scholarly journals Introducing Health Data Research Network Canada (HDRN Canada): A New Organization to Advance Canadian And International Population Data Science

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Kimberlyn McGrail ◽  
Brent Diverty ◽  
Lisa Lix
Keyword(s):  
Data Science ◽  
Data Access ◽  
Indigenous Communities ◽  
Health Data ◽  
Research Strategy ◽  
Research Network ◽  
Case Definitions ◽  
Extensive Experience ◽  
Data Infrastructure ◽  
Metadata Standards

IntroductionNotwithstanding Canada’s exceptional longitudinal health data and research centres with extensive experience transforming data into knowledge, many Canadian studies based on linked administrative data have focused on a single province or territory. Health Data Research Network Canada (HDRN Canada), a new not-for-profit corporation, will bring together major national, provincial and territorial health data stewards from across Canada. HDRN Canada’s first initiative is the $81 million SPOR Canadian Data Platform funded under the Canadian Institutes of Health Research Strategy for Patient-Oriented Research (SPOR). Objectives and ApproachHDRN Canada is a distributed network through which individual data-holding centres work together to (i) create a single portal and support system for researchers requesting multi-jurisdictional data, (ii) harmonize and validate case definitions and key analytic variables across jurisdictions, (iii) expand the sources and types of data linkages, (iv) develop technological infrastructure to improve data access and collection, (v) create supports for advanced analytics and (vi) establish strong partnerships with patients, the public and with Indigenous communities. We will share our experiences and gather international feedback on our network and its goals from symposium participants. ResultsIn January 2020, HDRN Canada launched its Data Access Support Hub (DASH) which includes an inventory listing over 380 datasets, information about more than 120 algorithms and a repository of requirements and processes for accessing data. HDRN Canada is receiving requests for multi-province research studies that would be challenging to conduct without HDRN Canada. Conclusion / ImplicationsThus far, HDRN Canada services and tools have been developed primarily for Canadian researchers but HDRN Canada can also serve as a prompt for an international discussion about what has/has not worked in terms of multi-jurisdictional research data infrastructure. It can also present an opportunity for the development of metadata, standards and common approaches that support more multi-country research.

scholarly journals Evolution or Revolution? Recommendations to Improve the Swiss Health Data Framework

2021 ◽  
Vol 9 ◽  
Author(s):  
Andrea Martani ◽  
Lester Darryl Geneviève ◽  
Sophia Mira Egli ◽  
Frédéric Erard ◽  
Tenzin Wangmo ◽  
...  
Keyword(s):  
Public Health ◽  
Data Access ◽  
Health Data ◽  
General Level ◽  
Unique Identifier ◽  
Data Infrastructure ◽  
Data Framework ◽  
Swiss Health ◽  
Mind Set ◽  
Societal Preferences

Background: Facilitating access to health data for public health and research purposes is an important element in the health policy agenda of many countries. Improvements in this sense can only be achieved with the development of an appropriate data infrastructure and the implementations of policies that also respect societal preferences. Switzerland is a revealing example of a country that has been struggling to achieve this aim. The objective of the study is to reflect on stakeholders' recommendations on how to improve the health data framework of this country.Methods: We analysed the recommendations collected as part of a qualitative study including 48 expert stakeholders from Switzerland that have been working principally with health databases. Recommendations were divided in themes and subthemes according to applied thematic analysis.Results: Stakeholders recommended several potential improvements of the health data framework in Switzerland. At the general level of mind-set and attitude, they suggested to foster the development of an explicit health data strategy, better communication and the respect of societal preferences. In terms of infrastructure, there were calls for the creation of a national data center, the improvement of IT solutions and the use of a Unique Identifier for patient data. Lastly, they recommended harmonising procedures for data access and to clarify data protection and consent rules.Conclusion: Recommendations show several potential improvements of the health data framework, but they have to be reconciled with existing policies, infrastructures and ethico-legal limitations. Achieving a gradual implementation of the recommended solutions is the preferable way forward for Switzerland and a lesson for other countries that are also seeking to improve health data access for public health and research purposes.

