6,563 research outputs found
A Blockchain-based Approach for Data Accountability and Provenance Tracking
The recent approval of the General Data Protection Regulation (GDPR) imposes
new data protection requirements on data controllers and processors with
respect to the processing of European Union (EU) residents' data. These
requirements consist of a single set of rules that have binding legal status
and should be enforced in all EU member states. In light of these requirements,
we propose in this paper the use of a blockchain-based approach to support data
accountability and provenance tracking. Our approach relies on the use of
publicly auditable contracts deployed in a blockchain that increase the
transparency with respect to the access and usage of data. We identify and
discuss three different models for our approach with different granularity and
scalability requirements where contracts can be used to encode data usage
policies and provenance tracking information in a privacy-friendly way. From
these three models we designed, implemented, and evaluated a model where
contracts are deployed by data subjects for each data controller, and a model
where subjects join contracts deployed by data controllers in case they accept
the data handling conditions. Our implementations show in practice the
feasibility and limitations of contracts for the purposes identified in this
paper
Randomized controlled trial of a coordinated care intervention to improve risk factor control after stroke or transient ischemic attack in the safety net: Secondary stroke prevention by Uniting Community and Chronic care model teams Early to End Disparities (SUCCEED).
BackgroundRecurrent strokes are preventable through awareness and control of risk factors such as hypertension, and through lifestyle changes such as healthier diets, greater physical activity, and smoking cessation. However, vascular risk factor control is frequently poor among stroke survivors, particularly among socio-economically disadvantaged blacks, Latinos and other people of color. The Chronic Care Model (CCM) is an effective framework for multi-component interventions aimed at improving care processes and outcomes for individuals with chronic disease. In addition, community health workers (CHWs) have played an integral role in reducing health disparities; however, their effectiveness in reducing vascular risk among stroke survivors remains unknown. Our objectives are to develop, test, and assess the economic value of a CCM-based intervention using an Advanced Practice Clinician (APC)-CHW team to improve risk factor control after stroke in an under-resourced, racially/ethnically diverse population.Methods/designIn this single-blind randomized controlled trial, 516 adults (≥40 years) with an ischemic stroke, transient ischemic attack or intracerebral hemorrhage within the prior 90 days are being enrolled at five sites within the Los Angeles County safety-net setting and randomized 1:1 to intervention vs usual care. Participants are excluded if they do not speak English, Spanish, Cantonese, Mandarin, or Korean or if they are unable to consent. The intervention includes a minimum of three clinic visits in the healthcare setting, three home visits, and Chronic Disease Self-Management Program group workshops in community venues. The primary outcome is blood pressure (BP) control (systolic BP <130 mmHg) at 1 year. Secondary outcomes include: (1) mean change in systolic BP; (2) control of other vascular risk factors including lipids and hemoglobin A1c, (3) inflammation (C reactive protein [CRP]), (4) medication adherence, (5) lifestyle factors (smoking, diet, and physical activity), (6) estimated relative reduction in risk for recurrent stroke or myocardial infarction (MI), and (7) cost-effectiveness of the intervention versus usual care.DiscussionIf this multi-component interdisciplinary intervention is shown to be effective in improving risk factor control after stroke, it may serve as a model that can be used internationally to reduce race/ethnic and socioeconomic disparities in stroke in resource-constrained settings.Trial registrationClinicalTrials.gov Identifier NCT01763203
- …