An ideal model of an assistive technology assessment and delivery process

The purpose of the present work is to present some aspects of the Assistive Technology Assessment (ATA) process model compatible with the Position Paper 2012 by AAATE/EASTIN. Three aspects of the ATA process will be discussed in light of three topics of the Position Paper 2012: (i) The dimensions and the measures of the User eXperience (UX) evaluation modelled in the ATA process as a way to verify the efficient and the evidence-based practices of an AT service delivery centre; (ii) The relevance of the presence of the psychologist in the multidisciplinary team of an AT service delivery centre as necessary for a complete person-centred assistive solution empowering users to make their own choices; (iii) The new profession of the psychotechnologist, who explores users needs by seeking a proper assistive solution, leading the multidisciplinary team to observe critical issues and problems. Through the foundation of the Position Paper 2012, the 1995 HEART study, the Matching Person and Technology model, the ICF framework, and the pillars of the ATA process, this paper sets forth a concept and approach that emphasise the personal factors of the individual consumer and UX as key to positively impacting a successful outcome and AT solution

Improving health and public safety through knowledge management

This paper reports on KM in public healthcare and public safety. It reflects the experiences of the author as a CIO (Chief Information Officer) in both industries in Australia and New Zealand. There are commonalities in goals and challenges in KM in both industries. In the case of public safety a goal of modern policing theory is to move more towards intelligence-driven practice. That means interventions based upon research and analysis of information. In healthcare the goals include investment in capacity based upon knowledge of healthcare needs, evidence-based service planning and care delivery, capture of information and provision of knowledge at the point-of-care and evaluation of outcomes. The issue of knowledge management is explored from the perspectives of the user of information and from the discipline of Information Technology and its application to healthcare and public safety. Case studies are discussed to illustrate knowledge management and limiting or enabling factors. These factors include strategy, architecture, standards, feed-back loops, training, quality processes, and social factors such as expectations, ownership of systems and politics

Data analytics and algorithms in policing in England and Wales: Towards a new policy framework

RUSI was commissioned by the Centre for Data Ethics and Innovation (CDEI) to conduct an independent study into the use of data analytics by police forces in England and Wales, with a focus on algorithmic bias. The primary purpose of the project is to inform CDEI’s review of bias in algorithmic decision-making, which is focusing on four sectors, including policing, and working towards a draft framework for the ethical development and deployment of data analytics tools for policing. This paper focuses on advanced algorithms used by the police to derive insights, inform operational decision-making or make predictions. Biometric technology, including live facial recognition, DNA analysis and fingerprint matching, are outside the direct scope of this study, as are covert surveillance capabilities and digital forensics technology, such as mobile phone data extraction and computer forensics. However, because many of the policy issues discussed in this paper stem from general underlying data protection and human rights frameworks, these issues will also be relevant to other police technologies, and their use must be considered in parallel to the tools examined in this paper. The project involved engaging closely with senior police officers, government officials, academics, legal experts, regulatory and oversight bodies and civil society organisations. Sixty nine participants took part in the research in the form of semi-structured interviews, focus groups and roundtable discussions. The project has revealed widespread concern across the UK law enforcement community regarding the lack of official national guidance for the use of algorithms in policing, with respondents suggesting that this gap should be addressed as a matter of urgency. Any future policy framework should be principles-based and complement existing police guidance in a ‘tech-agnostic’ way. Rather than establishing prescriptive rules and standards for different data technologies, the framework should establish standardised processes to ensure that data analytics projects follow recommended routes for the empirical evaluation of algorithms within their operational context and evaluate the project against legal requirements and ethical standards. The new guidance should focus on ensuring multi-disciplinary legal, ethical and operational input from the outset of a police technology project; a standard process for model development, testing and evaluation; a clear focus on the human–machine interaction and the ultimate interventions a data driven process may inform; and ongoing tracking and mitigation of discrimination risk

Methods and Processes of Developing the Strengthening the Reporting of Observational Studies in Epidemiology – Veterinary (STROBE-Vet) Statement

BACKGROUND Reporting of observational studies in veterinary research presents challenges that often are not addressed in published reporting guidelines. OBJECTIVE To develop an extension of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement that addresses unique reporting requirements for observational studies in veterinary medicine related to health, production, welfare, and food safety. DESIGN Consensus meeting of experts. SETTING Mississauga, Canada. PARTICIPANTS Seventeen experts from North America, Europe, and Australia. METHODS Experts completed a pre-meeting survey about whether items in the STROBE statement should be modified or added to address unique issues related to observational studies in animal species with health, production, welfare, or food safety outcomes. During the meeting, each STROBE item was discussed to determine whether or not rewording was recommended and whether additions were warranted. Anonymous voting was used to determine consensus. RESULTS Six items required no modifications or additions. Modifications or additions were made to the STROBE items 1 (title and abstract), 3 (objectives), 5 (setting), 6 (participants), 7 (variables), 8 (data sources/measurement), 9 (bias), 10 (study size), 12 (statistical methods), 13 (participants), 14 (descriptive data), 15 (outcome data), 16 (main results), 17 (other analyses), 19 (limitations), and 22 (funding). CONCLUSION The methods and processes used were similar to those used for other extensions of the STROBE statement. The use of this STROBE statement extension should improve reporting of observational studies in veterinary research by recognizing unique features of observational studies involving food-producing and companion animals, products of animal origin, aquaculture, and wildlife

Advances in Teaching & Learning Day Abstracts 2004

Proceedings of the Advances in Teaching & Learning Day Regional Conference held at The University of Texas Health Science Center at Houston in 2004

Medicaid's Role in the Health Benefits Exchange: A Road Map for States

Examines issues for integrating Medicaid into the administration, operation, and coverage continuum of insurance exchanges. Discusses eligibility, enrollment, and outreach; contracting, standards, and requirements; benefits design; and infrastructure

Methods for Population Adjustment with Limited Access to Individual Patient Data: A Review and Simulation Study

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, we undertake a comprehensive simulation study to compare standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non-collapsible. Standard indirect comparisons are systematically biased, particularly under stronger covariate imbalance and interaction effects. Standard errors and coverage rates are often valid in MAIC but the robust sandwich variance estimator underestimates variability where effective sample sizes are small. Interval estimates for the standard indirect comparison are too narrow and STC suffers from bias-induced undercoverage. MAIC provides the most accurate estimates and, with lower degrees of covariate overlap, its bias reduction outweighs the loss in effective sample size and precision under no failures of assumptions. An important future objective is the development of an alternative formulation to STC that targets a marginal treatment effect.Comment: 73 pages (34 are supplementary appendices and references), 8 figures, 2 tables. Full article (following Round 4 of minor revisions). arXiv admin note: text overlap with arXiv:2008.0595