Design and Development of a Medical Clinic Service System ^†

Abstract

This article describes a system for servicing and enhancing the efficiency of medical clinic operations. The developed system facilitates the patients of the medical clinic by providing rapid and convenient access to their comprehensive medical records. Leveraging advanced technology, this system is poised to significantly improve patient health outcomes. The experiments are made as average time for response from web service and application testing with patient feedback. The quality of patients’ health is expected to improve significantly with the smart system using advanced technology.

1. Introduction

An information system is a combination of information technology and the service to patients of the clinic, according to their needs. Medical clinic software applications serve as an essential tool that is revolutionizing healthcare for both providers and patients [1,2]. The multifaceted benefits it offers include streamlined access to medical information, improved time efficiency, patient convenience, and a host of features that contribute to proactive healthcare management.

One of the key benefits of a medical clinic app is its ability to optimize the time of both patients and healthcare providers. Patients can speed up the appointment scheduling process, view available time slots, and receive timely reminders, thereby reducing clinic wait times. This time efficiency is not only convenient for patients, but also contributes to the overall operational efficiency of the healthcare facility.

Furthermore, software applications extend the healthcare sphere beyond the confines of physical clinics [3]. Patients can connect remotely with healthcare providers through telemedicine services, enabling virtual consultations. This is proving particularly valuable for routine check-ups, follow-up appointments and discussion of laboratory results, removing the need for unnecessary in-person visits and improving accessibility to healthcare services.

Patient convenience is further enhanced by features such as prescription refills and secure messaging with healthcare professionals [4]. Patients can request prescription refills through the application, saving them from having to visit the clinic for routine medication needs. Secure messaging promotes effective communication by allowing patients to seek clarification, ask questions and receive guidance from healthcare professionals, strengthening the doctor–patient relationship.

Such a software product can also serve as a comprehensive health management tool [5]. Patients can monitor and track various health parameters, such as blood pressure, glucose levels or physical activity, contributing to a more holistic approach to healthcare. Automated alerts and reminders for medication schedules, upcoming appointments or preventive checkups further enhance the app’s functionality in promoting health and well-being.

Including a blog section in the app adds an educational dimension. Patients can access health-related articles, tips and updates that enrich their knowledge about specific conditions, preventative measures, and general wellness practices [6]. This educational content not only informs patients, but also encourages them to actively participate in their health journey. For healthcare providers, the app streamlines administrative tasks, from appointment management to electronic record keeping. Efficient document handling and the ability to maintain patient records digitally contribute to a more organized and efficient healthcare workflow.

In conclusion, medical clinic software has emerged as a transformative tool that not only provides easy access to information but also redefines the patient experience by saving time, promoting convenience and active participation in healthcare management. Its multifaceted features contribute to a more patient-centric and efficient healthcare ecosystem [7,8].

A single mobile app for medical clinics can offer several benefits to both healthcare providers and patients. Here are some reasons why such an app could be valuable:

• Access to medical information: patients can access their medical records, details of examinations and results via the mobile app.
• Time Efficiency: Patients can save time by using the app to schedule appointments, view available time slots and receive reminders.
• Patient convenience: the mobile app allows patients to manage their healthcare from the comfort of their own home.
• Remote consultations: some mobile apps enable telemedicine services by allowing patients to consult with medical professionals remotely.
• Health monitoring and alerts: Patients can use the app to track and monitor their health parameters. such as blood pressure, glucose levels or physical activity.
• Educational content.
• Improved communication.
• Appointment reminders and notifications.

In summary, a mobile app for medical clinics can improve the overall patient experience by providing easy access to information, improving communication, saving time, and encouraging proactive healthcare management [9].

2. System Development

2.1. Conceptual Model Scheme

The considered system works with a relational database consisting of thirteen tables (Figure 1). All applications work with the database. The visibility and permissions for the different tables are built in a hierarchical manner. The privilege levels are determined by the visibility of the user logged into the system.

The conceptual scheme of database model contains 13 main tables.

• Medical Examination Heart Disease Result;
• Medical Examination Diabetes Result;
• Medical Examination;
• Medical prescription;
• Prescription drugs;
• Medicine;
• Medicine type;
• Patient;
• Doctor;
• Specialty;
• Science degree;
• Category;
• Blog topic.

