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Review

Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review

by
Urška Šajnović
1,2,
Helena Blažun Vošner
1,2,
Jernej Završnik
1,2,
Bojan Žlahtič
3 and
Peter Kokol
1,3,*
1
Community Healthcare Center dr. Adolf Drolc Maribor, 2000 Maribor, Slovenia
2
Health Sciences, University Alma Mater Europaea, 2000 Maribor, Slovenia
3
Faculty of Electrical Engineering and Computer Sciences, University of Maribor, 2000 Maribor, Slovenia
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(18), 3642; https://doi.org/10.3390/electronics13183642
Submission received: 2 August 2024 / Revised: 4 September 2024 / Accepted: 4 September 2024 / Published: 12 September 2024
(This article belongs to the Special Issue Internet of Things, Big Data, and Cloud Computing for Healthcare)
Browse Figures
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Abstract

:
Background: The IoT and big data are newer technologies that can provide substantial support for healthcare systems, helping them overcome their shortcomings. The aim of this paper was to analyze the relevant literature descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: Synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping, and content analysis. For this analysis, the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found; these were published between 1985 and 10 June 2024. The first article in this field was published in 1985. Until 2012, the production of such literature was steadily increasing; after that, exponential growth began, peaking in 2023. The most productive countries were the United States, India, China, the United Kingdom, South Korea, Germany, and Italy. The content analysis resulted in eight themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (8 from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that the IoT and big data have become key technologies employed in preventive healthcare. The study outcomes might represent a starting point for the further development of research that combines the multidisciplinary aspects of healthcare.

1. Introduction

The COVID-19 pandemic has highlighted the limitations of traditional healthcare systems in responding to sudden, unpredictable crises. Technologies such as smart, connected wearables (Internet of things—IoT) and big data analytics have the potential to enhance these systems, both during emergencies and for routine operations. IoT devices can gather context-specific data on patients’ physical, behavioral, and psychological health, while big data analytics can process this information in real time, extracting valuable insights and making predictions that empower healthcare professionals and decision makers to improve services [1,2,3,4,5].
Although recent reviews and bibliometric studies] have explored the use of IoT and big data in healthcare, they have not focused on preventive medicine. Moreover, these studies often rely on small samples of publications, which limits their ability to provide a comprehensive overview of the field. Traditional bibliometric analyses tend to emphasize quantitative aspects, neglecting formal qualitative assessments that could reveal emerging themes, concepts, and research maturity. Additionally, many studies fail to examine IoT and big data in tandem, missing the integrative impact of these technologies.
Our study addresses this gap by offering insights for researchers, healthcare professionals, and managers seeking to better understand this rapidly evolving field. It also serves as a resource for novice researchers, health institution managers, and patients who may be unfamiliar with this area, helping them to grasp key research dimensions and practical applications. Ultimately, our study aims to inform and inspire further research, providing a foundation for more formal knowledge synthesis and evidence-based approaches.
To achieve this, we seek to answer the following research questions:
  • What are the volume and dynamics of the research regarding the use of IoT and big data in preventive healthcare?
  • How is this research geographically distributed?
  • Which sources of information are the most influential in the scientific community, and how do they facilitate the dissemination of research findings?
  • Which funding bodies are the most productive?
  • What are the predominant research themes, concepts, and future directions?
  • How have research themes evolved over time?
  • What are the potential research gaps?

