteristics, and the interaction between indicators in evaluating the service quality of Medical Caring and Nursing Combined Institutions for the Aged; based on reviewing relevant literature, combined with the actual service quality of Medical Caring and Nursing Combined Institutions for the Aged. This study constructs a service quality evaluation index system for Medical Caring and Nursing Combined Institutions for the Aged. It proposes a service based on the interval Mahalanobis-Taguchi system (MTS)— grey cloud clustering model. This study aims to establish a scientific and reasonable evaluation system for the service quality of Medical Caring and Nursing Combined Institutions for the Aged and provide a reference for decision-makers to evaluate the service quality of these institutions.

Contributions of this paper include: (1) Based on the Service Quality (SERVQUAL) model, a service quality evaluation index system for Medical Caring and Nursing Combined Institutions for the Aged is constructed from five dimensions: tangibles, security, reliability, responsiveness, and empathy. (2) The interval MTS was introduced into the grey cloud clustering model, and a new method was proposed for evaluating the quality of medical care services. (3) An IPA model was constructed based on important indicators and the average service quality score, which provides an effective analysis tool for improving the service quality of Medical Caring and Nursing Combined Institutions for the Aged.

2 Literature review

2.1 Research on the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged

The construction of a service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged requires careful consideration of multiple factors. Li et al. regarded the five factors of operation management, safety protection, and basic health as the main components of the evaluation index system. They pointed out that many indicators have comparative advantages and strong correlations [4]. Xiao et al. introduced eight factors into the evaluation index system such as the resource input, service capability, and material resources. Zhu et al. considered 15 factors, such as environment, system, and service completion rate, when constructing the evaluation index system [6]. Han et al. included nine factors, such as operating conditions, life care, and service personnel, into the evaluation index system [7]. Zhang et al. included eight factors, such as human resources, facilities and equipment, and medical and nursing service satisfaction when constructing the evaluation index system [8]. Subsequently, to build a more scientific index system, scholars applied the SERVQUAL model to construct the service quality evaluation index system of the Medical Caring and Nursing Combined Institutions for the Aged, and it has been widely used. Based on the SERVQUAL model, Zhang and Han [9] constructed a service quality evaluation index system of 20 indicators from five dimensions of tangibles, reliability, assurance, responsiveness, and empathy based on SERVQUAL model. Similarly, Wang et al. constructed a service quality evaluation index system of 28 criteria from these five dimensions [10].

2.2 Research on the evaluation of service quality of Medical Caring and Nursing Combined Institutions for the Aged

The scientific and reasonable evaluation method of the service quality of Medical Caring and Nursing Combined Institutions for the Aged determines the accuracy and effectiveness of the evaluation. Scholars have applied a variety of methods in this field. Anderson et al. researched the service quality of Medical Caring and Nursing Combined Institutions for the Aged, and they believed that the service quality of these institutions needs to be improved [11]. Chen et al. evaluated the service quality of long-term care institutions in Taiwan using the importance-satisfaction model and service quality performance matrix and proposed suggestions for improvement [12]. Ko et al. used the SERVQUAL model to measure the satisfaction of the elderly and analyzed the importance of the evaluation attributes of long-term care institutions through IPA [13]. Song analyzed the service quality of Korean nursing homes through logistic regression and found that the structure of the nursing staff was positively correlated with the quality of care [14]. Grøndahl evaluated the quality of care in four Norwegian nursing homes using a cluster analysis [15]. Yeh and Chen applied the analytic hierarchy process to calculate index weights and used quality function expansion and grey correlation analysis to improve Taiwan’s nursing homes [16]. Shen et al. used TOPSIS, weighted rank-sum ratio, and weighted composite index weighting techniques to evaluate the service quality of 15 Medical Caring and Nursing Combined Institutions for the Aged in China [17]. Wang et al. used the entropy method to calculate the attribute weights. They used TOPSIS to rank the service quality of the Medical Caring and Nursing Combined Institutions for the Aged in 13 areas of Wuhan and classified them using the cluster analysis [18].