2.2. System Modules

The system is made up of five modules (three applications):

• The designed applications contain a common database. which allows communication between different applications. Mobile application and both application programming interfaces use the same database.
• The mobile app “Medican” provides the ability for all clinic patients to be able to view all of their electronic medical prescriptions, be able to filter them, view test results, view blogs, book clinic appointments, view and edit patient data.
• ASP.NET Core Web API—To allow communication between patients and their mobile devices with the data that reside on the server, a server application, API, needs to be implemented to allow them to view and filter medical prescriptions, load their test results, read a blog to keep appointments, and edit their personal data. The API is implemented in C# programming language with ASP.NET Core framework and Entity Framework. (Figure 2)

• Flask API—The AI modules will be integrated into a dedicated API, a web application that will be accessible by all patients who have blood tests related to heart disease or blood sugar tests predisposing to diabetes. To be able to access the AI modules from the mobile application for this purpose, it is necessary to implement a server application, API. It is implemented in Python using the Flask framework (Figure 3 and Figure 4). This API gives access to the following modules:

  ○

  Artificial Intelligence module to predict possible heart diseases.

  ○

  Artificial intelligence module to predict possible diabetes diseases.

• Artificial intelligence modules aim to classify the presence of heart disease or diabetes in patients, providing precise classifications and assisting diagnosis in the medical clinic. Designing a process for learning artificial intelligence modules can be seen in Figure 5.

3. Artificial Intelligence Process

Artificial intelligence process consists of six phases.

• Study of a suitable dataset;
• Data analysis and visualization of various trends;
• Normalization to the data;
• Processing;
• Training the models with artificial intelligence;
• Exporting the model so it can be integrated into the Flask Web API.

3.1. Features for Hearth Disease Dataset [10]

This is a multivariate type of dataset, providing or including a variety of separate mathematical or statistical variables, i.e., multivariate numerical data analysis. It consists of 14 characteristics: age, gender, type of chest pain, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximal heart rate achieved, exercise-induced angina, old peak—ST depression, exercise-induced vs. rest, peak exercise ST-segment slope, large vessel number, and thalassemia. This database includes 76 attributes, but all published studies refer to the use of a subset of 14 of them.

The Cleveland database is the only one used by ML researchers to date. One of the main tasks of this dataset is to predict based on the given attributes of a patient whether that particular person has a heart disease or not, and another is the experimental task of diagnosing and locate various insights from this dataset that could help to understand the problem better (

Table 1. Description of features for Hearth Disease dataset.

Feature	Role	Type	Demographic	Description	Units
age	characteristic	int	yes	Age of the patient	years
sex	characteristic	category	yes	Gender (Male/Female)
cp	characteristic	category	no	type of chest pain (typical angina, atypical angina, non-anginal, asymptomatic)
trestbps	characteristic	int	no	resting blood pressure (on admission to hospital)	mm Hg
chol	characteristic	int	no	serum cholesterol	mg/dL
fbs	characteristic	category	no	fasting blood sugar > 120 mg/dL
restecg	characteristic	category	no	electrocardiographic results at rest
thalach	characteristic	int	no	maximum heart rate reached
exang	characteristic	category	no	angina pectoris induced by exercise
oldpeak	characteristic	int	no	ST depression induced by exercise versus rest
slope	characteristic	category	no	peak ST-segment slope during exercise
ca	characteristic	int	no	number of large vessels (0–3) stained by fluorosopia
thal	characteristic	category	no	a blood disorder called thalassemia (normal; fixed defect; reversible defect)
condition	target value	int	no	diagnosis of heart disease

). The dataset consists of about 300 records.

• Data normalization for Hearth Disease AI Model

The features cp, thal, slope are of type Category and have values that have different weights for assessing whether a patient has heart problems. Columns need to be added for:

• Cp—cp_0, cp_1, cp_2, cp_3;
• thal—thal_0, thal_1, thal_2;
• slope—slope_0, slope_1, slope_2.

After adding the new columns, the cp, thal and slope columns are removed.

X_normalized = 〖X-X _minimal〗/(X_maximal-X_minimal).

(1)

The dataset is split into 80% for training data and 20% for test data, and 10% of the data are scrambled. This action was taken in order not to overfit the model, i.e., so that it recognizes only the training data.

At the end of the training experiments performed, results were obtained with an accuracy of 85.00%.

3.2. Features for Diabetes Dataset [11]

This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The purpose of the dataset is to diagnostically predict whether or not a patient has diabetes based on certain diagnostic measurements included in the dataset (

Table 2. Description of features for Diabetes dataset.

