IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v328y2023i1d10.1007_s10479-022-04955-2.html
   My bibliography  Save this article

Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations

Author

Listed:
  • Görkem Sariyer

    (Yasar University, Department of Business Administration)

  • Mustafa Gokalp Ataman

    (Department of Emergency Medicine)

  • Sachin Kumar Mangla

    (Jindal Global Business School, O P Jindal Global University)

  • Yigit Kazancoglu

    (Yasar University, Department of Logistics Management)

  • Manoj Dora

    (Sustainable Production and Consumption School of Management Anglia Ruskin University)

Abstract

Grounded in dynamic capabilities, this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs, it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients, the average daily length of stay (LOS), and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19, and includes data from 238,152 patients. Comparing statistics on daily patient volumes, average LOS, and resource usage, both before and during the COVID-19 pandemic, we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period, these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model, we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158,347, it decreased to 79,805 during-COVID-19. On the other hand, while the average daily LOS was 117.53 min before-COVID-19, this value was calculated to be 165,03 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies, it empirically investigates the impact of different policies on ED operations.

Suggested Citation

  • Görkem Sariyer & Mustafa Gokalp Ataman & Sachin Kumar Mangla & Yigit Kazancoglu & Manoj Dora, 2023. "Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations," Annals of Operations Research, Springer, vol. 328(1), pages 1073-1103, September.
    • Handle: RePEc:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04955-2
    DOI: 10.1007/s10479-022-04955-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04955-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10479-022-04955-2?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. Wamba, Samuel Fosso & Gunasekaran, Angappa & Akter, Shahriar & Ren, Steven Ji-fan & Dubey, Rameshwar & Childe, Stephen J., 2017. "Big data analytics and firm performance: Effects of dynamic capabilities," Journal of Business Research, Elsevier, vol. 70(C), pages 356-365.
    2. Tsan‐Ming Choi & Stein W. Wallace & Yulan Wang, 2018. "Big Data Analytics in Operations Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1868-1883, October.
    3. David J. Teece & Gary Pisano & Amy Shuen, 1997. "Dynamic capabilities and strategic management," Strategic Management Journal, Wiley Blackwell, vol. 18(7), pages 509-533, August.
    4. Qi Feng & J. George Shanthikumar, 2018. "How Research in Production and Operations Management May Evolve in the Era of Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1670-1684, September.
    5. Vikas Thakur & Sachin Kumar Mangla & Binita Tiwari, 2021. "Managing healthcare waste for sustainable environmental development: A hybrid decision approach," Business Strategy and the Environment, Wiley Blackwell, vol. 30(1), pages 357-373, January.
    6. Verma, Surabhi & Gustafsson, Anders, 2020. "Investigating the emerging COVID-19 research trends in the field of business and management: A bibliometric analysis approach," Journal of Business Research, Elsevier, vol. 118(C), pages 253-261.
    7. Lee, Sang M. & Trimi, Silvana, 2021. "Convergence innovation in the digital age and in the COVID-19 pandemic crisis," Journal of Business Research, Elsevier, vol. 123(C), pages 14-22.
    8. Ajay Kumar & Ravi Shankar & Alok Choudhary & Lakshman S. Thakur, 2016. "A big data MapReduce framework for fault diagnosis in cloud-based manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7060-7073, December.
    9. Papadopoulos, Thanos & Baltas, Konstantinos N. & Balta, Maria Elisavet, 2020. "The use of digital technologies by small and medium enterprises during COVID-19: Implications for theory and practice," International Journal of Information Management, Elsevier, vol. 55(C).
    10. Maurizio Zollo & Sidney G. Winter, 2002. "Deliberate Learning and the Evolution of Dynamic Capabilities," Organization Science, INFORMS, vol. 13(3), pages 339-351, June.
    11. Nada R. Sanders & Ram Ganeshan, 2018. "Big Data in Supply Chain Management," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1745-1748, October.
    12. Deepa Mishra & Angappa Gunasekaran & Thanos Papadopoulos & Stephen J. Childe, 2018. "Big Data and supply chain management: a review and bibliometric analysis," Annals of Operations Research, Springer, vol. 270(1), pages 313-336, November.
