Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

doi:10.21203/rs.3.rs-2443699/v1

Download PDF

Research Article

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

https://doi.org/10.21203/rs.3.rs-2443699/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

This work presents a case study on The University of Hong Kong-Shenzhen Hospital (HKU-SZH), which was the first to implement an outpatient appointment registration system. This study provides an anatomy of the hospital outpatient process through various methods and theories, such as literature review, field research, expert consultation, business process improvement theory and Information technology, with the objective of identifying objectives and strategies of the hospital case in improving its outpatient process. By means of outpatient process improvement, this study aim to increase the case hospital’s efficiency and raise its patients’ satisfaction so that the hospital may enhance its comprehensive competence. In addition, an effective and operable methodology will be generated, which is expected to serve as a reference for other hospitals to improve their operation and their management. Therefore, the research question is to know which factors most influence patient satisfaction. It was found that service attitude, service value, and waiting time have a significant influence on patient satisfaction.

Outpatient Process

Business Process Improvement

Information Technology

Local Adaptation

Hospitals

China

With the increasing competition between the major hospitals, patients tend to seek higher quality medical services. In the past, hospitals used to adopt a hierarchical management system driven by power and a functional management pattern based on division of labor. However, this pattern has gradually fallen short of the need for competition in the socialist economy. If a hospital wants to have a sustained competitive edge, it should unremittingly raise its medical service level. At the same time, it should also foster a service philosophy of “treating patients like customers”. In addition, it should establish a strategic business process to respond to the changing internal and external environment. By renewing operational and management methods and rebuilding outpatient processes, a hospital can build a process management model in the interests of patients.

After 30 years of development, remarkable achievements have been made in China’s economy. In addition, people's living standards and purchasing power have also enhanced significantly. As people become increasingly rich, they start to attach more importance to their health. However, with such a large population and gradually deepening urbanization, China has experienced a drastic increase of urban population. These urban residents are in urgent need of high-quality services from hospitals and medical personnel. In China, large and medium hospitals are mainly concentrated in cities and the scale of these hospitals is determined by the registered population in these cities. As a result, medical resources in cities are inadequate and this situation is unlikely to be reversed in a short period. Currently, as the appointment registration model has not yet been widely implemented in large and medium hospitals, large crowds rush to see physicians in the morning. In this way, patients have to spend longer time in registration, payment, and drug dispensing, while they have far less time in the critical diagnosis by the doctor. To allocate medical resources in a reasonable way and reduce the waiting time of waiting to ensure sufficient time for their treatment, it has become inevitable to optimize the outpatient process management model. By introducing the appointment mechanism, patients can arrange their schedule to see a doctor in advance, which saves their unnecessary queuing time and minimizes conflicts between doctors and patients.

Wait time is a key driver of patient satisfaction, along with medical expertise, specialist availability, hospital location, cost, success rate, clinical care, and confidentiality [1]. Long wait lists in outpatient clinics are a widely recognized problem [2]. One of the most scarce resources in healthcare is physician time and there can be several reasons why a doctor does not spend enough time with patients [3]. Due to uncertainty in the demand for appointments, it is difficult to provide an exact match between planned physician availability and appointment requests [4].

Chen (2003) found that currently most domestic hospitals still adopt the traditional outpatient process model [5], namely: queue for registration → wait to see the doctor → see the doctor (doctor issues a checklist or prescription) → queue to pay fees → queue for body check (or drug dispensing → further consultation with the doctor (doctor issues a treatment list or prescription) → queue to pay fees → queue for treatment (or drug dispensing). This traditional outpatient process model is very common in major hospitals, but has many shortcomings, particularly in large, overcrowded hospitals. In this model, only a small part of the patient’s time in the hospital is for treatment, severely harms the social and economic benefits of the hospital.

Strategic and tactical capacity planning are critical decisions that hospitals must face. While these problems have received significant attention, current queueing-based approaches do not address realistic healthcare constraints such as blocking, transient arrivals, transient capacity assignments, and surge capacities [6].

This study uses BPI Theory,, Queuing Theory and System Theory with information technology as its core to analyze the process management model of outpatient appointment in The University of Hong Kong-Shenzhen Hospital (HKU-SZH). As far as we know, these theories had never been combined to study patient satisfaction in a hospital, and so this is the gap that we intend to fill.

The research question is to know which factors influence the most patient satisfaction. A quantitative analysis is performed using a questionnaire survey to identify defects and weaknesses of the current model. The Principles, Methods, and Techniques of BPI The theory will be used to analyze various problems existing in the outpatient process and the extent of their influence. A structural equation model has been established for scientific and quantitative analysis. This model can help identify the goals of optimization and measure improvement of the outpatient process and the patient satisfaction. The aim is to maximize the existing resources in outpatient departments, optimize the management model and the hospital operation service model, reduce unnecessary waiting time for patients, increase effective hospital visit rate per unit of time, eliminate the phenomenon of “three longs and one short’, build for hospitals a social image as providers of efficient, convenient and premium medical services, and enable patients to have easy access to medical services. In the literature review part, BPI Theory, Queuing Theory and Six Sigma Theory are reviewed and commented on. These theories served as a conceptual framework that allowed the construction of the questionnaire. In the case study, a questionnaire survey is conducted on the outpatient appointment process management model in HKU-SZH is conducted. By establishing a structural equation model of the current outpatient process, this study analyzes the influence of factors in the outpatient process on patient satisfaction so that objective and measures of improvement can be identified. In this sense, three hypotheses were defined:

H1: Service attitude influences patient satisfaction.

H2: Service value influences patient satisfaction.

H3: Waiting time influences patient satisfaction.

2.1. Process Management Theory

Process management, as an important management method in modern enterprise management, has been playing a bigger and bigger role in enhancing business performance and raising customer satisfaction level. It has also attracted widespread attention from management specialists in the business circle. Its successful application in enterprise management has also attracted great attention from hospital management scholars.