Feature	Role	Type	Demographic	Description	Units
Pregnancies	characteristic	int	no	Number of pregnancies	years
Glucose	characteristic	int	no	2-h plasma glucose concentration in an oral glucose tolerance test
BloodPressure	characteristic	int	no	Diastolic blood pressure	mm Hg
SkinThickness	characteristic	int	no	Triceps skinfold thickness	mm
Insulin	characteristic	int	no	2-h serum insulin	mu U/mL
BMI	characteristic	int	no	Body mass index (BMI)	weight in kg/(height in m)2
DiabetesPedigreeFunction	characteristic	int	no	Pedigree function of diabetes
Age	characteristic	int	yes	Patient age	Years
Outcome	target value	int	no	Diagnosed diabetes

). Several constraints were placed on the selection of these instances from a larger database. Notably, all patients here are women at least 21 years of age of Pima Indian heritage.

The dataset consists of about 800 records.

• Data Normalization for Diabetes Dataset

In this dataset, there is no need for data normalization because we have medical values that do not allow this operation to be performed.

When interpreting features in some cases, especially when the features represent specific medical measures, the original values should be preserved in order to easily interpret the results. When the features represent physiological measurements, such as blood pressure or insulin levels, leaving the original values may be medically preferred.

As regard feature scaling, if the features are on appropriate scales, for example if they are measured in the same units, and the scale differences are not significant, then normalization may not be so critical.

The dataset is split into 80% for training data and 20% for test data, and 42% of the data are scrambled. This action was taken in order not to overfit the model, i.e., so that it recognizes only the training data.

At the end of the training experiments performed, results were obtained with an accuracy of 77.92%.

3.3. Artificial Intelligence Algorithms for both AI Models

Logistic regression is a commonly used algorithm in medical applications, including heart disease detection, for these reasons:

Output is the probability of an event—logistic regression produces an output in the interval [0, 1], which can be interpreted as the probability of having heart disease. This is useful in medical applications where the interest is in predicting the probability of a particular event.

Interpretation of coefficients: The coefficients in logistic regression can be interpreted as the influence of each characteristic on the logarithm of the odds ratio. This gives a clear picture of which factors are associated with greater or lesser chances of heart disease.

Relative ease of training: Logistic regression is generally faster to train than more complex models and requires fewer training data. This can be an advantage, especially when you have limited medical data.

Dealing with highly correlated factors: Logistic regression is relatively robust to the problem of multicollinearity (highly correlated predictors). This is important because medical data often have multiple interrelated characteristics.

Interpretability: Logistic regression results are easy to interpret and explain, which can be important in the medical field where clear communication with doctors and patients is needed.

Ability to work with linear and non-linear interactions: Logistic regression can capture linear and non-collinear interactions between characteristics. This is useful because complex interactions between different factors are often encountered in medical data.

Robustness to the problem of outliers: Logistic regression is relatively robust to outliers in the data, which is important in the medical context, where data can often be affected by outliers.

Relatively easy to train: Logistic regression can be trained quickly, especially if the data are relatively large. This makes the algorithm suitable for use with medical data, which is often huge and diverse.

AI Models settings:

The model was trained with the scikit learn library, sklearn.linear_model.LogisticRegression

Features: Penalty: L2 regularization (Ridge).

$$J\left(\Theta \right)=Targetfunction{\sum }_{i=0}^n{\Theta }_1^2$$

(2)

where J(θ)—Target function; λ—tuning parameter specifying the strength of regularization; ${\sum }_{i=0}^n{\Theta }_1^2$—the sum of the squares of the parameter values.

LBFGS is an optimization algorithm that is used to find the minimum of a function. LBFGS (Limited-memory Broyden–Fletcher–Goldfarb–Shanno) is the most widely used version of the Broyden–Fletcher–Goldfarb–Shanno method. It is a method for optimizing functions of multiple variables where no information about the second derivatives of the function is required.

4. Experiments

4.1. API Response Rate

Another type of experiment fpr after the implementation of the system was to check the response speed from the application programming interface (API) (

Table 3. Average time for response from web service—ASP.NET Web API.

Method Type	Description	Response Time
GET	Load all categories	4 ms
GET	Load blogs by category	4 ms
GET	Load blog by identifier	4 ms
GET	Load all specialties	5 ms
GET	Load all doctors by specialty	5 ms
POST	Book an appointment for a medical examination	6 ms
GET	Loading test results for heart disease	4 ms
GET	Load test results for diabetes	5 ms
GET	Loading electronic medical prescriptions	5 ms
GET	Loading electronic medical prescriptions with filter	6 ms
GET	Refill medication from electronic medical prescription	4 ms
GET	Download an electronic medical prescription file	498 ms
GET	Logging into a patient profile	5 ms
GET	Load patient personal data	4 ms
POST	Saving a patient’s personal data	5ms

and

Table 4. Average time for response from web service—Flask Web API.

