Table 3 PRISMA 2019 checklist – Results

Ristevski B, Chen M. Big Data Analytics in Medicine and Healthcare [49]

• To understand the big data

• To find the challenges mining

• To understand data privacy and security

Qualitative

Qualitative

One

1, China

A hardware innovations in processor technology, newer kinds of memories/network architecture will minimize the time spent in moving the data from storage to the processor in a distributed setting

Big data characterstics, big data analtyics, chanllanges in big data analytics and big data privacy and security

Big data analytics in medicine and healthcare is an auspicious process of integrating, exploring, and analysing large amounts of complex heterogeneous data: biomedical data, experimental data, electronic health records data and social media data. Furthermore, integrating such diverse data makes big data analytics intertwine several fields, such as bioinformatics, medical imaging, sensor informatics, medical informatics, health informatics and computational biomedicine

Gamache R, Kharrazi H, Weiner JP. Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities [21]

To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations

Search of the literature from July 1, 2016 to September 30, 2017

Systmatic review

One

3

• The newly emerging public health informatics vision and infrastructure

• The alignment of informatics aims, goals, and outcomes across the oftentimes separate fields of public health and population health

• The increased incorporation by both public and population health informatics professionals of SDH data

Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health

Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this "bridging" is to identify proactively, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities

Zhang X, Pérez-Stable EJ, Bourne PE, Peprah E, Duru OK, Breen N, Berrigan D,

Wood F, Jackson JS, Wong DWS, Denny J. Big Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st Century [50]

• To find opportunities to Use Big Data Science to Advance Minority Health and Reduce Health Disparities

• To understand Potential Challenges of Using Big Data for Minority Health and Health Disparities Research

Quantitaive

6

One

Ethics, Privacy, and Trust

Missing Data and Statistical Uncertainty

Data Access and Sharing

Data Science Training and Workforce Diversity

This article focuses on three priority opportunities that Big Data science may offer to the reduction of health and health care disparities. One opportunity is to incorporate standardized information on demographic and social determinants in electronic health records in order to target ways to improve quality of care for the most disadvantaged populations over time. A second opportunity is to enhance public health surveillance by linking geographical variables and social determinants of health for geographically defined populations to clinical data and health outcomes. Third and most importantly, Big Data science may lead to a better understanding of the etiology of health disparities and understanding of minority health in order to guide intervention development

Specific recommendations include investing in data collection on small sample populations, building a diverse workforce pipeline for data science, actively seeking to reduce digital divides, developing novel ways to assure digital data privacy for small populations, and promoting widespread data sharing to benefit under-resourced minority-serving institutions and minority researchers. With deliberate efforts, Big Data presents a dramatic opportunity for reducing health disparities but without active engagement, it risks further widening them

Madanian S, Parry DT, Airehrour D, Cherrington M. mHealth and big-data

integration: promises for healthcare system in India. BMJ Health Care Inform [51]

• Review healthcare challenges in India, their context and investigate its current status—with a specific focus on mHealth

• Identify opportunities in Indian healthcare system for deploying mHealth and big-data

• Propose a mHealth model and provide recommendations for its efficient use to improve healthcare and action policies by authorities

Systmatic review

A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards

One

One, India

This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage

This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage

Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems

Pastorino R, De Vito C, Migliara G, Glocker K, Binenbaum I, Ricciardi W,

Boccia S. Benefits and challenges of Big Data in healthcare: an overview of the

European initiatives [31]

To find the initiatives utilize big data in health care system in EU countries

To study the potential benefits of Big Data for healthcare in the European Union

Systmatic review

Review the EU supported initiatives concerning activities that involve the use of Big Data in public health in Europe from 2012 to 2018

12, European Countries

The potential benefits of Big Data for healthcare in the European Union

Big Data in public health

Ethical and legal issues for the effective use of Big Data in healthcare

The implementation of precision medicine remains contingent on significant data acquisition and timely analysis to determine the most appropriate basis on which to tailor health optimization for individual prevention, diagnosis and disease treatment. Achieving effective and proportionate governance of health-related data will be essential for the future healthcare systems, and it requires that stakeholders collaborate and adapt the design and performance of their systems to reach the maximum innovative potential of information and innovation technology on health in the EU