scholarly journals The opportunities and ethics of big data: practical priorities for a national Council of Data Ethics

2016 ◽  
Vol 374 (2083) ◽  
pp. 20160116 ◽  
Author(s):  
Olivia Varley-Winter ◽  
Hetan Shah
Keyword(s):  
Big Data ◽  
Data Science ◽  
Professional Competence ◽  
Data Access ◽  
National Council ◽  
Data Infrastructure ◽  
Ethical Frameworks ◽  
Data Ethics ◽  
Address Data ◽  
The Uk

In order to generate the gains that can come from analysing and linking big datasets, data holders need to consider the ethical frameworks, principles and applications that help to maintain public trust. In the USA, the National Science Foundation helped to set up a Council for Big Data, Ethics and Society, of which there is no equivalent in the UK. In November 2015, the Royal Statistical Society convened a workshop of 28 participants from government, academia and the private sector, and discussed the practical priorities that might be assisted by a new Council of Data Ethics in the UK. This article draws together the views from that meeting. Priorities for policy-makers and others include seeking a public mandate and informing the terms of the social contract for use of data; building professional competence and due diligence on data protection; appointment of champions who are competent to address public concerns; and transparency, across all dimensions. For government data, further priorities include improvements to data access, and development of data infrastructure. In conclusion, we support the establishment of a national Data Ethics Council, alongside wider and deeper engagement of the public to address data ethics dilemmas. This article is part of the themed issue ‘The ethical impact of data science’.

scholarly journals Unifying the UK’S Data for Respiratory Science: BREATHE, The Health Data Research UK (HDR UK) Respiratory Health Data Research Hub

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Chris Orton ◽  
David Ford ◽  
Aziz Sheikh ◽  
John Norrie ◽  
Monica Fletcher ◽  
...  
Keyword(s):  
Data Sharing ◽  
Data Science ◽  
Respiratory Health ◽  
Tertiary Care ◽  
Data Access ◽  
Health Data ◽  
Quality Improvement Effort ◽  
Domain Expertise ◽  
The Uk ◽  
Collaborative Efforts

IntroductionThe BREATHE Health Data Research Hub is a consortium of five academic institutions and several industry partners seeking to facilitate and accelerate respiratory science initiatives and outcomes. Unlocking organisational, jurisdictional, and scientific challenges, such as differing and inherent complexities with data standards, incongruous governance, and disparate data access mechanisms for over 100 diverse UK datasets are key aims. Objectives and ApproachCentral to the data effort is the UK Secure eResearch Platform (SeRP UK), and its flagship tenancy, the SAIL Databank. Onboarding datasets, making them remotely available to the respiratory research community, is a key approach. Datasets targeted range from population cohort studies, to respiratory trials data, routine healthcare datasets, and specialist ‘omics data. Partnerships with national safe havens and providers such as eDRIS and NHS Digital will enable BREATHE to expedite and improve wider sharing of datasets for the respiratory science. Data improvements focus on datasets from primary, secondary, and tertiary care from national healthcare systems, ‘respiratorising’ these datasets and increasing utility for academic and industry respiratory scientists. Incorporating dataset metadata and access permutations into national cataloguing systems at HDR UK, standardising metadata, and interoperability for in-scope datasets form a concerted data quality improvement effort. ResultsFacilitating data sharing through initiatives such as BREATHE will increase visibility and accessibility for datasets within respiratory science, whilst addressing national cultural and governance issues to data sharing. BREATHE data sharing processes will allow for team science to be undertaken in a highly collaborative manner and allow for best practise in data collection and sharing to flow to nationwide datasets in respiratory science. Conclusion / ImplicationsCollaborative hubs with scientific domain expertise can be created and leveraged to accelerate data sharing and data science within the scientific area. These collaborative efforts can however be translated to other disease-specific efforts, and indeed disease agnostic platform solutions.

scholarly journals Multi-province epidemiological research using administrative data in Canada: Challenges and opportunities

