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The current health care system is complex and inefficient. A simple and reliable health monitoring system that can help patients perform medical self-diagnosis is seldom readily available. Because the medical system is vast and complex, it has hampered or delayed patients in seeking medical advice or treatment in a timely manner, which may potentially affect the patient’s chances of recovery, especially those with severe sicknesses such as cancer, and heart disease.
The purpose of this paper is to propose a methodology in designing a simple, low cost, Internet-based health-screening platform.
This health-screening platform will enable patients to perform medical self-diagnosis over the Internet. Historical data has shown the importance of early detection to ensure patients receive proper treatment and speedy recovery.
The platform is designed with special emphasis on the user interface. Standard Web-based user-interface design is adopted so the user feels ease to operate in a familiar Web environment. In addition, graphics such as charts and graphs are used generously to help users visualize and understand the result of the diagnostic. The system is developed using hypertext preprocessor (PHP) programming language. One important feature of this system platform is that it is built to be a stand-alone platform, which tends to have better user privacy security. The prototype system platform was developed by the National Cheng Kung University Ergonomic and Design Laboratory.
The completed prototype of this system platform was submitted to the Taiwan Medical Institute for evaluation. The evaluation of 120 participants showed that this platform system is a highly effective tool in health-screening applications, and has great potential for improving the medical care quality for the general public.
Around the world, some parts of the population live in areas distant from primary medical care facilities. Generally, these people have limited access to receiving proper preventive medical care, which often results in delayed treatment [
With the rapid advancement of telecommunication technology, some potential applications to certain medical practices, previously deemed infeasible and impractical, have recently been opened up. Further, this also allows an important channel for residents in rural areas to seek medical or diagnostic advice. For instance, with the advent of telecommunication technology, physicians are able to perform medical diagnostics, and enable medical devices (MRI, CAT scans, etc), using technologies such as video conferencing and the Internet, even though the patient is hundreds or thousands of miles away [
Internet-based medical technology has quickly become a critical part of modern health care systems and medicine [
The feasibility of using Web-based applications to perform medical diagnostics is limited [
The aim of this paper is to propose a simple, easy to implement, efficient, and reliable system for a telemedicine service as a preliminary medical diagnostic tool. The system is designed to enable the user to record the results of the diagnostic, and the results could be used by a physician in conjunction with future diagnostics. This is an application purposely built with a user-friendly, graphical interface and various services are implemented as dynamic Web pages. In addition, the application is developed with specific emphasis on patient privacy and ease of use. It is vital to create an environment in which the patient does not feel intimidated [
The initial step was to conduct a comprehensive analysis of the study’s scenario. Two types of users were identified to use the system: physicians in medical centers, which hosted the system platform, and patients located in remote and underserved areas that have concern for his/her health. Both users were required to have access to any active Internet connection and the ability to run popular Web browsers such as Internet Explorer and Mozilla Firefox. Since these Web browsers are supported by multiple operating systems, the users were able to access the application from any computer system with ease. In addition, the system design architecture was not limited to one unique type of medical symptom. The system design architecture was applicable to all types of medical symptoms or conditions with small updates to certain parts of the system. In this study, the medical symptoms due to neck and shoulder disorders were used as an example, and a proven Shoulder Fatigue Scale-30 Items (SFS-30) diagnostic scale [
We provide a hypothetical situation based on a patient who is suspected of suffering some form of neck and shoulder disorder, and is not sure the symptoms are severe enough to see a physician. This patient chose to get an initial diagnosis using a Web-based application from the medical center s/he preferred. The patient may have needed to pay a small fee to access the Web-based diagnostic application, and was required to input all the symptoms and a description of his/her concerns. Next, the data was submitted to the server for analysis and reviewed by the medical center’s attending physician. The final diagnosis was then provided to the patient in electronic form. The patient accessed the Web-based system to see the diagnosis and determined if s/he was required to seek treatment.
Note that the system service platform also provided a link that the patient was able to use to communicate with the medical center’s physicians. All Web-based medical diagnostic systems have certain limitations. Hence, when the final diagnosis was prepared, either comprehensive or broad, the patient was able to contact the medical center’s physician swiftly to get additional information, clarification, and prevent further deterioration of the patient’s medical-related conditions.
The system interface was designed with the assistance of 10 experts in the area of interface design, information system design, and prototype testing. The system’s overall performance and satisfaction were acquired through questionnaires (questionnaires are available upon request). Lastly, the Heuristic Evaluation Method was used to determine the best method in diagnosing neck and shoulder pain symptoms, and also used to further refine and improve the overall system design.
