Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.

Combined quality function deployment and logical framework analysis to improve quality of emergency care in Malta

Purpose – The purpose of this paper is to develop an integrated patient-focused analytical framework to improve quality of care in accident and emergency (A&E) unit of a Maltese hospital. Design/methodology/approach – The study adopts a case study approach. First, a thorough literature review has been undertaken to study the various methods of healthcare quality management. Second, a healthcare quality management framework is developed using combined quality function deployment (QFD) and logical framework approach (LFA). Third, the proposed framework is applied to a Maltese hospital to demonstrate its effectiveness. The proposed framework has six steps, commencing with identifying patients’ requirements and concluding with implementing improvement projects. All the steps have been undertaken with the involvement of the concerned stakeholders in the A&E unit of the hospital. Findings – The major and related problems being faced by the hospital under study were overcrowding at A&E and shortage of beds, respectively. The combined framework ensures better A&E services and patient flow. QFD identifies and analyses the issues and challenges of A&E and LFA helps develop project plans for healthcare quality improvement. The important outcomes of implementing the proposed quality improvement programme are fewer hospital admissions, faster patient flow, expert triage and shorter waiting times at the A&E unit. Increased emergency consultant cover and faster first significant medical encounter were required to start addressing the problems effectively. Overall, the combined QFD and LFA method is effective to address quality of care in A&E unit. Practical/implications – The proposed framework can be easily integrated within any healthcare unit, as well as within entire healthcare systems, due to its flexible and user-friendly approach. It could be part of Six Sigma and other quality initiatives. Originality/value – Although QFD has been extensively deployed in healthcare setup to improve quality of care, very little has been researched on combining QFD and LFA in order to identify issues, prioritise them, derive improvement measures and implement improvement projects. Additionally, there is no research on QFD application in A&E. This paper bridges these gaps. Moreover, very little has been written on the Maltese health care system. Therefore, this study contributes demonstration of quality of emergency care in Malta.

Discovering biomarkers for antidepressant response:protocol from the Canadian biomarker integration network in depression (CAN-BIND) and clinical characteristics of the first patient cohort

Background: Major Depressive Disorder (MDD) is among the most prevalent and disabling medical conditions worldwide. Identification of clinical and biological markers ("biomarkers") of treatment response could personalize clinical decisions and lead to better outcomes. This paper describes the aims, design, and methods of a discovery study of biomarkers in antidepressant treatment response, conducted by the Canadian Biomarker Integration Network in Depression (CAN-BIND). The CAN-BIND research program investigates and identifies biomarkers that help to predict outcomes in patients with MDD treated with antidepressant medication. The primary objective of this initial study (known as CAN-BIND-1) is to identify individual and integrated neuroimaging, electrophysiological, molecular, and clinical predictors of response to sequential antidepressant monotherapy and adjunctive therapy in MDD. Methods: CAN-BIND-1 is a multisite initiative involving 6 academic health centres working collaboratively with other universities and research centres. In the 16-week protocol, patients with MDD are treated with a first-line antidepressant (escitalopram 10-20 mg/d) that, if clinically warranted after eight weeks, is augmented with an evidence-based, add-on medication (aripiprazole 2-10 mg/d). Comprehensive datasets are obtained using clinical rating scales; behavioural, dimensional, and functioning/quality of life measures; neurocognitive testing; genomic, genetic, and proteomic profiling from blood samples; combined structural and functional magnetic resonance imaging; and electroencephalography. De-identified data from all sites are aggregated within a secure neuroinformatics platform for data integration, management, storage, and analyses. Statistical analyses will include multivariate and machine-learning techniques to identify predictors, moderators, and mediators of treatment response. Discussion: From June 2013 to February 2015, a cohort of 134 participants (85 outpatients with MDD and 49 healthy participants) has been evaluated at baseline. The clinical characteristics of this cohort are similar to other studies of MDD. Recruitment at all sites is ongoing to a target sample of 290 participants. CAN-BIND will identify biomarkers of treatment response in MDD through extensive clinical, molecular, and imaging assessments, in order to improve treatment practice and clinical outcomes. It will also create an innovative, robust platform and database for future research. Trial registration: ClinicalTrials.gov identifier NCT01655706. Registered July 27, 2012.