2.3 Research on the Mahalanobis-Taguchi system

The MTS is a pattern recognition method based on quality engineering proposed by Dr. Genichi Taguchi in 2002 [19]. The MTS can solve the interference caused by the correlation between indicators through an orthogonal table, Mahalanobis distance, and signal-to-noise ratio (SNR) and can ensure the independence and effectiveness of decision-making information [20]. Currently, this method is widely used in multi-attribute decision-making.Yuan et al. proposed a new multi criteria decision-making method for the selection of RIES schemes based on MTS and Choquet integral [21]. Chang and Cheng proposed a fuzzy integral multi-attribute decision-making method based on MTS and transformation and verified the feasibility of this method through an example [22]. On this basis, Chang et al. introduced interval numbers into the MTS, and proposed an interval number multi-attribute decision-making method of the MTS and TOPSIS and compared it with other methods to prove the feasibility of this method [23]. Li et al. proposed a risk decision-making method based on cloud model, MTS and grey cumulative prospect theory [24].With further research by scholars, the MTS has been extended to determine index weights. Ye et al. determined the weights of industrial operation quality evaluation indicators based on the MTS, eliminating the subjectivity caused by human empowerment [25]. Chen et al. used the MTS to determine the weight of enterprise epidemic prevention, control risk assessment indicators, and eliminate the correlation between the indicators [26].

2.4 Research on grey cloud clustering

Traditional grey clustering only considers the incompleteness of information, ignoring the ambiguity and randomness of the evaluation information and membership levels. To solve this problem, Wang et al. introduced the cloud model theory to grey clustering and, for the first time, proposed the concept of grey cloud clustering, which was later widely used by scholars in various fields [27]. Peng et al. used the improved analytic hierarchy process and grey cloud clustering to evaluate the operational effectiveness of a submarine-to-air missile weapon system [28]. Li and Yang evaluated the risk of water and mud inrush in a tunnel based on normal grey cloud clustering [29]. Luo et al. used the grey relational analysis method, maximum deviation, maximum entropy principle, and grey cloud clustering model to evaluate the drought risk of agricultural disasters [30]. Chao et al. used the grey entropy method, AHP, expert evaluation method, and grey cloud clustering model to evaluate offshore oilfield development programs [31] comprehensively. Li et al. evaluated the emergency evacuation plan of port ships by improving the AHP and grey cloud clustering model [32].

2.5 Summary of literature review

To sum up, the research on the service quality evaluation of Medical Caring and Nursing Combined Institutions for the Aged has made some progress. However, there are still some problems. The existing evaluation methods cannot solve the ambiguity, randomness, grey characteristics, and the interaction between indicators in evaluating the service quality of Medical Caring and Nursing Combined Institutions for the Aged. Furthermore, security-related indicators have become an important factor that affects the perception of service quality for the elderly [33]. However, scholars seldom consider them when constructing the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged. Therefore, this study considers the importance of safety for the service quality of medical and elderly care institutions. Based on the SERVQUAL model, a service quality evaluation index system was constructed based on tangibles, security, reliability, responsiveness, and empathy. In order to solve the problems of ambiguity, randomness, grey characteristics, and the interaction between indicators encountered in the evaluation process, this research proposes an interval MTS— grey cloud clustering model for Medical Caring and Nursing Combined Institutions for the Aged. The service quality evaluation model and the Medical caring and Nursing Combined Institutions for the Aged was applied in four typical cities in the Jiangsu Province as examples to verify the feasibility and effectiveness of the method proposed in this paper. Finally, through the IPA analysis of city A, targeted improvement measures were proposed, which provided stronger theoretical support for its quality improvement.

3 The construction of the service quality evaluation system of Medical Caring and Nursing Combined Institutions for the Aged

3.1 The construction of the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged

The SERVQUAL model has a milestone role in service quality assessment. In the existing research on service quality evaluation, the evaluation dimensions are mainly developed based on SERVQUAL quality evaluation method, including five dimensions of tangibles, reliability, assurance, responsiveness, and empathy, with a total of 22 evaluation items [34]. Later, this scale has been tested in many service industries such as hotels, shops and express delivery, showing good reliability and validity.The SERVQUAL model solves the problem that service quality evaluation is difficult to quantify and it points out that the service quality is the difference between the user’s expected service and the service actually obtained [35]. It is the most important part of the user’s evaluation of the quality of the service. It has been widely used in the evaluation of service quality in elderly care institutions [36]. Therefore, in this study,the SERVQUAL model was used to evaluate the service quality of Medical Caring and Nursing Combined Institutions for the Aged. Meanwhile,to ensure the accuracy of the SERVQUAL model applied to the service quality evaluation of Medical Caring and Nursing Combined Institutions for the Aged, it should be improved according to the characteristics of the services it provides.

This study is based on the research on the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged [37 –41] and the “Guidelines for the management of Medical Caring and Nursing Combined Institutions for the Aged “, combined with the results of in-depth interviews with the elderly, and revised the indicators according to the opinions of experts in related fields to build the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged.This paper combines “reliability” and “guarantee” in the SERVQUAL model into “reliability”, and at the same time adds a new dimension “security”, which is finally composed of 5 dimensions, namely tangibles, security, reliability, responsiveness,and empathy. The specific content is shown in Table 1. Meanwhile, we have explained these dimensions in combination with the characteristics of the services of Medical Caring and Nursing Combined Institutions for the Aged.

