Shui Yee Wong

Assessing new product development project risk by Bayesian network with a systematic probability generation methodology

New product development (NPD) is a crucial process to keep a company being competitive. However, because of its inherent features, NPD is a process with high risk as well as high uncertainty. To ensure a smooth operation of NPD, the risk involved in the process need to be assessed and the uncertainty should also be addressed properly. Facing these two tasks, in this paper, the critical risk factors in NPD are first analyzed. Since Bayesian network is specialized in dealing with uncertainties, those risk factors are then modeled into a Bayesian network to facilitate the assessing of the risk involved in an NPD process. To generate the probabilities of different kinds of nodes in a Bayesian network, a systematic probability generation approach is proposed with emphasis on generating the conditional probabilities of the nodes with multi-parents. A case study is also given in the paper to test and validate the critical risk factors as well as the probability generation approach.

Organizational innovation management: An organization‐wide perspective

Purpose – Organizational innovation management (OIM) is one of the critical means to sustain competitiveness in organizational innovation in the long term. Although literature in innovation management has discussed the notions of OIM, an organization‐wide OIM framework has not yet been developed and validated. This project is thus carried out to develop and validate an organization‐wide OIM framework.Design/methodology/approach – From an extensive review of literature, core values and concepts of an OIM conceptual system are developed. Then, OIM critical factors are identified and validated by questionnaires and interviews in the HK/PRD manufacturing industry.Findings – The OIM critical factors are validated by examining the importance of OIM critical factors and the relationship between company accomplishment in individual factors and performance in organizational innovation. Also an organization‐wide OIM hierarchical framework is formulated based on the OIM factors, with an evaluation of the HK/PRD manu...

Recent Research and Developments in Temporal and Spatiotemporal Surveillance for Public Health

The objective of public health surveillance is to systematically collect, analyze, and interpret public health data (about chronic or infectious diseases) to understand trends; detect changes in disease incidence and death rates; and plan, implement, and evaluate public health practices. Recently, studies have been conducted to develop methods and algorithms for health surveillance and disease detection. This paper attempts to review recent research on temporal and spatiotemporal surveillance methods. We have addressed specific challenges and research gaps in the relevant research. Lastly, we discuss a comparative example using a dataset of male thyroid cancer cases in New Mexico.

A neural network-based approach of quantifying relative importance among various determinants toward organizational innovation

Innovation management is the practice of managing new ideas and insights into business environment so as to increase market competitiveness. Therefore, it is vital to address the critical determinants of effective innovation management within an organizational context, hence called organizational innovation. Based on some existing frameworks of managing organizational innovation, several key determinants can be identified. Using industrial survey data, we then proposed and used a neural network-based approach to quantify the connectivity between each of these determinants and organizational innovation. First, the modeling accuracy of our proposed method was examined and benchmarked with multiple linear regression and non-linear least square fitting methods. Next, the relative importance among various key determinants toward organizational innovation can be computed using our method. We then compared the results with the qualitative judgment of field experts and professionals using the analytical hierarchy process. Based on the comparison outcomes, our proposed method is proved to be reasonably useful and practical in capturing the comparative influence among determinants toward organizational innovation.

A hybrid case-based reasoning CAD system for injection mould design

Injection mould design generally lies on the critical path of a new product development. The design efficiency will have significant impact on the overall lead time of a new product. This paper presents a prototype injection mould-design system using a hybrid case-based reasoning (HCBR) approach. Case-based reasoning (CBR) is a solving paradigm that uses previous episodes on solving problems similar to the problem at hand (the new problem) as the basis for solving the new problem. In this hybrid system, CBR is incorporated with generalized design knowledge, and provides a flexible and comprehensive model of design. The knowledge base of the system would be accessed by mould designers through interactive programs so that their own intelligence and experience could also be incorporated with the total mould design. The approach provides a workable model of mould design system with CBR and knowledge-based expert system intelligent support, which could suggest good and proven design solutions to new design problems quickly, avoiding the time necessary to create those designs from scratch, for the plastic products manufacturing industry.

