IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v241y2019ics0277953619305271.html
   My bibliography  Save this article

Values, challenges and future directions of big data analytics in healthcare: A systematic review

Author

Listed:
  • Galetsi, P.
  • Katsaliaki, K.
  • Kumar, S.

Abstract

The emergence of powerful software has created conditions and approaches for large datasets to be collected and analyzed which has led to informed decision-making towards tackling health issues. The objective of this study is to systematically review 804 scholarly publications related to big data analytics in health in order to identify the organizational and social values along with associated challenges. Key principles of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology were followed for conducting systematic reviews. Following a research path, we present the values, challenges and future directions of the scientific area using indicative examples from relevant published articles. The study reveals that one of the main values created is the development of analytical techniques which provides personalized health services to users and supports human decision-making using automated algorithms, challenging the power issues in the doctor-patient relationship and creating new working conditions. A main challenge to data analytics is data management and security when processing large volumes of sensitive, personal health data. Future research is directed towards the development of systems that will standardize and secure the process of extracting private healthcare datasets from relevant organizations. Our systematic literature review aims to provide to governments and health policy-makers a better understanding of how the development of a data driven strategy can improve public health and the functioning of healthcare organizations but also how can create challenges that need to be addressed in the near future to avoid societal malfunctions.

Suggested Citation

  • Galetsi, P. & Katsaliaki, K. & Kumar, S., 2019. "Values, challenges and future directions of big data analytics in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 241(C).
  • Handle: RePEc:eee:socmed:v:241:y:2019:i:c:s0277953619305271
    DOI: 10.1016/j.socscimed.2019.112533
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953619305271
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.socscimed.2019.112533?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gunasekaran, Angappa & Papadopoulos, Thanos & Dubey, Rameshwar & Wamba, Samuel Fosso & Childe, Stephen J. & Hazen, Benjamin & Akter, Shahriar, 2017. "Big data and predictive analytics for supply chain and organizational performance," Journal of Business Research, Elsevier, vol. 70(C), pages 308-317.
    2. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    3. Mihaela-Laura IVAN & Manole VELICANU, 2015. "Healthcare Industry Improvement with Business Intelligence," Informatica Economica, Academy of Economic Studies - Bucharest, Romania, vol. 19(2), pages 81-89.
    4. Margrét Bjarnadóttir & Sana Malik & Eberechukwu Onukwugha & Tanisha Gooden & Catherine Plaisant, 2016. "Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data," PharmacoEconomics, Springer, vol. 34(2), pages 169-179, February.
    5. Aghaei Chadegani, Arezoo & Salehi, Hadi & Md Yunus, Melor & Farhadi, Hadi & Fooladi, Masood & Farhadi, Maryam & Ale Ebrahim, Nader, 2013. "A Comparison between Two Main Academic Literature Collections: Web of Science and Scopus Databases," MPRA Paper 46898, University Library of Munich, Germany, revised 18 Mar 2013.
    6. Cuquet, Martí & Fensel, Anna, 2018. "The societal impact of big data: A research roadmap for Europe," Technology in Society, Elsevier, vol. 54(C), pages 74-86.
    7. Oztekin, Asil & Al-Ebbini, Lina & Sevkli, Zulal & Delen, Dursun, 2018. "A decision analytic approach to predicting quality of life for lung transplant recipients: A hybrid genetic algorithms-based methodology," European Journal of Operational Research, Elsevier, vol. 266(2), pages 639-651.
    8. Lowton, Karen & Hiley, Chris & Higgs, Paul, 2017. "Constructing embodied identity in a ‘new’ ageing population: A qualitative study of the pioneer cohort of childhood liver transplant recipients in the UK," Social Science & Medicine, Elsevier, vol. 172(C), pages 1-9.
    9. Amankwah-Amoah, Joseph, 2016. "Emerging economies, emerging challenges: Mobilising and capturing value from big data," MPRA Paper 85625, University Library of Munich, Germany.
    10. Margrét V. Bjarnadóttir & Sana Malik & Eberechukwu Onukwugha & Tanisha Gooden & Catherine Plaisant, 2016. "Understanding Adherence and Prescription Patterns Using Large-Scale Claims Data," PharmacoEconomics, Springer, vol. 34(2), pages 169-179, February.
    11. Lupton, Deborah & Jutel, Annemarie, 2015. "‘It's like having a physician in your pocket!’ A critical analysis of self-diagnosis smartphone apps," Social Science & Medicine, Elsevier, vol. 133(C), pages 128-135.
    12. Martin Hilbert, 2014. "Technological information inequality as an incessantly moving target: The redistribution of information and communication capacities between 1986 and 2010," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 821-835, April.
    13. Amankwah-Amoah, Joseph, 2016. "Emerging economies, emerging challenges: Mobilising and capturing value from big data," Technological Forecasting and Social Change, Elsevier, vol. 110(C), pages 167-174.
    14. O'Connor, Cliodhna & Kadianaki, Irini & Maunder, Kristen & McNicholas, Fiona, 2018. "How does psychiatric diagnosis affect young people's self-concept and social identity? A systematic review and synthesis of the qualitative literature," Social Science & Medicine, Elsevier, vol. 212(C), pages 94-119.
