Identifying Root Causes of Important Service Failures across Medical Examination Processes with Integration of 4M1E, ITLV, GRA, DEMATEL and FMEA

Medical examination plays an essential role in most medical treatment processes, and thus, the quality of service relevant to medical examination has great impact on patient satisfaction. The targeted hospital has long been faced with the problem that patient satisfaction of its medical examination department is below average. An assessment model, integrating 4M1E, ITLV, GRA, DEMATEL and FMEA, was developed in this study to identify the root causes of important service failures across medical examination processes, where (1) a cause-and-effect diagram was enhanced with 4M1E, identifying the list of failure modes relevant to service quality over the medical examination process with the 4M1E analysis framework, (2) FMEA experts were enabled to report their assessment results in their preferred ways by using the ITLV scheme, (3) causes of failure to failure modes with was figured out with DEMATEL, and (4) the evaluation results were improved by integrating GRA. Experimental results obtained by the proposed approach are compared with several benchmarks, and it was observed that (1) the results obtained by the proposed model are more suitable when FMEA experts prefer using different assessment languages versus other approaches; (2) the proposed model can figure out the key root causes according to their impact on overall failure modes.


Introduction
Medical examination plays an essential part in the majority of medical treatment processes, and thus, the quality of service relevant to medical examination processes has great impact on patient satisfaction. Shanghai Pulmonary Hospital, a medium-size specialized hospital located in Shanghai, China, has long been faced with the problem that patient satisfaction of its medical examination department is below average, according to patient satisfaction reports of this hospital. Considering that more and more hospitals have regarded patient satisfaction as the most important indicator measuring service quality in recent years [1], it is essential to reveal the key factors resulting in nonsatisfaction of patients and to give valuable suggestions for improving service quality for the targeted medical examination department.
According to the literature, it was observed that there are few research studies on improving the service quality of medical examination, and most studies focus on either specific problems or certain medical examination activities such as the performance of Carm X-ray machines, service assessments of radiographers' experiences relevant to patient safety incidents, etc. [1][2][3][4][5]. However, few studies assess the service quality relevant to medical examination from the perspective of the entire process, i.e., from the time the patient makes an appointment for examination to receiving the final examination report.
When the literature review was extended to include studies concerning service quality improvement for organizations other than the medical examination department, it was observed that many hybrid assessment models have been developed to draw on each The general procedure of the FMEA model, an advanced service quality assessment model developed in this study, is as follows: Step 1: Confirming central theme In this study, the FMEA model is developed to identify potential risks involving the quality of services relevant to the medical examination process in the targeted hospital.
Step 2: Establishing a team with authority and representativeness Five professionals, including two technicians, two nurses and one report physician, who have been responsible for various job positions across the medical examination process in the targeted hospital over one year, are invited as FMEA experts.
Step 3: Drawing the flowchart The list of activities involved in the examination process are identified based on full discussions with the invited FMEA experts, and the flowchart of process is confirmed by those FMEA experts as well.
Step 4: Identifying the list of failure modes, failure causes and effects of failure After full discussions with the expert group, we identify not only the failure modes and their possible effect, but also failure causes with the cause-and-effect diagram enhanced by the 4M1E structure.
Step 5: Scoring and computing the RPN First, the ITLV (interval 2-tuple linguistic variables) is applied to integrate the evaluation data collected from FMEA experts with multi-grained language.
Then, GRA is used to ranking FMs (failure modes) according to their RPNs. Afterward, DEMATEL is applied to identify the root causes for each failure mode.
Step 6: Devising and carrying out solutions Based on the results obtained in Step 5, suggestions are given to improve the service quality across the medical examination process in the targeted hospital.
For better understanding, the contribution of the hybridization mechanism to improve the performance of traditional FMEA is depicted in Figure 1, and details about how each component works are given in the rest of this section.

