Privacy Implications of Health Information Seeking on the Web

This article investigates privacy risks to those visiting health- related web pages. The population of pages analyzed is derived from the 50 top search results for 1,986 common diseases. This yielded a total population of 80,124 unique pages which were analyzed for the presence of third-party HTTP requests. 91% of pages were found to make requests to third parties. Investigation of URIs revealed that 70% of HTTP Referer strings contained information exposing specific conditions, treatments, and diseases. This presents a risk to users in the form of personal identification and blind discrimination. An examination of extant government and corporate policies reveals that users are insufficiently protected from such risks

Identifying factors likely to influence compliance with diagnostic imaging guideline recommendations for spine disorders among chiropractors in North America: a focus group study using the Theoretical Domains Framework

Background: The Theoretical Domains Framework (TDF) was developed to investigate determinants of specific clinical behaviors and inform the design of interventions to change professional behavior. This framework was used to explore the beliefs of chiropractors in an American Provider Network and two Canadian provinces about their adherence to evidence-based recommendations for spine radiography for uncomplicated back pain. The primary objective of the study was to identify chiropractors’ beliefs about managing uncomplicated back pain without xrays and to explore barriers and facilitators to implementing evidence-based recommendations on lumbar spine xrays. A secondary objective was to compare chiropractors in the United States and Canada on their beliefs regarding the use of spine x-rays. Methods: Six focus groups exploring beliefs about managing back pain without x-rays were conducted with a purposive sample. The interview guide was based upon the TDF. Focus groups were digitally recorded, transcribed verbatim, and analyzed by two independent assessors using thematic content analysis based on the TDF. Results: Five domains were identified as likely relevant. Key beliefs within these domains included the following: conflicting comments about the potential consequences of not ordering x-rays (risk of missing a pathology, avoiding adverse treatment effects, risks of litigation, determining the treatment plan, and using x-ray-driven techniques contrasted with perceived benefits of minimizing patient radiation exposure and reducing costs; beliefs about consequences); beliefs regarding professional autonomy, professional credibility, lack of standardization, and agreement with guidelines widely varied (social/professional role & identity); the influence of formal training, colleagues, and patients also appeared to be important factors (social influences); conflicting comments regarding levels of confidence and comfort in managing patients without x-rays (belief about capabilities); and guideline awareness and agreements (knowledge). Conclusions: Chiropractors’ use of diagnostic imaging appears to be influenced by a number of factors. Five key domains may be important considering the presence of conflicting beliefs, evidence of strong beliefs likely to impact the behavior of interest, and high frequency of beliefs. The results will inform the development of a theorybased survey to help identify potential targets for behavioral-change strategies

Enhancing reuse of data and biological material in medical research : from FAIR to FAIR-Health

The known challenge of underutilization of data and biological material from biorepositories as potential resources formedical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability—entitled FAIR Principles (Findability, Accessibility, Interoperability, and Reusability)—are likely to address only parts of the problem. In this article,we argue that biologicalmaterial and data should be viewed as a unified resource. This approach would facilitate access to complete provenance information, which is a prerequisite for reproducibility and meaningful integration of the data. A unified view also allows for optimization of long-term storage strategies, as demonstrated in the case of biobanks.Wepropose an extension of the FAIR Principles to include the following additional components: (1) quality aspects related to research reproducibility and meaningful reuse of the data, (2) incentives to stimulate effective enrichment of data sets and biological material collections and its reuse on all levels, and (3) privacy-respecting approaches for working with the human material and data. These FAIR-Health principles should then be applied to both the biological material and data. We also propose the development of common guidelines for cloud architectures, due to the unprecedented growth of volume and breadth of medical data generation, as well as the associated need to process the data efficiently.peer-reviewe

Examining Training Motivations Among Public Health Workers

CONTEXT: As public health needs and priorities evolve, maintaining a trained public health workforce is critical to the success of public health efforts. Researchers have examined training needs in various contexts and subpopulations, but a nationally representative study of what motivates public health workers to seek out training has yet to be conducted. By understanding these motivations, public health agencies and policy makers can appeal to worker motivations in both training programs and organizational incentives. OBJECTIVE: The purpose of this article was to describe overall training motivations and identify patterns of training motivations among public health workers. This study also explored whether or not training needs differ across prevalent motivational patterns. DESIGN AND PARTICIPANTS: Using data from the 2017 Public Health Workforce Interests and Needs Survey (PH WINS), the study used latent class analysis (LCA) to identify motivational patterns and logistic regression to analyze associations with training needs. RESULTS: The most prominent motivation to seek training was personal growth (82.7% of respondents). LCA identified 4 motivational classes of public health workers: those motivated by organizational pressure and requirements (31.8%), those motivated indiscriminately by all factors (28.4%), those motivated primarily by personal growth (21.7%), and those motivated by organizational accommodations and supports (18.2%). Motivational class was not associated with indicating training needs in any of 8 training domains, nor was it associated with indicating any training need in any domain. CONCLUSIONS: Public health agencies should consider the different motivational classes present in the public health workforce. In particular, motivational classes that represent organizational choices suggest that public health agencies should both motivate workers with organizational requirements and pressure from managers and offer institutional support via paid travel and covered time for training

AI management an exploratory survey of the influence of GDPR and FAT principles

As organisations increasingly adopt AI technologies, a number of ethical issues arise. Much research focuses on algorithmic bias, but there are other important concerns arising from the new uses of data and the introduction of technologies which may impact individuals. This paper examines the interplay between AI, Data Protection and FAT (Fairness, Accountability and Transparency) principles. We review the potential impact of the GDPR and consider the importance of the management of AI adoption. A survey of data protection experts is presented, the initial analysis of which provides some early insights into the praxis of AI in operational contexts. The findings indicate that organisations are not fully compliant with the GDPR, and that there is limited understanding of the relevance of FAT principles as AI is introduced. Those organisations which demonstrate greater GDPR compliance are likely to take a more cautious, risk-based approach to the introduction of AI

Initial impacts of global risk mitigation measures taken during the combatting of the COVID-19 pandemic

This paper presents an analysis of risk mitigation measures taken by countries around the world facing the current COVID-19 outbreak. In light of the current pandemic the authors collated and clustered (using harmonised terminology) the risk mitigation measures taken around the globe in the combat to contain, and since March 11 2020, to limiting the spread of the SARS-CoV-2 virus known to cause the Coronavirus disease 2019 (COVID-19). This overview gathers lessons learnt, provides an update on the current knowledge for authorities, sectors and first responders on the effectiveness and may allow enhanced prevention, preparedness and response for future outbreaks. Various measures such as mobility restrictions, physical distancing, hygienic measures, socio economic restrictions, communication and international support mechanisms have been clustered and are reviewed in terms of the nature of the actions taken and their qualitative early-perceived impact. At the time of writing, it is still too premature to express the quantitative effectiveness of each risk mitigation cluster, but it seems that the best mitigation results are reported when applying a combination of voluntary and enforceable measures.JRC.E.7-Knowledge for Security and Migratio