The development and testing of a contextual model for healthcare quality improvement using Lean and the Model for Understanding Success in Quality (MUSIQ) : a thesis presented in partial fulfilment of the requirements for the degree of Master of Quality Systems at Massey University, Palmerston North, New Zealand

This study developed a new theoretical model of quality improvement (QI) contextual factors, for QI activity undertaken at the healthcare microsystem level. The Model for Understanding Success in Quality (MUSIQ) (Kaplan, Provost, Froehle, & Margolis, 2012), was aligned with Lean improvement activity using the Toyota Way framework. The aim of the research was to improve the effectiveness of healthcare quality improvement initiatives by providing more understanding of the associations, relative importance and precise functioning of critical contextual factors. A new survey instrument, based on the literature, was developed to collect data and the hypothesised theoretical relationships were tested using the partial least squares path modelling (PLSPM) technique. QI practitioners at a large New Zealand District Health Board were surveyed on a range of contextual factors hypothesised to influence improvement outcomes. All survey participants had recently completed a small-scale improvement project using Lean, or were participants in training programmes that introduced them to Lean thinking and methods. Some participants worked autonomously on improvements of their own selection; others were part of a wider training programme derived from the National Health Service’s (UK) ‘productive ward’ programme. In the healthcare organisational context, the majority of these improvement initiatives were carried out at the microsystem level – initiated and delivered by the teams responsible for the work processes being modified. Survey responses were first analysed via principal components analysis (to examine the dimensionality of the scales) and then PLSPM. The defined contextual factors for ‘Teamwork’, ‘Respect for People’, ‘Lean Actions’ and the influence of negatively motivating factors all reached significance. Defined contextual factors for ‘Previous Experience’ and the influence of positive motivating factors did not reach significance at 5% level. The final model showed a statistically significant, moderate predictive strength, with an overall adjusted R2 of 0.58. This result was an encouraging validation of the microsystem-level layer of the MUSIQ model using Lean as the QI method (context). The relative influence of ‘Teamwork’, ‘Respect for People’, ‘Motivation’, and a mediating mechanism for making process changes (in this instance, Lean) were measured and found to be consistent with the MUSIQ model. Identifying more detailed causal mechanisms (the present model was intentionally parsimonious due to the time frame allowed and the resources available for the research), refining the operational definitions, and developing and testing predictive models for the defined contextual factors are the proposed next steps in the research

Management in the Portsmouth Block Mill, 1803-1812

The Portsmouth Block Mills’ operations are assessed using archival materials showing staff numbers, hours and work assignments, providing insight into scheduling and workload management, capacity availability and use, and overall facility organization and design. A review of production records reveals items made specifically to meet individual production requirements and those made for “stock” and later use, and the Mill’s internal lines ran in a relatively“lean” fashion

Advanced Methods for Dose-Response Assessment: Bayesian Approaches—Final Report

Resources for the Future (RFF), in conjunction with the U.S. Environmental Protection Agency, the Society for Risk Analysis, and the Electric Power Research Institute, held a workshop September 18–20, 2000, at the RFF Conference Center in Washington, D.C. The intent was to discuss how Bayesian approaches could be useful in improving techniques for estimating exposure–response functions. Ten distinguished scholars from a range of fields (medical biostatistics, decision sciences, environmental engineering, and toxicology) served as faculty. Approximately 80 people attended the workshop. Bayesian methods have been applied to a variety of problems in biomedical research and environmental risk analysis, including design of clinical trials, estimation of exposures to humans and local environments, and, in a few cases, estimation of exposure–response functions. Bayesian methods offer two signal advantages: their use requires careful analysis of problem logic, which has intrinsic utility, and disparate data can be incorporated into calculations. Although application of formal Bayesian analysis can be computationally challenging, widely available computer programs now greatly reduce this burden. Participants identified several factors that may impede the dissemination of Bayesian approaches among practitioners of dose–response assessment and made some recommendations for overcoming these hurdles. EPA, other regulatory agencies that use dose–response assessment as part of their processes, and the private sector all should take steps to foster the use of Bayesian approaches. EPA and other agencies should work to persuade professional societies (for example, Society for Risk Analysis, Society of Toxicology) to seek out and recognize meritorious analyses that use Bayesian approaches. EPA and private-sector organizations should consider sponsoring research into using Bayesian approaches, demonstration analyses that use them, and using the results of this work to help educate peers in the risk analysis and toxicology professions. EPA should request all staff and contractor scientists who develop mathematical models to use Bayesian techniques to calibrate models. EPA should consider ways to inform its staff, contractors, and the research community as to the utility of Bayesian analyses. EPA should consider improving its research planning by making use of Bayesian techniques (including value-of-information analyses).Bayesian analysis, dose–response, regulation, risk assessment, arsenic

MAINE'S LOBSTER FISHERY - MANAGING A COMMON PROPERTY RESOURCE

Resource /Energy Economics and Policy,