The Impact of Big Data Analytics on Health Care: A Systematic Review

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The Impact of Big Data Analytics on Health Care: A Systematic Review

https://doi.org/10.21203/rs.3.rs-4995748/v1

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The extension of Big Data analytics to healthcare has radically altered how healthcare-related data is managed and used, presenting unequalled chances of augmenting patient experiences, boosting operational effectiveness, and customising treatment regimens. The prospective advantages of Big Data in medical fields have been ameliorated to a larger extent due to recent technological breakthroughs. Regardless of the noteworthy progress, an in-depth comprehension of the exact manner in which Big Data analytics improves numerous healthcare applications is lacking. The abrupt advancement of these technological innovations and their utilisation in the healthcare sector demands a revised amalgamation of the existing research patterns, approaches, and outcomes. In order to answer six particular research questions on Big Data analytics in healthcare, this comprehensive analysis examined 127 research articles that were released between 2015 and 2024. The evaluation used a methodically organised approach that included determining the parameters for inclusion and exclusion, data sources, search tactics, quality evaluation, and data coding and analysis. To facilitate a thorough and honest review procedure, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were applied. According to the research, the fields with the highest representation in the literary works include sleep apnoea monitoring, digital health records, and BDA healthcare applications and platforms. The machine learning algorithms that are most commonly used are supervised learning approaches, including Linear Regression and Support Vector Machines. A geographic study showed that China, India, and the United States have made substantial contributions. The temporal study revealed a substantial spike in research endeavours between 2020 and 2023, indicating heightened interest in the fusion of Big Data with the healthcare industry. The year 2024 witnessed an abrupt reduction in publications, indicating either a level of saturation or a shift in the focus of the study. The results highlight how Big Data analytics may redefine healthcare by improving operational effectiveness, individualised treatment regimens, and diagnostic accuracy. The report also emphasises the significance of having strong ethical standards and legal frameworks in place in order to cope with data security and privacy issues. Future studies should concentrate on investigating upcoming technologies, multidisciplinary approaches, flawless integration with current systems, and the lasting effects of these technologies. Furthermore, encouraging international cooperation can improve the exchange of resources and ideal practices, expanding the scope of Big Data healthcare analytics globally.

Big Data analytics

Healthcare

Machine Learning

Systematic Literature Review

The use of Big Data analytics in the healthcare industry over the past ten years has completely altered how medical data is consumed and analysed (Ogundipe, 2024). As the healthcare industry continues to produce immense data (S. Kumari, 2018), encompassing digital health records (EHRs) (Yadav, Steinbach, Kumar, & Simon, 2018), genomic sequencing information (Church & Gilbert, 1984), and outputs from wearable and implantable gadgets (Wise, 2009), the ability to use this information for substantial amelioration of patient care has risen exponentially. This revolutionary potential is not just present as a theory but is also demonstrated by significant improvements in patient outcomes, operational effectiveness, and personalisation of treatment regimens.

Newly emerging Big Data innovations have made it possible to aggregate and analyse diverse medical data at an exceptional pace and scale (Banerjee, Chakraborty, Kumar, & Biswas, 2020). According to (Adeniyi, Arowoogun, Chidi, Okolo, & Babawarun, 2024), electronic health records (EHRs) lend a thorough record of patient communications with healthcare facilities, providing information regarding patient histories, treatment results, and health patterns across time. With the use of these gigantic databases, the clinical conditions of patients may be understood more deeply, enabling proactive intervention, better disease administration, and precise prognosis predictions.

Furthermore, the introduction of Big Data analytics to the healthcare domain has played a pivotal role in optimising operational effectiveness. For instance, predictive analytics (Wang & Hajli, 2017) forecasts future occurrences using past and present data, allowing healthcare institutions to better allocate resources, shorten wait times, and enhance service quality (Shi, 2023). Reduction in delays and the most optimal utilisation of existing resources enable these efficiencies to decrease healthcare expenses and elevate patient care quality.

Precision medicine, often known as personalised medicine, is one of the most exciting advantages of applying Big Data in the medical field (Cirillo & Valencia, 2019; Elemento, 2020). Healthcare professionals may determine the best courses of action for each patient by scrutinising trends of voluminous datasets, taking into account their distinctive genetic composition, lifestyle, and surroundings (Ogundipe, 2024; Yoo et al., 2012). This strategy markedly juxtaposes the 'one-size-fits-all' approach, providing a customised treatment plan that is incredibly successful in addressing and treating illnesses.

