Abstract：
Proper Health-Care Waste Management (HCWM) and integrated documentation in this sector of hospitals require analyzing massive data collected by hospital\'s health experts. This study presented a quantitative software-based index to assess the HCWM process performance by integrating ontology-based Multi-Criteria Group Decision-Making techniques and fuzzy modeling that were coupled with data mining. This framework represented the Complex Event Processing (CEP) and Corporate Performance Management (CPM) types of Process Mining in which a user-friendly software namely Group Fuzzy Decision-Making (GFDM) was employed for index calculation.
Assessing the governmental hospitals of Shiraz, Iran in 2016 showed that the proposed index was able to determine the waste management condition and clarify the blind spots of HCWM in the hospitals. The index values under 50 were found in some of the hospitals showing poor process performance that should be at the priority of optimization and improvement.
The proposed framework has distinctive features such as modeling the uncertainties (risks) in hospitals\' process assessment and flexibility enabling users to define the intended criteria, stakeholders, and number of hospitals. Having computer-aided approach for decision process also accelerates the index calculation as well as its accuracy which would contribute to more willingness of hospitals\' experts and other end-users to use the index in practice. The methodology could efficiently be employed as a tool for managing hospitals\' event logs and digital documentation in big data environment not only for the health-care waste management, but also in other administrative wards of hospitals.
Assessing the governmental hospitals of Shiraz, Iran in 2016 showed that the proposed index was able to determine the waste management condition and clarify the blind spots of HCWM in the hospitals. The index values under 50 were found in some of the hospitals showing poor process performance that should be at the priority of optimization and improvement.
The proposed framework has distinctive features such as modeling the uncertainties (risks) in hospitals\' process assessment and flexibility enabling users to define the intended criteria, stakeholders, and number of hospitals. Having computer-aided approach for decision process also accelerates the index calculation as well as its accuracy which would contribute to more willingness of hospitals\' experts and other end-users to use the index in practice. The methodology could efficiently be employed as a tool for managing hospitals\' event logs and digital documentation in big data environment not only for the health-care waste management, but also in other administrative wards of hospitals.
摘要：
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