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基于传感器的医疗器械供应商选择问题的混合多准则方法。

A Hybrid Multi-Criteria Approach to the Vendor Selection Problem for Sensor-Based Medical Devices.

机构信息

National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, 011455 Bucharest, Romania.

Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics, Romanian Academy, Calea 13 Septembrie, No.13, 050711 Bucharest, Romania.

出版信息

Sensors (Basel). 2023 Jan 9;23(2):764. doi: 10.3390/s23020764.

Abstract

Sensors for health are a dynamic technology and sensor-based medical devices (SMD) are becoming an important part of health monitoring systems in healthcare centers and ambulatory care. The rapid growth in the number, diversity and costs of medical devices and Internet of Things (IoT) healthcare platforms imposes a challenge for healthcare managers: making a rational choice of SMD vendor from a set of potential SMD vendors. The aim of this paper is to develop a hybrid approach that combines a performance evaluation model and a multi-objective model for the SMD vendor selection problem. For determining the criteria weights in the performance evaluation model, an original version of the best worst method (BWM) is applied, which we call the flexible best worst method (FBWM). The multi-objective model has two objective functions; one is to maximize the SMD performance and the other is to minimize the SMD cost. A case study for the application of the hybrid approach for SMD procurement in a healthcare center is analyzed. The hybrid approach can support healthcare decision makers in their SMD procurement decisions.

摘要

用于健康监测的传感器是一项极具活力的技术,基于传感器的医疗设备(SMD)正逐渐成为医疗中心和移动医疗护理中健康监测系统的重要组成部分。医疗器械和物联网(IoT)医疗平台数量、多样性和成本的快速增长,给医疗保健管理人员带来了挑战:需要从一系列潜在的 SMD 供应商中,为 SMD 供应商选择做出合理决策。本文旨在开发一种混合方法,将性能评估模型和多目标模型相结合,用于 SMD 供应商选择问题。为了确定性能评估模型中的标准权重,应用了最佳最差方法(BWM)的原始版本,我们称之为灵活最佳最差方法(FBWM)。多目标模型有两个目标函数;一个是最大化 SMD 性能,另一个是最小化 SMD 成本。通过对医疗中心 SMD 采购应用混合方法的案例研究进行分析。该混合方法可以为医疗保健决策者的 SMD 采购决策提供支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a688/9863984/e923bf90fc3a/sensors-23-00764-g001.jpg

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