In this context, EU Member States should agree on international technical standards, taking also into account openness that is considered as the basic paradigm for digital transformation. Additionally, new approaches must be found for translating the vast amount of data into meaningful information that healthcare professionals can use. Further efforts must be made to make information for doctors and health professionals more accessible and understandable

To achieve this, existing training and education programmes for healthcare professionals should integrate the issues of data handling in the curricula to ensure the development of the necessary skills and competencies. This is one of the objectives of the ‘European network staff eXchange for integrAting precision health in the health Care sysTems’ consortium (ExACT)20 project that aims to integrate precision health in European health systems by training a new generation of healthcare professionals across and outside of the EU

we are living in fast-moving times, not least in terms of healthcare innovation. Whilst there are pressing needs for more personalized and sustainable health services, science and technology are offering a host of potentially invaluable new tools to deliver them. A cooperation at the EU level is needed to facilitate investments both in new technology and in the human capital, in order to guide citizens into this new frontier of human health and well-being where data are becoming a significant corporate asset, a vital economic input and the foundation of new business models

Adibuzzaman M, DeLaurentis P, Hill J, Benneyworth BD. Big data in healthcare

- the promises, challenges and opportunities from a research perspective: A case

study with a model database [1]

• Identify and present the initial work addressing the relevant challenges of utilizing big data in clinical care, in three broad categories: data, accessibility, and translation

Qualitative

A case study with a model database

2

• Identify tow initiatives of Medical Information Mart for Intensive Care (MIMIC III) and “Informatics for Integrating Biology and the Bedside (i2b2)” where big data and the electronic medical record used to find a clinical outcome or conduct clinical research

• Identify the challenges of big data utilization in three broad categories: data, accessibility, and translation

The big data is considered as the future promise to enhance the health care system but still the available software system, polices doesn’t support the improvement in these areas. The study focused on data, accessibility and translation. To make data more understandable and valuable. We need a larger cohort of institutions to share complete, precise, and time-stamped data as well as with greater willingness to invest in technologies for de-identifying private patient data for it to be shared broadly for scientific research. At the same time, as more and more “big data” systems are developed, the scientific and regulatory communities need to figure out new ways of understanding causal relationship from data captured during routine health care, that would complement current gold standard methods such as RCTs as well as identify the relationship between clinical practice and outcomes, as there is a wide disparity in the quality of care across the country

Murphy S, Castro V, Mandl K. Grappling with the Future Use of Big Data for

Translational Medicine and Clinical Care [52]

• Identify the reasons for the lack of integration of the big data by most the electronic medical record system

• Identify the area of extracting the big data integration into clinical care

One, USA

• Data are often positioned outside the EMRS with their own distinct semantic and technical structures. Integrating big data into clinical systems requires web services organized around a common information model to reach out to the Big Data Repositories as they exist in cloud-native infrastructures

• Harassing of big data can be used in phenotyping, hypothesis generation, predication and clinical decision support

• Features that are important to healthcare often need to be computed from, rather than being readily available from, the Big Data

• Apps that will run inside and outside the EMRS can provide support for complex decisions and workflows that involve genomics, imaging, and personal health repositories. Accurate phenotyping can become a routine part of clinical care. An infrastructure based on the SMART API will allow an ecosystem of apps to be shared across healthcare institutions using web service standards such as FHIR. This provides a vision for a new type of EMRS, which is expected to play out over the next five to ten years

Roca J, Tenyi A, Cano I. Paradigm changes for diagnosis: using big data for

prediction. [53]

(i) Regional deployment of Integrated Care services for chronic patients with a personalized medicine approach; (ii) Development of a test-bed, willing for international leadership, for the use of ICT in novel services that generate value in the healthcare system of Catalonia; and, (iii) Development and monetization of novel products and services with a high level of transferability to other healthcare systems, contributing to strengthen Catalan industrial competences

Case study

One, Spain

ACSV

• The study describes how to implement innovation and big data analysis in managing chronic disease, a case of ‘chronic pulmonary obstructive disease’ prescribed as an example. Then the concept of “Integrated care” utilizing a different source of data stored in cloud platform to analyse population conditions and predicted using modelling method in the outcome

• The study illustrated four challenges: Enhanced clinical predictive modelling, Technology related and security and data storing issue and application of evaluation and adoption of decision support system (DSS)