Table 1
Service quality evaluation index system

Dimensions Codes Sub-dimensions Measurement items

Tangibles A1 Life facilities Modern living facilities

A2 Medical facilities Well-equipped medical facilities

A3 Living Environment Comfortable living environment

A4 Staff appearance Staffs should be well dressed and appear neat

Security B1 Food security Ensure the safety and health of food

B2 Medical safety Ensure the safety of medical services

B3 Information security Ability to protect personal privacy

B4 Personal security Ability to ensure life safety

Reliability C1 Service attitude Staffs are polite, enthusiastic

C2 Professional staff Ratio of staff with certificates

C3 Staff training Qualified rate of staff training

C4 Communication skills Staff have good communication skills

Responsiveness D1 Service speed Time required to provide services

D2 Complaint handling Completion rate of complaints

D3 Service accuracy Provide the necessary services accurately

D4 Emergency capability Ability to handle emergencies

Empathy E1 Service cost Service cost is reasonable

E2 Service assistance Provide assistance with meals and bath services

E3 Special care Provide tranquillity services, hospice care, etc

E4 Personalized service Meet the individual needs of the elderly

Dimensions	Codes	Sub-dimensions	Measurement items
Tangibles	A1	Life facilities	Modern living facilities
	A2	Medical facilities	Well-equipped medical facilities
	A3	Living Environment	Comfortable living environment
	A4	Staff appearance	Staffs should be well dressed and appear neat
Security	B1	Food security	Ensure the safety and health of food
	B2	Medical safety	Ensure the safety of medical services
	B3	Information security	Ability to protect personal privacy
	B4	Personal security	Ability to ensure life safety
Reliability	C1	Service attitude	Staffs are polite, enthusiastic
	C2	Professional staff	Ratio of staff with certificates
	C3	Staff training	Qualified rate of staff training
	C4	Communication skills	Staff have good communication skills
Responsiveness	D1	Service speed	Time required to provide services
	D2	Complaint handling	Completion rate of complaints
	D3	Service accuracy	Provide the necessary services accurately
	D4	Emergency capability	Ability to handle emergencies
Empathy	E1	Service cost	Service cost is reasonable
	E2	Service assistance	Provide assistance with meals and bath services
	E3	Special care	Provide tranquillity services, hospice care, etc
	E4	Personalized service	Meet the individual needs of the elderly

(1) Tangibles

Compared with traditional pension institutions, Medical Caring and Nursing Combined Institutions for the Aged can provide elderly care services and medical services at the same time. Before moving in, the elderly will consider whether the infrastructure and environment of the institution can meet their daily needs and medical needs, such as living facilities, medical facilities, living environment, and staff appearance.

(2) Security

Security refers to the sense of security and trust that the elderly generates when receiving services, which protects them from danger, risks and worries. Because Medical Caring and Nursing Combined Institutions for the Aged include both elderly care services and medical services, security mainly includes: food security, medical safety, information security, and personal security.

(3)Reliability

Reliability refers to the ability of service personnel to provide professional services.Due to the particularity of the elderly, the service personnel of Medical Caring and Nursing Combined Institutions for the Aged should also have the corresponding professional knowledge. Assurance indicators are manifested in the service quality of Medical Caring and Nursing Combined Institutions for the Aged mainly include: service attitude, professional staff, staff training, and communication skills.

(4) Responsiveness

Responsiveness mainly relates to the service enthusiasm of Medical Caring and Nursing Combined Institutions for the Aged in the process of providing elderly care services and the timeliness of responding to the needs of the elderly.Most of the elderly in Medical Caring and Nursing Combined Institutions for the Aged suffer from chronic diseases, and some elderly people still have mobility problems.When the elderly need it, whether the service staff can actively and quickly provide help to these elderly people is a key issue for the elderly.

(5) Empathy

Empathy refers to Medical Caring and Nursing Combined Institutions for the Aged can consider and provide personalized services for the elderly. Considering the inconvenient movement of some elderly in Medical Caring and Nursing Combined Institutions for the Aged and the demand for medical services, the design of empathy indicators also needs to consider two factors: service assistance and special care.

