Privacy Preference Signals: Past, Present and Future

Privacy preference signals are digital representations of how users want their personal data to be processed. Such signals must be adopted by both the sender (users) and intended recipients (data processors). Adoption represents a coordination problem that remains unsolved despite efforts dating back to the 1990s. Browsers implemented standards like the Platform for Privacy Preferences (P3P) and Do Not Track (DNT), but vendors profiting from personal data faced few incentives to receive and respect the expressed wishes of data subjects. In the wake of recent privacy laws, a coalition of AdTech firms published the Transparency and Consent Framework (TCF), which defines an opt-in consent signal. This paper integrates post-GDPR developments into the wider history of privacy preference signals. Our main contribution is a high-frequency longitudinal study describing how TCF signal gained dominance as of February 2021. We explore which factors correlate with adoption at the website level. Both the number of third parties on a website and the presence of Google Ads are associated with higher adoption of TCF. Further, we show that vendors acted as early adopters of TCF 2.0 and provide two case-studies describing how Consent Management Providers shifted existing customers to TCF 2.0. We sketch ways forward for a pro-privacy signal

The economic benefit of timely, adequate, and adherence to Parkinson's disease treatment: the Value of Treatment Project 2

Background: Parkinson's disease (PD) is a chronic progressive neurological disorder with a high psychosocial and economic burden. As part of the European Brain Council (EBC)-led Value of Treatment project, this study aimed to capture the economic benefit of timely, adequate, and adherence to PD treatment. Methods: The EBC Value of Treatment Initiative combined different stakeholders to identify unmet needs in the patients’ journey according to Rotterdam methodology. The economic evaluation focused on three major topics identified as major gaps: start of treatment; best treatment for advanced disease; and adherence to treatment. Two separate healthcare systems (Germany and the UK) were chosen. Cost-effectiveness was determined by using decision-analytical modelling approaches. Effectiveness was expressed as quality-adjusted life-years (QALYs) gained and incremental cost-effectiveness ratio (ICER). Results: Treatment intervention in PD was found to be cost-effective regardless of the initial health state of the patient receiving the treatment. Cost savings were between -€1000 and −€5400 with 0.10 QALY gain and -€1800 and -€7600 with 0.10 QALY gain for Germany and the UK, respectively. Treatment remains cost-effective within the National Institute for Health and Care Excellence thresholds. Availability of adequate treatment to more patients was also found to be cost-effective, with an ICER of €15,000–€32,600 across country settings. Achieving the target adherence to treatment would generate cost-savings of €239,000–€576,000 (Germany) and €917,000–€2,980.000 (UK) for every 1,000 patients treated adequately. Conclusions: The analyses confirmed that timely, adequate, and adherence to PD treatment will not only improve care of the patients but is also cost-effective across healthcare systems. Further studies with a distinct identification of gaps in care are necessary to develop better and affordable care

Application of the quick scan audit methodology in an industrial filter production process

The quick scan audit methodology (QSAM) is an established investigative tool to assess the health of business processes and supply chains within short schedules. This study extends the standard QSAM procedure to include the simulation step. It also extends the QSAM to a wider industry platform by applying it into the precision mechanical engineering industry, where managers have been under competitive pressure to reduce an industrial filter production lead time. Following a review of the relevant literature, this paper presents the research design adopted in the study. The QSAM has been conducted using various data collection techniques (such as observations, process activity mapping, interviews, questionnaires, brainstorming and access to company documents) and data analysis methods (including cause and effect analysis, Pareto analysis and time series plot). This is followed by the development of a set of improvement strategies, namely, direct information sharing, priority planning, and additional data recording and analysis. In addition to testing the potential benefits of changing scheduling approaches for the paint plant, simulation has been utilized in this study as a communication means to increase employee participation in the QSAM process and enhance the audit accuracy. It has also provided the case company with a better understanding of the behaviour and characteristics of the system under study, thus facilitating more thoughtful decisions to improve the system. The paper concludes with further research opportunities derived from this study