The cloud-based data analytics platform has been proposed to successfully address the implicated potentials of

health risk assessment and stratification and to facilitate

large-scale adoption of Integrated Care of chronic patients

[17, 39], contributing to enhance healthcare outcomes

and patient experience of care while reducing costs and

improving the health of populations. Applying holistic

strategies for subject-specific risk prediction and stratification, that consider multilevel covariates influencing

patient health, would increase the predictive accuracy

and facilitate clinical decision-making based on sound

estimates of individual prognosis

Thompson ME, Dulin MF. Leveraging Data Analytics to Advance Personal,

Population, and System Health: Moving Beyond Merely Capturing Services Provided [54]

Explore the facets of the promise, perils, trends, and trajectories of health informatics and analytics

Explore the causes of underutilizing big data in health care

Identify the role and opportunity of introducing the new policy ( North Carolina's new modernized health information exchange policy)

Qualitiaive study

One

One

The causes of underutilization of big data in North Carolina categorized into 5 points:

• the Health Information Technology Act and the Affordable Care Act (ACA), he payment-for-services side of the process is well developed and functional. The shift to value-based payments has yet to materialize. limit investment to utilize EMR data

• Data governance is lacking in consistency and rigor, within and across organizations. As Shannon Fuller notes in his article, without assurances of data quality, integrity, and security, any conclusions drawn from analyses are suspect at best

• uniform standards for capturing and reporting the largely unstructured data found in medical records are absent. Aside from a few high-level data elements, the interoperability and easy aggregation and pooling of analytic data promised by the federal Health Information Exchange (HIE) are undeveloped

• The sharing, linking, aggregating, and disaggregating of patient, system, and community data, while now technically feasible, are simultaneously required and prohibited by a variety of conflicting laws and policies

• The processes needed to link and analyze these disparate data, and make them accessible to patients, providers, payers, and researchers, also create the risks (and liabilities) of breach and abuse

Big data and analytics hold much promise, but are under-developed in health care when compared to other sectors. Given the many pitfalls, security concerns, and risks engendered by our lack of a universal coverage/single risk pool system, perhaps we are fortunate to have the added breathing space to more fully develop our governance structures, ensure data quality and security, and align our policies before charging ahead into the brave new electronically integrated world. Building systems and networks based upon trust and transparency among health care's many stakeholders will take time and effort outside the scope of the technology that makes it possible. The move toward valued-based payments will reinforce the need for health care industries to adapt to a systems-thinking-driven model and will further foment a culture of data-driven learning organizations

Carney TJ, Kong AY. Leveraging health informatics to foster a smart systems

response to health disparities and health equity challenges [55]

• Identified health disparities and health equity

• Identify big data challenges in health disparities and health equity

• Identify the domains of health informatics and how to apply smart data analysis within each domains

Qualitiaive study

One

one,USA

• Define the health disparities and health equality and the value to apply this in country to understand health related issues

• Identify four groups to apply smart health and big data which are: Public health, population health, community health and consumer health informatics

• Identify the challenges and available resources for each domains and what required to improve health informatics in each domains

• This presentation of collective intelligence and the corresponding terms of smart health, knowledge ecosystem, enhanced health disparities informatics capacities, knowledge exchange, big-data, and situational awareness are a means of demonstrating the complex challenges informatics professional face in trying to model, measure, and manage an intelligent and smart systems-level response to health disparities. Study outlined public and population health disparities challenges across four distinct domains of informatics. Study also introduced a concept of performance-based health disparities that we operationally define in our models as those that are generated or triggered by breaches or defects in the continuum of care that may negatively influence the quality, cost, safety, efficiency, or effectiveness of population health and health promotion

Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-

Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di

Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG,

Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J,

Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J,

Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic

M, Pliakos K, Rattray M, I Màs JR, Schneider R, Sengstag T, Serra-Picamal X,

Spek W, Vaas LA, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno

JA, Wubbe JP, Zanetti G. Making sense of big data in health research: Towards an

EU action plan [9]

Addressing big data barriers in European countries

To find the opportunities to creating the European Single Market for health, which will improve health and healthcare for all Europeans