In the Eras of Taylor and Ford, maximization of operation efficiency was the primary target. As a result, the idea of process management gradually developed into a formal management theory. Management master Taylor first proposed the idea of job analysis, followed by the idea of process management in 1920. In 1980, the idea of quality management became very popular. In 1984, the US-based International Business Corporation, abbreviated as IBM, proposed Quality Focus Business Process (QFBP) and listed “quality” as the core element of process. In 1988, IBM officially changed QFBP into Business Process Management (BPM). In 1990, American management master Michael Hammer and James Champy first proposed the idea of ‘business process reengineering” in their best-seller Reengineering the Corporation [7]. This idea fitted the economic development requirements of the new period because Hammer and Champy both agreed that enterprises should adapt to the changing external environment, gradually establish the customer-oriented operating principle, and reengineer the business processes to suit the external business development. In response to business process reengineering, IBM formally put forward the idea of Process Management (PM).

In 1995, Michael Hammer and Steven Stanton jointly published another book entitled The Reengineering Revolution Handbook [8], which started an upsurge in “organizational reengineering” among Western countries. At that time, some scholars hold that ‘organizational reengineering’ is the latest progress in management science. It will definitely bring about a brand new management revolution. At the core is business process re-engineering.

In another study, also conducted in the same hospital (HKU-SZH), apart from assessing resistance related to human factors, it was concluded that rethinking the business process is also important, and the findings suggested that process reengineering and innovation is a complicated, difficult and multiple dimensional organizational change [9].

In the healthcare sector, management scholars have always maintained a positive attitude towards the successful management theories and methods in the business circle. While findings on application of management theories such as Business Process Reengineering, Business Process Management, and Process Management have been published in documentary research, empirical studies on application in other aspects have also been published in various academic journals. For example, Wong, Doyle and Straus gave an explanation of process management in 1975 [10].

Process management approaches (such as TQM, lean and six sigma) originally intended for manufacturing settings are seeing increasing application in the healthcare sector [11, 12]. In fact, these process management approaches created awareness of the operational organizational aspects of care treatments [13].

2.2. Business Process Improvement Theory

Business Process Improvement (BPI) is a methodology aimed at keeping the competitive edge of an enterprise by constantly perfecting and optimizing the business process. The process must be continuously improved in its design and implementation so as to achieve the best result. BPI means to do the right thing in the right way. Its aim is to increase service time and improve efficiency of valuable services, as well as minimize the time spent on nonvalue-added services. Usually there are three stages in BPI: first, description of business process, which includes a clear definition of the business or operation process of the target company through field research in the enterprise; second, analysis of business process, which means looking into the current process of the target company to identify major causes of existing problems; third, formulation and implementation of improvement plans, in which redundant or overlapping processes are redesigned and optimized on the basis of the identified problems and their causes.

According to Ding and Sun (2010), most Chinese hospitals follow a serial working mode, which means the work is divided step by step, and only after one step finishes can the next start [14]. Such a process is rarely value-added since it leads to unreasonable distribution of medical resources, low degree of information sharing capacity and low operating efficiency. Zhao (2006) suggests that the effective treatment time of outpatients is only 10% of their stay in a hospital, as patients have to queue up for treatment and payment. If the percentage can be raised to 30%, then the stay of patients in hospitals will be cut by two thirds, and if raised to 50%, the stay will be reduced by four fifths [15]. However, while carrying out BPI projects, some hospitals have faced great difficulties brought about by their system and structure. Improvement in patient service satisfaction in these hospitals is particularly difficult. Lin and Yang (2004) insists that Hospital Business Process Management (HBPM) should be applied [16].

Much success has been achieved with respect to the use of BPI in the healthcare domain. However, there is also still a need for more efficient and effective methods to support and overcoming the shortfalls of existing methods. The complex nature of the healthcare domain requires comprehensive BPI that are able to deal with its common problems and challenges [17].

2.3. Queuing Theory

According to Li and Xu (2003), Queuing Theory, or Stochastic Service System Theory, is an important branch of Operations Research [18]. It studies the probabilistic properties of queuing in various service systems so as to achieve optimal service system efficiency. In 1909, Agner Krarup Erlang, a Danish engineer, published The Theory of Probabilities and Telephone Conversations, the first monologue on Queuing Theory, which mainly calculates and studies service time and queuing efficiency. Lu (1994) finds in his research that Queuing Theory has been constantly improved and enriched with the rapid development of IT technology and has been applied extensively in various sectors such as transportation, health care, shopping and banking [19]. In the healthcare industry, queuing theory has been implemented, mainly with the implementation of distributed systems. Distributed systems can be usefully applied to realise lean and flexible architectures that facilitate healthcare structure interoperability. Systems used in healthcare structures are characterized by proprietary data flow formats and encoding systems, which hinder the possibility of sharing data in a standard format and extracting atomic data for further analysis [20].

The queuing theory model can be used to measure the service efficiency of different departments in public hospitals, which can improve the service efficiency and patient satisfaction [21].

2.4. Six Sigma Theory

According to Liu and Ma (2005), sigma (σ), a Greek letter, represents the level of the process or operation. Sometimes, the process capability is denoted by Z. For example, Z = 6 means that the process or operation has a process capability of 6σ [22]. The analysis of process capability usually focuses on whether the level of a process or operation can meet the expectation and to what extent the expectation is met. Usually, Critical to Quality (CTQ) is employed as an indicator for analysis.

Chen (2010) finds in his research that in 2009 Taizhou Hospital in Zhejiang Province reduced the defect rate from 39.6–12.9% by using Six Sigma Theory management method to define, measure, analyse, improve and control the defect in the dripping speed of outpatient venous transfusion [23].