2. Materials and Methods

The research landscape of IoT and big data in regards to preventive healthcare was shaped by synthetic knowledge synthesis (SKS) [6]. SKS was developed to address the challenges of synthesizing research evidence in the face of rapid knowledge expansion and the digital revolution. It integrates quantitative and qualitative knowledge synthesis by combining descriptive bibliometrics, bibliometric mapping, and content analysis. By automating parts of the synthesis process and reducing the required resources, SKS overcomes some of the limitations of traditional approaches. This allows for the synthesis of thousands, or even tens of thousands, of publications, thereby resolving sampling issues faced by traditional systematic reviews and enhancing the reproducibility. Namely, instead of analyzing just a few dozen publications, all available publications are synthesized [7,8]. Additionally, the triangulation used in SKS offers a more comprehensive view of the phenomena under study. This approach enhances the validity, credibility, dependability, confirmability, and transferability (ecological validity) of the research synthesis [9]. To further strengthen the study’s validity and minimize bias, content analysis was conducted from both medical and computer science perspectives. The SKS process was executed using the following algorithm:
  • Research publications were harvested from the Scopus bibliographic database using the search string TITLE-ABS-KEY ((“internet of things” OR iot OR big-data) AND prevent*) AND (LIMIT-TO (SUBJAREA, “MEDI”) OR LIMIT-TO (SUBJAREA, “HEAL”) OR LIMIT-TO (SUBJAREA, “NURS”)).
  • Descriptive bibliometric analysis was performed using Scopus’s built-in functionality and the Bibliometrics software [10].
  • Author keywords were used as meaningful units of information in content analysis. First, bibliometric mapping was performed using VOSViewer [6]. Next, using content analysis on the most popular authors’ keywords, the node size, links, and proximity between author keywords in individual clusters and their borders presented in the bibliometric map were analyzed from the medical and computer science viewpoints to form categories, identify concepts, and name the research theme.
  • Next, the representative themes and subcategories’ author keywords/terms were applied to form search strings to locate relevant publications associated with describing categories and the scope of the themes.
  • The authors’ keywords for the landscape timeline were induced and used along with reference publication year spectroscopy (RPYS) to identify seminal publications and to historically analyze knowledge development [11]. The future research themes were identified by comparing different time slices of the timeline landscape [12].
Scopus (Elsevier, The Netherlands) served as the primary bibliographic database for this study. As the largest abstract and citation database for peer-reviewed research literature, Scopus offers robust analytics services and supports the export of up to 20,000 records simultaneously. Furthermore, Scopus covers most of the source titles covered by other bibliographic databases, including Web of Science and all PubMed indexed publications. The search query was constructed based on the recommendations of Farooq et al. [13], who analyzed and synthesized search strategies employed in related review papers. The search was conducted on 10 June 2024. To identify relevant keywords for the synthetic knowledge synthesis (SKS), Zipf’s law was applied, while Bradford’s law was used to determine the number of core journals. [14].

3. Results and Discussion

3.1. Descriptive and Production Bibliometrics

3.1.1. Volume of Research

The search yielded a total of 2272 publications, including 971 articles, 669 conference papers, 363 reviews, 159 book chapters, and 110 other types of publications, all featuring 6195 unique author keywords. These works were produced by 9770 authors affiliated with 2446 institutions across 72 countries and published in 1165 different source titles. On average, each publication had 5.2 co-authors, with 166 publications being single-authored. Additionally, 21.4% of the publications were international collaborations. Each publication received an average of 15.11 citations. Over time, the number of contributing countries increased from 29 during 1985–2015, to 40 in 2016–2020, and finally, to 71 countries in the 2021–2024 period.

3.1.2. The Dynamics of the Research Literature Production

The first paper in this field was published in 1985, followed by a period of sparse production until 2012, when exponential growth began. This growth reached its peak in 2023, with 438 publications (Figure 1) noted. While the number of conference papers continues to show a positive trend, the number of articles, reviews, and book chapters has declined. Overall, the annual growth rate in the number of publications stands at 9.5%.

3.1.3. Prolific Information Sources

A total of 1165 source titles have published articles on the use of IoT and big data in preventive healthcare. According to Bradford’s law, 108 of these source titles fall within the core zone, based on their productivity. The substantial number of core zone journals suggests that the research area is still maturing, and archival journals dedicated specifically to this field have yet to be firmly established. The 10 most productive source titles are listed in Table 1.
The H-index of the source titles mentioned above ranges from 26 to 197, and the Scopus Journal Rank (SJR) falls between 0.15 and 2.02. This places most of these source titles within the Q1 category, reflecting the high quality of these prolific information sources. Interestingly, the five most cited papers (Table 2) were not published in the most productive source titles, with the exception of the most cited paper, which appeared in a Q1 journal with an SJR higher than that of the most productive source titles. Nevertheless, all five journals are part of the core zone journals.