3.2 Service quality evaluation model of Medical Caring and Nursing Combined Institutions for the Aged based on interval MTS-grey cloud clustering

In actual survey of service quality of Medical Caring and Nursing Combined Institutions for the Aged, the questionnaires collected are often missing and incomplete. Traditional evaluation methods make it difficult to solve such grey problems. The grey clustering method is specially used to evaluate and classify objects with insufficient known information. It can solve the problem of “small samples and poor information” in evaluating the service quality of Medical Caring and Nursing Combined Institutions for the Aged. As there are some qualitative indicators in the service quality evaluation indicators, they need to be converted into quantitative values, and ambiguity and randomness will inevitably occur in the conversion process. The traditional whitening function cannot express the randomness of the decision maker’s subjective judgment. It is necessary to introduce the cloud model into the whitening weight function to form a grey cloud clustering model. Therefore, this study will use the grey cloud clustering model to evaluate the service quality of Medical Caring and Nursing Combined Institutions for the Aged. However, when determining the weights of indicators, decision-makers cannot give exact values when making decisions due to the uncertainty of actual problems and the limitations of human cognition. At the same time, correlations between the evaluation indicators cannot be eliminated. As a quality engineering tool, the MTS can solve the problem of correlation between indicators. It is an effective method to deal with the problem of multi-attribute uncertainty; the interval number can solve the problem as the MTS cannot handle fuzzy language. Therefore, this study combines interval numbers with the MTS to improve the grey cloud clustering model, considers the ambiguity of decision information, eliminates the correlation between indicators, and makes the evaluation results more accurate. The decision steps were:

Step 1. Define the digital feature peak value λ_x, left and right boundary values (L_x, R_x) expectation E_x, entropy E_n, and super entropy H_e of the cloud model. The algorithm for generating normal grey clouds is as follows:

In the interval [L_x, R_x], a normal random number is generated with C_x as the expectation and E_n = (R_x - L_x)/6 as the standard deviation.

Generate a normal random number E′_n with E_n = (R_x - L_x)/6 as the expectation, and H_e = E_n/α (α usually takes 6–8) as the standard deviation.

Calculate $u_{i} = exp (- \frac{(x - C_{x})^{2}}{2 E'_{n}^{2}})$ and let (x_i, u_i) represent the cloud droplets.

The above steps were repeated until the required m cloud drops were generated.

Step 2. Based on the above algorithm, the normal whitening weight function of the upper limit measure of the j index with respect to the k-grey class is obtained as $f_{j}^{k} (x) = {\begin{matrix} exp (- \frac{(x - C_{x})^{2}}{2 E'_{n}^{2}}), & x \in [L_{x}, C_{x}] \\ 1, & x \in [C_{x}, R_{x}] \\ 0, & x \notin [L_{x}, R_{x}] \end{matrix}$ (1)

The moderate measure of grey-like whitening weight function: $f_{j}^{k} (x) = {\begin{matrix} exp (- \frac{(x - C_{x})^{2}}{2 E'_{n}^{2}}), & x \in [L_{x}, R_{x}] \\ 0, & x \notin [L_{x}, R_{x}] \end{matrix}$ (2)

The lower limit measures the grey-like whitening weight function: $f_{j}^{k} (x) = {\begin{matrix} 1, & x \in [L_{x}, C_{x}] \\ exp (- \frac{(x - C_{x})^{2}}{2 E'_{n}^{2}}), & x \in [C_{x}, R_{x}] \\ 0, & x \notin [L_{x}, R_{x}] \end{matrix}$ (3)

Step 3. Evaluate the evaluation decision matrix $A = [a_{ij}^{L}, a_{ij}^{U}]_{m \times n}$ of the expert group on the n evaluation indicators of m evaluation objects and construct the evaluation matrix $B = [\begin{matrix} \begin{matrix} max (a_{il}) & \dots & max (a_{in}) \end{matrix} \\ \begin{matrix} min (a_{il}) & \dots & min (a_{in}) \end{matrix} \end{matrix}]$ of abnormal samples according to the decision matrix.

Step 4. Standardize the evaluation matrix, namely: $[z_{kj}^{L}, z_{kj}^{U}] = [\frac{b_{kj}^{L} - {\bar{X}}_{j}}{S_{j}}, \frac{b_{kj}^{U} - {\bar{X}}_{j}}{S_{j}}]$ (4)

In the formula, the mean and variance of the evaluation matrix are, respectively: ${\bar{X}}_{j} = \frac{1}{2 m} \sum_{i = 1}^{m} (a_{tj}^{U} + a_{tj}^{U})$ (5)

$S_{j} = \sqrt{\frac{1}{3 m} \sum_{t = 1}^{m} {[(a_{tj}^{U})^{2} + a_{tj}^{U} a_{tj}^{L} + (a_{tj}^{L})^{2} - \frac{1}{4 m} [\sum_{t = 1}^{m} (a_{tj}^{U} + a_{tj}^{L})]}^{2}}$ (6)

