Risk-based Quality Research Articles

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge, Dongyang River, one tributary of Qiantang River, Zhejiang Province, China, is established in order to model water quality in areas under small data. Then, based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data, a conditional linear Gaussian Bayesian network is constructed. A “two-constraint selection” procedure is proposed to estimate potential parameter values under small data. Among all potential parameter values, the ones that are most probable are selected as the “representatives”. Finally, the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed. The final results show that conditional linear Gaussian Bayesian network and “two-constraint selection” procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

The evidence for a risk-based approach to Australian higher education regulation and quality assurance

This paper explores the nascent field of risk management in higher education, which is of particular relevance in Australia currently, as the Commonwealth Government implements its plans for a risk-based approach to higher education regulation and quality assurance. The literature outlines the concept of risk management and risk-based approaches to quality assurance across numerous private and public sector environments, primarily in the United Kingdom and Australian contexts. The objective is to shed some light on the appropriateness of such an approach, based on the success (or otherwise) of risk-based quality assurance models deployed elsewhere. In the final analysis, in seeking to find answers to the research question posed, this paper throws up many questions about this relatively new field. In doing so, it provides some sense of how this issue can be further explored to enhance the possibilities for achievement of Australia's higher education objectives.

W09-03 - Surveillance in clinical trials

The FDA, EMA and MRHA draft guidance of late 2011 recommends the adoption of more risk-based methods by sponsors to monitor clinical investigations. In neuroscience trials, the data verification and site monitoring process is effective in confirming adherence to good clinical practices and adherence to the protocol guidelines; however, does not adequately assess the actual quality of the outcome measure data from a clinical perspective and, too often, does not account for serious protocol deviations in a timely manner. This session discusses data driven risk-based quality control methods currently utilized in neuroscience clinical trials to determine the quality of outcome measure data. In this session, participants will learn how robust data monitoring platforms and sophisticated analytics ensure that emerging risk signals are identified early, addressed in a consistent way, and corrected in a timely manner. From this workshop, clinicians and statisticians in industry and academia will learn: Identifying discrepancies and inconsistencies in data that reflect possible risk signals; Evaluating the severity of the risk signal and how they influence study outcome; Preventing risk signals from becoming challenges that could impact the results of the study.

Clinical pathology services: remapping our strategic itinerary

Both technological advances and economic drivers have led to major changes in clinical laboratories across the world, with vastly improved testing productivity. However, the production process capability advances have far outpaced the clinical pathologists' success in assuring optimal test utilization and interpretation. While productivity of 'commodity' testing increases, our healthcare value productivity decreases. Such developments constitute a serious threat to our clinical pathology specialty, not only because pathologists may lose direct control of the commodity testing production activities, but also because the present evolution exposes a failure of our core clinical activities, the pathologist's knowledge processes that translate 'commodity' results into medical outcomes optimization. At a time when a revolution in health care organization is inescapable in the years ahead, clinical pathology must proceed from a merely reactive strategy (to fulfill the 'more with less' demands) to a proactive strategy where we build excellence and visibility in knowledge services on a strong foothold of operational excellence. Based on a Strengths-Weaknesses-Opportunities-Threats analysis, we argue that clinical pathology should safeguard and expand its healthcare value productivity by assuming leadership in building integrated laboratory services networks. We also suggest that the core knowledge processes deserve a system approach, for example, by applying a risk-based quality management system.

Risk-based Quality by Design (QbD): A Taguchi Perspective on the Assessment of Product Quality, and the Quantitative Linkage of Drug Product Parameters and Clinical Performance

For good reason, quality by design (QbD) has become a topic of significant interest within the pharmaceutical industry. Whereas regulatory agencies and standard-setting organizations are moving swiftly to establish QbD guidance relevant to the needs of pharmaceutical manufacturing, much of the core science of QbD was established long ago by Dr. Genichi Taguchi and other leaders in the field of quality management. In order for the pharmaceutical industry to fully capitalize on QbD, new methods for quantitatively assessing product quality need to be established. This perspective article presents a risk-based strategy for quality assessment which uses model-based simulation to link variation in drug product parameters and clinical performance. This work is intended to provide background and introduction for a series of manuscripts dealing with QbD based on probabilistic risk assessment.