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Amanda Butler ◽  
Mark Smith ◽  
Wayne Jones ◽  
Carol Adair ◽  
Simone Vigod ◽  
...  
Keyword(s):  
Health Care ◽  
Administrative Data ◽  
Data Access ◽  
Health Data ◽  
National Standards ◽  
Epidemiological Research ◽  
Data Infrastructure ◽  
Administrative Health Data ◽  
Challenges And Opportunities ◽  
Time Required

IntroductionCanada has a publicly-funded universal health care system with information systems managed by 13 provinces and territories. This context creates inconsistencies in data collection and challenges for epidemiological research conducted at the national or multi-jurisdictional level. Objectives and ApproachUsing a recent five-province research project as a case study (BC, AB, MB, ON, QC), we will discuss the strengths and challenges of using Canadian administrative health data in a multi-jurisdictional context. Our goal is to contribute to a better understanding of these challenges and the development of a more integrated and harmonized approach to conducting multi-jurisdictional research. ResultsMulti-jurisdictional data work is feasible but requires detailed coordination and extensive cooperation from all involved. There were noteable variations across provinces in this multi-province study. For example, time required to access the data varied greatly across the five provinces (from 4 to 9 months), and thus there were sequencing challenges, with some provinces being well into the analysis stage while others were still waiting for data. Access to human resources varied across provinces and in some cases led to delays in data abstraction. Cost of data (or analytic support) also varied across provinces, from $12,000 – $15,000. Critical to the success of the project was a coordinating group with expertise in both administrative health data and cross-provincial project coordination. Conclusion/ImplicationsThis project demonstrated the value of comparable data infrastructure with equitable access policies. Many of the disadvantages to multi-province projects using health care administrative data, such as potential coding errors and inconsistencies, can be managed by developing national standards and protocols, and tools that are shared for data cleaning and validation.

scholarly journals Assessing and piloting interoperability for population health research

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
F Estupiñán-Romero ◽  
J Gonzalez-García ◽  
E Bernal-Delgado
Keyword(s):  
Case Studies ◽  
Population Health ◽  
Health Research ◽  
Network Architecture ◽  
Digital Health ◽  
Data Extraction ◽  
Health Data ◽  
Research Network ◽  
Technical Solution ◽  
Privacy And Security

Abstract Issue/problem Interoperability is paramount when reusing health data from multiple data sources and becomes vital when the scope is cross-national. We aimed at piloting interoperability solutions building on three case studies relevant to population health research. Interoperability lies on four pillars; so: a) Legal frame (i.e., compliance with the GDPR, privacy- and security-by-design, and ethical standards); b) Organizational structure (e.g., availability and access to digital health data and governance of health information systems); c) Semantic developments (e.g., existence of metadata, availability of standards, data quality issues, coherence between data models and research purposes); and, d) Technical environment (e.g., how well documented are data processes, which are the dependencies linked to software components or alignment to standards). Results We have developed a federated research network architecture with 10 hubs each from a different country. This architecture has implied: a) the design of the data model that address the research questions; b) developing, distributing and deploying scripts for data extraction, transformation and analysis; and, c) retrieving the shared results for comparison or pooled meta-analysis. Lessons The development of a federated architecture for population health research is a technical solution that allows full compliance with interoperability pillars. The deployment of this type of solution where data remain in house under the governance and legal requirements of the data owners, and scripts for data extraction and analysis are shared across hubs, requires the implementation of capacity building measures. Key messages Population health research will benefit from the development of federated architectures that provide solutions to interoperability challenges. Case studies conducted within InfAct are providing valuable lessons to advance the design of a future pan-European research infrastructure.

scholarly journals COVID-19 and Children’s Well-Being: A Rapid Research Agenda

2021 ◽  
Author(s):  
Rebecca N. Dudovitz ◽  
Shirley Russ ◽  
Mary Berghaus ◽  
Iheoma U. Iruka ◽  
Jessica DiBari ◽  
...  
Keyword(s):  
Nonprofit Organizations ◽  
Research Agenda ◽  
Well Being ◽  
Research Network ◽  
Negative Effects ◽  
Research Partnerships ◽  
Data Infrastructure ◽  
Community Based Research ◽  
National Measurement ◽  
Resilient Response

