1. Introduction

In the age of big data, the healthcare sector is experiencing unparalleled transformations, with data emerging as a crucial instrument for enhancing the efficiency and quality of healthcare services. During the COVID-19 pandemic, health officials can enhance the execution of preventative and control measures through the analysis of epidemic data.

Despite the widespread use of the phrase "big data," a globally agreed definition has yet to be established. McKinsey & Company defines big data as data collections that exceed the capacity of conventional database software tools for capture, storage, management, and analysis. Gartner uses the "three Vs" to summarize the popular definition of Big data: Big data is massive (Volume, fast (Velocity), many types (Variety) information assets (Table 1) [1]. The amount of data involved in big data is usually huge, from TB (terabytes) to TB (petabytes) or more, the speed is the frequency of data generation, processing and analysis, and the diversity of big data refers to the diversity of data sources and types. Data can be unstructured, such as text, images, videos, etc., or structured, such as tabular data in a database, or semi-structured, such as log files.

The application of big data enhances the efficiency and quality of medical services while facilitating precision medicine and individualized therapy. Pramanik et al. assert that healthcare professionals anticipate that big data utilization would enhance their industry, prompting them to study data for novel insights, so acquiring substantial information that may be transformed into inventive and purposeful activities [2]. By conducting comprehensive analyses of patient data, medical experts may formulate tailored treatment plans for everyone, thus minimizing the waste of medical resources and enhancing treatment outcomes. In the healthcare sector, big data mostly encompasses the following categories of information: (1) Clinical data encompasses information derived from electronic medical records, hospital information systems, imaging centers, laboratories, pharmacies, and other medical service providers, in addition to patient-generated health data, physicians' medical records, genomic data, and physiological monitoring data. (2) Monitor biometric data from several devices, including weight, blood pressure, and blood glucose levels [3].

Table 1: Three dimensions of big data

It comes from organizational units with varying attributes, following the establishment of a big data chain, the aggregated data can be utilized to compare and identify the clinical treatments and physiological characteristics of patients, thereby facilitating the processes of collecting, managing, analyzing, utilizing, and re-collecting big data. In addition, the concept of big data is evolving, and now it is no longer focused on the massive amount of data itself, but more on the process of creating value from the data. Bresnick asserts that big data analytics significantly enhances the healthcare sector across several dimensions, including diagnostics and care, clinical decision support, and population health management, among others [4]. During the peak of the COVID-19 pandemic, big data analysis significantly contributed to epidemic prevention and control. In response to the global medical crisis, researchers and professionals have created a range of technologies to transform the medical system into a complete big data framework aimed at mitigating disease transmission and enhancing preventive and control efficacy. Majeed and Lee have created a technique for the acquisition of personal data to mitigate the transmission of COVID-19 [5]. One potential contribution of this research is to use computing technology to provide people with COVID-19 related knowledge to raise awareness of the disease.

The European Commission's Health Agency for Research and Innovation has currently proposed a precise definition of the importance of big data in health research: in this domain, big data encompasses a vast and varied array of biological, clinical, environmental, and lifestyle information regarding the health and wellness status of an individual or multiple time points, ranging from a single person to a large population. In addition, some scholars believe that "big" is no longer the only defining parameter of big data, equally important is how "intelligent" the data is, concentrating on whether the data amount adequately yields relevant insights. Big data possesses significant potential to enhance health; yet it’s worth can only be fully realized when utilized to support decision-making. Facilitating evidence-based decision-making necessitates effective techniques for analyzing substantial data volumes and transforming them into significant insights.

2. The application of big data in healthcare

Big data can be applied in almost all areas of healthcare management, including epidemic transmission prediction, clinical applications, designing medical devices, health insurance, personalized patient care and production, and drug development.

According to Aceto et al., the primary application scenarios in healthcare encompass long-term senior care, home monitoring, emergency medical services, and rehabilitation systems [6]. These technologies effectively and precisely gather, identify, and analyze a continually expanding volume of diverse data, hence advancing the evolution of big data in healthcare. Due to the Internet, cloud storage, and big data, researchers and practitioners can develop innovative solutions to current issues that effectively enhance and optimize healthcare practices, as well as yield new breakthroughs to tackle and mitigate persistent healthcare challenges.