Six Sigma in itself does not address the cultural or interpersonal aspects of quality improvement and it is limited further as it looks at individual processes rather than taking a system-wide approach. Additionally, poorly developed care processes inevitably reveal themselves when attempts are made to apply a Six Sigma program in a hospital [24]. To be effective, Six Sigma is dependent on high quality data and statistical expertise. Front line clinicians must therefore have access to appropriate systems and support (both technical and statistical), so that they can easily collect robust appropriate data for analysis [17].

2.5. Summary

In Table 1 we can see which are the main constructs that result from the theories studied and which are the main items related to the hospital environment in the outpatient service, which were used for the construction of the questionnaire.

Table 1

Main constructs and items in the outpatient service
Theory	Main constructs	Outpatient service
Process Management	Business performance Service attitude Customer satisfaction	Diagnosis an treatment Medical guidance Sign and guidance system Attending doctor Registration system
Business Process Improvement	Results Efficiency Service value Customer satisfaction	Environment Sanitary conditions Technical level Payment Treatment outcome Cost
Queuing	Waiting time Customer satisfaction
Six Sigma	Quality improvement Customer satisfaction

3.1. Satisfaction Measurement for the Outpatient Service

Based on the literature review, on-the-spot convenience sampling to conduct an on-the-spot questionnaire survey that lasted for a month in the hospital. The survey collects basic information, their general satisfaction with the outpatient process, and scores of evaluating each survey item. Then, the structural equation modeling is established to analyze the effects of basic factors on the degree of satisfaction of patients for the outpatient process. To ensure that the model can better fit the data, we optimize and revise the model and draw a standardized coefficient path graph. Through the analysis of the direct and indirect effect of key variables on the degree of satisfaction, the current ‘bottleneck problem’ in the hospital’s outpatient process is finally found and reasons for the obstacles in the present outpatient process are summarized.

In order to know the needs of patients and the status quo of the process of outpatient business, it was conducted a convenience sampling of questionnaires in April and May 2014 among patients who went to the hospital for treatment. The survey is an on-the-spot questionnaire survey, meaning that patients will anonymously have a self-administered questionnaire survey on the spot. Clerks of the IT department gave questionnaires to the patients who had finished treatment, who were ready to leave the hospital and who were waiting in the waiting room, the injection room, and the clinical laboratory. The questionnaires were collected when the patients completed them.

Out of 1,500 questionnaires sent, 1,132 have been collected, with an effective rate of 88.3%. According to the statistical validity and reliability tests, the survey data is valid.

The questionnaire consists of three parts. In the first part, basic information is introduced, including their sex, age, marital status, profession, degree in education, place of residence, category of payment category, and their family’s monthly income, designed for collecting basic information from patients so as to classify and compare. The second part includes 24 items, including appointment registration, communication between doctors and patients, medical cost, medical effect, hospital signs, service attitude, hospital environment, hospital hygiene and waiting time in each link of treatment. Based on variables of satisfaction survey, this part aims to let patients respond to each item and get score of each patient’s attitude with cumulating method. The average score of all the patients can indicate the overall attitude of investigators. Each item adopts the Likert-type six-level scale method. From the negative to the positive, number 1, 2, 3, 4, 5 and 6 respectively correspond to very dissatisfied, not very satisfied, a little dissatisfied, a little satisfied, quite satisfied and very satisfied. The answer of each item will no longer be confined to “Yes” or “No”, instead, it will be divided into several types, ranging from “satisfied” to “dissatisfied” and the medium term of which is neutral. Due to increasing types, it can fully embody the difference in patient attitude. The third part contains open questions which patients need to answer to make suggestions for the hospital’s outpatient work and to give recommendations for improvement. Table 2 shows the scores for outpatient service items.

Table 2

Scores of the Outpatient Service Items
No.	Name for Variables	Items of Satisfaction Survey	Mean	Variance
1	B23	Will you choose our hospital for treatment again?	4.56	0.86
2	B20	Are you satisfied with the environment of the hospital and feel comfortable in the hospital?	4.48	0.66
3	B19	Are you satisfied with the sanitary conditions of the hospital?	4.31	0.74
4	B5	Are you satisfied with the technical level for general practitioners in our hospital?	4.21	0.62
5	B6	Are you satisfied with the diagnosis and treatment of the attending doctor?	4.09	0.71
6	B2	Are you satisfied with the patience shown by medical guidance personnel when they answer your questions?	3.96	0.79
7	B21	Are you satisfied with the sign and guidance system of our hospital?	3.91	0.78
8	B7	Are you satisfied with the attitude when the attending doctor asks about your illness, examines and treats you?	3.78	0.73
9	B1	Are you satisfied with the service of the registration system of our hospital?	3.72	0.6
10	B15	What do you think of the one-time charge which contains the entire fee for general practice?	3.69	0.91
11	B12	Are you satisfied with the service of the personnel in the clinical laboratory?	3.64	0.72
12	B14	Are you satisfied with the service of the personnel in the pharmacy?	3.58	0.65
13	B9	Are you satisfied with the privacy protection of our hospital for you?	3.52	0.77
14	B16	Are you satisfied with the waiting time to go to the dispensary?	3.47	0.64
15	B3	Are you satisfied with the waiting time n front of the triage station?	3.36	0.74
16	B18	Are you satisfied with the treatment outcome?	3.24	0.8
17	B4	Can you choose your doctor according to your own will?	3.16	0.79
18	B8	Does the doctor respect your opinion when choosing the therapeutic schedule?	3.05	0.67
19	B22	Do you think that the cost you paid can do justice to the medical treatment you get?	2.94	0.84
20	B17	Are you satisfied with the service of the personnel in the cashier?	2.92	0.91
21	B10	Are you satisfied with the waiting time to have a test (such as routine urine test and blood routine examination)?	2.9	0.83
22	B11	Are you satisfied with the waiting time to have a test (such as B ultrasonic and CT)?	2.72	1.09
23	B13	Are you satisfied with the waiting time to take medicine?	2.51	1.16

A total of 1322 valid questionnaires were obtained in this survey. After the statistical validity test and the reliability test, the data proves to be valid. After calculating statistics, the overall degree of patient satisfaction of the hospital is 83.13 points.