Quanatative

Health Directorate of the

Directorate-General for Research and Innovation at the

European Commission (EC), the executive body of the

EU, organized in Luxembourg a workshop entitled “Big

data in health research: an EU action plan

6

Multiple, European countries

The potential benefits of big data for healthcare

The challenges ahead for the effective use of big data in healthcare

Data quality, acquisition, curation, and visualization

Legal and regulatory aspects

Recommendations for an EU action plan:

Launch pilot projects on the application of big data to inform health

Leverage the potential of open and citizen science for the exploitation of big data in health

Catalyze the involvement of all relevant stakeholders in projects

The digital revolution is underway. A number of industries have already transformed their activities or have now become inoperative. The driving forces are miniaturization, automation, and now increasingly the convergence of artificial intelligence, deep learning, and robotics. Healthcare will not escape these developments. In fact, big data as a driving force will play an even more important role than in most industries. In Europe, working across borders is the only way to master the challenges of this scientific, technological, and industrial revolution. The single most important factor is the workforce. Countries that are ahead in ICT competence and have an understanding of cultural differences and an ability and willingness to work together have the best chance to succeed

Beckmann JS, Lew D. Reconciling evidence-based medicine and precision

medicine in the era of big data: challenges and opportunities. [56]

To define the challenges and opportunities for achieving clinical utility in precision medicine

To find the opportunity of evidence-based precise medicine

Qualitative

Multiplicity of stakeholders and disciplines

•Analyses of big data

•Heterogeneity of complex, multilayered data types, and formats

•Harmonization of data semantics (clinical, laboratory, and others): vocabularies, terminologies, classification and coding systems, ontologies

•Standardization of data entry and storage

•Integration of multiple data types (such as laboratory, clinical, behavioral, lifestyle, environmental)

•Secure, sustainable, and effective data storage and sharing

•Necessity for new analytic tools and algorithms

•Multiplicity and lack of semantic and technical interoperability of electronic health record systems

•Extremely dynamic and fast-changing field, with new tools constantly emerging

•Training and education of the different stakeholders (medical staff, patients, and decision-makers)

•Ethical, legal, social, and consent issues

•Uberization of medicine

The role of clinical bioinformatics in precision medicine

Aggregation of heterogeneous data sets into electronic health records

Reconciling evidence-based medicine and precision medicine

Citizen-centered medicine

The economics of precision medicine

Challenges of evidence-based precision medicine

We consider that evidence-based precision medicine rests on three pillars: (i) responsible inter-institutional sharing of large clinical and laboratory interoperable, harmonized data sets; (ii) data on vital signs and behavior collected by empowered citizens; and (iii) clinical bioinformatics required to convert this complex information into clinically useful knowledge, which will be returned by the medical practitioners to the individuals concerned.. The net outcome could be better clinical diagnosis or prognostication; this could facilitate clinical decision-making, improve medical care or treatment, and most importantly, could contribute to disease delay or even prevention. Clinical bioinformatics has a central role to play in this revolutionary person-centric effort by contributing to care delivery innovations and improved health preservation, and shifting the emphasis more and more from therapy to prevention, and from disease to wellness

Kumar S, Singh M. Big data analytics for healthcare industry: impact, applications, and tools. Big Data Mining and Analytics. 2018 Oct 15;2(1):48–57. [57]

To analyse Big data analytics for healthcare industry: impact, applications, and tools

Qualitative

Observation based study

The health industry sector has been confronted by the need to manage the big data being produced by various sources, which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector

Balsam Alkouz, Zaher Al Aghbari, Jemal Hussien Abawajy. Tweetluenza: Predicting Flu Trends from

Twitter Data. Big Data Mining and Anyalytics 2019, 2(4): 273–287. [58]

The authors develop a new Influenza prevalence prediction model, called Tweetluenza, to predict the spread of the Influenza in real time using cross-lingual data harvested from Twitter data streams with emphases on the United Arab Emirates (UAE). Based on the features of tweets, Tweetluenza filters the Influenza tweets, utilizing big data mining from social media account

Quantitative

To monitor the growth of Influenza, the reporting tweets were employed. Furthermore, a linear regression model leverages the reporting tweets to predict the Influenza-related hospital visits in the future. We evaluated Tweetluenza empirically to study its feasibility and compared the results with the actual hospital visits recorded by the UAE Ministry of Health