Method Type	Description	Response Time
GET	Obtaining a prediction from an artificial intelligence model for diabetes	4 ms
GET	Obtaining a prediction from an artificial intelligence model for heart problems	4 ms

), and the requests were generated using the mobile application.

4.2. Data Protection

Data security is a very important system because it stores personal data of patients and doctors. Data include:

• Unified Civil Number (UCN).
• Password.

This type of data is not stored in its pure form. The password is encrypted with SHA-512 for maximum protection. This algorithm belongs to the SHA-2 (Secure Hash Algorithm 2) family and provides a longer hash value length. It uses one protection layer.

4.3. App Testing with Feedback (User Survey)

Mobile application for patients (

Table 5. Application feedback for mobile application from young patients (15–44 years).

Gender, Age, Specialty	Smartphone and Computer Technology Skills	Feedback
Female, 24, Procurement and Logistics officer	Yes	“The design is clean and it is very easy and pleasant for every user to navigate his choice. The status of the recipe is clearly and distinctly expressed by means of a bright color that attracts attention and gives a clear answer to the question what the recipe is—“Active” or “Inactive”. The window that appears while it is loading is a great idea. It gives the user a visual of what is happening and that they need to wait a moment to be able to proceed with the next action. Scheduling an appointment and getting results is easy and quick and saves an enormous amount of time in our busy lives”.
Male, 23, Computer science student	Yes	“Quite well-structured interface. Minimalistic design providing quite intuitive actions. I don’t think there will be a user who will get confused about what to do, regardless of their age. The recipe viewer is clean and informative. The description is hidden, but it’s colored like a link and it renders quite well and only when you want to view it then you click it, so it doesn’t fill the screen with unnecessary information, which is quite clever. The fact that you can download the electronic recipe is very nice. QR code sharing is pretty good, especially when you’re in a pharmacy and you want to get your prescriptions. Very nice work you have done, Bravo!”
Male, 23, Software Developer	Yes	“I have no comments and recommendations. Scheduling an appointment at the clinic is a very good functionality. Being curious about the processes in the human body I would follow the blog with quite a lot of interest. Just everything is presented in a very good way”.
Male, 25, Medical student “General practitioner”	Yes	“Everything is very useful as an idea and we like it very much. It would be nice to have a chat with an on-call doctor if needed, something like 112 for the private clinic”.

,

Table 6. Application feedback for mobile application from middle-aged patients (45-79 years).

Gender, Age, Specialty	Smartphone and Computer Technology Skills	Feedback
Female, 53 years old, Unemployed	Yes	“The phone app is very handy. Everything is structured and described properly. The most important and basic processes in a medical clinic are presented. Scheduling an appointment is very easy to use. Editing personal data is good because the patient will be able to keep it up to date. There are no disadvantages. It would be nice to be able to extend you electronic prescriptions with a request through the app”.
Male, 59 years old, Driver of a special fire truck	Yes	“The colors are nice and soft, not intrusive. It’s nice that there is a contact for feedback in case of a problem. It’s nice being able to see with precision which medicine can be drunk when and what its description is. The blog is very nice it allows me to read interesting things about my health”.

and

Table 7. Application feedback for mobile application from elderly age patients (75-89 years).

Gender, Age, Specialty	Smartphone and Computer Technology Skills	Feedback
Female, 84. Retired	No	“There aren’t many buttons. There is contrast in the colors. I found it easy to navigate where to click and find the recipes. The result viewer thing is nice because I live out of town and can hardly go to the hospital just to check them. As someone who likes to read blogs, the blog is a very nice option to keep myself healthy”.

):

5. Conclusions

The current development proposes an information system for a medical clinic service. It provides information security against known cyber-attack methods. The software allows patients to retrieve medical prescriptions, share or download electronic medical prescriptions, preview medical examinations, blog, and schedule appointments.

Despite the available functionalities, the system could be improved and developed with the following future developments:

• Integrate an option to use supplemental health insurance when booking an appointment;
• Notify patient of upcoming appointments with doctor;
• Digital authorization for medical prescription renewal in order to continue treatment;
• Remote consultations;
• Physical activity monitoring;

  ○

  Health parameters:

  ○

  Record and store blood pressure values;

  ○

  Recording and storage of blood oxygen values;

  ○

  Record and store blood glucose values;

  ○

  Integration of specialized glucose monitoring sensor (Continuous Glucose Monitors, CGM), for blood glucose levels.

Keeping track of these patient health indicators will help collect data on their health. This data will be used to analyze the patients’ condition and to structure datasets. These datasets will be used to train various AI modules that will help doctors in diagnosing patients.