General architecture of the system.
The system performed two primary functions. The first function was to evaluate the data provided by the user. Next, the analyzed data was used to develop the final diagnosis and medical advice. In order to perform these functions, the three major components required for this system to perform its task were (1) data (inputted by the user), (2) database (storage and retrieval of data), and (3) personal computer and internet network (to complete the expert system;
For this research, the system platform was developed using the Microsoft Windows XP operating system, and is designed for the Internet Explorer 6.0 or newer browser. The Web page is best viewed with 1024 × 768 screen resolution. The Pietty software package version 0.3.27 was used to develop the system platform. This software package was selected for its simple user interface, customizable window display, its support of multiple languages, and its advantage of direct drag and drop file upload.
The diagnosis data is reformatted using a PHP class that allows PDF files to be generated with pure PHP, also known as FPDF, and saved in a commonly used PDF format. This enables the user to store the file electronically and readily accessible. In addition, this reduces paper usage, which is beneficial for the environment. Next, a commonly used Java script was added to detect an incomplete input field. This was done to ensure all required input fields had been entered properly. To ensure the system platform could be displayed and function properly, additional PHP code was added to ensure compatibility with all types of browsers (
Comparison of functional requirements in three types of testing platforms.
Functional requirements/types | Expert | Standard | Simple |
Usage requirement | Physician | Onsite self-diagnostic | Web-based self-diagnostic |
Target user | District hospital | Clinic | Public |
Operating environment | Reside in user terminal | Reside in user terminal | Access through Internet |
Programing language | JAVA | VB | PHP |
Type of storage | None, printable | None, printable | |
Output format | Text, chart | Text, chart | Text, chart, figure |
Development cost | High | Low | Low |
System development requirements of the testing platform.
Operating system | Ubuntu 4.1 |
Operating environment | Microsoft Windows XP Pro SP2 |
Browser | IE 6.0 and Newer |
Development Tool | Pietty 0.3.27 |
Programming language | PHP/5.2.6-3, Apachie/2.2.11 |
Interface design | CSS |
Chart design | Google Chart Tools, Flash |
Data storage | MYSQL, FPDF |
Testing | JavaScript |
Analysis | SFS-30 questionnaire |
System services.
Since this is a Web-based application, all data was transmitted through the Internet, and hence there was a potential risk of the data being intercepted or manipulated by someone other than the user. To ensure the safety and confidentiality of the database, all users were required to register and obtain permissions from the system administrator prior to accessing the system platform. For all registered users, s/he had the confidence of accessing the medical information easily and safely.
The system administrator was also responsible for establishing the database with relevant medical information and inputting from physicians. This was essential for the user to obtain complete, up-to-date, and accurate information.
The user interface is a user-center graphical interface design. The first thing the user saw after launching the SFS-30 website is shown in
In
Step 3 of
Step 4 of
Main window.
Personal information.
SFS-30 Scale.
Results.
When predicting or explaining the behavior of individuals, the “Intention model” is considered to be a complete model. The Intention model factors in attitude, beliefs, and affection, therefore predicting an individuals’ behavior. If someone wants to predict and explain whether a person will act in a specific manner, we have to understand their intentions.
Fishbein and Ajzen [
Based on the TRA and with the application of information system, Davis et al [
User evaluation and validation was conducted to evaluate the clinical trial of this system. The evaluation method used was a prototype system developed by Cheng Kung University Laboratory of Human Factors Engineering. The prototype system combined task-technology fit (TTF) and TAM models. Yen et al [
The evaluation and validation were conducted at Taipei Veterans General Hospital (Taiwan) in November 2010. There was a total of 120 patients who participated in this study. Among the 120 patients, there were 79 males and 41 females with an average age of 34-years old.
The causal structure of the proposed research model was conducted using SEM. SEM is a modeling method that can handle a series or group of independent variables and the relationship between the dependent variable. In this study, LISREL 8.51 software was used to calculate the SEM fit indices. The recommended value and the numerical results of this study are listed in
Demographic information of the respondents.