3.1.4. Geographical Distribution of Research

The most productive countries, each with over 100 publications, were the United States (n = 477), followed by India (n = 445), China (n = 388), the United Kingdom (n = 179), South Korea (n = 131), Germany (n = 114), and Italy (n = 109). This ranking closely aligns with the Scimago Country Rankings (Elsevier, Amsterdam, The Netherlands), in which China, the United States, and India occupy the top three positions in the field of computer science, and the United States, China, and the United Kingdom lead in the field of medicine. Notably, all of these top-producing countries are also among the 11 most productive in both categories and are members of the G20 [15]. The United States, China, and the United Kingdom also prevail among the most productive institutions; among the first 20, 19 are from those three countries.
The most cooperative countries (Figure 2) are the United States, Canada, the United Kingdom, Australia, and Germany, with 28, 27, 26, 26, and 24 co-author-based cooperation links, respectively. The strongest collaborations are observed between the United States and Canada; Australia and the United Kingdom; the United Kingdom, Germany, the Netherlands, and Sweden; and India, the United States, and Malaysia. This pattern of cooperation reflects a regional and cultural concentration of research. To enhance the global impact of preventive healthcare, it is important to overcome these regional limitations. Expanding collaboration to include less developed regions would allow regional researchers to contribute to global knowledge, share unique datasets, and leverage specialized equipment, regional insights, and distinct research environments.
The United States, Canada, Denmark, and Sweden have, on average, the oldest publications in the field, while Belgium, the Russian Federation, India, Japan, Hong Kong, Malaysia, Pakistan, and Saudi Arabia yield the most recent examples. This suggests that preventive healthcare research has a longer history in the former group of countries, whereas the latter group represents regions where attention to this field has increased more recently.
The eight most productive institutions, each with more than 20 publications, are Harvard Medical School (n = 35), the University of Oxford (n = 26), Peking University (n = 24), the University of Toronto (n = 24), Harvard T.H. Chan School of Public Health (n = 23), Stanford University (n = 22), Brigham and Women’s Hospital (n = 22), and the Chinese Center for Disease Control and Prevention (n = 22).

3.1.5. Most Prolific Funding Bodies

Another important indicator of the research state within a scientific field or sub-field is the level of research funding [16]. Our analysis revealed that 37.0% of the publications were funded, which is significantly higher than the results for many other fields, although lower than the number in comparable subfields, such as the use of AI in pediatrics, in which 47.4% [17], and for software engineering, in which 41.1% [18] of publications were funded. The most productive funding sponsors, each supporting more than 20 publications, include the National Natural Science Foundation of China (n = 96), the National Institutes of Health, USA (n = 85), the Horizon 2020 Framework Program, EU (n = 28), the National Key Research and Development Program of China (n = 28), the National Research Foundation of Korea (n = 23), and the National Science Foundation, USA (n = 22). As expected, the majority of these top funding sponsors are from China and the United States, which aligns with the significant investment these countries make in preventive healthcare. In particular, the USA is known for its substantial spending in this area, while China has recently undertaken a comprehensive health system reform, supported by strong political and financial backing [19].

3.2. Most Prolific Research Themes

Content analysis was conducted using SKS Steps 3 and 4, along with VOSViewer software, version 1.6.20 (Leiden University, Leiden, The Netherlands). In accordance with Zipf’s law, 79 author keywords were deemed sufficiently relevant for inclusion in both SKS and content analysis, meaning that all keywords occurring ten or more times were analyzed. The author’s keyword landscape is depicted in Figure 3, and the synthesized results are presented in Table 3. The content analysis identified eight themes—four from a computer science perspective and four from a medical perspective—along with 21 concepts, comprising 8 from the computer science viewpoint and 13 from the medical viewpoint.

