STC G6

Figure 1. Theoretical Framework of the Study

In the Philippines, where a large percentage of the population lacks access to basic medical care and one million people live below the poverty line as a result of high medical costs, the research discusses the poor healthcare situation. Macroenvironmental factors—politics, socioeconomics, technology, the environment, and legal considerations—all contribute to the current healthcare gap, which causes this urgent issue. The study aims to positively impact emergency response and the overall healthcare system by addressing disparities in service availability, treatment costs, medication shortages, and the shortage of medical experts.

To tackle these challenges, the research proposes a solution in the form of a mobile health application that caters to individuals in remote and impoverished areas, providing easier monitoring of patients, information sharing, and treatment management. The ultimate goal is the development of a simplified telemedicine app that is accessible to all, thereby eliminating barriers to healthcare services. Structuring the research involves a focused examination of the widespread use of telemedicine during the COVID-19 pandemic, with an emphasis on addressing lower patient satisfaction in developing countries like the Philippines. The researchers plan to develop a user-friendly app with an accurate symptom checker, addressing specific research questions related to accuracy ratings, user experience, interface design correlation, and differences in accuracy for various illnesses. The study's significance lies in the potential of a telemedicine application to revolutionize healthcare delivery, democratize medical services, enhance efficiency, and ensure accessible and user-friendly healthcare for everyone.

Figure 2. Procedural Framework Showing the Data Collection Methods and Analysis

The research utilized an experimental research design since the study aims to determine the cause and effect relationships among the variables. The independent variable—the algorithm and user interface design—is developed and manipulated by the researchers in order to observe its causal effect on the dependent variable—accuracy and overall user satisfaction. By utilizing this design, the researchers could confidently draw conclusions from the causation, because there is more control over the variables. The study was conducted in St. Theresa’s College Quezon City with participants from 6 sections of Grade 12 with 220 students. The sample size is 38 with 36 participants from Grade 12 STEM C and 2 medical professionals from the clinic due to time and resource constraints. The student participants were randomly selected through a name generator.

The data was collected through simulations and surveys. The procedure is separated into three phases: pre-simulation, peri-simulation, and post-simulation. Pre-simulation is the preparation of the materials required for the simulation, such as the telemedicine app containing a symptom checker with the illnesses (i.e. Common Cold, Flu, COVID-19, Cholera, Gastritis, Gastroenteritis) and the ascribed symptoms and prescriptions. Other features include geolocations of hospitals within the 1 km radius from the simulation area and electronic health records linked to the person’s user account.

Before conducting the simulation, the researchers would need to collect the list of participants and their signed Plain Language Statements. The peri-simulation phase starts with the student participants registering their personal account. They will be asked to explore the app before proceeding to the symptom checker. They will be given a set of symptoms to input which would result into the top 3 illnesses with prescriptions. The observed results should match the expected results to be considered a successful trial. Afterwards, the student participants will be given an User Interface Survey. For the medical professional participants, they will be asked to explore the app, including the symptom checker, before being given the User Interface Survey. The post-simulation phase starts with the data analysis using descriptive statistics (i.e. Mean, Mode, Standard Deviation) and inferential statistics (i.e. One-way ANOVA and Point-Biserial Correlation). A normal distribution generator was used for the One-way ANOVA.

In this study, the researchers meticulously addressed several ethical considerations to ensure the responsible and secure use of their telemedicine app. Firstly, the potential for harm was recognized, given that the app collects data from individuals to assess their symptoms and possible illnesses. To mitigate this risk, a simulation was conducted, involving participants from the Grade 12 batch of St. Theresa’s College, Quezon City, representing rural and disadvantaged communities. This approach would minimize the safety risks associated with involving real sick individuals and the researchers themselves. Data privacy also emerged as another concern. This was addressed through requiring the users to check a checkbox in the registration page, which signifies their agreement to the terms and conditions. Furthermore, in addressing concerns regarding the accuracy and reliability of the symptom checker algorithm, the app prominently featured a disclaimer. This disclaimer emphasized that the information provided concerning illnesses and prescriptions was not guaranteed to be 100% accurate, wherein the users are strongly urged to consult a medical professional. This disclaimer also addresses the issue of generating unnecessary alarm or anxiety among users.

The study's main discovery is that frequency distribution allowed the researchers to effectively communicate their conclusions. The majority of the six surveys received a rating of five from the chosen respondents, indicating that users are generally satisfied with the app.

The medical experts also gave their app a rating mean of 3.17, which is regarded as a low variability rating, indicating that it is an improvement. Additionally, they discovered that the majority of the participants' average results fell within the range of 2, 3, 4, and 5. After computing, they discovered that the average rating for telemedicine apps was 4.45, meaning that over half of the participants approved of it.