Linguistic Variables 2.2.1. 2-Tuple Linguistic Variables
2-tuple linguistic variables were firstly identified by Herrera and Martinez based on the concept of symbol translation [1]. In this study, applied is the generalized 2-tuple linguistic variables model raised by Chen and Tai in 2005 based on the concept of 2-tuple linguistic variables to deal with and compare linguistic variables from linguistic evaluation sets with different granularity [27]. Here below are some definitions relevant to 2-tuple linguistic variables. Definition 1. Let S = S 0 , S 1 , . . . , S g be a linguistic term set and β ∈ [0, 1] be a value representing the result of standardization. Then, the generalized translation function ∆ used to obtain the 2-tuple linguistic variable equivalent to β can be defined as follows [1]: where: (a) round means rounding off operation; where: (a) round means rounding off operation; (b) • means normal multiplication; Definition 2. Let S = S 0 , S 1 , . . . , S g be a linguistic term set and (S i , α) be a 2-tuple. The function ∆ −1 that can translate a 2-tuple linguistic variable to its equivalent numerical value β ∈ [0, 1], which can be used to compare different multi-granularity linguistic terms, can be defined as follows: Definition 3. Let (S k , α 1 ) and (S l , α 2 ) be two 2-tuples, and define the following rules: 1.

Interval 2-Tuple Linguistic Variables (ITLV)
Definition 5. Let S = S 0 , S 1 , . . . , S g be a linguistic term set. An interval 2-tuple linguistic variable is composed of two 2-tuples, denoted by (S i , α 1 ), S j , α 2 , with i < j and α 1 < α 2 . S i S j and α 1 (α 2 ) represent the linguistic label of the predefined linguistic term set S and symbolic translation.

Grey Relation Analysis (GRA)
Grey theory was proposed in 1982 and is widely used in situations with multiple input, imperfection and uncertain information, especially in [28]. Grey theory is composed of 6 main research methods, and GRA is one of the most important of them.
x 0 (k) and x i (k) are the kth elements of reference and comparative sequence, and their grey relation coefficient can be calculated through the following equation: The grey relational degree between x 0 and x i is indicated by the following equation:

Decision-Making Trial and Evaluation Laboratory (DEMATEL)
DEMATEL was proposed by the Battelle Memorial Institute of the Geneva Research Center to resolve complex social issues through setting up a matrix to compute the direct and indirect relationships between elements [28].
DEMATEL can be divided into 5 steps as follows.
Step 1: List and define the factors in a complex system, and then, design a form to demonstrate their causal relationship.
Step 2: Establish an initial direct-relation matrix X. Value in the matrix represents the incidence between factors, obtained by pairwise comparisons in terms of influences and directions. x ij is denoted as the degree to which factor i affects factor j.
Step 3: Normalize the initial direct-relation matrix X. The normalized direct-relation matrix (N) can be obtained by the following equation: Step 4: Calculate the total relation matrix (T).
Step 5: Calculate the sum of every row and column in matrix T. Let R i be sum of the i th row and C j be sum of the j th column. R j and C j contain the direct and indirect relationships among factors.
2.5. GRA-DEMATEL-Based FMEA (GD-FMEA Method) In this subsection, the GD-FMEA method is combined with the interval 2-tuple linguistic variables, where GRA and DEMATEL will be demonstrated.
Supposing that (a) an expert team is composed of l expert (DM k , k = 1, 2, . . . , l), and λ k is their associated weights, in which k = 1, 2, . . . l, λ k > 0, and ∑ l k=1 λ k = 1; (b), there are m causes of failure (CF i , i = 1, 2, . . . , m) with n risk factors RF j , j = 1, 2, . . . , n . Weights represent the importance of each expert when they evaluate the risk. Every expert can use a different linguistic term set S (S = S 0 , S 1 , . . . , S g ) to evaluate CFs. Let D k = d k ij m×n be the linguistic evaluation matrix of the kth expert, where d k ij is the linguistic assessment provided by DM k on the assessment of CF i , with respect to RF j . Let w k j be the linguistic weight of risk factor RF j given by DM k to reflect its relative importance in the determination of risk priorities of causes of failure and failure modes. Based on the previously mentioned assumptions, the GD-FMEA method can be divided into the following steps: Step 1: Establish a team with authority and representativeness, and then, list flowchart, failure modes, causes and effect of failure.
Step 2: Experts use different linguistic term sets to evaluate the risk factors and CFs; then, convert the linguistic decision matrix Step 3: Aggregate the experts' opinions to construct a collective interval 2-tuple linguistic decision matrix R = r ij m×n and obtain the aggregated 2-tuple linguistic weight of each risk factor ω = ω j , α ωj 1×n , where Step 4: Calculate the weight of risk factors (S, O, D) Based on the aggregated weights of risk factors ω j , α ωj 1×n , the normalized risk factor weights can be obtained as follows: Step 5: This step is related to GRA; thus, it can be subdivided into four steps: (1) Determine the reference sequence r 0 (2) Calculate the distances (differences) between the comparative sequences and the reference one, and establish the distance (3) Calculate the grey relational coefficient γ ij The grey relational coefficient represents how close r ij is to r 0j . The larger the grey relational coefficient is, the closer r ij and r 0j are: Healthcare 2022, 10, 2283 Step 6: Construct the initial relation matrix Y based on the result of Step 5.
Step 7: Normalize matrix Y to obtain the normalized relation matrix H. This step can be subdivided into two steps.
(1) Calculate the sum of every row and column and use Equation (12) to obtain the reciprocal of the maximum of summed columns and rows. (2) Use Equation (13) to obtain the normalized relation matrix H.
Step 8: Calculate the direct and indirect relative severity matrix T. Use Equation (14) to obtain the direct and indirect severity matrix T.
(R + C) represents the total relationships between the cause and effect of specific criteria.
(R − C) represents influence, the differences between the cause and effect of specific criteria [29].