UAE

The experiments demonstrate the practicality of Tweetluenza, which was verified by the high correlation between the Influenza-related Twitter data and hospital visits due to Influenza. Furthermore, the evaluation of the analysis and prediction of Influenza shows that combining English and Arabic tweets improves the correlation results

To investigate the impact of Twitter data on the prediction of Influenza.ediction. To show that the combination of tweet counts and hospital visits counts improves the prediction of future hospital visits, we repeated the second experiment twice to predict the number of hospital visits: once using only the counts of tweets and once using only the counts of hospital visits. From Table 20, we note that the prediction by using either tweet counts only or the hospital visits counts only produces higher RMSE error than using them combined as compared to the result of

In this paper, author proposed Tweetluenza system that uses Twitter streams for Influenza surveillance and forecasting in a cross-lingual and cross-dialec. This show example the utlization of social media into predict health care system utlizing the big data infromation form the social media

Gravili G, Manta F, Cristofaro CL, Reina R, Toma P. Value that matters: intellectual capital and big data to assess performance in healthcare. An empirical analysis on the European context. Journal of Intellectual Capital. 2020 Jul 30. [59]

The aim of this paper is to analyze and measure the effects of intellectual capital (IC), i.e. human capital (HC), relational capital (RC) and structural capital (SC), on healthcare industry organizational performance and understanding the role of data analytics and big data (BD) in healthcare value creation

Qualitative

The study has a twofold approach: in the first part, the authors operated a systematic review of the academic literature aiming to enquire the relationship between IC, big data analytics (BDA) and healthcare system, which were also the descriptors employed. In the second part, the authors built an econometric model analyzed through panel data analysis, studying the relationship between IC, namely human, relational and structural capital indicators, and the performance of healthcare system in terms of performance. The study has been conducted on a sample of 28 European countries, notwithstanding the belonging to specific international or supranational bodies, between 2011 and 2016

Multiple

The relationship between IC indicators and performance could be employed in other sectors, disseminating new approaches in academic research. Through the establishment of a relationship between IC factors and performance, the authors implemented an approach in which healthcare organizations are active participants in their economic and social value creation

The paper proposes a data-driven model that presents new approach to IC assessment, extendable to other economic sectors beyond healthcare. It shows the existence of a positive impact (turning into a mathematical inverse relationship) of the human, relational and structural capital on the performance indicator, while the physical assets (i.e. the available beds in hospitals on total population) positively mediates the relationship, turning into a negative impact of non-IC related inputs on healthcare performance. The result is relevant in terms of managerial implications, enhancing the opportunity to highlight the crucial role of IC in the healthcare sector

The authors provide a new holistic framework on the relationship between IC, BDA and organizational performance in healthcare organizations through a systematic review approach and an empirical panel analysis at a multinational level, which is quite a novelty regarding the healthcare

Gu D, Li J, Li X, Liang C. Visualizing the knowledge structure and evolution of big data research in healthcare informatics. International journal of medical informatics. 2017 Feb 1;98:22–32. [60]

To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics

Qualitative

A series of bibliometric analyses on the related literature, including papers’ production trends in the field and the trend of each paper’s co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis

3

By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People’s Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing)

This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions

Dhagarra D, Goswami M, Sarma PR, Choudhury A. Big Data and blockchain supported conceptual model for enhanced healthcare coverage. Business Process Management Journal. 2019 Oct 14. [61]

Despite recent progress in ensuring improved access to health care in past decade or so, disparities across gender, geography and socioeconomic status continue to persist. Fragmented and scattered health records and lack of integration are some of the primary causes leading to uneven healthcare service delivery. The devised framework is intended to address these challenges. The paper aims to discuss these issues

Qualitative

In this research a Big Data and blockchain anchored integrative healthcare framework is proposed focusing upon providing timely and appropriate healthcare services to every citizen of the country. The framework uses unique identification number (UID) system as formalized and implemented by the Government of India for identification of the patients, their specific case histories and so forth

1

A key component of our evolved framework is the Big Data analytics-based framework that seeks to provide structured health data to concerned stakeholders in healthcare services. The model entails all pertinent stakeholders starting from patients to healthcare service providers

The key characteristic of our proposed framework is that it provides easy access to secure, immutable and comprehensive medical records of patients across all treatment centers within the country. The model also ensures security and privacy of the medical records based upon the incorporation of biometric authentication by the patients for access of their records to healthcare providers