3.2.1. Literature Review Based on Induced Themes and Categories

A more detailed description of the categories structured by themes, as seen in Table 3, in terms of the most influential articles from each category area is presented below.
The role of artificial intelligence in personal, precision, and preventive healthcare/the role of AI in personalized medicine (genetics, genomics) in the field of the most common diseases of the modern population (cardiovascular diseases, dementia, obesity, asthma, SARS-CoV-2, cancer) is evaluated, as follows:
○
The use of artificial intelligence and Omics in personalized and precision medicine according to health policies: Precision, preventive, and personalized (3PM) medicine, in combination with omics, environmental data, and big data analytics, is an emerging approach in modern public health, with vast implications for future healthcare, as well as for future health care policy formulation [20]. The concept of 3PM emerged in response to epidemics of non-communicable diseases and emerging cases of suboptimal, but still reversible, health conditions like sleep disorders [21], kidney injury, and diabetes [22].
○
The use of machine learning in risk prediction: Big data and machine learning have been used to predict the risks of various diseases like stroke [23], coronary artery diseases [24,25], diabetes [26], COVID-19 [27], breast cancer [28], and suicide [29], and they are also used for risk prediction in occupational medicine.
○
The role of personalized medicine in chronic disease management: The integration of AI and big data with mobile health has significantly increased, demonstrating considerable potential to assist individuals and healthcare professionals in managing and preventing chronic diseases within a person-centered paradigm [30,31].
○
The use of AI in the genetics and genomics of cardiovascular diseases, cancer, dementia, obesity, and asthma: Pathogenetic processes are most often the result of interactions between various environmental and genetic factors. The use of AI, based on available biological and clinical datasets, can contribute to greater accuracy in predicting the risk of developing the most common chronic diseases in a given person [32]. In addition, the above combination of technologies is also widely used to aid in diagnosing and prognosticating diseases, optimizing treatment, and in developing new drugs [33]. In the pathophysiology of the diseases listed above, AI relies mainly on the emerging fields of molecular biology (genomics, glycomics, proteomics, lipidomics, and transcriptomics) [34].
○
Investigating an individual’s risk for the most common chronic diseases: AI plays a crucial role in assessing an individual’s risk of developing chronic diseases by analyzing the complex network formed by the physical environment, human factors, technological devices, and healthcare quality. Studies have shown that AI is a promising tool for enhancing patient safety, identifying and analyzing disease risk, and detecting errors in clinical settings. However, it is important to note that AI still requires human supervision and cannot fully replace the expertise of clinical staff [19]. The strength of AI in risk identification lies in its ability to accurately and efficiently process vast amounts of data [35]. Additionally, AI serves as a vital tool for improving communication with patients and supports various healthcare applications [36].
○
Use of AI in SARS-CoV-2 management: Digital technologies leveraging smartphone sensors have been extensively deployed to support the response to COVID-19. These efforts have focused on collaboration among big data analysts, telecommunication systems, and public health authorities [21], with objectives including promoting healthy lifestyles among the elderly [37], managing COVID-19 diagnosis [38] and vaccination [23], and enhancing surveillance of zoonotic diseases [24].
The role of big data in public health/the role of big data and databases in public health, especially in the field of prevention, epidemiology, and surveillance, are assessed as follows:
○
Big data mining of social media and electronic health records in epidemiology, predictive analysis, and prevention: The mining of big data from real-world sources [25,26,27] has become increasingly important in predictive epidemiology. This approach is used to manage epidemics [28], control urban epidemiology [39], and predict conditions such as hospital-induced delirium [40].
○
Big data analysis in public health surveillance: Digital epidemiology emerged as a novel discipline that employs big data analytics and IoT to enhance traditional surveillance methods [41]. In addition to supporting the management of COVID-19, digital epidemiology has also been used in response to infectious diseases in Bangladesh [42], as well as in urban epidemiology control [39], influenza trend surveillance [43], and zoonotic disease response [24].
○
Use of big data and databases in public health: AI has become increasingly important in public health, particularly in detecting diseases at early stages, interpreting disease progression, optimizing treatment regimens, and researching new intervention strategies [42]. Big data analysis in public health involves the collection, processing, and analysis of large-scale datasets from diverse sources, including electronic health records, social media, and portable devices. These data provide valuable insights into disease patterns, risk factors, healthcare, and population health trends [43]. Additionally, big data analysis [44] enables real-time monitoring of disease incidence, spread, and transmission patterns [45]. Analyzing data from social media and mobile health applications also sheds light on health-related behaviors and attitudes within the population. Understanding these behaviors allows policymakers to design more effective, targeted health promotion campaigns [25,46].
○
Use of databases in epidemiology: Medical databases analyzed by AI algorithms play a crucial role in diagnosing and treating diseases, particularly during pandemics, as they facilitate easier disease control. These databases are vital for epidemiology, as they enable the rapid management of infectious diseases, support the implementation and assessment of trends, trace the sources of infection and treatment, and assist in the development of vaccines and drugs [47]. This is especially important because understanding the epidemiological landscape is essential for studying the distribution, pathogenesis, and spread of diseases [48]. Furthermore, these databases allow for the identification of demographic, environmental, genetic, and behavioral risk factors, aiding in the development of predictive models to assess an individual’s likelihood of developing a disease [49,50].
○
Planning and researching prevention and survival in COVID-19: AI significantly enhanced disease control and prevention during the COVID-19 pandemic [47] by utilizing both passive (existing epidemiological data) and active surveillance (targeted search for specific information on the disease) [51]. The information gathered through these surveillance methods improved the efficiency and effectiveness of health services [52].