The data from the collected questionnaires are entered through Epidata platform, issued by EpiData Association from Denmark, analyzed by SPSS 15.0 and AMOS7.0 is used to analyze variables through structural equation modelling. In addition, statistical approaches such as descriptive statistics analysis and Wilcoxon rank sum test are also used.

Generally, questionnaires are analyzed using the following methods:

a. Descriptive statistics analysis

The description of the basic information is included as well as the frequency distribution and percentage analysis to understand the distribution of the sample. Generally, a descriptive analysis of the data is conducted at first to find the inner law and then further analysis approach is adopted. Descriptive analysis can be used to analyze all the relevant data of variables in the survey population, mainly including frequency analysis, central tendency analysis,, dispersion degree analysis and distribution of the data, as well as statistical graphics. Common indicators include mean value, median, mode, variance, and standard deviation.

b. Cronbach’s reliability coefficient analysis

Reliability refers to the consistency, stability, and dependability of test results. Generally, the level of reliability is indicated by inner consistency. The higher the reliability coefficient, the more consistent, stable, and dependable the result is. In addition, analyzing measured items of variables can shed light on the inner consistency of the measurement dimension. Furthermore, when Cronbach’s is higher than 0.7, its reliability is high, and when Cronbach’s is lower than 0.7, its reliability is low. And the lowest acceptable reliability level is 0.5.

c. Exploratory Factor Analysis and Confirmatory Factor Analysis

Exploratory factor analysis and confirmatory factor analysis are used to test the convergent validity and discriminant validity of measured items in each dimension. Reliability alone is not enough, because a high reliability measurement may probably turn out to be completely invalid or invalid to some degree. Therefore, a validity test is necessary. Validity refers to whether tools can get the expected result when they are designed. The convergent validity test is decided by the capacity of each item and measured concept factors. And discriminant validity test is to calculate the correlation index of related concepts according to confirmatory factor analysis to test whether 95% of confidence interval of correlation index includes 1.0. If not, it can be regarded to be discriminant valid.

d. Structural Equation Modelling Analysis

Structural equation modelling combines factor analysis with path analysis and incorporates simultaneous equations in econometrics. Therefore, several variables can be dealt with simultaneously. Moreover, measurement errors of independent and dependent variables are allowed and the factor structure and the factor relation can be estimated at the same time. A measurement model of greater flexibility is allowed and the fit degree of the overall model can be estimated so that it can be applied to the causal relationship of the overall model. In the estimation of the model parameter, maximum likelihood (ML) estimation is adopted. While testing the fit degree, indicators of preliminary fit criteria, overall model fit and fit of the model’s internal structure are used as criteria. As for the criteria of overall model fit, chi-square/df value, GFI, RMSR, and RMSEA are used as indicators to study whether the estimated parameters reach a significant level.

The basic information of the surveyed patients is shown in Table 3. According to data of A9, it was found that 34.3% patients go to the University of Hong Kong-Shenzhen Hospital for the first time and 65.7% of patients go there for more than twice. Therefore, it can be seen that the surveyed objects are mainly patients who go to the hospital repeatedly. Therefore, their evaluation of the hospital is stable and there is less error in the data. According to questionnaires, the main reason for patients to choose the hospital is its reputation and the advanced medical skills of doctors, respectively accounting for 53.8% and 33.7% in all reasons, followed by simplified treatment process, advanced medical technology and equipment, and good service attitude, respectively accounting for 22.4%, 16.5% and 12.7%. Paragraph: use this for the first paragraph in a section, or to continue after an extract.

Table 3

Basic Information of Surveyed Outpatients
Item	Percentage (%)	Item	Percentage (%)
A2. Gender		A6. Education degree
Male	43.8	Master and above	9.6
Female	56.2	University and College diploma	58.3
A3. Age		Technical Secondary School, Secondary Technical School and Senior High School	10.9
Under 18	13.8	Junior High School	7.2
Between 18 and 25	10.9	Primary School and below	14
Between 26 and 35	14.1	A7. Place of residence
Between 36 and 45	42.6	Districts Inside Shenzhen Special Economic Zone	68.2
Between 46 and 55	7.4	Districts Outside Shenzhen Special Economic Zone	26.4
Between 56 and 65	6.8	Districts Outside Shenzhen	5.4
66 and above	4.4	A1. Clinical Departments
A4.Marital Status		General Practice Outpatient	21.7
Single	22.3	Internal Medicine Department	15.3
Married	74.8	Surgical Department	5.1
Divorced or Separated	2	Obstetrics and Gynecology Department	3.5
Widowed	0.9	Department of Pediatrics	19.4

[Table 3 near here]

3.2. Factors Influencing Patient Satisfaction with the Outpatient Service

SEM can be divided into two models: Measurement Equation and Structural Equation. The expression is as follows:

(1) Measurement modelling: is used to describe the relationship between measurement variables and latent variables. The mathematical expression is as follows.

X = Uxξ + δ

Y = Uyη + ε

The meanings for the above-mentioned mathematical expressions are as follows:

ξ represents exogenous latent variables. They serve as independent variables and remain unchanged regardless of influences from other variables in the model.

η represents endogenous latent variables. They change accordingly with the change of other variables in the model.

X represents vectors made up of exogenous measurement variables.

Y represents vectors made up of endogenous measurement variables.

Ux represents the rotated component matrix of exogenous measurement variables in exogenous latent variables.