3. Personalized medical care

Personalized medicine seeks to be patient-centered, necessitating the collection of data from diverse sources, including the patient and their environment, as the analysis of this data informs medical and social care decisions that align with implementation. Common data sources include wearable devices, as well as implanted micro and nanotechnology equipped with sensors or therapeutic devices, such as fall detectors, implantable insulin pumps, and defibrillator vests.

The healthcare sector in the era of big data is characterized by user-centricity and the integration of data from many sources, including wearables, to deliver highly tailored services. IBM's Watson Robot Doctor exemplifies advanced computing capabilities, sophisticated natural language processing technology, and a comprehensive professional knowledge base. It can analyze 500GB of multi-dimensional health and medical data—including clinical, laboratory testing, pathology, and biological sample information—per second, thereby addressing the practical requirements of clinical decision support, customizing individualized treatment and medication plans for patients, and mitigating the risks of adverse reactions, drug toxicity, and other detrimental events. It can be seen that personalized medicine relies heavily on the collection of genetic information about each individual, and that a comprehensive understanding of each individual's biological information can have a significant impact on treatment, screening, diagnosis, prognosis, pharmacogenomics, and monitoring of the recovery period. Therefore, the role of big data analytics cannot be ignored when implementing personalized healthcare for individual users and user groups.

4. Medicare & fraud detection

Medicare firms are rapidly utilizing big data technologies in order to increase the efficiency of underwriting, the detection of health care fraud, and the handling of claims. Not only may the implementation of these technologies result in an improvement in the quality of service, but it can also result in expenses and risks being reduced. For instance, insurers are able to build more suitable insurance policies and conduct more accurate risk assessments by analyzing enormous volumes of consumer data. This data includes the individual's health status, claims history, and behavior on social media platforms.

When it comes to the detection of fraudulent activity in the healthcare business, the industry is implementing analytical controls at every stage of the treatment process and incorporating them into the claims review process. Analyzing the patient's treatment methods and drugs prescribed against instances with comparable symptoms is part of the review process, which combines data analysis and prediction models. The goal of this study is to assess whether or not the treatments and medications used are legal and in compliance with applicable regulations [7].

In recent years, there has been a huge improvement in the way individuals manage themselves, particularly in the nursing and healthcare industries. The analysis of large amounts of data allows for the identification and modification of risk factors that have an impact on health, as well as the development of interventions that aim to alter health habits, which ultimately contributes to the prevention of illness. For instance, the treatment of chronic diseases, which is an essential component of self-health management, may make use of big data technology to provide individuals individualized guidance and instruction, to encourage the development of healthy eating habits [8], and to devise suitable exercise programs [8].

When it comes to the design of intelligent services, academics are investigating the ways in which services may be designed to deliver feedback that is more precise and efficient than just presenting and temporarily storing data. The goal is to accomplish dynamic evaluation and early warning of health condition through the utilization of real-time monitoring and in-depth mining of individual health data. In addition, new models of health management services are being investigated as a result of the development of technology that utilizes artificial intelligence and big data. By way of illustration, the quantification of self-data allows for the construction of an accurate image of personal health, which is necessary in order to accomplish the shift from concentrating on treatment to concentrating on preserving health and avoiding disease. Not only does the use of these technologies enhance the effectiveness and quality of medical services, but it also offers individuals alternatives for health management that are more individualized and precise. In terms of the management and prevention of obesity, the National Health Commission of China has issued the Guidelines for the Diagnosis and Treatment of Obesity (2024 edition). These guidelines highlight the physical and psychological harm that is caused by overweight and obesity, and they put forward recommendations for diagnosis and treatment that correspond to these harms. This further demonstrates the application of big data in individual self-management, which has the potential to deliver more effective management and treatment methods for patients who are obese by means of precise data collecting and analysis [9].

Therefore, big data technology offers robust assistance and creative solutions for individual self-management, particularly in the treatment of chronic diseases and the prevention of obesity. Not only is it possible to enhance the effectiveness of therapy by employing approaches that are driven by accurate data, but it is also possible to successfully prevent the onset of disease and to positively influence the health of individuals.