The role of IoT, cloud computing, deep learning, and blockchain in secure and safe healthcare/the role of IoT and deep learning in the security and privacy of healthcare
  • IoT, cloud computing, deep learning, and blockchain in secure and safe healthcare: Blockchain, mobile health, the Internet of things, and other recent ICT technologies have been used to determine safe COVID-19 vaccination strategies, to ensure the safe management of vaccination, to deliver safe and transparent vaccination certificates, and to provide postvaccination surveillance [23,53]. In this manner, the above technologies supported the development of safe, dependable, and efficient Healthcare 4.0 applications [54].
  • Application of deep learning and IoT in healthcare: The primary task of IOT in healthcare is to make patients’ lives easier by monitoring their health status. This facilitates the decision making of attending physicians [55]. IoT offers a wide range of applications in healthcare, including remote monitoring of the patient’s health status, tracking of patient treatments, and administration of medication to patients [56,57]. In addition, IoT represents an important area for enabling progress in healthcare delivery in nursing homes [58]. Additionally, IoT exhibits great potential for improving the quality of health services and reducing costs based on the early detection and prevention of diseases [59,60].
  • Security and privacy of IoT and deep learning: IOT-based deep learning is essential in bio- and medical informatics and medical applications in medicine, as it enables the analysis and interpretation of large amounts of complex and diverse data that humans cannot process without the help of technology. The ability to perform such analyses in real time can further increase the efficiency of healthcare systems. Deep learning applications using IOT include diagnostics, treatment recommendations, clinical decision support, and new drug discovery [61] and can also be used in disease self-management and remote patient health monitoring [62,63]. However, this immense analytic power also has its dangers; thus, solving security and privacy issues is of utmost importance [55,62]
  • Sensitivity of the sensors for the acquisition of IoT: IoT can introduce new services and solutions in various healthcare applications [45,64]. This is result is possible through smart sensors that can assess the population’s health. These have gradually emerged in public health as multiplexed biosensors and data acquisition systems with flexible substrate and body attachments for improved wearability, portability, and reliability. These sensors offer the potential for the early detection, diagnosis, and management of diseases. They enable the real-time assessment of abnormal conditions of physical or chemical components in the human body [65].
  • Importance of sensors for deep learning: IoT and wireless sensor networks (WSN) [66] can collect and feed crucial patient health data to deep learning algorithms, enabling the continuous monitoring of patients’ health status. These sensors include sensors for blood pressure, pulse, oxygen level, airflow, patient position, muscle and heart activity [67], breathing patterns, and glucose levels [66]. This technology allows for the remote monitoring of patients in medical institutions, as well as in their home environments, thereby improving the quality of medical care and reducing costs [67]. In medical applications, sensors as part of deep learning have shown their importance in recognizing and assessing diseases (epilepsy, dementia, autism, stroke, depression, sudden cardiac arrest, and even Parkinson’s disease [68].
The role of digital health in monitoring and telemedicine/the role of ethics in telemedicine and digital health:
○
Mobile health and wearable devices in monitoring mental health: The concept of intelligent health (iHealth) in mental healthcare integrates AI and big data analytics [69]. It has been introduced in various areas, including community mental health services [70], preventive mental healthcare [71], student mental healthcare prediction [72], and the management of mental well-being [73].
○
Digital health use in telemedicine: COVID-19 has significantly transformed the global healthcare infrastructure, accelerating the shift toward digital healthcare. This transformation encompasses various technologies, including AI, big data, telemedicine, robotics, IoMT, federated learning, computer vision and audition, blockchain, cloud and fog computing, and other ICT innovations [30,74,75]. Recently, IoT and big data analytics have further enhanced telemedicine, and they have been applied in managing chronic obstructive pulmonary disease, in sleep medicine [21], in transgender healthcare services, and in monitoring cardiac arrhythmia [76].
○
Ethical aspects of digital health and telemedicine: The digitization of healthcare is a global phenomenon that permeates professional and private life [77]. It comprises three levels of e-health services: 1. general online services (provide advice, information, and guidance on health and social services), 2. various ordering services in social and health care (tracking personal data), and 3. digitized services (various video conferencing and remote services in education, diagnosis and provision of medical care). Telemedicine and e-health are the main e-environments in digitized healthcare [78], and according to Kaplan [79] is an ongoing natural experiment, which also brings with it questions of legal and ethical aspects, such as the issue of privacy, accuracy, security, responsibility, availability, and transparency of data and patient consent [43,80].
○
Data monitoring for eHealth. The expansion of knowledge and technological advancements has led to increased digitization and automation of data exchange within health systems [81]. E-health technology, combined with AI, has been integrated into existing health information and communication systems, such as electronic health records, bringing numerous benefits, including enhanced privacy, accuracy, security, responsibility, availability, and data transparency [81]. Among these advantages are improved interoperability [82], the potential for data re-use [83], and better decision support [84]. Technological developments in e-health have also enabled healthcare delivery at the patient’s home, shifting away from traditional hospital settings while ensuring secure data collection [85].
○
Ethical aspects of monitoring an individual’s mental health. Social concepts regarding the boundaries between public and private data, as well as medical and non-medical information, are not clearly defined. Recommending the use of digital technology for patients with mental illness can inadvertently cause harm [86]. While e-mental health offers new opportunities in mental healthcare, particularly during pandemic situations, its effectiveness and efficiency must be carefully evaluated before it is integrated into routine mental health services [87]. AI can provide innovative solutions for managing mental health issues and enhancing care quality [88]. However, ethical concerns regarding the use of AI in mental health primarily revolve around data ownership and obtaining informed consent from patients [89].