Uy represents the rotated component matrix of endogenous measurement variables in endogenous latent variables.

δ is the error item, which is caused by exogenous measurement variable X.

ε is the error term, which is caused by endogenous measurement variable Y.

(2) Structural Modelling: it is used to reflect the relationship between latent variables. The mathematical expression is η = Bη + Γξ + ζ.

The meanings for above mentioned mathematical expressions are as follows:

B represents the interaction among endogenous latent variables.

Γ represents the effect and influence of exogenous latent variables upon endogenous latent variables.

ζ is the error term of the structural equation.

Coefficient matrix composed of B and Γ as well as error vector ζ serve as the link between endogenous latent variables and exogenous latent variables,

In this research, AMOS7.0 (Analysis of Moment Structures) is used to construct structural equation modelling and to carry out parameter estimation and relevant test analysis. This software can utilize path graph to customize models and use mapping tools to change path graph so as to change models. It can also graphically display parameter estimation and fitting measurement, as well as display the degree of freedom at any time of mapping in the path graph. In this software, the rectangle represents the measurement indicators. The oval represents latent variables that cannot be measured directly. The circle represents residual error. Double arrow indicates correlation and single arrow represents cause and effect. Exogenous latent variables do not have residual errors while endogenous latent variables have these errors.

The graphic interface provided by AMOS is used to map out the causal relationship path graph. In mapping, the corresponding factor for every latent variable in the measure indicator is supposed to be 1. That is to say, the unit of measurement for latent variables and the corresponding measure indicators are the same. It is also supposed that the default value for the system error of observed variables is 1 and at least one path coefficient for the measure indicators has 1 as its initial value. Furthermore, AMOS7.0 will automatically set 1 as the value of the path coefficient, which is one indicator of the measure of the same latent variables. After the parameters of causal relationship path graph (Fig. 1).

It is estimated that there exist four latent variables in this model, namely, service attitude, service value, waiting time and patient satisfaction. Explanation and definition for the four variables are as follows:

(a) Service attitude: it is defined as the level of patience, conscientiousness and respect of outpatient medical staff for patients
(b) Service value: it is defined as the expertise of medical workers, the curative effects, the rationality of the medical service charge and the sanitary environment of hospitals.
(c) Waiting time: defined as the queue time and the order of patients throughout the outpatient process.
(d) Patient satisfaction: It is defined as the overall satisfaction rate of patients with outpatient service in the hospital. The satisfaction rate is influenced by the three variables.

It was used 24 constructs from B1 to B24 to measure the above mentioned four variables. Among them, the corresponding variables for patient satisfaction are patient loyalty and overall evaluation score. The variables e1 to e24 represent the measurement errors caused by the measurement variables. The variables from zate1 to zate3 represent the error for endogenous latent variables.

The fitting situation for the model is shown in Table 4. The indicators of the fitting effect show that P value which is corresponded by chi-square has certain statistical significance. The degree of freedom is 1 and the probability value is 0.672, which is higher than the threshold value 0.05. As a result, null hypothesis is accepted and the model is considered to be appropriate. The ratio of chi-square to the degree of freedom is more than 2. The values for GFI, AGFI and CFI are all less than 0.9. The value for RMSEA is more than 0.08. Generally speaking, the GFI value should be more than 0.9 while the RMR value and the RMSEA value should be less than 0.05 and the smaller the value, the better. Therefore, this indicates that the fitting between data and model is not ideal and this model should be revised. Usually the fitting indicator RMSEA (root mean square error of approximation), which is very sensitive to error model, is a relatively ideal indicator and the smaller its value is, the better. When its value is less than 0.08, it shows good fitting. By comparison, it is better for the values of GFI (goodness of fit index) and AGFI (adjusted goodness of fit index) to be large. The value of NFI (normed fit index) ranges from 0 to 1. When it surpasses 0.9 the model is acceptable, and the nearer the value is towards 1, the better.

Table 4

Model Fit Measures
	CMIN	DF	P	RMR	GFI	AGFI	NFI	RFI	CFI	RMSEA
Default Model	367.194	149	0.000	0.079	0.81	0.786	0.859	0.832	0.897	0.105

In the estimation of regression coefficient, except that the regression coefficient P value for wait-༞satisfaction, attitude-༞B4, wait-༞B3 does not have statistical significance, the other regression coefficient reaches more than 95% in the hypothesis testing of significance. Whether non-standardized regression coefficient is meaningful is determined by whether P value is significant (three asterisks indicate that the value is significant). Insignificant path needs to be removed in the model revising process afterwards.

The theories studied allowed the creation of the questionnaire and the theoretical model. The results came to demonstrate that these theories can be useful for the study of patient satisfaction, confirming previous studies. After an analysis of the theoretical model, the relationship between measured variables and latent variables is found to be clear. After a check on the correction factors, the M.I value between e7 and e15 is found to be the largest, that is, 15.426. If the correlation between the two variables is strengthened, the Chi-square value will at least decrease to 15.426. Therefore, it can be concluded that there is a strong relationship between these two variables. In the causal relationship path diagram, if the correlation of measurement errors between e7 and e15 is strengthened, a revised model can be obtained.

The fitting situation of the revised model is shown in Table 5, from which it can be seen that the new model is better matched to the data. Furthermore, the revised Chi-square value of significance is p = 0.00. The ratio of Chi-square to the degree of freedom is 2.06, slightly larger than 2.0. But the RMSEA value is 0.057, far less than 0.08. The values for GFI, NFI, and CFI are all greater than 0.9 while the values for AGFI and RFI are all smaller than 0.9. This proves that the revised model matches its data compared with the previous one. The reason why the Chi-square value of significance is 0 is probably connected with the abnormal distribution of the data. After an analysis of regression coefficient, it is found that, except the P value for attitude-༞B4, which does not have statistics significance, other regression coefficients are all greater than 95% in the hypothesis testing of significance.

