Data-driven approaches to sustainable referral system design integrating the offline channel and the online channel

Wan, M; Shukla, N; Li, J; Pradhan, B

Data-driven approaches to sustainable referral system design integrating the offline channel and the online channel

Wan, M Shukla, N Li, J Pradhan, B

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Cleaner Production, 2023, 414, pp. 137691
Issue Date:: 2023-08-15

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Field	Value	Language
dc.contributor.author	Wan, M
dc.contributor.author	Shukla, N
dc.contributor.author	Li, J
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2024-04-11T20:14:22Z
dc.date.available	2024-04-11T20:14:22Z
dc.date.issued	2023-08-15
dc.identifier.citation	Journal of Cleaner Production, 2023, 414, pp. 137691
dc.identifier.issn	0959-6526
dc.identifier.uri	http://hdl.handle.net/10453/177771
dc.description.abstract	The Chinese healthcare referral system is a hierarchical system that includes medical institutions at different levels to ensure high-quality medical resources available and accessible to patients in need. In this research, we formulate a mixed-integer nonlinear programming model to minimize the total cost of patients, including their transportation cost, treatment cost and purchase of home-care services in a novel two-way referral system incorporating telemedicine and home-care services. To deal with the uncertain requests in teleconsultation, an adaptive robust optimization method is applied to reformulate the chance constraint into a linear form. Moreover, the uncertainty set under the measurement of Kullback–Leibler divergence is introduced to use the information and tractably approximate the chance constraint. We have used real-world data to conduct numerical experiments to determine the sensitivity of the results concerning the changes in the parameters such as the government subsidy, the maximum service capacity and the cure rate of diseases in the telemedicine center. We also prove that our optimal strategy of patient allocation in the novel referral system with the current strategy achieves environmental and social sustainability by reducing travel to healthcare facilities(less GHG emissions) and less crowdedness in the comprehensive hospitals compared to the current strategy in the traditional system. Finally, managerial insights are provided to achieve better performance in the healthcare referral system.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Cleaner Production
dc.relation.isbasedon	10.1016/j.jclepro.2023.137691
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0907 Environmental Engineering, 0910 Manufacturing Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Environmental Sciences
dc.subject.classification	33 Built environment and design
dc.subject.classification	40 Engineering
dc.title	Data-driven approaches to sustainable referral system design integrating the offline channel and the online channel
dc.type	Journal Article
utslib.citation.volume	414
utslib.for	0907 Environmental Engineering
utslib.for	0910 Manufacturing Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-11T20:14:20Z
pubs.publication-status	Published
pubs.volume	414

Abstract:

The Chinese healthcare referral system is a hierarchical system that includes medical institutions at different levels to ensure high-quality medical resources available and accessible to patients in need. In this research, we formulate a mixed-integer nonlinear programming model to minimize the total cost of patients, including their transportation cost, treatment cost and purchase of home-care services in a novel two-way referral system incorporating telemedicine and home-care services. To deal with the uncertain requests in teleconsultation, an adaptive robust optimization method is applied to reformulate the chance constraint into a linear form. Moreover, the uncertainty set under the measurement of Kullback–Leibler divergence is introduced to use the information and tractably approximate the chance constraint. We have used real-world data to conduct numerical experiments to determine the sensitivity of the results concerning the changes in the parameters such as the government subsidy, the maximum service capacity and the cure rate of diseases in the telemedicine center. We also prove that our optimal strategy of patient allocation in the novel referral system with the current strategy achieves environmental and social sustainability by reducing travel to healthcare facilities(less GHG emissions) and less crowdedness in the comprehensive hospitals compared to the current strategy in the traditional system. Finally, managerial insights are provided to achieve better performance in the healthcare referral system.

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http://hdl.handle.net/10453/177771