3.3. Timeline of the Recent Research and Seminal Publications

Among the seminal papers identified by RPYS, the first to be noted is Leibniz’s paper published in 1671, in which he develops ideas about the advancement of medical knowledge through empirical and experimental approaches and discusses public health [90]. The next two influential papers, published nearly 350 years later in 1920 and 1953, focus on public health [91] and the role of observation and experimentation in medicine [92]. The remaining two ground-breaking papers address the analysis of epidemics, with the first introducing their mathematical theory and the second, published in 2019, exploring the use of computer science and informatics in preventive healthcare to detect influenza epidemics [93].
Historically (Figure 4), the application of big data and IoT in preventive healthcare began with more traditional ICT technologies, such as data mining/science, the Internet, Twitter, mobile health, telemedicine, personalized medicine, and genomics, particularly in the management of chronic diseases. In the subsequent phase, cloud and fog computing, data mining/science, natural language processing, and more advanced social media were employed for predictive analytics, disease management, preventive healthcare, and epidemiology. In the most recent period, digital health, advanced sensors, AI, and machine learning have been increasingly utilized in health policy making, epidemic prevention, prognosis, and surveillance.

3.4. Hot Topics

Some interesting hot spots were identified by comparing different time slices in the research landscape. The Internet of medical things (IoMT) is used in occupational safety monitoring, for the rapid diagnosis of viral respiratory infections, and in precision medicine [94]. In combination with federated learning, it is used for handling privacy-sensitive medical data, for analyzing wireless long-tailed data [95], and in smart healthcare [96]. IoT and big data collected from smartphones, in combination with machine learning and AI, are used to improve the management, diagnosis, and treatment of high-prevalence diseases like hypertension [95], dementia and cognitive impairment [97], or sleep disorders [98], as well as in trends analysis [99], injury prevention [100] and in solving privacy [101] and safety issues [102].

3.5. Limitations, Research Gaps, and Challenges for Future Research

As already mentioned above, IoT and big data exhibit great potential to improve the healthcare system and consequently, the many dimensions of patient health. However, to be successful and widely adopted, research regarding ecosystems consisting of IoT, big data, big data analytics, and healthcare must overcome some limitations and resolve several research gaps and open challenges [103,104], some of which are presented below:
  • The heterogeneous nature of equipment and devices involved in the use of big data and IoT in preventive healthcare makes their application problematic [105].
  • Healthcare data is already prone to large security and privacy risks; however, adding IoT and big data significantly increases the risk of information exposure [106].
  • Clinical-grade medical devices require approval and clearance from various supervisory entities, which can present new challenges for the regulatory and legislative bodies [107].
  • In order to reach meaningful and clinically relevant decisions based on data collected from the various IoMT tools, all IoMT devices and big data algorithms must be interoperable, resolving the problems regarding the interoperability and standardization of data [108].
  • IoT and big data software/hardware systems require a high initial investment that might act as a barrier to IoMT [109].
  • Many current health institutions’ networks are neither secure nor robust enough to operate the new IoMT/big data platforms [110].
  • While IoMT/big data is becoming increasingly popular in preventive healthcare, ensuring future growth scalability and broader adoption might be problematic [111].
  • Protecting the confidentiality and privacy of patient data and information [112] is an ongoing concern.
  • Assuring the dependability and accessibility of IoT infrastructure [113] is essential.
  • Assuring the quality, trustworthiness, and accuracy of the data collected by IoT devices and stored in big data databases and big data analytics algorithms [114] must be addressed.
  • The education and training of health professionals and patients are required for interpreting and understanding the use and outputs of big data analytics.
  • Resolving ethical issues arising from using patients’ data, such as patient consent, preventing data misuse, and assuring the ethical relevance of AI-generated diagnoses and treatment suggestions [91] is essential.
To solve the above challenges, the future of big data analytics and IoT research should focus on the deeper integration of the advanced technologies described above, advancing big data analytics through cognitive computing, enabling predictive analytics in real time using fog computing, improving cybersecurity with blockchain and related technologies focusing on patient consent management, as well as through the use of implacable wireless medical devices, big data fusion, and digital biomarkers.