Table 5

Revised Model Fit Measures
	CMIN	DF	P	RMR	GFI	AGFI	NFI	RFI	CFI	RMSEA
Default Model	288.163	128	0.00	0.067	0.903	0.853	0.914	0.856	0.909	0.057

Through the standardized coefficients of variables in the output model of AMOS7.0, the correction and degree of correlation between different variables can be found. In this way, it is easier to figure out which factors are the bottleneck that influences patients’ satisfaction and then help to make efficient improvement plans. In the software, the covariance between two variables can be shown in sample moments. The coefficients between different latent variables can be shown in Fig. 2.

(1) Correlation analysis of Latent Variables

The coefficient between latent variables means that the change of one variable will cause the change of another variable. This degree of influence is reflected in the correlation coefficient. It can be seen in Fig. 2 that:

a) The coefficient of influence between service value and patient satisfaction is 0.552. This shows that an addition or improvement of 1% in service value will directly lead to an increase of 0.552% in patient satisfaction.

b) The direct influence coefficient between service attitude and patient satisfaction is 0.499. The indirect influence coefficient caused by service value is 0.758×0.552 = 0.418. Therefore the total influence coefficient is 0.499 + 0.418 = 0.917.

c) The direct influence coefficient between waiting time and patient satisfaction is 0.198. The indirect influence coefficient caused by service attitude is 0.413×0.917 = 0.379. Therefore the total influence coefficient is 0.198 + 0.379 = 0.577.

As a result, the three variables of service attitude, service value, and waiting time have a huge influence on patient satisfaction, leading to the verification of the three hypotheses, especially service attitude, for its coefficient of influence reaches 0.917. Waiting time alone does not have a significant influence on patient satisfaction (its direct influence coefficient is only 0.198). But if the indirect influence caused by service attitude is also taken into consideration, the influence coefficient will become 0.577, which deserves the attention of the hospital.

The scores for latent variables are shown in Table 6. It can be seen that patients give the highest score for service attitude and the next highest score for service value, while giving the lowest score for waiting time. Therefore, reducing the waiting time is the key to making patients more satisfied.

Table 6

Scores for Latent Variables
Latent Variables	Measurement Variables	MEAN	SD
Service value	B5、B15、B18、B19、B20、B22	3.45	0.43
Service attitude	B1、B2、B4、B6、B7、B8、B9、B12、B14、B17、B21	3.68	0.40
Waiting time	B3、B10、B11、B13、B16	2.91	0.52
Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

(2) Analyzing the Correlation between Measurement Variables and Latent Variables

The correlation between "service value" and the price/performance ratio of medical service is the closest, with the correlation coefficient reaching 0.769. The next closest correlation is between inspection fees and therapy effectiveness. Although service value is also related to general doctors’ medical expertise as well as hospitals’ environment, comfortableness and sanitary conditions, the degree of correlation is not significant.

The correlation between service attitude and physicians' diagnosis and treatment is the closest, with the correlation coefficient reaching 0.732. The degree of conscientiousness of doctors, namely their work attitude while examining and treating patients, has the second closest correlation with service attitude, with the correlation coefficient reaching 0.682. Furthermore, patients' satisfaction level with appointment service, pharmacy staff’ service, hospital cashiers’ service and hospital guidance system have the third closest correlation with service attitude, with each correlation coefficient reaching more than 0.3. Data show that the attitude of hospital patient guide when answering questions, patients’ choice of doctors by patients according to their own will, the respect for patients while choosing treatment protocols, the protection of patients' privacy, the attitude of the personnel of the Medical Examination Department are also correlated with the service attitude. But the degree of such correlation is not significant, with the correlation coefficients all below 0.3.

Waiting time is most closely related to medical examination, laboratory test, and drug dispensing wait, with the correlation coefficients reaching 0.516, 0.462 and 0.327 respectively. But waiting time has a weak correlation with queuing and waiting for triage and waiting to pay fees, with the correlation coefficients both below 0.3. After a comparison of the values of all correlation coefficients, it can be known that the waiting time for medical examination, laboratory test, and medicine dispensing is relatively long and can significantly influence patient satisfaction. This should be given priority in process optimization. Though patients have to wait for a long time to get treatment, it does not have a huge influence on patients' satisfaction, which is probably because patients have the lowest expected value or the highest degree of tolerance towards waiting to be treated.

Given that the normality of the survey data could not be guaranteed, it was decided to conduct the Wilcoxon rank sum test to analyze the influence of some basic factors, such as gender, occupation, and age, on outpatient service satisfaction. As a result, factors such as gender, age, payment method, and clinic department will significantly influence outpatient service satisfaction. The conclusions are as follows:

a) Male and female have quite different comments on service attitude and waiting time. To be specific, the comments from men are much higher than that of female.

b) Age also has a certain influence on the satisfaction of service attitude and waiting time. Specifically, people 36 to 45 give the highest comments while people 46 to 55 years old produce the most negative comments. Furthermore, the older people make the higher comments on waiting time.

c) Payment method also has certain influence on customer loyalty. To be specific, patients with medical insurance that covers part of their medical costs rank the first in terms of loyalty, while patients enjoying free treatment or medical insurance that covers all the costs rank the second, with patients who seek for medical treatment at their own expense ranking the last.

d) Clinic department is also a factor that influences the waiting time. It was found that the waiting time for treatment in Departments of Gynecology, Obstetrics and Pediatrics is much longer than that in other departments. Therefore, the hospital should focus on the Departments of Gynecology, Obstetrics and Pediatrics while improving its process.