Case Study
In this section, it will be demonstrated how the GD-FMEA is applied to derive the root causes of failures resulting in poor service quality across the medical examination process in the targeted hospital located in Shanghai, China, and the steps are as follows.
Step 1: Define the list of failure modes (FMs) and corresponding causes of failure (CFs). As shown in Figure 2, the process relevant to a medical examination in the targeted hospital consists of three stages, where the pre-inspection process starts from the registration of the patient until the arrival of the patient at the medical technology department at the appointment time; the per-inspection process consists of all the relevant activities carried out during the inspection; and the post-inspection process refers to obtaining the examination report after the inspection. CFs are identified with the cause-and-effect diagram enhanced by the 4M1E mechanism, as shown Figure 3, for FM1, as an example.

CF1
Patients are not familiar with treatment process, which makes them queue repeatedly CF2 Times for activities are not well arranged by the staff Based on literature review and after full discussion with the FMEA experts, the list of FMs defined in this study is constructed as shown in Table 1, where the corresponding CFs are identified with the cause-and-effect diagram enhanced by the 4M1E mechanism, as shown Figure 3, for FM 1 , as an example. Patients are not familiar with treatment process, which makes them queue repeatedly CF 2 Times for activities are not well arranged by the staff CF 3 Technician missed certain inspection items or performed the wrong inspection CF 4 Report physician writes wrong patient's name on report CF 5 Staff acts wrongly out of personal Medical technical inspection equipment is not regularly maintained CF 18 Self-service number machines are not regularly maintained CF 19 Self-service reporting machines are not regularly maintained CF 20 Software system of medical technical inspection equipment failures CF 21 Software system of self-service number machine failures CF 22 Software system of self-service reporting machine failures Environment CF 23 The instrument placement environment does not meet the requirements FM 6 Report does not tally with patient's symptom Man CF 4 Report physician writes wrong patient's name on report CF 24 Report physician inputs error of examination result CF 25 Technician uninforms precautions FM 7 Patients do not fetch report on time Man CF 16 Patients do not use the self-service reporting machine according to the regulations Machine CF 22 Software system of self-service reporting machine failures CF 26 Self-service reporting machine goes wrong Material CF 27 Insufficient material such as paper and ink symptom CF25 Technician uninforms precautions FM7 Patients do not fetch report on time Man CF16 Patients do not use the self-service reporting machine according to the regulations Machine CF22 Software system of self-service reporting machine failures CF26 Self-service reporting machine goes wrong Material CF27 Insufficient material such as paper and ink Step 2: Collect individual assessment results from FMEA experts.
To ensure the quality of assessment data, the FMEA expert team is composed of five experts, called decision makers (DMs) hereafter, who are senior professionals invited from the medical technology department in the targeted hospital, including two technicians responsible for medical examination operations, two nurses for on-site services at the targeted medical examination department, and one report physician. Considering the job responsibilities of these experts can be regarded as having equally important impact on the satisfaction of the patient partaking in the medical examination; their respective weights are set the same, i.e., 0.2 for each.
Furthermore, when collecting individual assessment data from the FMEA team, three linguistic term sets, whose structures are shown in Table 2, are provided in the questionnaires to enable the experts to fill in the questionnaires using their favorite rating system.  Step 2: Collect individual assessment results from FMEA experts.