Step 5. The MTS usually uses a 2-level orthogonal table L_p (2^q) to measure the impact of indicators, where P represents the number of experiments and q represents the number of indicators. The Mahalanobis distance $Q = [q_{ij}^{L}, q_{ij}^{U}]$ of each evaluation object is obtained by an orthogonal experiment, and the formula is as follows:

$\begin{matrix} Q = [q_{ij}^{L}, q_{ij}^{U}] = min / max [\sqrt{\frac{z_{kj}^{L} R_{q}^{- 1} (Z_{kq}^{L})^{T}}{| Y_{q} |}}, \\ \sqrt{\frac{z_{kj}^{U} R_{q}^{- 1} (Z_{kq}^{U})^{T}}{| Y_{q} |}}] \end{matrix}$ (7)

Step 6. Calculate the SNR of each indicator; ${SNR}_{j} = - 10 {log}_{10} [\frac{1}{n} \sum_{i = 1}^{n} q_{ij}^{U} q_{ij}^{L}]$ (8)

Step 7. Calculate the information Gain of index j by using the SNR_(i1) of the index at level 1 and the SNR_(i2) at level 2, and use the Sigmoid function to calculate the weight w_j of index j. $w_{j} = \frac{φ (x_{j})}{\sum_{i = 1}^{n} φ (x_{j})}$ (9)

Where $φ (x_{i}) = \frac{1}{1 + exp (- σ Gain)}$ Gain = SNR_(i1) - SNR_(i2)

Step 8. Calculate the clustering coefficient of the indicator. The index data x_i is substituted into the grey cloud whitening weight function of each evaluation level, and the grey cloud clustering coefficient $f_{j}^{k} (x_{j})$ of index j with respect to the grey category k is calculated. $f_{j}^{k} (x_{j}) = f_{{jE}_{x}}^{k} (x_{j}) / \sum_{k = 1}^{n} f_{{jE}_{x}}^{k} (x_{j})$ (10)

Where $f_{{jE}_{x}}^{k} (x_{j}) = (f_{j 1}^{k} (x_{j}) + f_{j 2}^{k} (x_{j}) + \dots + f_{jq}^{k} (x_{j})) / q$

Step 9. Determine the comprehensive clustering coefficient. The index weight calculated by the interval MTS is substituted into the comprehensive grey cloud clustering coefficient $σ_{i}^{k}$ of i with respect to the evaluation level k. $σ_{i}^{k} = \sum_{j = 1}^{m} f_{j}^{k} (x_{j}) \times w_{j}$ (11)

Step 10. Determine evaluation level. If $max {σ_{i}^{k}} = σ_{i}^{k *}$ , then object i is classified into grey class k.

4 Empirical analysis

4.1 Data collection

Jiangsu Province is one of the first provinces in China to enter an aging society, and the degree of aging is second only to Shanghai and Beijing. At present, Jiangsu has 650 Medical Caring and Nursing Combined Institutions for the Aged with 137,000 beds to promote further the development of these Institutions and improve the service quality of these Institutions. The Jiangsu Province has formulated the “Implementation Plan for Improving the Service Quality of Medical Caring and Nursing Combined Institutions for the Aged in the Jiangsu Province.” This study designed a questionnaire based on the service quality evaluation index system defined in Section 3.1 and selected four typical cities, A, B, C, and D in the Jiangsu Province, with Medical Caring and Nursing Combined Institutions for the Aged as the evaluation objects. The questionnaire is mainly aimed at the elderly in Medical Caring and Nursing Combined Institutions for the Aged. It comprises two parts (the basic situation of the survey object and the main research content of the questionnaire). A five-point Likert scale (very dissatisfied = 1 to very satisfied = 5) was used for scoring according to the respondent’s satisfaction. The higher the score, the higher the satisfaction. A total of 232 questionnaires were collected through on-site and online surveys, and 216 valid questionnaires were collected, with an effective recovery rate of 93.1%. The average scores of the elderly on the service quality evaluation of the four typical cities with Medical Caring and Nursing Combined Institutions for the Aged are shown in Table 2.