The goal of this comprehensive evaluation includes consolidating existing knowledge and highlighting significant fields of study in Big Data analytics for the healthcare industry. This analysis delves into 127 research publications that were issued during 2015-2024 in order to discover the major trends, approaches, and technologies that have influenced the evolution of Big Data in the healthcare industry. Based on a retrospective assessment of the chosen publications, this review tackles certain specific research queries mentioned below to outline the significance and extent of Big Data analytics in healthcare:

1. What are the main research goals explored in the surveyed articles?

2. Which nations provide the majority of the contributions to this field?

3. What kinds of machine learning technology are usually employed?

4. What machine learning methods are mostly applied in these studies?

5. Which academic fields are prominently featured in the examined literature?

6. In what ways have the practices around the use of Big Data analytics in healthcare changed over time?

This analytical investigation offers an exhaustive review of Big Data applications in healthcare, which benefits the academic and medical domains. It points out areas that demand further research, highlights research gaps, and emphasises the significance of multidisciplinary association in exploiting the power of Big Data analytics in its entirety. It is anticipated that this study's outcomes will inspire academicians, legislators, and healthcare professionals to enhance the application of Big Data technology to attain superior patient care and healthcare operations. The findings of the analysis also demonstrate the indispensable role of machine learning in converting medical data into usable information, thereby shifting the discipline closer to more personalised, preventative, and predictive medicine. This paper lays the foundation for future research by outlining the scope of Big Data analytics in the healthcare sector.

The application of Big Data analytics has revolutionised the healthcare domain via the incorporation and examination of a wide range of diverse data sources (Mukarambi, 2024; Singhal & Sharma, n.d.). (Raghupathi & Raghupathi, 2014) deliberate the transformative potential of Big Data in the healthcare sector for augmented patient care by employing predictive analytics and offering customised treatment regimens to the patients. In a similar vein, (Murdoch & Detsky, 2013) underscore the impending implementation of Big Data in healthcare, projecting significant gains in effectiveness and accomplishments of incorporating data analytics into healthcare processes to a greater extent.

Big Data has exacerbated the operational effectiveness of the healthcare sector. It enables the identification of high-risk and high-cost patients, thereby driving the optimal allocation of resources and delivery of enhanced patient care (Bates, Saria, Ohno-Machado, Shah, & Escobar, 2014). (Krumholz, 2014) articulates the vitality of Big Data in building a healthcare system that learns continually by analysing care procedures and their results.

The emergence of Big Data has significantly expedited the transition to personalised medicine. (Schork, 2015) explains the use of data-driven methods to increase the effectiveness of interventions by customising medical therapies to each patient's unique genetic composition. In concurrence with the above perspective, (Joyner & Paneth, 2019) claim that Big Data-enabled precision medicine signifies a fundamental change in the way medical therapies are created and delivered.

Among the multiple innovative uses of Big Data in healthcare, predictive analytics has been particularly noteworthy. The use of machine learning algorithms to forecast the effects on patients is dealt with in this study (Siddiq, 2022), which may facilitate proactive treatments and more effective disease management techniques. These technologies, which are both predictive as well as prescriptive, furnish prospective treatment pathways to be embraced by healthcare practitioners.

Regardless of the technological progress, the implementation of Big Data in healthcare poses considerable difficulties and ethical dilemmas. (Mittelstadt & Floridi, 2016) study revolves around the ethical concerns linked with threats to privacy and the potential for data breaches, representing significant obstacles in the application of Big Data analytics. The technological issues associated with data integration and the requirement for strong data governance systems have been addressed in this study (Wiens et al., 2019) to ensure the precision and accessibility of healthcare data.

Although abundant studies have focused on the advantages and uses of Big Data in healthcare, there is still a dearth of investigations concerning the long-lasting impacts of Big Data analytics on healthcare expenses and the confidentiality of patients. Furthermore, the existing research fails to adequately understand or confront the ethical quandaries of data usage, especially with regard to permission and transparency. This disparity suggests that additional in-depth investigation is required that carefully looks at the long-term effects of Big Data analytics in the healthcare industry in addition to examining its possible advantages.

The systematic review was carried out using a methodically outlined approach that included many separate steps, as explained in the following subsections and in the extensive instructions provided by Kitchenham & Charters (2007). During this procedure, inclusion and exclusion criteria were determined, data sources were chosen, search techniques were developed, the quality of the data was evaluated, and the gathered data was coded and analysed.