Toward a Practical Method for Setting Screening-Level, Ecological Risk-Based Water Temperature Criteria and Monitoring Compliance

We integrated basic concepts from fisheries science and toxicological risk assessment to form a potential method for setting screening-level, risk-based, site-specific water quality objectives for temperature. In summary, the proposed approach: (a) uses temperature impacts upon specific growth as a measure of chronic (cumulative) temperature effects; (b) explicitly incorporates the consequences of both magnitude and cumulative duration of exposures; (c) adjusts the result for local watershed conditions, reducing the likelihood that naturally warm systems are identified as thermally polluted while helping to ensure that naturally cool systems are closely monitored for ecologically harmful changes in thermal regime; and (d) expresses the net result both graphically and as a risk quotient, RQ, closely analogous to that used in toxicological risk assessments. The latter yields a site-specific, risk-based water quality objective and may serve as a straightforward decision rule for environmental managers. The method was applied to historical data from a small British Columbia stream subject to increasing urban development pressures. We also illustrate how the technique might be used to explore potential climatic change impacts, using coupled general circulation model predictions in conjunction with empirical downscaling. Overall, the method and results are presented as an introduction and illustration of concept, intended as a step toward the development of a logistically feasible risk-based approach to establishing screening-level, site-specific water temperature objectives, and monitoring compliance, in the context of large-scale, many-site, environmental monitoring networks. With further work, the technique offers potential to fill the gap between the temperature threshold-based targets typically specified in regulatory guidelines, which may be hydroecologically unrealistic, and detailed biophysical modelling, which typically is logistically infeasible in a day-to-day environmental monitoring and management context.

Resource oriented selection of rule-based classification models: An empirical case study

The amount of resources allocated for software quality improvements is often not enough to achieve the desired software quality. Software quality classification models that yield a risk-based quality estimation of program modules, such as fault-prone (fp) and not fault-prone (nfp), are useful as software quality assurance techniques. Their usefulness is largely dependent on whether enough resources are available for inspecting the fp modules. Since a given development project has its own budget and time limitations, a resource-based software quality improvement seems more appropriate for achieving its quality goals. A classification model should provide quality improvement guidance so as to maximize resource-utilization. We present a procedure for building software quality classification models from the limited resources perspective. The essence of the procedure is the use of our recently proposed Modified Expected Cost of Misclassification (MECM) measure for developing resource-oriented software quality classification models. The measure penalizes a model, in terms of costs of misclassifications, if the model predicts more number of fp modules than the number that can be inspected with the allotted resources. Our analysis is presented in the context of our Rule-Based Classification Modeling (RBCM) technique. An empirical case study of a large-scale software system demonstrates the promising results of using the MECM measure to select an appropriate resource-based rule-based classification model.

Assessment of a New Three-Group Software Quality Classification Technique: An Empirical Case Study

The primary aim of risk-based software quality classification models is to detect, prior to testing or operations, components that are most-likely to be of high-risk. Their practical usage as quality assurance tools is gauged by the prediction-accuracy and cost-effective aspects of the models. Classifying modules into two risk groups is the more commonly practiced trend. Such models assume that all modules predicted as high-risk will be subjected to quality improvements. Due to the always-limited reliability improvement resources and the variability of the quality risk-factor, a more focused classification model may be desired to achieve cost-effective software quality assurance goals. In such cases, calibrating a three-group (high-risk, medium-risk, and low-risk) classification model is more rewarding. We present an innovative method that circumvents the complexities, computational overhead, and difficulties involved in calibrating pure or direct three-group classification models. With the application of the proposed method, practitioners can utilize an existing two-group classification algorithm thrice in order to yield the three risk-based classes. An empirical approach is taken to investigate the effectiveness and validity of the proposed technique. Some commonly used classification techniques are studied to demonstrate the proposed methodology. They include, the C4.5 decision tree algorithm, discriminant analysis, and case-based reasoning. For the first two, we compare the three-group model calibrated using the respective techniques with the one built by applying the proposed method. Any two-group classification technique can be employed by the proposed method, including those that do not provide a direct three-group classification model, e.x., logistic regression and certain binary classification trees, such as CART. Based on a case study of a large-scale industrial software system, it is observed that the proposed method yielded promising results. For a given classification technique, the expected cost of misclassification of the proposed three-group models were significantly better (generally) when compared to the technique?s direct three-group model. In addition, the proposed method is also evaluated against an alternate indirect three-group classification method.