To ensure the quality of assessment data, the FMEA expert team is composed of five experts, called decision makers (DMs) hereafter, who are senior professionals invited from the medical technology department in the targeted hospital, including two technicians responsible for medical examination operations, two nurses for on-site services at the targeted medical examination department, and one report physician. Considering the job responsibilities of these experts can be regarded as having equally important impact on the satisfaction of the patient partaking in the medical examination; their respective weights are set the same, i.e., 0.2 for each.
Furthermore, when collecting individual assessment data from the FMEA team, three linguistic term sets, whose structures are shown in Table 2, are provided in the questionnaires to enable the experts to fill in the questionnaires using their favorite rating system.
According to the questionnaires collected from the FMEA experts, noted as DM 1 to DM 5 , it was observed that DM 1 preferred set C, DM 4 selected set B, and the other three experts, i.e., DM 2 , DM 3 and DM 5 , used set A.
As Interval 2-tuple linguistic variables for the importance of risk factors are shown in Table 6.
Step 3: Construct an aggregated interval 2-tuple linguistic decision matrix. Use Equation (17) to construct the aggregated interval 2-tuple linguistic decision matrix R = r ij m×n , and use Equation (18) to obtain the aggregated 2-tuple linguistic weight of each risk factor ω = ω j , α ωj 1×n , as shown in Table 7.          Step 4: Normalize the weights of risk factors. Use Equation (19) to normalize the weights of risk factors, as shown in the last line of Table 7.
Step 5: Set reference sequence. Use Equation (20) to determine the following reference sequence. Step 6: Establish distance matrix. Use Equation (21) to calculate the distances (differences) between the comparative sequences and the reference one, and then, establish the distance matrix D 0 = d r ij , r 0j m×n , as shown in Table 8.
Step 7: Calculate grey relational coefficients. Determine the grey relational coefficient γ ij by using Equation (22), of which the results are shown in Table 9.
Step 8: Estimate grey relational degrees. Determine the grey relational degree ϕ i by using Equation (23), of which the results are shown in Table 10.
Step 9: Construct initial relation matrix among FMs and CFs. Use the grey relational degrees obtained in Step 8 to construct the initial relation matrix Y among FMs and CFs, as shown in Table 11.   Step 10: Construct direct and indirect relation matrix among FMs and CFs. Use Equation (13) and Equation (14) to obtain the direct and indirect relation matrix T among FMs and CFs, as shown in Table 12.
Step 11: Calculate the direct and indirect relative relationships. Use Equation (15) and Equation (16) to calculate R for CFs and C for FMs, as shown in Tables 13 and 14, respectively.   Step 12: Rank the priority of FMs and CFs.
Since the worst case is set as the reference sequence, it is reasonable to conclude that the bigger the value of R, the more significant a CF is, and similarly, the bigger C is, the higher priority FM must have. According to the results shown in Table 13, it can be observed that CF 22 , CF 16 , CF 4 , CF 3 , and CF 5 , whose values are close to 1, are considered as significant.
With the results shown in Table 14, FM 5 , FM 1 and FM 7 are ranked as the top three failures modes resulting in poor service quality at the targeted medical examination department.
Considering that some failure causes may have impact on more than one failure mode, it is also interesting to further analyze the overall impact of causes to those three critical failure modes: FM 5 , FM 1 and FM 7 . Considering the meanings of R and C, the priority of CFs relevant to these three critical FMs is ranked according to the descending order of (R + C), such that the bigger the value of (R + C) obtained, the higher priority a CF has, and more attention must be paid to improve the activities relevant to this CF. As shown in Table 15, it can be observed that the top five CFs have a firm relationship with the top three FMs and the top two CFs have great impact on FM 5 and FM 7 . Furthermore, a comparison among the proposed GD-FMEA, an enhanced VIKOR (CFFS-VIKOR) [13], and a GRA-FMEA (G-FMEA) [21] is conducted to evaluate the performance of GD-FMEA. The results obtained by CFFS-VIKOR contain two parts, where CFFS-VIKOR-A corresponds to the assessment data collected with the five-granularity assessment language (Set A) and CFFS-VIKOR-C corresponds to the nine-granularity assessment language (set C). The results regarding the priorities of FMs and critical FM-related CFs are shown in Tables 16 and 17, respectively.