3.6. Study Strengths and Limitations

This article exhibits particular strengths and limitations. The first strength is that the article is based on the Scopus database—one of the largest databases of peer-reviewed literature, offering access to literature published over a more extended period of time, as well as comprising PubMed publications. At the same time, the article combines the computer and medical aspects of IoT and big data as applied in preventive healthcare, with its findings significantly contributing to the application of this knowledge in practice. Another strength of the article is the use of SKS, which is considered a proven knowledge synthesis method, and which offers a holistic view of knowledge development’s thematic, spatial, and historical aspects.
On the other hand, the article also displays certain limitations, of which we must mention the qualitative and consequently subjective interpretation of the results obtained using SKS. One of the limitations is that only the Scopus database was used in the SKS, so there is a possibility that part of the literature, especially those studies published in various preprint services, was not considered when defining themes and categories. However, all available research literature in available bibliographic databases was searched in preparing the literature review presented in Section 3.2.1. Other databases will be included in future work.

4. Conclusions

Our study offers a holistic and interdisciplinary synthesis of the research literature regarding the use of IoT and big data in preventive healthcare. This field has seen rapid and robust development in recent years. Big data health databases, particularly those comprised of electronic health records, provide extensive data on patients and diseases, forming a critical foundation for health policymakers. This is especially true in epidemiology, where the monitoring and prediction of infectious diseases demand fast, reliable, and concrete decisions based on information stored in various health databases and on social media, along with other evidence that can be supported by deep learning and IoT. Medical sensors and wearable devices, key components of IoT, enable the continuous remote monitoring of patient functions and treatments. As demonstrated in our paper, IoT, big data, and machine learning—under the broader umbrella of artificial intelligence—can significantly support decision making in the fields of personalized and precision medicine, risk prediction for both population-level and individual patient treatments, genomics and other omics, chronic disease management, epidemic prediction and management, and public health surveillance and monitoring. From an information technology perspective, the Internet of medical things, cloud computing, fog computing, and deep learning comprise the leading technologies in this area. Additionally, it is important to note that a significant amount of research also addresses the security, privacy, and ethical challenges associated with the use of advanced information technologies in healthcare.

