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Clinical concern for acute coronary syndrome (ACS) is one of emergency medicine's most common patient encounters. This study aims to develop an ensemble learning-driven framework as a diagnostic support tool to prevent misdiagnosis.We obtained extensive clinical electronic health data on patient encounters with clinical concerns for ACS from a large urban emergency department (ED) between January 2017 and August 2020. We applied an analytical framework equipped with many well-developed algorithms to improve the data quality by addressing missing values, dimensionality reduction, and data imbalance. We trained ensemble learning algorithms to classify patients with ACS or non-ACS etiologies of their symptoms. We used performance evaluation metrics such as accuracy, sensitivity, precision, F1-score, and the area under the receiver operating characteristic (AUROC) to measure the model's performance.The analysis included 31,228 patients, of whom 563 (1.8%) had ACS and 30,665 (98.2%) had alternative diagnoses. Eleven features, including systolic blood pressure, brain natriuretic peptide, chronic heart disease, coronary artery disease, creatinine, glucose, heart attack, heart rate, nephrotic syndrome, red cell distribution width, and troponin level, are reported as significantly contributing risk factors. The proposed framework successfully classifies these cohorts with sensitivity and AUROC as high as 86.3% and 93.3%. Our proposed model's accuracy, precision, specificity, Matthew's correlation coefficient, and F1-score were 85.7%, 86.3%, 93%, 80%, and 86.3%, respectively.Our proposed framework can identify early patients with ACS through further refinement and validation.

Identification, interpretation and response to customer requirements are the key success factors for companies, regardless of their industry. Failing to satisfy customer requirements can damage a company's reputation and cause heavy losses. In this study, we have developed a new approach for properly interpreting and analyzing the fuzzy voice of the customer using association rule learning and text mining. This unique methodology converts textual and qualitative data into a common quantitative format which is then used to develop a mapped Integrated Customer Satisfaction Index (ICSI). ICSI is a framework for measuring customer satisfaction. Previous measures of customer satisfaction ratio failed to incorporate the cost implications of resolving customer complaints/issues and the fuzzy impact of those complaints/issues on the system. In addition to including these important and unique factors in the present study, we have also introduced a dynamic Critical to Quality (CTQ) concept, a novel method that provides a real-time system to monitor the CTQ list through an updated CTQ library. Finally, a procedure for customer feedback mining and sentiment analysis is proposed that handles typographical errors, which are unavoidable in every real database. The results of this study suggest that incorporating the fuzzy level of negativity and positivity of comments into the model instead of treating negative and positive comments as binary variables, leads to more reasonable outcomes. In addition, this study provides a more structured framework for understanding customer requirements.

The reprocessing of reusable medical equipment (RME) is complex due to the difficulty of eliminating infections. Healthcare providers have expressed dissatisfaction with the difficulty faced in cleaning and disinfecting medical equipment after use. Reprocessed RMEs include endoscopes, valves, adapters etc. This research aims to increase the ease of reprocessing and decrease the risk of infection using a collaborative modular architecture framework. The methodology is divided into four steps. First, we identify and define the product’s functional and physical decompositions. Secondly, based on stakeholders’ input, parameters such as design, human factors, and cost were identified to be the main factors affecting the reprocessing of an endoscope. The parameters’ subsequent metrics are selected for performance requirements. Thirdly, surveys are developed to collect data about the performance of different endoscope models. Fourthly, we utilize a linear multi-objective optimization model which aims at generating representative solutions on the true Pareto front of the problem that maximizes the similarity among module members in terms of the factors. Finally, we combine the module information with efficiency feedback to derive recommendations for the users. A case study is presented using hospital data. The results indicate that there is a high risk of infection caused by human errors during reprocessing for endoscopes clustered in Module 1 compared to Module 2 and 3. To ensure that the safety and quality of medical care rendered to patients are not compromised, we recommend that healthcare providers utilize endoscopes in Modules 2 and 3 as they are safe and easy to reprocess. © 2019 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Voice of the customer (VOC) is a critical analysis procedure that provides precise information regarding customer input requirements for a product/service output. The ability to conduct a voice of the customer analysis, which could be gained through direct and indirect questioning, will enable engineers and other decision makers to successfully understand customer needs, wants, perceptions, and preferences. The information obtained from the customers is then translated into critical targets that will be used to ultimately satisfy the customer requirements. During this research project, different forms of customer input, including qualitative and quantitative data, were transformed to a common data format to develop a correlation between design input requirements and product/service outputs. We have developed a new method for measuring customer satisfaction ratio (CSR) by considering the following: mining both textual and quantitative data, multiple design parameters, mapping output on a scale of 0-1, and a decision template for means of measure. Previous measures of CSR fail to incorporate the cost implication of fixing customer complaints/issues; however, we include this important and unique measure in our research. The implication of this research will reduce Things Gone Wrong (TGW's) and engineering development time and will achieve improvements in JD Power ratings, quality perception, marketing tools, and customer satisfaction.