5. Drug intake monitoring and smart pharmaceuticals

Due to cognitive decline or memory loss, it is typical for patients with chronic conditions and the elderly to take medications against the recommendation of their doctors. As a result of this phenomena, monitoring drug consumption with big data technologies can significantly improve the situation. For instance, it is feasible to detect risk factors that impact drug adherence by evaluating patient medication data. This allows for the development of treatments that will enhance treatment outcomes.

Additionally, the researchers are working on establishing intelligent service systems that are able to not only monitor the medicine that patients are taking in real time by utilizing sensor networks and RFID technology, but also make suggestions for the management of patients' health in a tailored manner.

When it comes to pharmacological therapy, the option of when to provide the medication is of utmost significance for it to be effective. The outcome of this is that a number of mobile applications have developed on the market that include features such as the ability to schedule reminders, reminders for prescriptions, and tracking of drug consumption in order to guarantee that patients take their meds at the appropriate times. In addition, there are a few cutting-edge solutions that are currently being developed to give patients with more complete health management. These solutions include sensors that may be worn or swallowed, as well as integrated Internet connectivity and integrated smart systems.

The term "smart drugs" refers to electronic packaging, drug delivery systems, or tablets that offer intelligent added value and may be connected to the Internet using wireless Bluetooth devices. Additionally, smart drugs can be used to compile, store, and analyze data through direct Internet connectivity with remote systems [10]. It is anticipated that in the future, smart medications will be able to gather information at both the micro and macro levels, offer fresh perspectives on the treatment of illnesses, and make it easier to build individualized healthcare.

6. Public health

The application of big data is progressively making its way into the field of public health in the European Union, particularly in the areas of prevention and the provision of quality treatment. The draft of the "Medical Data Space Regulation" that was created by the European Commission seeks to further improve the exchange of medical data and to encourage the direct and indirect use of medical data. This will be accomplished by defining standardized data regulations, technological standards, and a data governance framework for infrastructure. Furthermore, this demonstrates that the European Union (EU) is placing a high premium on the use of data as well as new information and communication technologies (ICTs) in order to enhance the quality of public health services and increase their efficiency [11].

By way of illustration, the DEXHELPP project of the European Union makes use of health data sources that are gathered on a daily basis in order to assess the performance of the health system, forecast future changes, and simulate the implementation of policies and interventions. The eHealth initiative in Estonia is mainly focused on increasing the quality and efficiency of healthcare services. This is accomplished by digitizing all patient information and prescriptions, and by establishing an environment known as X-Road in which all healthcare practitioners are able to use data. The EU is making efforts to support the building of health data space, and these initiatives not only illustrate the promise of big data in the medical sector, particularly in the sphere of public health, but they also represent those efforts. With these measures, the EU intends to be able to respond more effectively to the rising number of people who are living with chronic diseases, determine the therapies that are the most successful, and harness the full potential of information and communication technologies (ICTs).

7. Conclusion

Big data technology is being used extensively in the healthcare business, which is causing the industry to undergo dramatic changes in ways that have never been seen before. In addition to providing patients with diagnostic and treatment services that are more precise and individualized, it not only dramatically enhances the quality of medical care but also significantly enhances the operating efficiency of medical facilities.

Using in-depth analysis, this study exposes the particular applications of big data in a variety of medical sectors as well as the far-reaching influence that it has. To begin, the utilization of big data in customized medical care has made it possible for surgeons to tailor treatment plans to individual patients, which has resulted in a significant improvement in the results of therapy. Second, the utilization of big data in the field of medical insurance and fraud detection not only successfully identifies and prevents insurance fraud, but it also ensures that medical insurance is both fair and sustainable.

People are able to better understand their current health state and take preventative steps at the appropriate time with the assistance of big data, which also plays a significant role in self-management, health monitoring, and illness prevention. On the other hand, the development of drug intake monitoring and smart medications has made it possible for patients to have a better understanding of the amount and timing of their medication, which has led to an improvement in the efficacy of drug therapy. In conclusion, the utilization of big data in the field of public health has shown to be an invaluable resource for the prevention of diseases, the monitoring of epidemics, and the management of emergency situations.

It is believed that big data will bring broader development prospects and more far-reaching social impact to the medical industry. This is despite the fact that the potential of big data in the medical industry has not yet been fully released. However, with the continuous progress of technology and the continuous deepening of application.