Table 2
Average service quality score

Index/City A B C D

A₁ 3.1 3.4 3.6 4.1

A₂ 3.4 3.1 2.9 3.4

A₃ 3.4 3.3 2.8 3.7

A₄ 3.5 2.4 2.9 3.2

B₁ 3.1 3.4 3.3 3.3

B₂ 3.3 3.6 3.5 3.9

B₃ 2.6 3.4 3.3 4.1

B₄ 3.5 3.3 3.4 3.9

C₁ 3.7 4.3 3.8 3.5

C₂ 2.8 3.1 2.9 3.8

C₃ 2.8 4.1 3.4 3.8

C₄ 2.7 3.5 3.8 3.6

D₁ 3.3 2.6 4.5 3.6

D₂ 3.0 2.9 2.6 3.4

D₃ 3.3 3.2 3.4 4.5

D₄ 2.4 3.3 3.2 3.7

E₁ 2.6 3.3 3.7 3.8

E₂ 2.8 3.4 3.5 3.4

E₃ 3.6 3.9 3.5 3.5

E₄ 2.3 2.7 4.6 4.3

Index/City	A	B	C	D
A₁	3.1	3.4	3.6	4.1
A₂	3.4	3.1	2.9	3.4
A₃	3.4	3.3	2.8	3.7
A₄	3.5	2.4	2.9	3.2
B₁	3.1	3.4	3.3	3.3
B₂	3.3	3.6	3.5	3.9
B₃	2.6	3.4	3.3	4.1
B₄	3.5	3.3	3.4	3.9
C₁	3.7	4.3	3.8	3.5
C₂	2.8	3.1	2.9	3.8
C₃	2.8	4.1	3.4	3.8
C₄	2.7	3.5	3.8	3.6
D₁	3.3	2.6	4.5	3.6
D₂	3.0	2.9	2.6	3.4
D₃	3.3	3.2	3.4	4.5
D₄	2.4	3.3	3.2	3.7
E₁	2.6	3.3	3.7	3.8
E₂	2.8	3.4	3.5	3.4
E₃	3.6	3.9	3.5	3.5
E₄	2.3	2.7	4.6	4.3

4.2 Reliability and validity analysis

In order to verify the stability and reliability of the questionnaire, the Cronbach Alpha reliability coefficient method and the structural validity factor analysis method were used to analyze the reliability and validity of the questionnaire. The alpha reliability coefficient and KMO value of the questionnaire were calculated using SPSS23— the results as shown in Table 3.

Table 3
Alpha reliability coefficient and KMO value

Tangibles Security Reliability Responsiveness Empathy

Alpha reliability coefficient 0.917 0.923 0.856 0.921 0.839

KMO value 0.926

	Tangibles	Security	Reliability	Responsiveness	Empathy
Alpha reliability coefficient	0.917	0.923	0.856	0.921	0.839
KMO value	0.926

As shown in Table 3, the alpha reliability coefficients for tangibles, securty, reliability,responsiveness, and empathy were all greater than 0.7, and the KOM value was greater than 0.7, indicating that the reliability and validity of the questionnaire met the requirements.

4.3 Service quality evaluation

Through steps (1) and (2), combined with the actual questionnaire survey data, the service quality evaluation level was divided into five levels: very good, good, medium, poor, and very poor. The corresponding grey cloud digital characteristics are presented in Table 4. The five levels of normal grey cloud whitening weight functions are shown in Fig. 1.

Fig. 1

Five-level normal cloud whitening weight function.

Table 4

Grey cloud characteristic value

level	very good	good	medium	poor	very poor
Grey range	[4,5, 4,5]	[3.5,4.5, 3.5,4.5]	[3,4, 3,4]	[2.5,3.5, 2.5,3.5]	[2,3, 2,3]
Ex	4.5	4	3.5	3	2.5
En	0.17	0.17	0.17	0.17	0.17

According to steps (3)–(6) and based on the expert group’s interval assessment decision matrix for the service quality of four typical cities with Medical Caring and Nursing Combined Institutions for the Aged, the standardization process is carried out using the formula (4). Since there are 20 indicators in this paper, it is adopted. The formula (7) calculates the Mahalanobis distance of the service quality evaluation of four typical cities with Medical Caring and Nursing Combined Institutions for the Aged. Finally, the average of each index was calculated using Equation (8). The SNR values are presented in Table 5.