In terms of improving the outpatient hospital service process in the interests of patients, it can be expected that there are mainly three medical service processes closely related to the interests of patients, including departmental treatment, test and examination, and diagnosis and treatment. Since the improvement of other processes such as registration, triage of treatment, payment and drug dispensing cannot bring significant benefits to patients, they are defined as non-value-added process. Therefore, the optimization of process can be carried out by reducing, integrating, and simplifying the nonvalue-added processes while ensuring the normal operation of the above-mentioned three processes. The theories studied made it possible to highlight these conclusions.

According to the revised Fig. 2 of standardized coefficients distribution, the waiting time required in different processes has different effects on patient satisfaction, which can be ranked from large to small as follows: examination (B11), testing (B10), drug dispensing (B13), triage of treatment (B3) and payment (B16). Their corresponding coefficients of relationship are 0.516, 0.462, 0.327, 0.283 and 0.276. Hence, reducing the waiting time of examination, testing, and drug dispensing can efficiently improve patient satisfaction.

After an analysis of the current outpatient service process, the source of inefficiency has been found and can be summed up into the following aspects.

(1) The overly subdivided departments and the lack of medical information sharing result in a poorly-organized and discontinuous outpatient process. Due to the over-subdivision of functions, patients have to go back and forth between different departments, leading to the great mobility of patients within the hospital. Currently, some consulting rooms, pharmacy, examination department, and testing department are even located in different floors. When a doctor ask his or her patient to have a laboratory test, the patient has to go out of the consulting room, find the medical laboratory with the test report, and then receive the testing. After the test, the patient has to wait for the result, go back to the consulting room again, ask the doctor to prescribe medication and pay for the fee. Only after the above processes are finished can the patient get the medicine. The outpatient process is even more complicated when the patient needs to be transferred to another hospital or the patient needs to visit different departments. Additionally, lack of information sharing will also result in poor coordination and connection between different departments of their business. For example, since a doctor knows nothing about the inventory of medicine when he or she prescribes, only when the patient goes to the cashier does he or she know that the medicine in the prescription has been sold out. The patient has to return to the consultation room to ask the doctor to change the prescription. Such situation occurs from time to time in the hospital, not only increasing the mobility of patients between the consulting room and cashier, but also affecting the orderliness and continuousness of the process.

(2) The unreasonable architectural layout of the outpatient service leads to increased patient mobility and waiting time. In fact, the layout of medical technology building and departments will directly influence the efficiency of process. For example, the lack of signs in hospital or the overly small font size of characters on signs will further increase patients' mobility and waiting time in different service processes. Besides, nurses for medical guidance, knowing little about the categories of diseases due to the lack of training, cannot provide efficient service for patients. Furthermore, the location of some departments, such as Orthopedics Department and Obstetrical Department, is not well-designed in the architectural layout. The Orthopedics Department is located on the second floor, while the Obstetric Department is on the third floor, which poses great difficulties for patients to move around.

(3) Medical and health resources are insufficient and unevenly distributed. Although a comprehensive outpatient appointment system has been established in the hospital for the consultation process, the number of medical staff and examination equipment still cannot meet the needs of patients, since the hospital has only been open for a short time. In light with the increasing number of outpatients, a further expansion of the hospital must be carried out. Besides, the comparatively uneven distribution of medical resources also leads to the uneven mobility of patients in different departments to some extent. As analyzed in the last chapter, the Department of Gynecology and Obstetrics, the Department and Pediatrics Department of the hospital receive the majority of patients. However, the dermatologists in the Department are comparatively insufficient.

The current situation of outpatient service process was analyzed. In addition, patient satisfaction of this hospital is also measured through field questionnaire and interviews. Totally, there were 250 patients of the hospital taking part in the survey anonymously. In this survey, patient satisfaction was evaluated according to the 6-grade scale. A validity test has also been carried out on the reliability coefficient. The analysis of this survey mainly focuses on the basic situation, percentage of overall satisfaction evaluation and the scores of all outpatient service items, covering 24 factors totally, including sex, age, marital status, occupation, degree of education, address, payment category, monthly income of family, reservation and registration, communication between doctors and patients, treatment effect, hospital sign, service attitude, hospital environment, hospital hygienic conditions, waiting time of each process.

To determine the specific process that has the greatest impact on patient satisfaction through the quantitative analysis method, which is also the bottleneck for the hospital outpatient process, the hospital, was adopted structural equation model was adopted. With this model, interactive relationship between multiple variables and relationship between multiple reasons and results, including some unobservable variables (latent variables), can be studied. It was assumed that there were four latent variables in the established model, including service attitude, service value, waiting time, and patient satisfaction.

Through regression analysis and parameter estimation, was added the relationship between e7 and e15, it was found that service attitude, service value and waiting time have significant influence on patient satisfaction. Especially, the influence coefficient of the service attitude reached 0.917. Meanwhile, the direct and indirect influence of waiting time on patient satisfaction cannot be neglected either, since these factors might be the key to improving patient satisfaction.

In this study, the diagnoses and analyses of the HKU-SZH outpatient process were made through field investigation and measurement of patient satisfaction. Rather than regression analysis, correlation analysis or factor analysis, the measurement is based on a structural equation model, through which the influence of different processes on patient satisfaction can be accurately calculated. This method not only embodies the service philosophy of “putting patients first”, but also serves as an entry point of process reengineering. An in-depth analysis was carried out on deficiencies of the appointment registration for all patients model. Information technology was used to reengineer the outpatient process of HKU-SZH so that its medical resources are utilized reasonably and efficiently. In this way, the difficulty of accessing medical services was solved to some extent. This empirical study is of practical significance as it is in line with the Outline of National Development Plan for Health Information.

The major limitations and suggestions for further study are as follows:

1. There is no quantitative analysis to evaluate the effects of process improvement. A method to improve and perfect the process improvement plan is as follows: a scientific measurement scale for patient satisfaction should be devised for a more in-depth investigation of patients and hospital staff. The effects of hospital progress should be evaluated using the Cost-Benefit Analysis Method. Special attention should be paid to the changes of patient satisfaction, especially in hospital process, before and after process reengineering.

