Gül E. Okudan Kremer

A simulation analysis of the impact of FAHP-MAUT triage algorithm on the Emergency Department performance measures

Emergency Department (ED) crowding is a major problem in the U.S. like in many other countries world-wide. This problem is adversely affecting the safety of the patients who rely on receiving a timely treatment in EDs. As a part of solving this problem, a triage process is utilized. Triage is a pre-hospital process by which patients are sorted according to the severity of their illnesses or injuries. Improvements to this process would affect the patient flow positively, and in turn would enhance patient satisfaction and quality of care. In a previous study, we developed a triage algorithm that uses Fuzzy Analytic Hierarchy Process (FAHP) and Multi-Attribute Utility Theory (MAUT) to rank the patients according to their characteristics: chief complaint, age, gender, pain level, and vital signs. The main purpose of this study is to compare two triage systems using Discrete Event Simulation (DES); one system uses the typical Emergency Severity Index (ESI), and the other uses the FAHP and MAUT algorithm. Overall, there was no strong statistical evidence that either system would do better than the other for all the performance measures when the average is taken across all ESI levels. On the other hand, the collected simulated data by each ESI level showed that the FAHP-MAUT algorithm tends to balance the time-to-bed (TTB) and length of stay (LOS) for ESI levels 2-5. In terms of the percentage of tardy patients, FAHP-MAUT system significantly outperforms the ESI system for ESI levels 4 and 5; 34% vs. 61% and 25% vs. 70%, respectively. Both systems were performing about equally for ESI level 1 and level 3 patients; 25% vs. 26% and 64% vs. 67%, respectively. While ESI system slightly outperforms FAHP-MAUT system for ESI level 2 patients, 56% vs. 66%. Based on these results, we recommend using FAHP-MAUT not only because it performs better in terms of minimizing the number of patients with longer than the allotted upper limits of wait times, but also it reduces potential bias and errors in decision making in clinical settings; and thus, it can be used as the basis of an expert system to advise triage nurses.

Supporting medical device development: a standard product design process model

This article describes the complex nature of the medical device development (MDD) process and presents a product design process model to aid designers engaged in MDD. Basically, the model serves as a conceptual framework and provides a set of formalisms to define the development landscape for medical devices. Specifically, the model describes the phases of MDD and their relationships, including the testing and approval environment that impacts this process. The Food and Drug Administration (FDA) is an essential part of this environment, acting as the regulatory agency for medical devices in the USA. FDA approval is a significant milestone which industry developers must achieve before the actual release of their devices in the US market. In this article, the relevant literature addressing the regulations and the MDD process models are reviewed. The development and organisation of the model are discussed in detail with descriptions of the building process, concepts, granularity, utility and overall contribution. The model is the result of a documentation analysis and is supported by a content validation survey and a case study. The model is proposed as an aid for designers to proactively use in handling the complexities of MDD.

A Modular Design Approach to Improve Product Life Cycle Performance Based on the Optimization of a Closed-Loop Supply Chain

As environmental concerns have grown in recent years, the interest in product design for the life cycle (DFLC) has exhibited a parallel surge. Modular design has the potential to bring life cycle considerations into the product architecture decision-making process, yet most current modular design methods lack the capability for assessing module life cycle consequences in a supply chain. This paper proposes a method for product designers, called the architecture and supply chain evaluation method (ASCEM), to find a product modular architecture with both low life cycle costs and low energy consumption at the early design stages. ASCEM expands the assessment scope from the products architecture to its supply chain network. This work analyzes the life cycle costs (LCCs) and energy consumption (LCEC) of two products designated within the European Unions directive on waste of electric and electronic equipment (WEEE) within a closed-loop supply chain to identify the most beneficial modular structure. In addition, data on 27 theoretical cases representing various products are analyzed to show the broader applicability of the proposed methodology. Our analysis shows that ASCEM can efficiently identify a good-quality modular structure having low LCC and LCEC in a closed-loop supply chain for both the two tested products and the hypothetical cases.

Dynamic patient grouping and prioritization: a new approach to emergency department flow improvement

The demand on emergency departments (ED) is variable and ever increasing, often leaving them overcrowded. Many hospitals are utilizing triage algorithms to rapidly sort and classify patients based on the severity of their injury or illness, however, most current triage methods are prone to over- or under-triage. In this paper, the group technology (GT) concept is applied to the triage process to develop a dynamic grouping and prioritization (DGP) algorithm. This algorithm identifies most appropriate patient groups and prioritizes them according to patient- and system-related information. Discrete event simulation (DES) has been implemented to investigate the impact of the DGP algorithm on the performance measures of the ED system. The impact was studied in comparison with the currently used triage algorithm, i.e., emergency severity index (ESI). The DGP algorithm outperforms the ESI algorithm by shortening patients’ average length of stay (LOS), average time to bed (TTB), time in emergency room, and lowering the percentage of tardy patients and their associated risk in the system.