    13. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    14. Hao Huang & Zongchao Peng & Hongtao Wu & Qihui Xie, 2020. "A big data analysis on the five dimensions of emergency management information in the early stage of COVID-19 in China," Journal of Chinese Governance, Taylor & Francis Journals, vol. 5(2), pages 213-233, April.
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Yucheng & Hou, Zhongwei & Yang, Feifei & Yang, Miles M. & Wang, Zhiling, 2021. "Discovering the evolution of resource-based theory: Science mapping based on bibliometric analysis," Journal of Business Research, Elsevier, vol. 137(C), pages 500-516.
    2. Vicky Ching Gu & Bin Zhou & Qing Cao & Jeffery Adams, 2021. "Exploring the relationship between supplier development, big data analytics capability, and firm performance," Annals of Operations Research, Springer, vol. 302(1), pages 151-172, July.
    3. Julia Eichholz & Nicole Hoffmann & Anja Schwering, 2024. "The role of risk management orientation and the planning function of budgeting in enhancing organizational resilience and its effect on competitive advantages during times of crises," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 35(1), pages 17-58, March.
    4. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    5. Hassani, Abdeslam & Mosconi, Elaine, 2022. "Social media analytics, competitive intelligence, and dynamic capabilities in manufacturing SMEs," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    6. Motamarri, Saradhi & Akter, Shahriar & Hossain, Md Afnan & Dwivedi, Yogesh K, 2022. "How does remote analytics empowerment capability payoff in the emerging industrial revolution?," Journal of Business Research, Elsevier, vol. 144(C), pages 1163-1174.
    7. Klöckner, Maximilian & Schmidt, Christoph G. & Wagner, Stephan M. & Swink, Morgan, 2023. "Firms’ responses to the COVID-19 pandemic," Journal of Business Research, Elsevier, vol. 158(C).
    8. Nayak, Bishwajit & Bhattacharyya, Som Sekhar & Krishnamoorthy, Bala, 2022. "Exploring the black box of competitive advantage – An integrated bibliometric and chronological literature review approach," Journal of Business Research, Elsevier, vol. 139(C), pages 964-982.
    9. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    10. Rodríguez-Espíndola, Oscar & Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Albores, Pavel & Emrouznejad, Ali, 2022. "Analysis of the adoption of emergent technologies for risk management in the era of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    11. Dubey, Rameshwar & Bryde, David J. & Dwivedi, Yogesh K. & Graham, Gary & Foropon, Cyril & Papadopoulos, Thanos, 2023. "Dynamic digital capabilities and supply chain resilience: The role of government effectiveness," International Journal of Production Economics, Elsevier, vol. 258(C).
    12. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    13. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
    14. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    15. Siddharth Gaurav Majhi & Ambuj Anand & Arindam Mukherjee & Nripendra P. Rana, 2022. "The Optimal Configuration of IT-Enabled Dynamic Capabilities in a firm’s Capabilities Portfolio: a Strategic Alignment Perspective," Information Systems Frontiers, Springer, vol. 24(5), pages 1435-1450, October.
    16. Efpraxia D. Zamani & Anastasia Griva & Kieran Conboy, 2022. "Using Business Analytics for SME Business Model Transformation under Pandemic Time Pressure," Information Systems Frontiers, Springer, vol. 24(4), pages 1145-1166, August.
    17. Dwivedi, Yogesh K & Shareef, Mahmud A & Akram, Muhammad S & Bhatti, Zeeshan A & Rana, Nripendra P, 2022. "Examining the effects of enterprise social media on operational and social performance during environmental disruption," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    18. Lin, Canchu & Kunnathur, Anand, 2019. "Strategic orientations, developmental culture, and big data capability," Journal of Business Research, Elsevier, vol. 105(C), pages 49-60.
    19. Shamim, Saqib & Zeng, Jing & Shafi Choksy, Umair & Shariq, Syed Muhammad, 2020. "Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level," International Business Review, Elsevier, vol. 29(6).
    20. Pedota, Mattia, 2023. "Big data and dynamic capabilities in the digital revolution: The hidden role of source variety," Research Policy, Elsevier, vol. 52(7).

    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:spr:annopr:v:328:y:2023:i:1:d:10.1007_s10479-022-04955-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.