The research embraced the systematic review methodologies suggested by (Moher, Liberati, Tetzlaff, & Altman, 2009a) for improving the organisation and performance of the systematic literature review (SLR). Implementation of a review procedure was the first step in this improved SLR approach, which was subsequently followed by methodical review preparation, execution, and evaluation. Specifically, the method of searching was chosen, the studies' quality was assessed, important research works were chosen for inclusion, the data was synthesised, the review process was documented, the data was retrieved, and the results were validated to guarantee the credibility of the results.

Additionally, developing research questions represented the critical first stage of the SLR process since it constituted the fundamental reference basis for the whole study. The six essential steps of the review process are depicted in Fig. 1. Creating a search plan to find firsthand information was integral to this process due to the necessity of identifying the search words and criteria. Following the completion of these early processes, the preliminary study outcomes were matched with the SLR's wider goals, guaranteeing a coherent and thorough investigation.

3.1 Inclusion/Exclusion Criteria

The review study will be centred on the assessment of papers that comply with the inclusion and exclusion criteria outlined in Table 1. In order to strengthen the overall profundity and authenticity of our results, only the most pertinent and superior research endeavours will be included in our meticulous analysis.

Table 1

Inclusion and exclusion criteria
Inclusion Criteria	Exclusion Criteria
Studies that specifically investigate the impact of Big Data analytics on health care outcomes, processes, efficiency, patient satisfaction, or cost-effectiveness.	Opinion pieces, editorials, commentaries, and non-systematic literature reviews should be excluded as they do not provide empirical evidence.
Include primary research articles such as randomised controlled trials (RCTs), cohort studies, case-control studies, and cross-sectional studies. Systematic reviews and meta-analyses can also be included for an overview of existing evidence.	Exclude studies focusing on Big Data applications outside of healthcare or those that do not directly address the impact of Big Data analytics in healthcare settings.
To ensure the review covers the most current understandings, include studies published within the last 5–10 years. Adjust this range based on the rapidity of developments in this field.	Studies with incomplete data, preliminary findings (e.g., abstracts without full reports), or those that lack clear methodology should be excluded to maintain the quality of the review.
Studies that detail the use of specific Big Data analytics techniques (e.g., machine learning, predictive analytics, data mining) and their application in healthcare settings.	Exclude studies that are duplicate publications or subsets of larger studies to avoid over-representation of data.
Studies must include clear outcome measures related to health care, such as improvements in diagnostic accuracy, treatment efficacy, patient outcomes, cost reductions, or workflow efficiency.	Exclude studies that focus solely on animal models unless they provide direct insight into human health care applications of Big Data analytics.
Studies published in English or for which English translations are available.	If your review has a specific geographical focus, exclude studies from regions outside of this focus unless their findings are broadly applicable.

3.2 Data sources and search strategies

In order to find pertinent subject matter, the article selection procedure for this comprehensive review began in October 2023 by making use of many prestigious scholarly databases, such as ACM Digital Library, Emerald, IEEE Xplore, SAGE Pub, ScienceDirect, Springer, Taylor & Francis Online, and Wiley Online Library. A list of keywords presented in Tables 2 and 3 served as an index for finding relevant research publications; these keywords are necessary to obtain pertinent academic papers, as mentioned in the cited sources (Costa & Monteiro, 2016). Figure 2 demonstrates the outcome of a preliminary search with these keywords that produced 318 articles. Out of these, 4 were removed from the list to avoid duplication, thereby rendering 314 articles for subsequent. Subsequently, every article was carefully examined in light of preset inclusion and exclusion parameters. In the end, 127 publications met the inclusion requirements and were chosen for in-depth examination throughout the review. The screening, eligibility scrutiny, and inclusion process were methodically recorded in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) standards (Moher, Liberati, Tetzlaff, & Altman, 2009b) demonstrated in the PRISMA flowchart presented in Fig. 3. This methodical technique led to a thorough and open selection process, which is essential to the credibility and robustness of the systematic evaluation.

Table 2

Keyword search
Core Keywords	Synonyms
Big Data Analytics	"Big Data," "data analytics," "data science," "predictive analytics," "machine learning," "AI in healthcare," "data mining".
Health Care	"healthcare," "health care," "medical care," "clinical care," "patient care".
Impact/Outcomes	"impact," "effect," "outcome," "efficiency," "improvement," "optimisation," "cost reduction," "patient satisfaction," "diagnostic accuracy," "treatment efficacy".