A dynamic multi-attribute utility theory-based decision support system for patient prioritization in the emergency department

The triage process may result in long waiting periods during which vital indicators of patients with apparently less urgent problems are not monitored after the initial triage. The integration of technology and decision theory has the potential to assist nurses in recognizing priorities by collecting data on the changing clinical information of patients and methodically organizing it. This study investigates the potential for integrating technology and multi-attribute utility theory (MAUT) to develop a dynamic decision support system (DSS) for patient prioritization in Emergency Department (ED) settings. An enhancement to the conventional MAUT model was made to incorporate changes in vital signs over time. A pilot study was conducted with data from 12 nurses and 47 patients. The dynamic MAUT model was assessed with a physician who made prioritization decisions independent of the model. A statistical analysis shows no significant difference between the recommendation proposed by the model and the decisions made by the physician. The results from the analysis give evidence for the potential benefits of combining technology with decision theory to aid nurses in prioritizing ED patients. These results can be used to further develop a DSS for dynamic patient prioritization in ED settings.

Product Modularity and Implications for the Reverse Supply Chain

Modularity has been widely used and studied in industry and academia. Modular products consist of detachable modules that can be manufactured, assembled, and serviced separately. Some module components (or overall modules themselves) can be reusable, recyclable, or remanufacturable after reaching the end of their original life cycle. The reverse supply chain represents all operations related to reuse of products, components, and their materials. Recently, environmental issues including energy usage and the carbon footprint (CF) implications of products have attracted attention. Thus, designing product modular architectures that consider not only the interactions across components but also component end-of-life (EOL) options (i.e., reuse, recycle, and disposal) has become important. In this article, we compare results of two modularity methods for their CF implications. The environmental impact (i.e., CF generation during assembly or manufacturing) was analyzed by dissecting a refrigerator and using SimaPro software for the recovered data. Significantly, a new factor—the carbon footprint—is introduced into the analysis of modularization comparisons. We discuss the implications of the differences in terms of CF for the reverse supply chain.

Strategic decision making for multiple-generation product lines using dynamic state variable models: The cannibalization case

Multiple-generation product lines require carefully planned strategies. Under a multiple-generation product development strategy, companies introduce a line of products to the market instead of introducing a single product to better utilize technology assets and resources in an elongated time span. For such product development and launch scenarios, cannibalization can occur, however. That is, multiple product generations compete in the same market and partition the companys market shares. In the paper, we propose a new framework to predict the sales and introduction timing for every product generation in a multiple-generation product line while considering cannibalization. We demonstrate a case study implementing the proposed framework on Apple Inc.s iPhone product line. The results show that the forecast performance of the model matches the realized data. Moreover, because the proposed framework is not computationally prohibitive, it can be used widely.

Professional Skills Assessment: Is a Model of Domain Learning Framework Appropriate?

Today’s global economy demands that new graduates excel in not only technical knowledge but also professional skills. In fact, the lack of professional skills in project teams has been identified among the most important factors contributing to the high failure rate of complex engineering projects. As a response, academic programs have incorporated professional skills in their curricula. However, there are challenges in the assessment of learning outcomes related to professional skills. This paper presents a novel assessment framework based on the Model of Domain Learning, to assess students’ development in professional skills across different disciplines. The proposed assessment model can be tailored to various learning objectives and student levels to facilitate integration of the assessment of professional skills into an overall program assessment plan. An empirical study, which assesses the teamwork communication skills, is presented to demonstrate the applicability of the proposed framework and its advantages as compared to other traditional assessment rubrics in engineering and technology education. Professional Skills Assessment: Is a Model of Domain Learning Framework Appropriate?

An investigation of critical factors in medical device development through Bayesian networks

In this paper, we investigate the impact of product, company context and regulatory environment factors for their potential impact on medical device development (MDD). The presented work investigates the impact of these factors on the Food and Drug Administrations (FDA) decision time for submissions that request clearance, or approval to launch a medical device in the market. Our overall goal is to identify critical factors using historical data and rigorous techniques so that an expert system can be built to guide product developers to improve the efficiency of the MDD process, and thereby reduce associated costs. We employ a Bayesian network (BN) approach, a well-known machine learning method, to examine what the critical factors in the MDD context are. This analysis is performed using the data from 2400 FDA approved orthopedic devices that represent products from 474 different companies. Presented inferences are to be used as the backbone of an expert system specific to MDD.

The Impact of Team-Based Product Dissection on Design Novelty

Although design novelty is a critical area of research in engineering design, most research in this space has focused on understanding and developing formal idea generation methods instead of focusing on the impact of current design practices. This is problematic because formal techniques are often not adopted in industry due to the burdensome steps often included in these methods, which limit the practicality and adoption of these methods. This study seeks to understand the impact of product dissection, a design method widely utilized in academia and industry, on design novelty in order to produce recommendations for the use or alterations of this method for supporting novelty in design. To investigate the impact of dissection, a study was conducted with 76 engineering students who completed a team-based dissection of an electric toothbrush and then individually generated ideas. The relationships between involvement in the dissection activity, the product dissected, the novelty and quantity of the ideas developed were investigated. The results reveal that team members who were more involved in the dissection activity generated concepts that were more novel than those who did not. In addition, the type of the dissected product also had an influence on design novelty. Finally, a positive correlation between the number of ideas generated and the novelty of the design concepts was identified. The results from this study are used to provide recommendations for leveraging product dissection for enhancing novelty in engineering design education and practice.