Demographic information | n (n=120) | Percentage | |
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Male | 79 | 65.8 |
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Female | 41 | 34.2 |
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Under 20 | 5 | 4.2 |
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21-30 | 53 | 44.2 |
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31-40 | 22 | 18.3 |
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41-50 | 24 | 20 |
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51-60 | 13 | 10.8 |
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Above 60 | 3 | 2.5 |
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High school | 5 | 4.2 |
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College | 74 | 61.7 |
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Graduate school | 39 | 32.5 |
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Doctorate/PhD | 2 | 1.7 |
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Typical white collar worker | 51 | 42.5 |
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Athlete | 8 | 6.7 |
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Service | 31 | 25.8 |
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Housewife | 13 | 10.8 |
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Porter | 3 | 2.5 |
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Other | 14 | 11.7 |
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Do not use the computer | 0 | 0 |
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Less than 1 hour/day | 13 | 10.8 |
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1-4 hours/day | 32 | 26.7 |
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5-8 hours/day | 55 | 45.8 |
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More than 8 hours/day | 20 | 16.7 |
Measures of model fit for measurement model.
Measures of model fit | Recommended value | Recommended by | Research value |
χ2 | -- | -- | 273.9 |
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-- | -- | 194 |
χ2 / |
<3 | Hayduk | 1.41 |
GFIb | >0.9 | Scott | 0.92 |
AGFIc | >0.8 | Scott | 0.89 |
CFId | >0.9 | Bagozzi & Yi | 0.96 |
RMSEAe | <0.05 | Bagozzi & Yi | 0.026 |
aChi-square value divided by the degrees of freedom
bGoodness-of-fit Index
cAdjusted Goodness-of-fit Index
d(Expectation for a) constant scale factor index
eRoot mean square error of approximation
From the reliability analysis, the overall scale of the Cronbach α value was found to be 0.946 (n=120). This indicated that the result of this questionnaire (questionnaires available upon request) had good internal consistency.
Next, composite reliability (CR) was conducted to check the consistency of internal dimension. The higher the CR value, the higher the correlation between the observed variables. Hair et al [
Average variance extracted (AVE) is a measure of the shared or common variance in a latent variable (LV), and the amount of variance that is captured by the LV in relation to the amount due to its measurement error. In another words, AVE is a measure of the error-free variance of a set of items. Per Fornell and Larcker [
Discriminant validity was used to calculate the degree of difference in dimensions and trait. Hair et al [
The last stage of the validation process was to use LISREL to calculate the γ and β values. These values are used to explain the observed variables and LV, and relations between each LV.
H1: User PU and BI show positive correlation.
H2: User PEOU and PU show positive correlation.
H3: User PEOU and BI show positive correlation.
H7: The TECH and user PEOU show positive correlation.
H8: The TECH and user PU show positive correlation.
H9: The TASK of improving the user’s neck and shoulder pain symptoms and the TTF show positive correlation.
H10: The TECH of improving the user’s neck and shoulder pain symptoms and the TTF show positive correlation.
Integrated theoretical model of TTF and TAM [
Reliability analysis.
Latent variable | Observed variable | Factor loading | Measurement error | Composite reliability | Average variance extracted | |
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BI1 | 0.86 | 0.24 | 0.872 | 0.773 |
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BI2 | 0.83 | 0.18 |
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PU1 | 0.61 | 0.03 | 0.939 | 0.857 |
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PU2 | 0.57 | 0.15 |
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PU3 | 0.67 | 0.07 |
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PU4 | 0.72 | 0.18 |
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PEOU1 | 0.84 | 0.22 | 0.924 | 0.781 |
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PEOU2 | 0.79 | 0.17 |
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PEOU3 | 0.9 | 0.29 |
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PEOU4 | 0.86 | 0.27 |
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TTF1 | 0.75 | 0.25 | 0.898 | 0.754 |
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TTF2 | 0.63 | 0.26 |
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TTF3 | 0.72 | 0.19 |
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TTF4 | 0.78 | 0.24 |
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TECH1 | 0.65 | 0.16 | 0.927 | 0.840 |
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TECH2 | 0.52 | 0.12 |
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TECH3 | 0.68 | 0.11 |
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TECH4 | 0.56 | 0.07 |
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TASK1 | 0.87 | 0.24 | 0.879 | 0.646 |
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TASK2 | 0.79 | 0.27 |
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TASK3 | 0.84 | 0.28 |
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TASK4 | 0.85 | 0.75 |
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The correlation coefficient matrix of the latent variable.