    15. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    16. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    17. Indranil Bardhan & Jeong-ha (Cath) Oh & Zhiqiang (Eric) Zheng & Kirk Kirksey, 2015. "Predictive Analytics for Readmission of Patients with Congestive Heart Failure," Information Systems Research, INFORMS, vol. 26(1), pages 19-39, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wei-Chih Lu & I-Ching Tsai & Kuan-Chung Wang & Te-Ai Tang & Kuan-Chen Li & Ya-Ci Ke & Peng-Ting Chen, 2021. "Innovation Resistance and Resource Allocation Strategy of Medical Information Digitalization," Sustainability, MDPI, vol. 13(14), pages 1-20, July.
    2. Secundo, Giustina & Riad Shams, S.M. & Nucci, Francesco, 2021. "Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management," Journal of Business Research, Elsevier, vol. 131(C), pages 563-572.
    3. Wen-Jing Suo & Chai-Lee Goi & Mei-Teh Goi & Adriel K. S. Sim, 2022. "Factors Influencing Behavioural Intention to Adopt the QR-Code Payment: Extending UTAUT2 Model," International Journal of Asian Business and Information Management (IJABIM), IGI Global, vol. 13(2), pages 1-22, August.
    4. Glory Urekwere Orlu & Rusli Bin Abdullah & Zeinab Zaremohzzabieh & Yusmadi Yah Jusoh & Shahla Asadi & Yousef A. M. Qasem & Rozi Nor Haizan Nor & Wan Mohd Haffiz bin Mohd Nasir, 2023. "A Systematic Review of Literature on Sustaining Decision-Making in Healthcare Organizations Amid Imperfect Information in the Big Data Era," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
    5. K. Katsaliaki & P. Galetsi & S. Kumar, 2022. "Supply chain disruptions and resilience: a major review and future research agenda," Annals of Operations Research, Springer, vol. 319(1), pages 965-1002, December.
    6. Siala, Haytham & Wang, Yichuan, 2022. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 296(C).
    7. Carboni, Chiara & Wehrens, Rik & van der Veen, Romke & de Bont, Antoinette, 2022. "Conceptualizing the digitalization of healthcare work: A metaphor-based Critical Interpretive Synthesis," Social Science & Medicine, Elsevier, vol. 292(C).
    8. Dal Mas, Francesca & Massaro, Maurizio & Rippa, Pierluigi & Secundo, Giustina, 2023. "The challenges of digital transformation in healthcare: An interdisciplinary literature review, framework, and future research agenda," Technovation, Elsevier, vol. 123(C).
    9. Sharma, Rohit & Jain, Geetika & Paul, Justin, 2023. "Does the world need to change its vaccine distribution strategy for COVID-19?," Technovation, Elsevier, vol. 126(C).
    10. Daniele Piovani & Stefanos Bonovas, 2022. "Real World—Big Data Analytics in Healthcare," IJERPH, MDPI, vol. 19(18), pages 1-3, September.
    11. Cruz, Taylor M. & Paine, Emily Allen, 2021. "Capturing patients, missing inequities: Data standardization on sexual orientation and gender identity across unequal clinical contexts," Social Science & Medicine, Elsevier, vol. 285(C).
    12. Cherif, Emna & Bezaz, Nora & Mzoughi, Manel, 2021. "Do personal health concerns and trust in healthcare providers mitigate privacy concerns? Effects on patients’ intention to share personal health data on electronic health records," Social Science & Medicine, Elsevier, vol. 283(C).
    13. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).
    14. Cruz, Taylor Marion, 2022. "The social life of biomedical data: Capturing, obscuring, and envisioning care in the digital safety-net," Social Science & Medicine, Elsevier, vol. 294(C).
    15. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2023. "Exploring benefits and ethical challenges in the rise of mHealth (mobile healthcare) technology for the common good: An analysis of mobile applications for health specialists," Technovation, Elsevier, vol. 121(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    2. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    3. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    4. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    5. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    6. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    7. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    8. Liedong, Tahiru Azaaviele & Rajwani, Tazeeb & Lawton, Thomas C., 2020. "Information and nonmarket strategy: Conceptualizing the interrelationship between big data and corporate political activity," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    9. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    10. Gupta, Shivam & Chen, Haozhe & Hazen, Benjamin T. & Kaur, Sarabjot & Santibañez Gonzalez, Ernesto D.R., 2019. "Circular economy and big data analytics: A stakeholder perspective," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 466-474.
    11. Anke Joubert & Matthias Murawski & Markus Bick, 2023. "Measuring the Big Data Readiness of Developing Countries – Index Development and its Application to Africa," Information Systems Frontiers, Springer, vol. 25(1), pages 327-350, February.
    12. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    13. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    14. Muhammad Noman Shafique & Ammar Rashid & Sook Fern Yeo & Umar Adeel, 2023. "Transforming Supply Chains: Powering Circular Economy with Analytics, Integration and Flexibility Using Dual Theory and Deep Learning with PLS-SEM-ANN Analysis," Sustainability, MDPI, vol. 15(15), pages 1-23, August.
    15. Qaisar Ali & Hakimah Yaacob & Shazia Parveen & Zaki Zaini, 2021. "Big data and predictive analytics to optimise social and environmental performance of Islamic banks," Environment Systems and Decisions, Springer, vol. 41(4), pages 616-632, December.
    16. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    17. Mohammad Ali Yamin, 2021. "Investigating the Drivers of Supply Chain Resilience in the Wake of the COVID-19 Pandemic: Empirical Evidence from an Emerging Economy," Sustainability, MDPI, vol. 13(21), pages 1-16, October.
    18. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    19. Harkaran Kava & Konstantina Spanaki & Thanos Papadopoulos & Stella Despoudi & Oscar Rodriguez-Espindola & Masoud Fakhimi, 2021. "Data Analytics Diffusion in the UK Renewable Energy Sector: An Innovation Perspective," Post-Print hal-03781046, HAL.
    20. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:241:y:2019:i:c:s0277953619305271. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.