In this paper, we present an integrated collaborative modular architecture method for medical device design and development. The methodology is focused on analyzing the input of stakeholder data from existing products and components to achieve an optimal number of modules. The methodology starts by defining a product’s functional and physical decompositions. Product parameters are selected such as quality, reliability, ease of development, and cost. These are prioritized using analytical hierarchy process (AHP) to determine the medical device manufacturers’ focus area. The parameters’ subsequent metrics are selected for performance requirements. Next, we evaluate the candidate modules by acquiring stakeholder data and converting them to crisp values by applying the Sugeno fuzzy-based method. Finally, we determine the subsequent optimal module values using a multi-optimization goal programming model. We present here a proof of concept using a typical glucometer. The implication of this work is the determination of the optimal number of product modules based on stakeholder constraints. Hence, an original equipment manufacturer (OEM) can work on fewer components per module without adversely affecting the integrity, quality, and reliability of the final product. Next is the improved quality of patient care by enabling cost reductions in product design and development, thereby improving patient safety. This methodology helps reduce product cycle time, thereby improving market competitiveness among other factors.
During a medical surge, resource scarcity and other factors influence the performance of the healthcare systems. To enhance their performance, hospitals need to identify the critical indicators that affect their operations for better decision-making. This study aims to model a pertinent set of indicators for improving emergency departments’ (ED) performance during a medical surge. The framework comprises a three-stage process to survey, evaluate, and rank such indicators in a systematic approach. The first stage consists of a survey based on the literature and interviews to extract quality indicators that impact the EDs’ performance. The second stage consists of forming a panel of medical professionals to complete the survey questionnaire and applying our proposed consensus-based modified fuzzy Delphi method, which integrates text mining to address the fuzziness and obtain the sentiment scores in expert responses. The final stage ranks the indicators based on their stability and convergence. Here, twenty-nine potential indicators are extracted in the first stage, categorized into five healthcare performance factors, are reduced to twenty consentaneous indicators monitoring ED’s efficacy. The Mann-Whitney test confirmed the stability of the group opinions (p < 0.05). The agreement percentage indicates that ED beds (77.8%), nurse staffing per patient seen (77.3%), and length of stay (75.0%) are among the most significant indicators affecting the ED’s performance when responding to a surge. This research proposes a framework that helps hospital administrators determine essential indicators to monitor, manage, and improve the performance of EDs systematically during a surge event.
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