Delays in service for non-emergent patients due to arrival of emergent patients in the emergency department: a case study in Hong Kong

Abstract Background In Hong Kong Emergency Departments (EDs), the timeliness of providing high-quality services has been compromised by the increasing attendance of non-emergent patients in addition to the unpredictable arrival of emergency patients. Objectives We sought to quantify the impact of the presence of emergent patients and other related factors on the delay in service for non-emergent patients. Methods We conducted a retrospective study in patients who visited the ED of a large hospital in Hong Kong from July 1, 2009 to June 30, 2010. We estimated waiting and length of stay (LOS) for individual non-emergent patients registered during day and evening shifts. Using multiple linear regression, we estimated waiting time and LOS as a function of the presence of emergent patients and other related factors such as patient demographics and clinical factors. In particular, we evaluated the influence of the arrival or presence of emergent patients on the odds of violating the 120-min waiting time target for semi-urgent patients. Results The arrival of a new emergent patient prolonged the waiting time and LOS of a non-emergent patient by 14.9% (95% confidence interval [CI] 14.2–15.5) and 10.8% (95% CI 10.6–11.0), respectively. An additional patient-hour needed for an emergent patient increased the probability of violating the waiting time target for non-emergent patients (odds ratio 2.3, 95% CI 2.2–2.4). Conclusions The arrival of an emergent patient significantly prolonged the waiting time and LOS for non-emergent patients. Discouraging non-urgent ED utilization and building a real-time decision-support system are critical methods needed to relieve staff pressure and guide contingent resource reallocation when emergent patients arrive.

A simulation study to achieve healthcare service quality improvement in accident & emergency department (AED)

It is understood that potential outbreaks of infectious diseases may cause significant impacts on hospital service quality. System simulation study provides essential grounds to quantitatively evaluate the robustness of current medical systems in preparing next outbreak. The project attempts to improve the accident and emergency department (AED) service quality by enhancing its operation efficiency. To achieve the target, a simulation study which focuses on investigating causes of failure in meeting waiting time service pledge, in particular for Category III and IV cases, is being carried out. This paper provides details of the planning and design of data collection system for AED simulation. The simulation model is able to facilitate AED workflow decision making and examine possible process improvement initiatives. The development of a valid model is able to facilitate clinical workflow decision-making, facility and resource allocation, capacity planning, evaluation of treatment efficacy, and process reengineering.

Using simulation and optimisation to characterise durations of emergency department service times with incomplete data

Simulation models of emergency departments (EDs) are often built based on incomplete data, for example, missing arrival times or service-time durations. The difficulty in collecting reliable and complete data can subsequently lead to invalid simulation results. To tackle this problem, we propose a simulation and optimisation method to characterise the unavailable durations of service times. Since many services in an ED are sequential and dependent on each other, this paper considers these multiple process steps cooperatively. We first use lognormal distributions to characterise the key service durations. Then we propose a new meta-heuristic approach, which combines an Improved Adaptive Genetic Algorithm (AGA) and Simulated Annealing (SA), IAGASA, to search for the optimal set of service-time distribution parameters. To address the difficulties of applying IAGASA when noise is involved in the performance measures and improve the simulation efficiency, we jointly apply IAGASA and Optimal Computing Budget Allocation (OCBA) technology. OCBA minimises the total simulation cost for achieving a desired level of probability of correctly selecting the best set of distribution parameters, which improves the search efficiency significantly. The experimental results indicate that our proposed method can find accurate estimates of service-time distribution parameters within a relatively short time.

Modeling patient visits to Accident and Emergency Department in Hong Kong

Simulation emerges as an important technique in recent years for modeling complex operational dynamics in various healthcare institutions and hence providing deep insights for potential improvement. In particular, Accident and Emergency Department (A&ED) has been a place for such research as it accounts for a large proportion of the total hospital visits and admissions. To create a viable simulation for A&ED, accurate description and forecast of patient visits is the foremost step. This paper investigates several contributing factors to A&ED visits, and various time-series methods of modeling A&ED visits with different triage categories and mode of arrival. All the methods are compared in terms of goodness-of-fit and forecast accuracy. The purpose of this research is two-fold. First, this research is part of attempt to build a simulation model for A&ED of a local hospital. Second, the results may be useful for reexamine the resource allocation plan of the A&ED.

Recent research in public health surveillance and health management

While the challenges of the next pandemic outbreak are overwhelming, either from swine flu, other infectious disease, bioterrorism, timely detection of disease outbreaks is most important for public health surveillance and society safety and stability. In public health surveillance, the objective is to systematically collect, analyze, and interpret public health data (chronic or infectious diseases) in order to understand trends, to detect changes in disease incidence and death rates, and to plan, implement, and evaluate public health practice. Recently much research has been conducted to develop methods and algorithms for health surveillance and disease detection. This paper presents an overview and reviews the recent research methods on temporal and spatiotemporal surveillance. Specific research challenges and future research directions are discussed. A real life example is used to compare the performance of three currently used surveillance methods, scan, EWMA, and CUSUM.