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The Social Data Foundation model: Facilitating health and social care transformation through datatrust services
Data & Policy ; 4, 2022.
Article in English | ProQuest Central | ID: covidwho-1683816
ABSTRACT
Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.
Keywords
Political Science; data governance models; data institutions; data stewardship; datatrust services; healthcare and social care; AI (artificial intelligence); API (application programming interface); CHIA (Care and Health Information Exchange Analytics); DARS (Data Access Request Service); DLT (distributed ledger technology); DPIA (data protection impact assessment); DPO (data protection officer); DSAP (data sharing and analysis project); GDPR (General Data Protection Regulation); HL7 FHIR (Health Level 7 Fast Healthcare Interoperability Resources); HRA (Health Research Authority); ICO (Information Commissioner’s Office); ICS (integrated care system); ISO (International Organization for Standardization); MELD (Multidisciplinary Ecosystem to study Lifecourse Determinants of Complex Mid-life Multimorbidity using Artificial Intelligence); ML (machine learning); MLTC-M (multiple long term conditions—multimorbidity); NHS (National Health Service (UK)); NHS REC (NHS Research Ethics Committee); NIHR (National Institute for Health Research (UK)); ONS (Office for National Statistics); OWASP (Open Web Application Security Project); PETs (privacy enhancing-technologies); PI (principal investigator); RDA (Research Data Alliance); SDF (Social Data Foundation); SD-WANS (software-defined wide area networks); TRE (trusted research environment); UK (United Kingdom); UKDS (UK Data Service); UKHDRA (UK Health Data Research Alliance); WSI (Web Science Institute); Health disparities; Data processing; Databases; Credibility; Public health; Wealth; Health authorities; Loss of control; Researchers; Data analysis; Data science; Information sharing; Data management systems; Management systems; Health education; COVID-19; Data management; Stakeholders; Health care industry; Transformation; Social care; Pandemics; Social factors; Medical research; Health services; Individualized; Medical personnel; Social welfare; Governance; Coronaviruses; United Kingdom--UK

Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Data & Policy Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Language: English Journal: Data & Policy Year: 2022 Document Type: Article