Table 5

SNR values

Test	1	2	3	4	5	6	. . .	17	18	19	20	21	22	23	SNR
	A₁	A₂	A₃	A₄	B₁	B₂		E₁	E₂	E₃	E₄
1	1	1	2	1	2	1	. . .	2	1	2	2	2	1	2	–16.92
2	2	2	2	1	1	2	. . .	2	1	1	2	1	2	1	–15.27
3	1	1	1	2	2	1	. . .	1	1	2	2	2	2	1	–13.01
4	2	2	1	1	2	2	. . .	1	1	2	1	1	1	2	9.31
5	1	2	2	2	2	1	. . .	1	1	1	1	1	2	2	–8.97
6	2	2	2	2	1	1	. . .	2	1	1	1	2	1	1	–6.33
7	2	1	1	1	2	2	. . .	2	2	2	1	1	2	1	7.10
8	1	2	2	1	2	2	. . .	2	1	2	1	2	2	1	5.15
9	2	1	2	2	1	2	. . .	1	2	2	1	2	1	1	–9.97
10	2	1	1	2	2	2	. . .	1	1	1	2	2	2	1	–10.19
11	2	2	2	1	2	1	. . .	1	2	1	2	2	1	1	–6.71
12	2	1	1	1	1	1	. . .	2	2	1	2	2	2	2	9.90
13	2	2	1	2	1	1	. . .	2	1	2	2	1	2	2	10.13
14	1	2	1	2	1	2	. . .	1	2	1	1	2	2	2	8.42
15	1	2	1	2	2	2	. . .	2	2	1	2	1	1	1	–9.24
16	1	1	1	1	1	1	. . .	1	1	1	1	1	1	1	8.06
17	1	1	2	2	1	1	. . .	2	2	2	1	1	2	1	–10.02
18	2	1	2	1	2	1	. . .	1	2	1	1	1	2	2	6.56
19	1	2	1	1	1	1	. . .	1	2	2	2	1	1	1	–11.38
20	1	1	1	1	1	2	. . .	2	1	1	1	2	1	2	9.37
21	2	1	2	2	1	2	. . .	1	1	2	2	1	1	2	–8.21
22	2	2	1	2	2	1	. . .	2	2	2	1	2	1	2	–6.67
23	1	1	2	2	2	2	. . .	2	2	1	2	1	1	2	–9.24
24	1	2	2	1	1	2	. . .	1	2	2	2	2	2	2	–13.68

Through step 7, using formula (9), according to the existing literature, let σ = 0.25 calculate the information Gain of index j, and use the Sigmoid function to calculate the weight w_j of index j, as shown in Table 6.

Table 6

Index weight

index	Weights	index	Weights
A₁	0.06697	C₃	0.08903
A₂	0.09230	C₄	0.01719
A₃	0.06984	D₁	0.01243
A₄	0.08424	D₂	0.03211
B₁	0.01836	D₃	0.02693
B₂	0.08811	D₄	0.02455
B₃	0.01858	E₁	0.01434
B₄	0.01453	E₂	0.08221
C₁	0.06901	E₃	0.02742
C₂	0.06441	E₄	0.08745

Through steps (8)–(10), the comprehensive grey cloud clustering coefficient of the service quality of the four typical cities with Medical Caring and Nursing Combined Institutions for the Aged is calculated, and the final evaluation grade is obtained. $max_{1 ⩽ k ⩽ 5} {σ_{A}^{k}} = σ_{A}^{1} = 0.208, max_{1 ⩽ k ⩽ 5} {σ_{B}^{k}} = σ_{B}^{2} = 0.197,$ $max_{1 ⩽ k ⩽ 5} {σ_{C}^{k}} = σ_{C}^{3} = 0.198, max_{1 ⩽ k ⩽ 5} {σ_{D}^{k}} = σ_{D}^{4} = 0.204 .$

Reesults show that the service quality level of the Medical Caring and Nursing Combined Institutions for the Aged in city A is “very poor,” city B is “poor,” city C is “poor,” and city D is “good.”

4.4 IPA analysis

The calculated results show that the service quality of Medical Caring and Nursing Combined Institutions for the Aged in the Jiangsu Province is generally low. Therefore, to further improve the service quality of Medical Caring and Nursing Combined Institutions for the Aged in Jiangsu, the evaluation level of “very poor” city A is selected for further IPA analysis. In this study, importance of the indicators calculated by the interval MTS and the average service quality score collected by the questionnaire were used as the coordinate axis of the IPA analysis. The average value was used as the division boundary of the quadrants, divided into the advantage area, maintenance area, opportunity area, and the four quadrants of the key improvement area, as shown in Fig. 3. The first quadrant is the advantage area, the area where the index importance and the average score of service quality are high. The second quadrant is the maintenance area, which belongs to the area with low indicator importance but a high average service quality score. The third quadrant is the opportunity area, which is an area where the importance of indicators and the average score of service quality are low; and the fourth quadrant is the key improvement area, which refers to a very important indicator, but the average service quality score is relatively low. Therefore, the importance of the above indicators and the evaluation results of service quality fully reflect the current advantages of City A and its main problems.