Discussions
According to the results shown in Table 13, the FMs whose evaluation results (C) are significantly higher than the others are FM 5 (C = 5.409, machines go wrong) and FM 1 (C = 3.423, waiting too long in the queue for examination), where the former is related firmly with machine breakdown and the latter reveals the patients' complaints about long waiting in the queue for examination. As for CFs, it can be observed that CF 22 (software system of self-service reporting machine does not work), CF 16 (patients do not use the self-service reporting machines according to the regulations), CF 4 (report physician writes wrong patient's name on report), CF 3 (technicians missed certain inspection items or performed the wrong inspection) and CF 5 (staff acts wrongly out of personal considerations) are ranked as the top five. Furthermore, their evaluation results (R) are quite close to 1, indicating that those causes have great impact on poor quality of relevant services in the targeted services. Further analysis of the relationship between these top FMs and CFs shows that the top two causes of failures (CF 22 and CF 16 ) firmly relate with FM 5 , which indicates that more and more patients derive the inspection report from the self-service reporting machine. Thus, not only the failures of the machine's operating system but also improper operations of the machine may prevent the patients from successfully obtaining their inspection report, resulting in complaints from patients, and the hospital should not only ensure the normal status of the reporting machines but should also improve on-site assistance service for patients to derive inspection reports from the reporting machines. On the other hand, regarding the analysis of the significant causes related with FM 1 , i.e., CF 3 , CF 4 and CF 5 , it can be concluded that all of them are human-related failures, although none of them relate to professionals, such as technicians and report physicians, but rather to nurses responsible for on-site reception and assistance services. Furthermore, it can be concluded that these three CFs are related to a lack of responsibility. Therefore, the suggestion for the targeted hospital is to organize a training program to improve the professionalism and responsibility of the staff, especially the examination technicians and the reporting physicians.
According to the comparison regarding the priority of FMs as shown in Table 16, it can be observed that the results of GD-FMEA and G-FMEA are the same, and the top two FMs identified by these two approaches are the same as the CFFS-VIKOR model. As for the CFFS-VIKOR, we observed that the results are not consistent when different assessment language sets are applied. Furthermore, it can be observed that regarding the results shown in Table 17, the results obtained by GD-FMEA are consistent with those obtained by G-FMEA and CFFS-VIKOR-A for the top four CFs, and the same results can be obtained through G-FMEA for the top six CFs. It is reasonable to conclude that the proposed assessment model is much more stable than the approaches using mono-assessment language set and is consistent with the other assessment approaches.

Conclusions
In this study, an assessment model, integrating 4M1E, ITLV, GRA, DEMATEL and FMEA, was developed to identify the root causes of important service failures across medical examination processes in a medium-sized specialty hospital, where (1) the causeand-effect diagram was enhanced with 4M1E to identify the list of failure modes relevant to service quality over the medical examination process with 4M1E analysis framework, (2) FMEA experts were enabled to report their assessment results in their preferred ways by using ITLV scheme, (3) the causes of failure to failure modes were figured out with DEMATEL, and (4) the evaluation results were improved by integrating GRA. The case study was conducted in the targeted specialized hospital, which has long been faced with the problem that patient satisfaction of its medical examination department is below average. A comparison among the proposed GD-FMEA, CFFS-VIKOR and G-FMEA models was conducted to evaluate the performance of GD-FMEA. According to the comparison, the results obtained by GD-FMEA are consistent with those obtained by G-FMEA and CFFS-VIKOR-A for the top four CFs, and the same results were obtained as G-FMEA for the top six CFs. It is reasonable to conclude that the proposed assessment model was much more stable than approaches using mono-assessment language sets and was consistent with the other assessment approaches. CF 22 (software system of self-service reporting machine does not work), CF 16 (patients do not use the self-service reporting machines according to the regulations), CF 4 (report physician writes wrong patient's name on report), CF 3 (technicians missed certain inspection items or performed the wrong inspection) and CF 5 (staff acts wrongly out of personal considerations) were ranked as the top five. Therefore, several suggestions are presented as follows: a.
Strengthen the daily maintenance of inspection machines and self-service machines, including software and hardware; b. Train volunteers to operate the self-service machines and assign them to instruct patients at the self-service machines; c.
Establish a two-person inspection mechanism for reports to reduce the occurrence of errors.