Evaluation of Home Care vs. Conventional Care Using Parametric Cost Estimation and the Fuzzy Analytical Hierarchy Process: A Case Study in Central Sweden
2021 (English)In: 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, Online, Singapore: Institute of Electrical and Electronics Engineers (IEEE), 2021, p. 1706-1710Conference paper, Published paper (Refereed)
Abstract [en]
Sweden is experiencing a demographic transition contributing to the rise in care-related costs. This study aims to evaluate whether home care is a better choice than conventional care from an economical and quality standpoint for people over the age of 65. The costs of the two care models were estimated using parametric cost estimation. The fuzzy analytic hierarchy process was used to determine which of the two care models was the most beneficial. The cost estimation implied that conventional care is less expensive than home care. However, the sensitivity analysis indicates that a minimal improvement in efficiency is sufficient enough for home care to become the least expensive option. In addition, the fuzzy analytic hierarchy process revealed that home care should be the prioritized option, even after a sensitivity analysis was conducted. Therefore, from an overall perspective, home care is the preferable care model. This paper evaluated a healthcare management problem using conventional methods, introducing new insight on how to perceive complex problems in today's healthcare environment.
Place, publisher, year, edition, pages
Singapore: Institute of Electrical and Electronics Engineers (IEEE), 2021. p. 1706-1710
Keywords [en]
Multicriteria analysis, healthcare management, decision analysis, cost economic relationship, sensitivity analysis
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:miun:diva-44178DOI: 10.1109/IEEM50564.2021.9672916ISI: 000821855600330Scopus ID: 2-s2.0-85125406491OAI: oai:DiVA.org:miun-44178DiVA, id: diva2:1634598
Conference
2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, [DIGITAL], December 13-16, 2021.
2022-02-022022-02-022022-08-02Bibliographically approved