Analyzing a Decision Support System for Resource Planning and Surgery Scheduling
2016 (English)In: Procedia Computer Science / [ed] Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E., Elsevier, 2016, Vol. 100, p. 532-538Conference paper, Published paper (Refereed)
Abstract [en]
This study aims to propose a decision support system based on optimization modelling for operating room resource planning and sequence dependent scheduling of surgery operations. We conduct a simulation experiment using real world data collected from the local hospital to evaluate the proposed model. The obtained results are compared with real surgery schedules, planned at the local hospital. The experiment shows that the efficiency of schedules produced by the proposed model are significantly improved, in terms of less surgery turnover time, increased utilization of operating rooms and minimized make-span, compared to the real schedules. Moreover, the proposed optimization based decision support system enables analysis of surgery scheduling in relation to resource planning.
Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 100, p. 532-538
Series
Procedia Computer Science, ISSN 1877-0509
Keywords [en]
Decision support system, Healthcare, Operating rooms, Optimization, Scheduling, Turnover time, Artificial intelligence, Decision support systems, Health care, Hospitals, Information systems, Project management, Resource allocation, Surgery, Optimization modelling, Real surgeries, Real-world, Resource planning, Sequence-dependent, Information management
National Category
Health Care Service and Management, Health Policy and Services and Health Economy Other Computer and Information Science
Identifiers
URN: urn:nbn:se:bth-13766DOI: 10.1016/j.procs.2016.09.192ISI: 000392695900065Scopus ID: 2-s2.0-85006942544OAI: oai:DiVA.org:bth-13766DiVA, id: diva2:1065416
Conference
Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, Porto
Part of project
Bigdata@BTH- Scalable resource-efficient systems for big data analytics, Knowledge Foundation2017-01-162017-01-162021-05-05Bibliographically approved