Abstract Purpose Understanding the full impact of COVID-19 on U.S. children, families, and communities is critical to (a) document the scope of the problem, (b) identify solutions to mitigate harm, and (c) build more resilient response systems. We sought to develop a research agenda to understand the short- and long-term mechanisms and impacts of the COVID-19 pandemic on children’s healthy development, with the goal of devising and ultimately testing interventions to respond to urgent needs and prepare for future pandemics. Description The Life Course Intervention Research Network facilitated a series of virtual meetings that included members of 10 Maternal and Child Health (MCH) research programs, their research and implementation partners, as well as family and community representatives, to develop an MCH COVID-19 Research Agenda. Stakeholders from academia, clinical practice, nonprofit organizations, and family advocates participated in four meetings, with 30–35 participants at each meeting. Assessment Investigating the impacts of COVID-19 on children’s mental health and ways to address them emerged as the highest research priority, followed by studying resilience at individual and community levels; identifying and mitigating the disparate negative effects of the pandemic on children and families of color, prioritizing community-based research partnerships, and strengthening local, state and national measurement systems to monitor children’s well-being during a national crisis. Conclusion Enacting this research agenda will require engaging the community, especially youth, as equal partners in research co-design processes; centering anti-racist perspectives; adopting a “strengths-based” approach; and integrating young researchers who identify as Black, Indigenous, and People of Color (BIPOC). New collaborative funding models and investments in data infrastructure are also needed.

scholarly journals Involving the Public in Data Linkage Research

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Mhairi Aitken ◽  
Annette Braunack-Mayer ◽  
Felicity Flack ◽  
Kimberlyn M McGrail ◽  
Michael Burgess ◽  
...  
Keyword(s):  
Public Engagement ◽  
Health Research ◽  
Consensus Statement ◽  
Data Linkage ◽  
Health Data ◽  
Advisory Council ◽  
Research Network ◽  
Plain Language ◽  
The Public ◽  
Data Intensive

Introduction“The Consensus Statement on Public Involvement and Engagement with Data-Intensive Health Research”, recent data breaches, and growing public awareness and controversy associated with secondary use of health data all highlight the need to understand what data sharing the public will support, under what circumstances, for what purposes and with whom. Objectives and ApproachThis symposium explores methods and findings from public engagement at all stages of data linkage research, beginning with short presentations (~6-8 minutes) on recent work: Mhairi Aitken: Consensus Statement - principles and an application using deliberative workshops to explore public expectations of public benefits from data-intensive health research Annette Braunack-Mayer/Felicity Flack: Surveys and citizens’ juries: Sharing government data with private industry Kim McGrail/Mike Burgess: Public deliberations on cross-sector data linkage, and combining public and private sources of data Alison Paprica: Plain language communication informed by Health Data Research Network Canada’s Public Advisory Council. Half the session will be spent interacting with the audience through live polling. The moderator will post a series of poll question such as “What is the most important thing for meaningful public engagement?” to prompt audience thinking on the topic. After the audience responses are revealed, panelists will share their own views about what they think is the best answer, and the main reason(s) behind their choice. The last 10-15 minutes of the session will be reserved for Q&A and dialogue with the audience. ResultsWe anticipate that this approach will surface emerging and tacit knowledge from presenters and the audience, and augment that through generative discussion. Conclusion / ImplicationsSession attendees will leave with a better understanding of the current state of knowledge and ways to talk about that understanding with other researchers, policy makers and the public.

scholarly journals Development of Data Infrastructure to Monitor Lung Cancer Screening Use and Outcomes in Four Cancer Research Network Sites

2016 ◽  
Vol 3 (3) ◽  
pp. 172
Author(s):  
V. Paul Doria-Rose ◽  
Lori C Sadoka ◽  
Christine M Neslund-Dudas ◽  
Debra P Ritzwoller ◽  
Heather S Feigelson ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Research ◽  
Cancer Screening ◽  
Lung Cancer Screening ◽  
Research Network ◽  
Cancer Research Network ◽  
Data Infrastructure

scholarly journals Alberta's Data and Analytic Strategy: Leveraging Linked Data to Drive Innovation