The readers should interpret the data such as the tables, figures, and graphs as an overview of what the app is really about which is the app may serve as a "band-aid" solution for those seeking information on suspected illnesses based on their symptoms, even if it might not be the greatest for wide public medical usage. It can also offer details on tactics and at-home treatments to lessen these symptoms. Based on this, the scores from participants as well as medical professionals suggest that there is space for improvement to the app.

Table 1. Post-Hoc Tests of One-Way ANOVA

In the inferential results, the one-way ANOVA revealed highly significant differences among different illness groups, and post-hoc tests confirmed these differences. The correlation between User Experience Rating and Symptom Checker Outcome was statistically significant, with a p-value of 0.012. These findings align with the research objectives and hypotheses, indicating the app's effectiveness in distinguishing between different illnesses and its strong user satisfaction.

Table 2. Table for the Point-Biserial Correlation Results

Noteworthy observations include consistently high ratings and significant differences in symptom diagnoses among various illnesses. Overall, the results highlight the app's positive user perception and diagnostic accuracy, supporting our research objectives and hypotheses.

The outcomes of the study promise to yield substantive insights through a meticulous examination of the symptom checker's accuracy, the participants’ user experience, and the relationship between these variables.

The obtained results offer insights that both align with and augment previous research in the field of telemedicine applications. Notably, the Symptom Checker's accuracy, evaluated by medical professionals, presents an average rating of 3.17, revealing varied assessments between professionals but agreeing on the helpfulness of symptoms and identifying areas for app enhancement. This correlates with prior studies emphasizing usability and content accuracy as crucial aspects of medical apps. Similarly, the positive correlation between user interface design and symptom checker accuracy aligns with existing research that stresses the influence of interface quality on user experience and system performance.

The findings have significant implications for the development and implementation of telemedicine applications. The varying app ratings by medical professionals signal a need for improvement, emphasizing the importance of refining usability and content presentation for better acceptance among healthcare professionals. Moreover, the strong correlation identified between user interface design and symptom checker accuracy underscores the criticality of a well-designed and accurate interface, enhancing user experiences and symptom identification. These outcomes contribute significantly to existing knowledge by validating the relevance of interface quality in the success of medical applications.

However, the study exhibits limitations that might affect the interpretation of the results. The sample size constraints in the simulation and reliance on the MIT App Inventor as the app creation platform might restrict the app's feature set, impacting its overall functionality. Moreover, the simulated environment's lack of representation of real-life conditions limits the generalizability of findings. Safety measures and the focus on diagnosing common illnesses with over-the-counter medications may have reduced the study's validity, minimizing potential implications arising from misdiagnoses.

The presented information highlights a substantial and positive association between user interface design and symptom checker accuracy. Enhancing interface correctness positively influences user experiences, indicated by higher UX Ratings, accentuating the importance of improving interface quality. Additionally, significant differences in symptom accuracy among illnesses (Common Cold, Flu, COVID-19, Gastritis, Gastroenteritis, and Cholera) were evident, emphasizing the necessity for accurate symptom identification for effective medical diagnosis and treatment planning.

Overall, the study's outcomes are in harmony with prior research, reinforcing the significance of interface design, symptom accuracy, and user experience in telemedicine applications while acknowledging limitations that might impact result interpretation.

SYNTHESIS

As for prospective research initiatives, in order to improve the accuracy and efficiency of the app, the researchers’ suggest augmenting the sample size to a minimum of 30 participants per illness set, leveraging well-established programming languages like Python and Java, opting for field experimentation over simulation, and fostering collaborative endeavors with external entities, notably medical companies, institutions, and governmental organizations. The developmental phase of the application's prototype presents itself as an instrument for subsequent researchers, affording opportunities to refine the efficacy of the symptom checker. Furthermore, positioned for utilization by the general public to address common medical needs, the application, bolstered by the insights and endorsements of medical professionals, holds potential applications within healthcare settings to enhance the precision and efficiency of medical practitioners in delivering accurate diagnoses and prescriptions. Additionally, the findings of the study validate the initial hypotheses posited by the researchers, highlighting a commendable user interface that enhances participant experiences and a systematic algorithm yielding reliable outcomes. The results obtained manifest a predominant satisfaction among participants, as discerned from the comprehensive analysis presented in Chapter 3.

Annika
"susulpot pagkatapos ng tulog"

Kristiana Abba
"guys deadline"

Gillian
"ano nangyayari mga dear"

Angela Ohnyle
"Abba hehe question"

Princess
"pwede pacheck"

Julienne Kyra
"reminding everyone yung dl natin"

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