Medical devices have a very high failure rate in their first prototype tests. According to the international testing body Intertek, out of every ten medical devices, nine fail in their first prototype tests—a 90% failure rate. In addition to the cost implication, quality is a key issue. To address this, we present an integrated, collaborative modular architecture method for medical device design and development. The methodology focuses on analyzing the input of stakeholder data from existing products and components to achieve an optimal number of modules. The objective of this research is to investigate the effect of rules modification on the final number of product modules. The methodology starts by defining a product's functional and physical decompositions. Next, product parameters are selected and prioritized using an analytical hierarchy process (AHP) to determine the medical device manufacturers’ focus area(s). Candidate modules are evaluated by acquiring stakeholder data and converting them to cris...

The goal of this research is to determine the effect of customer ratings on the optimal number of modules for medical device design. Medical devices have a 90% failure rate in their first prototype tests according to the international testing body, Intertek. To address this key issue of quality, we present an integrated, collaborative, modular architecture method for medical device design and development. A typical glucometer is used as proof of concept to demonstrate the methodology and analyze the impact of changing the customer ratings on the optimal number of modules and minimum deviation. The implication of this research is to generate scholarly work and to reduce the number of potential failure points in medical devices by determining the optimal number of modules.

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.

The purpose of this project is to develop a modular architecture framework for the design and manufacture of medical devices. This modular framework aims to incorporate design variables and criteria that are unique to the medical domain to facilitate reliable operation, easier maintenance, and faster product development time. Central to this research effort is the need for inputs from the range of stakeholders. The specific goals for this effort are to determine design criteria by collaborating with users and manufacturers of medical equipment and literature search, to translate user inputs to specific design targets, to develop a preliminary modular design framework using multicriteria optimization methods, and to test a preliminary modular architecture using a simple medical device such as a glucometer. The importance of the research with respect to its application in the medical arena can be very significant. With the product interaction with humans, both on the manufacturing level and the user level, the issue of safety is paramount. Some of the other significant contributions are in the improvement of the following: product quality and reliability, product life cycle issues, and an enabler for the medical community.

The purpose of this project is to develop a modular architecture framework for the design and manufacture of medical devices. This modular framework aims to incorporate design variables and criteria that are unique to the medical domain to facilitate reliable operation, easier maintenance, and faster product development time. Central to this research effort is the need for inputs from range of stakeholders. The specific goals for this effort are: to determine design criteria by collaborating with users and manufacturers of medical equipment and literature search; to translate user inputs to specific design targets; to develop a preliminary modular design framework using multi criteria optimization methods; to test preliminary modular architecture using a simple medical device such as a glucometer. The importance of the research with respect to its application in the medical arena can be very significant. With the product interaction with humans, both on the manufacturing level, and the user level, the issue of safety is paramount. Some of the other significant contributions are in the improvement of the following: product quality and reliability; product life cycle issues; an enabler for the medical community.