Table 3

Boolean operators and the search string
Keywords
("Big Data" OR "data analytics" OR "data science" OR "predictive analytics" OR "machine learning" OR "AI in healthcare" OR "data mining") AND ("healthcare" OR "health care" OR "medical care" OR "clinical care" OR "patient care") AND ("impact" OR "effect" OR "outcome" OR "efficiency" OR "improvement" OR "optimisation" OR "cost reduction" OR "patient satisfaction" OR "diagnostic accuracy" OR "treatment efficacy")

3.3 Data coding and analysis

The following elements were coded to ensure reliable research methodology: (a) publication year; (b) prominent research theme in the field of Big Data analytics in healthcare; (c) particular research area (e.g., asthma, digestive medicine, electronic health records); (d) study location; (e) source database (e.g., Springer, ScienceDirect, IEEE); (f) machine learning category employed (Supervised, Unsupervised, Reinforcement); and (g) machine learning algorithms applied (e.g., SVM, Naive Bayes, CNN).

The current systematic analysis examined 127 academic publications on Big Data analytics in healthcare published from 2015 to 2024 in order to respond to 6 particular research questions. The goal of this comprehensive analysis was to synthesise a comprehensive array of observations and conclusions from over 10 years of study in this rapidly evolving field. By analysing these papers, this investigation seeks to identify significant trends, approaches, and effects of Big Data technology in the healthcare industry. Assessing the impact of Big Data analytics on patient care, healthcare operations, and medical research is essential for laying the basis for future developments in the healthcare sector.

4.1 RQ1: what are the overarching research objectives pursued in the surveyed articles?

Examination of Fig. 4 illustrates that the examined papers' overall objective mostly centres on the use of Big Data analytics in several healthcare fields. The most popular category, "BDA Healthcare Applications and Platforms," includes 19 articles recommending the prioritised creation and utilisation of Big Data instruments and platforms to improve healthcare services. Additional major disciplines that emphasise the use of data analytics in patient record management and health surveillance include "Electronic Health Records" and "Sleep Apnoea Monitoring," which are addressed in 13 and 7 publications, respectively. Furthermore, there is an increasing tendency to implement Big Data to anticipate health outcomes, as evident in the large number of studies that concentrate on predictive analytics in fields like "Diabetes Prediction," "Cardiovascular Diseases Prediction," and "Mental Illness Prediction." This range of goals demonstrates how extensively people are interested in using Big Data technology to enhance patient outcomes, upgrade system efficiency, and offer healthcare more effectively.

4.2 RQ2: from which countries do the primary contributions originate?

There is a notable emphasis on Big Data analytics in the US healthcare settings as evidenced by the 26 publications that make up the key contributions to the research evaluation within this comprehensive review. After the USA, China and India also emerge as prominent contributors, with 21 and 14 papers, respectively. With ten publications apiece, the UK and Europe also contribute significantly, demonstrating the regions' intense involvement and effectiveness in Big Data techniques for healthcare research. Three articles, each from other nations, like Australia, Canada, and Italy, signify their contributions, although to a lesser degree. With substantial involvement and outputs from both Western and non-Western nations, this pattern highlights the enthusiasm towards Big Data healthcare analytics throughout the world and shows a varied geographic dispersion in the research environment (See Fig. 5).

4.3 RQ3: what types of machine learning technologies are most commonly utilised?

The most frequently used machine learning technology in the examined study is supervised learning, featured in 39 publications. With 15 publications, the second most popular genre combines supervised and unsupervised learning, demonstrating a tendency towards using both labelled and unlabelled data to create more sophisticated models. Remarkably, a sizable portion of the publications (64) with the label N/A did not indicate the kind of machine learning employed. More intricate amalgamations of machine learning types, such as supervised with unsupervised and semi-supervised or reinforcement learning, were less commonly observed, each in three or fewer publications. The observed pattern highlights the industry's significant affinity for supervised learning approaches, which can be attributed to their efficacy in areas that make labelled historical data readily available for training predictive models (See Fig. 6).

4.4 RQ4: Which machine learning algorithms are frequently employed in these studies?

The most commonly engaged machine learning methods used in the examined papers include Linear Regression (LR) and Support Vector Machines (SVM), cited in 66 and 31 sources, respectively. The algorithms that come after these include Naive Bayes (NB) and k-Nearest Neighbours (KNN), featured in 29 and 21 articles, respectively. Although less prevalent yet noteworthy, Principal Component Analysis (PCA) and Classification and Regression Trees (CART) methods have been used in 13 and 11 investigations, respectively. These results exhibit an inclination for conventional statistical learning algorithms like LR and SVM, alongside simpler, fundamental methods like NB and KNN, providing both resilience and simplicity for various predictive modelling projects within healthcare analytics (See Fig. 7).