Author Contributions

Conceptualization, U.Š. and P.K.; methodology, U.Š., P.K., B.Ž. and H.B.V.; software, P.K.; validation, H.B.V., J.Z. and U.Š.; formal analysis, P.K. and B.Ž.; investigation, U.Š. and H.B.V.; resources, J.Z.; data curation, U.Š.; writing—original draft preparation, U.Š., P.K., H.B.V., B.Ž. and J.Z.; writing—review and editing, all authors; visualization, P.K.; supervision, H.B.V. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. The dynamics of research literature production.
Figure 1. The dynamics of research literature production.
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Figure 2. Country cooperation network.
Figure 2. Country cooperation network.
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Figure 3. Author keywords landscape, including author keywords occurring ten or more times. Each colored cluster presents a theme.
Figure 3. Author keywords landscape, including author keywords occurring ten or more times. Each colored cluster presents a theme.
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Figure 4. Timeline keywords landscape, including authors’ keywords occurring ten or more times.
Figure 4. Timeline keywords landscape, including authors’ keywords occurring ten or more times.
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Table 1. Most productive source titles.
Table 1. Most productive source titles.
Source TitleNumber of PublicationsH-INDEX in ScopusScopus SJRQuarter
Eai Springer Innovations in Communication and Computing55260.15Q4
International Journal of Environmental Research and Public Health541980.81Q2
Journal of Medical Internet Research361972.02Q1
Studies in Health Technology and Informatics28670.29Q3
Frontiers in Public Health271010.90Q1
Journal of Healthcare Engineering24570.51Q2
Safety Science201541.28Q1
Accident Analysis and Prevention171881.90Q1
BMC Public Health121971.25Q1
BMJ Open121600.97Q1
Table 2. Most cited papers.
Table 2. Most cited papers.
AuthorsTitlePublication YearSource TitleCited bySJR 2023Core Journal
Tomczak, K. et al.The Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge2015Wspolczesna Onkologia14520.532 (Q2)Yes
Peeri, N.C. et al.The SARS, MERS, and Novel Coronavirus (COVID-19) Epidemics are the Newest and Biggest Global Health Threats. What Lessons Have We Learned?2021International Journal of Epidemiology9872.663 (Q1)Yes
Vaishya, R. et al.Artificial Intelligence (AI) Applications for the COVID-19 Pandemic2020Diabetes and Metabolic Syndrome: Clinical Research and Reviews9271.313 (Q1)Yes
Dimitrov, D.V.Medical Internet of Things and Big Data in Healthcare2016Healthcare Informatics Research6451.628 Q1)Yes
Brisimi, T.S. et al.Federated Learning of Predictive Models from Federated Electronic Health Records2018International Journal of Medical Informatics5521493 (Q1)Yes
Table 3. The concepts and themes including IoT and big data.
Table 3. The concepts and themes including IoT and big data.
Cluster Color (Number of Keywords)Representative Author Keywords (ICT Viewpoint in Upper Cell/Medical Viewpoint in Lower Cell)Concepts
(ICT Viewpoint in Upper Cell/Medical Viewpoint in Lower Cell)		Theme
(ICT Viewpoint in Upper Cell/Medical Viewpoint in Lower Cell)
Red (n = 26)Artificial intelligence (n = 206), machine learning (n = 205), precision medicine (n = 64), personalized medicine (n = 32), risk prediction (n = 31), health policy (n = 17).
-
Use of artificial intelligence and omics in personalized and precision medicine, according to health policies;
-
the use of machine learning in risk prediction;
-
the role of personalized medicine in chronic disease management.
The role of artificial intelligence in personal, precision, and preventive healthcare.
Artificial intelligence (n = 206), personalized medicine (n = 32), SARS-CoV-2 (n = 23), cardiovascular diseases (n = 20), genetics (n = 17), genomics (n = 19), obesity (n = 15), asthma (n = 15), cancer (n = 12), dementia (n = 11).
-
Use of AI in the genetics and genomics of cardiovascular diseases, cancer, dementia, obesity, asthma.
-
Investigating an individual’s risk for the most common chronic diseases.
-
Use of AI in SARS-CoV-2 management.
The role of AI in personalized medicine (genetics, genomics) in the most common diseases of the modern population (cardiovascular diseases, dementia, obesity, asthma, SARS-CoV-2, cancer).
Green (n = 20)Big data (n = 494), COVID-19 (n = 153), prevention (n = 52), social media (n = 33), public health (n = 43), predictive analytics (n = 33), epidemiology (n = 32).
-
Big data mining of social media and electronic health records used in epidemiology, predictive analysis, and prevention;
-
big data analysis in public health surveillance.
The role of big data in public health.
Big data (n = 494), COVID-19 (n = 153), prevention (n = 52), public health (n = 43), surveillance (n = 29).
-
Use of big data and databases in the field of public health;
-
use of databases in epidemiology;
-
planning and researching prevention and survival in COVID-19.
The role of big data and databases in public health, especially in the fields of prevention, epidemiology, and surveillance.
Blue (n = 14)IoT (n = 439), deep learning (n = 83), healthcare (n = 63), cloud computing (n = 49), blockchain (n = 48).
-
Use of IoT, cloud computing and deep learning, and blockchain in secure and safe healthcare.
The role of IoT, cloud computing, deep learning, and blockchain in secure and safe healthcare.
IoT (n = 439), deep learning (n = 83), healthcare (n = 63), security (n = 49), sensors (n = 38), privacy (n = 24).
-
Application of deep learning and IoT in healthcare;
-
security and privacy of IoT and deep learning;
-
sensitivity of the sensors for the acquisition of IoT;
-
importance of sensors for deep learning.
The role of IoT and deep learning in the security and privacy of health care.
Yellow (n = 13)Digital health (n = 39), telemedicine (n = 39), mobile health (n = 30), monitoring (n = 17), suicide (n = 16).
-
Mobile health and wearable devices in monitoring mental health;
-
digital health use in telemedicine.
The role of digital health in monitoring and telemedicine.
Telemedicine (n = 39), digital health (n = 39), monitoring (n = 26), mental health (n = 15), eHealth (n = 14), ethics (n = 12).
-
Ethical aspects of digital health and telemedicine;
-
data monitoring for eHealth;
-
ethical aspects of monitoring an individual’s mental health.
The role of ethics in telemedicine and digital health.
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Šajnović, U.; Vošner, H.B.; Završnik, J.; Žlahtič, B.; Kokol, P. Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review. Electronics 2024, 13, 3642. https://doi.org/10.3390/electronics13183642

AMA Style

Šajnović U, Vošner HB, Završnik J, Žlahtič B, Kokol P. Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review. Electronics. 2024; 13(18):3642. https://doi.org/10.3390/electronics13183642

Chicago/Turabian Style

Šajnović, Urška, Helena Blažun Vošner, Jernej Završnik, Bojan Žlahtič, and Peter Kokol. 2024. "Internet of Things and Big Data Analytics in Preventive Healthcare: A Synthetic Review" Electronics 13, no. 18: 3642. https://doi.org/10.3390/electronics13183642

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