SWS Academic Research eLibrarySocial Sciences & Art

Scholarly record

MANAGING HOSPITAL LOGISTICS USING AI

Ewa Stawiarska, Piotr Stawiarski, Maciej Stawarski

First published: 2024https://doi.org/10.35603/sws.iscss.2024/s16/92View metrics

Abstract

Artificial intelligence (AI) is becoming increasingly important in various sectors, including healthcare. One of the key areas where AI can significantly improve patient care and hospital efficiency is hospital logistics management. [1], [2] This paper presents the potential of artificial intelligence (AI) to support the strategic management of logistics infrastructure; operational management for hospital supply planning; and operational improvement i.e.: logistics process flows. The paper uses a monographic research method to describe two hospitals using AI in hospital logistics management. The examples show that the management of resources, internal transport, medical waste and patient data are activities that require advanced technological solutions. The examples show that AI is already being successfully implemented in healthcare units.

Publication Impact Profile

PlumX
  • Captures
  • Mendeley - Readers: 1

Publication details

Title
MANAGING HOSPITAL LOGISTICS USING AI
Authors
Ewa Stawiarska, Piotr Stawiarski, Maciej Stawarski
Proceedings
Proceedings of 11th SWS International Scientific Conference on Social Sciences - ISCSS 2024
Publisher
SGEM WORLD SCIENCE (SWS) Scholarly Society
Year
2024
Pages
547-554
SWS Citekey
Stawiarska202433547554
ISSN
2682-9959
ISBN
978-3-903438-14-9
Language
en
Publication type
Proceedings Paper
Proceedings contents
Open official contents
Keywords
References23
  1. Allahham M., Sharabati A., Hatamlah H., Bani A., Sabra S. Daoud M. (2023). Big Data Analytics and AI for Green Supply Chain Integration and Sustainability in Hospitalsm Wseas transactions on environment and development. 19. 1218-1230. DOI: 10.37394/232015.2023.19.111.

  2. Gallo H. Khadem A., Alzubi A. (2023). The Relationship between BigData Analytic-Artificial Intelligence and Environmental Performance: A Moderated Mediated Model of Green Supply Chain Collaboration (GSCC) and Top Management Commitment (TMC). Discrete Dynamics in Nature and Society. 2023. 1- 16.DOI: 10.1155/2023/4980895.

  3. Stawiarska, E.; Stawiarski, M. (2023). Assessment of Patient Treatment and Rehabilitation Processes Using Electromyography Signals and Assessment of Patient Treatment and Rehabilitation Processes Using Electromyography Signals and Selected Industry 4.0 Solutions, International Journal of Environmental Research and Public Health (IJERPH) 20(4) DOI: 10.3390/ijerph20043754

  4. Fosso W., Gunasekaran A. Papadopoulos T. Ngai E.. (2018). Big data analytics in logistics and supply chain management, The International Journal of Logistics Management, 29,DOI: 10.1108/IJLM-02-2018-0026.

  5. Dubey R., Gunasekaran A., Childe S. J., Roubaud D., Wamba S. F., Giannakis M.,Foropon C., 2019, Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain.International Journal of Production Economics, 210, 120-136. DOI: 10.1016/j.ijpe.2019.01.023.

  6. Benzidia, S., Makaoui, N. Bentahar O. (2021), The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance., Technological Forecasting and Social Change, 165, p.120557.DOI: 10.1016/j.techfore.2020.120557.

  7. GS1 Healthcare Reference Book 2022-2023 Stories of successful implementations of GS1 standards, https://gs1pl.org/app/uploads/2023/02/reference_book_2022- 2023_fullbook.pdf

  8. Mikalef P., Krogstie J., Pappas I. O., Pavlou P., (2020), Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities, Information &Management, 57(2), 103169.DOI: 10.1016/j.im.2019.05.004.

  9. 16 Choi T. M., Wallace S. W., Wang Y., (2018), Big data analytics in operations management, Production and Operations Management, 27(10), 1868- 1883.DOI: 10.1111/poms.12838.

  10. Favour O., Potter K., Doris L. (2024). Optimizing Hospital Management with AI. Information Technology in Hospitality.

  11. Ammar M., Haleem A., Javaid, M., Walia R., Bahl. S., (2021) Improving material quality management and manufacturing organizations system through Industry 4.0 technologies. In Materials Today: Proceedings; Elsevier: Amsterdam, The Netherlands,;5089–5096.

  12. Gunasekaran A., Papadopoulos T., Dubey R.,Wamba S. F., Childe S. J., Hazen B., Akter S., (2017), Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70, 308-317.DOI: 10.1016/j.jbusres.2016.08.004.

  13. Iyer K. N., Srivastava P., Srinivasan M.,(2019), Performance implications of lean in supply chains: Exploring the role of learning orientation and relational resources. International Journal of Production Economics, 216, 94-104. DOI: 10.1016/j.ijpe.2019.04.012.

  14. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. (2017) 2(4):230–43. DOI: 10.1136/svn-2017-000101

  15. Davenport T, Kalakota R. The potential for artificial intelligence in Healthcare. Future Healthc J. (2019), 6(2):94–8. DOI: 10.7861/futurehosp.6-2-94.

  16. Chen J., Huang S., BalaMurugan, S., Tamizharasi, G.S., (2021), Artificial intelligence-based e-waste management for environmental planning. Review of the environmental impact assessment, 87, 106498.2.

  17. Sharma P., Vaid, U. (2021), The emerging role of artificial intelligence in waste management practice. In IOP Conference Series: Earth and Environmental Sciences (889, no 1, s. 012047), IOP.5.

  18. Das S., Lee SH., Kumar P., Kim KH, Lee SS., Bhattacharya SS. (2019). Solid waste management: the scope and challenge of sustainable development. Cleaner Production Journal, 228, 658-678.11.

  19. Godinho F.M., Roubaud D.,(2018). Industry 4.0 and the circular economy: aproposed research agenda and originalroadmap for sustainable operations, Annals of Operations Research, 270(1-2), 273-28.DOI: 10.1007/s10479-018-2772-8

  20. Mageto J. (2021), Big Data Analytics in Sustainable Supply Chain Management: AFocus on Manufacturing Supply Chains. Sustainability. DOI: 13. 7101.DOI: 10.3390/su13137101.

  21. Benzidia S., Makaoui N. Bentahar O., (2020), The impact of big data analytics and artificial intelligence on green supply chain process integration and hospital environmental performance. Technological Forecasting and Social Change,165,DOI: 10.1016/j.techfore.2020.120557.

  22. Zhang, Q., Gao B., Luqman, A., (2022), Linking green supply chain management practices with competitive ensuring covid 19: The role of big data analytics. Technology in Society. 70.DOI: 10.1016/j.techsoc.2022.102021.

  23. Al-Khatib A. (2022), Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation andtechnological intensity. Business Process Management Journal,28.DOI: 10.1108/BPMJ-07-2022-0332.

View or Download full articleAccess options
Full paper accessChoose SWS login, librarian support, or instant article download.

SWS access login

Login as SWS Scientific Committee

Authors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.

For librarian assistance: [email protected]

Purchase Instant Access

48-hour online accessComing soon
Online-only accessComing soon
Download the full article in PDF formatEUR 35
  • Article can be downloaded after successful payment.
  • Article may be used according to SWS library access terms.
  • Article cannot be redistributed.
Get full paper

Back to publication list