4.5 RQ5: What disciplines are most represented in the surveyed literature?

The studied literature's most featured domains, as seen in Fig. 8, demonstrate a wide range of research priorities in the field of Big Data analytics in healthcare. The most popular field, "BDA Healthcare Applications and Platforms," cited in 19 instances, highlights the expansive investigation of Big Data platforms and technologies aimed at augmenting healthcare offerings. "Electronic Health Records" and "Sleep Apnoea Monitoring" come closely behind, with 10 and 7 references, respectively, signalling an emphasis on enhancing data management systems to enhance patient care as well as targeted studies on certain health issues. Such a broad range of specialisations demonstrates how Big Data is widely applied in numerous domains of healthcare, from overall system enhancements to focused condition monitoring.

4.6. RQ6: How have the trends in the application of Big Data analytics in healthcare evolved over time?

Figure 9 illustrates how Big Data analytics patterns within the healthcare industry have changed with time in a noticeable way. After an inactive year in 2015, there was a noticeable upsurge in interest and research in this field in 2023, witnessing 36 publications. This trend shows how the potential advantages of Big Data analytics are becoming more widely acknowledged in healthcare. The dramatic rise in research outputs between 2020 and 2023 points to a growing interest that is probably being sparked by technological developments and a rise in the use of data-driven healthcare approaches. Nonetheless, the notable decline in 2024 limiting up to 5 publications may be indicative of a saturation point, changes in the focus of the study, or the completion of particular large-scale endeavours. This evolving trend underscores the ever-changing nature of studies in this discipline, influenced by both technological progress and changing academic and corporate inclinations.

Comparable to several other systematic studies on the topic of Big Data analytics in healthcare, this analysis has numerous methods and areas of focus shared with earlier research works. This evaluation strongly reiterates earlier assessments' emphasis on the significance of machine learning approaches, a key theme reinforced in this study. By emphasizing different uses and developments in Big Data technology, this study expands on the fundamental comprehension of the prospects of Big Data in healthcare as indicated by many reviews (Raghupathi & Raghupathi, 2014).

This study differs from previous initiatives in its specific emphasis on current technology developments in the relatively recent and emerging field of Big Data analytics in healthcare. This analysis focuses on the mechanism of enhancement of healthcare technology and techniques due to Big Data analytics, whereas previous evaluations (Bates et al., 2014) concentrating on identifying high-cost patients have examined Big Data applications extensively across many healthcare industries.

Furthermore, the historical patterns examined in this study shed light on the development of technology and the focal areas of research that have not been well covered in earlier studies. As reflected in Fig. 9, the notable surge in articles from 2020 to 2023 indicates a growing emphasis on more recent implementation of Big Data in healthcare, in contrast to previous assessments that were more centred around traditional technologies like predictive analytics and electronic health records.

The conclusions of this systematic study have profound clinical ramifications, especially when it comes to using Big Data analytics to improve patient care. The combination of these cutting-edge technologies can result in more precise diagnoses, individualised treatment regimens, and efficient chronic illness monitoring. The adoption of machine learning algorithms in conjunction with electronic health records (EHRs) might yield predictive data to assist physicians make better decisions, which may lead to lowered rate of medical lapses and enhanced patient outcomes. The operational aspect of this evaluation underscores the capacity of Big Data analytics to optimise resource management and expedite healthcare procedures. The employment of predictive analytics and real-time data processing assists healthcare professionals in enhancing the quality of service delivery, minimising wait times, and optimising scheduling. Consequently, reduced cost and efficient use of healthcare resources facilitate prompt and effective care to the patients. The growing focus on healthcare applications and platforms within academia highlights the sector’s commitment to improved efficiency and effectiveness in healthcare delivery processes.

The present research stresses crucial policy-related and ethical issues. With the growing prevalence of Big Data, it becomes imperative to put in place strong data governance models in order to safeguard patient privacy and guarantee data safety. Challenges linked with the ethical handling of patient data, permission, and data sovereignty need to be tackled by policymakers. Maintaining public trust and ensuring the appropriate implementation of technology in healthcare need constant consideration of the ethical concerns covered in the review, especially with regard to confidentiality and data breaches.