An evaluation of HACCP implementation status in UK small and medium enterprises in food manufacturing

To reduce foodborne illnesses, hazard and risk-based quality management systems are essential. Small and medium sized companies (SMEs) tend to have a poor understanding of such systems and limited adoption of the Hazard Analysis Critical Control Point system (HACCP). The requirement for full HACCP implementation by 2006 will place an even greater burden on these businesses. The aim of this project is to assess the current levels of understanding of hazards and risks in SMEs in the manufacturing sector. A questionnaire survey was made of 850 SMEs, including microbusinesses. This determined the industry sector and processes carried out, whether the company operated hazard-based quality management and the knowledge of the technical manager regarding the associated hazards and risks. Follow-up visits to the manufacturing plant observed the processes and the operatives to determine their level of understanding. A benchmarking audit was carried out and each company was rated. The results show that the majority of respondents stated that they operated hazard analysis-based quality management. The ability of the respondents to correctly define a hazard or risk or identify different types of hazard was, however, poor. There was no correlation between business type and audit score. The microbusinesses did, however, perform significantly less well than the larger SMEs.

Resource-oriented software quality classification models

Developing high-quality software within the allotted time and budget is a key element for a productive and successful software project. Software quality classification models that provide a risk-based quality estimation, such as fault-prone ( fp) and not fault-prone ( nfp), have proven their usefulness as software quality assurance techniques. However, their usefulness is largely dependent on the availability of resources for deploying quality improvements to modules predicted as fp. Since every project has its own special needs and specifications, we feel a classification modeling approach based on resource availability is greatly warranted. We propose and demonstrate the use of a resource-based measure, i.e., “Modified Expected Cost of Misclassification” ( mecm), for selecting and evaluating classification models. It is an extension of the “Expected Cost of Misclassification” ( ecm) measure, which we have previously applied for model-evaluation purposes. The proposed measure facilitates building resource-oriented classification models and overcomes the limitation of ecm, which assumes that enough resources are available to enhance all modules predicted as fp. The primary aspect of mecm is that it penalizes a model, in terms of costs of misclassifications, if the model predicts more number of fp modules than the number that can be enhanced with the available resources. Based on the resources available for improving quality of software modules, a practitioner can use the proposed methodology to select a model that best-suits the projects goals. Hence, the best possible and practical usage of the available resources can be achieved. The application, analysis, and benefits of mecm is shown by developing models using Logistic Regression. It is concluded that the use of mecm is a promising approach for practical software quality improvement.

Ambient Air Quality and Human Health: Current Concepts, Part 2

This second of two parts continues with the development of a framework for understanding air quality issues and their relationship to human health. Recognized health effects associated with air pollution are described and current controversies regarding ozone and PM10 are briefly outlined. Epidemiological methods of investigating air quality effects are discussed, comparing recent landmark studies in Canada. Comparative prevalence studies do not reflect the state of the art in air pollution epidemiology but are frequently cited and conducted in Canada as if they were definitive. The implications of setting air quality standards and objectives on this basis or to meet arbitrary levels of risk of health effects are examined. The current state of the art does not support risk‐based air quality standards. A policy of continuous improvement is most protective of both human health and the environment.

Ambient Air Quality and Human Health: Current Concepts—Part 1

There is a fundamental reevaluation of the association between air quality and human health taking place. This reevaluation is motivated by several recent developments: increasing interest in air quality as an environmental issue; interest in the unanswered questions regarding the epidemiology of asthma; and the reduced prevalence of the principal hazard to respiratory health, cigarette smoking, the control of which invites interest in second-order determinants of health. This article attempts to provide a framework for understanding air quality issues that pertain to human health. The objective is to provide the specialist in respiratory medicine with an overview that will assist in educating patients and in responding to their inquiries, and to equip the physician to respond to requests for assistance or interpretation when called upon to comment on public policy issues involving air pollution. The implications of setting air quality standards or objectives to meet arbitrary levels of risk of health effects are examined. The current state of the art does not support risk-based air quality standards. A policy of continuous improvement is most protective of both human health and the environment.