2. As regards resource sharing of treatment information, it is necessary to seek help from government departments like Shenzhen Health and Family Planning Commission. Only with their help can a medical information-sharing platform be built to ensure sharing of treatment information between different hospitals.

3. It is recommended to communicate and negotiate with medical security institutions to recommend a functional integration of social security cards and co-branded cards. In this way, patients only need one card for medical services, which is convenient and efficient.

Ethical Approval: not applicable

Consent to Participate: Informed consent was obtained from all individual participants included in the study. This study is not a clinical trial.

Competing interests: Jingsong Chen declares that has no competing interests, and Bráulio Alturas declares also that has no competing interests.

Authors' contributions: Both authors, Jingsong Chen and Bráulio Alturas, participated in writing and reviewing the manuscript.

Funding: This study was partially funded by the Portuguese Foundation for Science and Technology (Grant UID/GES/00315/2019).

Availability of data and materials: not applicable

Silas S, Rajsingh EB (2019) A novel patient friendly IT enabled framework for selection of desired healthcare provider. Int J Med Eng Inform 11:14–40. https://doi.org/10.1504/IJMEI.2019.096890
Lewis AK, Taylor NF, Carney PW, Harding KE (2020) Reducing the waitlist of referred patients in a medical specialist outpatient clinic: an observational study. J Health Organ Manag 35:115–130. https://doi.org/https://doi.org/10.1108/JHOM-08-2020-0321
Grad JD (1996) Some unhealthy aspects of managed health care. Business and Society Review 53–54
Aslani N, Kuzgunkaya O, Vidyarthi N, Terekhov D (2020) A robust optimization model for tactical capacity planning in an outpatient setting. Health Care Manag Sci 24:26–40. https://doi.org/https://doi.org/10.1007/s10729-020-09528-y
Chen ZH (2003) Optimizing the process of outpatient service through general appointment system. China Journal of Modern Medicine 13:142–145
Curry GL, Moya H, Erraguntla M, Banerjee A (2022) Transient queueing analysis for emergency hospital management. IISE Trans Healthc Syst Eng 12:36–51. https://doi.org/10.1080/24725579.2021.1933655
Hammer M, Champy J (1993) Reengineering the corporation: a manifesto for business revolution. Harper Collins, New York, NY, USA
Hammer M, Champy J (1995) Reengineering revolution: a Handbook. Harper Collins, New York, NY, USA
Xu XP, Ke DG, Deng DN, et al (2019) An innovative medical consultation model in mainland China: Acceptance and perceptions. Int J Health Care Qual Assur 32:1055–1071. https://doi.org/10.1108/IJHCQA-02-2017-0033
Wong P, Doyle M, Straus D (1975) Problem solving through “process management.” Journal of Nursing Administration 5:37–39. https://doi.org/10.1097/00005110-197501000-00017
Kaissi A (2012) Learning from other industries: lessons and challenges for health care organizations. Health Care Manager 31:65–74. https://doi.org/10.1097/HCM.0b013e318242d399
Daultani Y, Chaudhuri A, Kumar S (2015) A decade of lean in healthcare: current state and future directions. Global Business Review 16:1082–1099. https://doi.org/10.1177/0972150915604520
de Regge M, Gemmel P, Meijboom B (2019) How operations matters in healthcare standardization. International Journal of Operations & Production Management 39:1144–1165. https://doi.org/10.1108/IJOPM-03-2019-0227
Ding YH, Sun W (2010) Application of business process improvement in hospital management. Soft Science of Health 5:412–415
Zhao Q (2006) Business reengineering and its application in hospital operation management. Journal of Qingdao Technical College 4:35–36
Lin QY, Yang JY (2004) Exploration on hospital process-oriented management. Chinese Health Service Management 1:26–28
Ahmed ES, Ahmad MN, Othman SH (2019) Business process improvement methods in healthcare: a comparative study. Int J Health Care Qual Assur 32:887–908. https://doi.org/10.1108/IJHCQA-07-2017-0116
Li J, XU J (2003) Operational Research - Optimization of Nonlinear System. Science Press, Beijing, China
Lu C (1994) Pai Dui Lun. Beijing University of Posts and Communications Press, Beijing, China
Muriana C, Gilia G, Mistretta V, et al (2019) A distributed integration system enabling electronic health records: An Italian experience. Int J Med Eng Inform 11:1–13. https://doi.org/10.1504/IJMEI.2019.096889
Zhang L (2020) Application of computer-based queuing theory in public hospital management. J Phys Conf Ser 1549:1–7. https://doi.org/10.1088/1742-6596/1549/5/052009
Liu W, Ma YZ (2005) Study on the theoretical basis of six sigma quality improvement. Journal of Henan University 35:
Chen HX (2010) The practice of reducing the dropping speed of venous transfusion in outpatient service through six sigma management. Journal of Traditional Chinese Medicine 6:21–23
Lifvergren S, Gremyr I, Hellström A, et al (2010) Lessons from Sweden’s first large-scale implementation of Six Sigma in healthcare. Operations Management Research 3:117–128. https://doi.org/10.1007/s12063-010-0038-y
Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Improvement of Outpatient Service Processes: A Case Study of the University of Hong Kong-Shenzhen Hospital

Status:

Version 1

Abstract

Figures

1. Introduction

2. Literature Review

2.1. Process Management Theory

2.2. Business Process Improvement Theory

2.3. Queuing Theory

2.4. Six Sigma Theory

2.5. Summary

3. Methodology

3.1. Satisfaction Measurement for the Outpatient Service

3.2. Factors Influencing Patient Satisfaction with the Outpatient Service

4. Results And Discussion

5. Conclusions

Declarations

References

Additional Declarations

Status:

Version 1