BI | PU | PEOU | TTF | TASK | TECH | |
BIa | 0.88 |
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PUb | 0.73 | 0.93 |
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PEOUc | 0.69 | 0.74 | 0.88 |
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TTFd | 0.58 | 0.66 | 0.72 | 0.87 |
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TASKe | 0.37 | 0.62 | 0.52 | 0.57 | 0.80 |
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TECHf | 0.77 | 0.76 | 0.76 | 0.71 | 0.51 | 0.92 |
aBehavioral intention
bPerceived usefulness
cPerceived ease of usefulness
dTask-technology fit
eTask characteristics
fTechnology characteristics
Structural model results.
Hypothesis | Hypothesis (H) | β | t Statistic | Results of hypothesis testing |
PUa→BIb | H1 | 0.52 | 7.46 | Supported |
PEOUc→PU | H2 | 0.26 | 3.52 | Supported |
PEOU→BI | H3 | 0.21 | 3.16 | Supported |
TTFd→PU | H4 | 0.12 | 1.32 | Not supported |
TTF→BI | H5 | 0.19 | 2.92 | Not supported |
TTF→PEOU | H6 | 0.13 | 1.30 | Not supported |
TECHe→PEOU | H7 | 0.47 | 4.28 | Supported |
TECH→PU | H8 | 0.35 | 3.33 | Supported |
TASKf→TTF | H9 | 0.22 | 3.30 | Supported |
TECH→TTF | H10 | 0.64 | 8.65 | Supported |
aPerceived usefulness
bBehavioral intention
cPerceived ease of usefulness
dTask-technology fit
eTechnology characteristics
fTask characteristics
Historically, a simple medical self-diagnostic scale tends to have low accuracy and minimal reference value. This type of self-diagnostic scale is also not readily available and printed on paper. At the present time, patients have limited options in receiving a proper medical diagnosis without seeing the physician in person. Hence, the Web-based, self-diagnosis system developed in this study will be a great alternative available to the patient.
The main finding of this study was achieved in developing a self-diagnosis system architecture that is beneficial to the general public. The system was developed with the primary objective in providing a low cost, self-diagnostic tool and can be accessed through any personal computer that is connected to the Internet. Further, the tool is developed with a user-friendly interface that is simple and intuitive to use without special instructions. Another important feature of the tool is that it enables users to save a copy of the diagnosis for their personal records, and can be used as a reference for future visits with the physician.
The proposed system was tested using a hypothesis model developed by Yen et al [
On the other hand, hypotheses H4, H5, and H6 are not supported. Per TTF model definition, this is used to measure the degree to which a technology can assist an individual in carrying out his/her tasks. The findings suggested that users themselves may already know the causes of his/her neck and shoulder pain symptoms, and this is probably due to bad habits, or an undesirable working environment that he/she has limited or no control over it. Therefore, the user does not feel compelled to use the diagnostic system in treating his/her problems, even with knowing the system is simple and beneficial.
Structural model results (*correlation is significant at the 0.01 level).
The accuracy of any diagnostic test depends on the inputs. Often, the user might not select the correct response and this could result in an inaccurate diagnosis. However, Liu’s [
The purpose of this study was to develop a medical, self-diagnostic system for neck and back pain patients. This system is supported by the inspection database, expert systems, and a decision-support mechanism. Upon the completion of the diagnostic, the system will generate a report consisting of charts, level of severity, and recommendations in a PDF format. Note that this report contains information based on research from medical doctors; hence, the information can be used to assist patient’s attending physician in developing the proper treatments.
In addition, the diagnosis identifies the potential root cause of the user’s neck and back pain symptoms, and provides recommendations that the user can choose to pursue in alleviating his/her conditions perhaps due to environmental factors or poor personal habits. In doing so, the diagnosis can aid the user in preventing his/her neck and back pain symptoms from becoming a health risk.
adjusted goodness-of-fit index
average variance extracted
behavioral intention
composite reliability
(expect for a) constant scale factor Index
a PHP class that allows PDF files to be generated with pure PHP
goodness-of-fit index
latent variable
perceived ease of usefulness
hypertext preprocessor
perceived usefulness
root mean square error of approximation
structural equation modeling
Shoulder Fatigue Scale-30 Item
task characteristics
Technology Acceptance Model
technology characteristics TRA: Theory of Reasoned Action TTF: task-technology fit
Theory of Reasoned Action
task-technology fit
Special thanks to Taipei Veterans General Hospital, Department of Physical Rehabilitation Medicine & Rehabilitation Duen-Ren Sung in providing assistance and insight in this study, and to the platform interface designer, Chao-Hsin Kuo. Also, to the National Science Council for the support of funding (NSC 102-2221-E-006-214).
None declared.