This assessment points forth a number of unmet research needs and potential study fields. The publication of articles discussing Big Data analytics increased significantly between 2020 and 2023, indicating growing interest and promise in this field. However, the steep drop in articles in 2024 suggests a change in study emphasis or a saturation point. It is expected that the upcoming research works to delve into the influences of these technologies on healthcare deliverables over the long run, along with injecting novel ideas in fields like real-time data analytics, inter-operational data systems, and creating easy-to-use platforms for medical professionals. Additionally, the use of Big Data analytics extends to medical education and training as well. The incorporation of simulations and real-time learning in these technologies can improve medical professionals' and students' educational experiences while also sharpening their knowledge and abilities. This, in turn, could produce healthcare professionals who are more competent and skilled in utilising cutting-edge technologies to enhance patient care. The upcoming generation of healthcare workers should be prepared by including training on these technological innovations in the curricula of higher learning institutions.

At last, there are significant financial ramifications to Big Data analytics' integration in the healthcare industry. The healthcare economy as a whole may be greatly impacted by the potential cost reductions from increased operational effectiveness, fewer medical lapses, and optimised resource allocation. Furthermore, by generating new employment opportunities and sectors centred around data analytics and healthcare technology, the advancement and application of these innovations could boost the economy. In addition to immediate financial gains, the widespread implementation of Big Data analytics may spur innovation, bolster the competitiveness of healthcare providers, and create more sustainable healthcare systems.

The revolutionary power and enormous promise of Big Data analytics in the healthcare sector have been highlighted by this comprehensive review. The analysis of 127 research publications between 2015 and 2024, reveals numerous significant findings of the study; for instance, major research disciplines encompassing BDA Healthcare Applications and Platforms, Electronic Health Records, and Sleep Apnoea Monitoring demonstrate a deliberate attempt to harness Big Data for augmenting patient care and healthcare delivery. The research highlights the dependence on established approaches to driving healthcare analytics by revealing a considerable inclination for supervised learning techniques, with the most often used algorithms, namely Linear Regression and Support Vector Machines. Substantial contributions of the USA, China, and India are assessed by geographic distribution analysis, signifying regional interest and investment in Big Data healthcare studies. The chronological patterns show a notable rise in research initiatives from 2020 to 2023, indicating a rising awareness of the benefits of Big Data in healthcare; however, a notable fall in research efforts in 2024 suggests a divergence of priorities or reallocation of resources. These outcomes have significant clinical and operational ramifications. On the clinical front, Big Data may personalise treatment regimens, increase diagnostic precision, and track chronic illnesses, whereas, on the operational front, it can improve resource management and simplify operations, thereby lowering costs and ameliorating patient care. To ensure the ethical and open use of technology, the research also emphasises the necessity of strong ethical principles and legislative frameworks to manage data privacy, safety, and ownership problems. Subsequent investigations ought to concentrate on the enduring consequences of Big Data analytics, multidisciplinary methodologies, smooth technological integration, discovery of innovative technologies, and boosting interdisciplinary collaboration. The healthcare sector will be able to make full use of the potential of Big Data by plugging these gaps and seeking novel avenues that will enhance patient care, operational effectiveness, and comprehensive healthcare results.

Author Contribution

I.A . Conceptualization; I.A. formal analysis; S.S and I.A. investigation; I.A. Methodology; S.S. project administration; I.A. and S.S. wrote the main manuscript text; All authors prepared figures; All authors reviewed the manuscript.Iman Akour: (I.A). and Said Salloum (S.S)

Acknowledgement

Not applicable

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The Impact of Big Data Analytics on Health Care: A Systematic Review

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Abstract

Figures

1. Introduction

2. Literature Review

3. Methods

3.1 Inclusion/Exclusion Criteria

3.2 Data sources and search strategies

3.3 Data coding and analysis

4. Results and Discussion

4.1 RQ1: what are the overarching research objectives pursued in the surveyed articles?

4.2 RQ2: from which countries do the primary contributions originate?

4.3 RQ3: what types of machine learning technologies are most commonly utilised?

4.4 RQ4: Which machine learning algorithms are frequently employed in these studies?

4.5 RQ5: What disciplines are most represented in the surveyed literature?

5. Similarities and Differences Between this Review Study and Previous Reviews

6. Study Implications

7. Conclusion and Future Work

Declarations

Author Contribution

Acknowledgement

References

Additional Declarations

Supplementary Files

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