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Larry Svenson
Keyword(s):  
Health System ◽  
Health Information ◽  
Data Warehouse ◽  
Data Access ◽  
Health Data ◽  
Direct Access ◽  
Safe Haven ◽  
For Profit ◽  
Secondary Use ◽  
Not For Profit

BackgroundThe Province of Alberta, Canada, maintains a mature data environment with linkable administrative and clinical data dating back up to 30 years. Alberta has a single payer, publicly funded and administered, universal health system, which maintains multiple administrative data sets. Main AimThe main aim of the strategy is to fully maximize the data assets in the province to drive health system health system innovation, with a focus on improving health outcomes and quality of life. Methods/ApproachThe Alberta Ministry of Health has created the Secondary Use Data Access (SUDA) initiative to leverage its administrative health data. SUDA envisions strengthening partnerships between the public and private sectors through two main data access approaches. The first is direct access to de-identified data held within the Alberta Health data warehouse by key health system stakeholders (e.g. academic institutions, professional associations, regulatory colleges). The second is indirect access to private and not-for-profit organizations, using a data access safe haven (DASH) approach. Indirect access is achieved through private sector investments to a trusted third party that hires analysts placed within the Ministry of Health offices. ResultsStaffing agreements and privacy impact assessments are in place. Indirect access includes a multiple stakeholder steering committee to vet and prioritize projects. Private and not-for-profit stakeholders do not have access to raw data, but rather receive access to aggregated data and statistical models. All data disclosures are done by Ministry staff to ensure compliance with Alberta's Health Information Act. Direct access has been established for one professional organization and one academic institution, with access restricted to de-identified data. ConclusionThe Secondary Use Data Access initiative uses a safe haven approach to leveraging data to provide a more secure approach to data access. It reduces the need to provision data outside of the data warehouse while improving timely access to data. The approach provides assurances that people's health information is held secure, while also being used to create health system improvements.

scholarly journals British Columbia’s Health Data Platform: Unleashing the Power of a Data Environment Commons for Health and Health System Improvement

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Shirley Wong ◽  
Victoria Schuckel ◽  
Simon Thompson ◽  
David Ford ◽  
Ronan Lyons ◽  
...  
Keyword(s):  
Health Sector ◽  
Data Access ◽  
Health Data ◽  
Decision Makers ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Data Accessibility ◽  
Data Platform ◽  
Access To Data ◽  
Data Consumer

IntroductionThere is no power for change greater than a community discovering what it cares about.1 The Health Data Platform (HDP) will democratize British Columbia’s (population of approximately 4.6 million) health sector data by creating common enabling infrastructure that supports cross-organization analytics and research used by both decision makers and cademics. HDP will provide streamlined, proportionate processes that provide timelier access to data with increased transparency for the data consumer and provide shared data related services that elevate best practices by enabling consistency across data contributors, while maintaining continued stewardship of their data. HDP will be built in collaboration with Swansea University following an agile pragmatic approach starting with a minimum viable product. Objectives and ApproachBuild a data sharing environment that harnesses the data and the understanding and expertise about health data across academe, decision makers, and clinicians in the province by: Enabling a common harmonized approach across the sector on: Data stewardship Data access Data security and privacy Data management Data standards To: Enhance data consumer data access experience Increase process consistency and transparency Reduce burden of liberating data from a data source Build trust in the data and what it is telling us and therefore the decisions made Increase data accessibility safely and responsibly Working within the jurisdiction’s existing legislation, the Five Safes Privacy and Security Framework will be implemented, tailored to address the requirements of data contributors. ResultsThe minimum viable product will provide the necessary enabling infrastructure including governance to enable timelier access, safely to administrative data to a limited set of data consumers. The MVP will be expanded with another release planned for early 2021. Conclusion / ImplicationsCollaboration with Swansea University has enabled BC to accelerate its journey to increasing timelier access to data, safely and increasing the maturity of analytics by creating the enabling infrastructure that promotes collaboration and sharing of data and data approaches. 1 Margaret Wheatley

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