Table 7
Comprehensive grey cloud clustering coefficient

Grey range A B C D

Very good 0.135 0.152 0.171 0.198

Good 0.160 0.174 0.180 0.204

Medium 0.195 0.196 0.198 0.201

Poor 0.204 0.197 0.189 0.169

Very poor 0.208 0.193 0.179 0.145

Grey range	A	B	C	D
Very good	0.135	0.152	0.171	0.198
Good	0.160	0.174	0.180	0.204
Medium	0.195	0.196	0.198	0.201
Poor	0.204	0.197	0.189	0.169
Very poor	0.208	0.193	0.179	0.145

Fig. 2

Analysis matrix.

Through analysis, city A’s C2(Professional staff), C3 (Staff training),E2 (Service assistance), and E4 (Personalized service) are in the average service quality score. However, the indicators are in the key improvement area Therefore, the following measures can be taken to improve the service quality of Medical Caring and Nursing Combined Institutions for the Aged: (1) Attractive talent policies should be introduced, and vocational qualification certification and title promotion system should be improved. It is necessary to systematically train more medical and pension compound talents in the field of elderly care in cooperation with universities. (2) The assessment of aged care workers should include not only the assessment of their professional knowledge and business ability, but also the assessment of staff’s service enthusiasm and learning situation. Rewards and punishments should be in accordance with relevant regulations, so as to stimulate staff’s work initiative and enthusiasm. (3) The assistance with meals and bath services should be provided for disabled elderly people, and some modern elderly care products can be added, such as automatic bathing machines and bath chairs. (4) Needs of the elderly should be known in advance, and personalized pension plans for the elderly should be formulated. For example, suitable activities for the elderly with mobility difficulties should be provided.

5 Conclusion and prospects

5.1 Conclusion

As elderly care institutions that can effectively solve the medical and care problems of the elderly, the Medical Caring and Nursing Combined Institutions for the Aged promote the further development of elderly care in China and also affects the realization of the goal of a “good life” for the elderly. In recent years, the service quality problems of many Medical Caring and Nursing Combined Institutions for the Aged have seriously harmed the interests of the elderly, and negatively impacted their development. The lack of an effective service quality evaluation system has also become one of the most important obstacles to their development. This study proposes a scientific and effective evaluation system for the service quality of Medical Caring and Nursing Combined Institutions for the Aged, which provides feasible solutions for evaluating and improving the service quality of Medical Caring and Nursing Combined Institutions for the Aged. It has a scope for further developing the Medical Caring and Nursing Combined Institutions for the Aged.

5.2 Prospects

Although the method proposed in this study can effectively evaluate the service quality of Medical Caring and Nursing Combined Institutions for the Aged, with the development of society, the needs of the elderly for integrated medical care services are constantly changing. Therefore, the evaluation index system must be updated and improved in the future. At the same time, due to the limitations of the samples in this study, in follow-up research, the sample selection can be expanded to the whole country, enhancing the scientificity, accuracy, and universality of the research. In addition, based on the evaluation of customers’ perceived service quality in Medical Caring and Nursing Combined Institutions for the Aged, the relationship between perceived service quality and customer satisfaction can be further studied. Optimizing medical care resources and rationally allocating resources are also issues that need attention.

Footnotes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant (No. 72074100).

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Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

Abstract

Keywords

1 Introduction

2 Literature review

2.1 Research on the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged

2.2 Research on the evaluation of service quality of Medical Caring and Nursing Combined Institutions for the Aged

2.3 Research on the Mahalanobis-Taguchi system

2.4 Research on grey cloud clustering

2.5 Summary of literature review

3 The construction of the service quality evaluation system of Medical Caring and Nursing Combined Institutions for the Aged

3.1 The construction of the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged

4.1 Data collection

Table 3 Alpha reliability coefficient and KMO value Tangibles Security Reliability Responsiveness Empathy Alpha reliability coefficient 0.917 0.923 0.856 0.921 0.839 KMO value 0.926

Table 7 Comprehensive grey cloud clustering coefficient Grey range A B C D Very good 0.135 0.152 0.171 0.198 Good 0.160 0.174 0.180 0.204 Medium 0.195 0.196 0.198 0.201 Poor 0.204 0.197 0.189 0.169 Very poor 0.208 0.193 0.179 0.145

5.1 Conclusion

5.2 Prospects

Footnotes

Acknowledgments

References

Table 3
Alpha reliability coefficient and KMO value

Tangibles Security Reliability Responsiveness Empathy

Alpha reliability coefficient 0.917 0.923 0.856 0.921 0.839

KMO value 0.926

Table 7
Comprehensive grey cloud clustering coefficient

Grey range A B C D

Very good 0.135 0.152 0.171 0.198

Good 0.160 0.174 0.180 0.204

Medium 0.195 0.196 0.198 0.201

Poor 0.204 0.197 0.189 0.169

Very poor 0.208 0.193 0.179 0.145