Report by the Open Data Policy Lab (The GovLab): “In July 2020, following severe economic and social losses due to the COVID-19 pandemic, the administration of New York City Mayor Bill de Blasio announced the NYC Recovery Data Partnership. This data collaborative asked private and civic organizations with assets relevant to New York City to provide their data to the city. Senior city leaders from the First Deputy Mayor’s Office, the Mayor’s Office of Operations, Mayor’s Office of Information Privacy and Mayor’s Office of Data Analytics formed an internal coalition which served as trusted intermediaries, assessing agency requests from city agencies to use the data provided and allocating access accordingly. The data informed internal research conducted by various city agencies, including New York City Emergency Management’s Recovery Team and the NYC…(More)”
Ten (not so) simple rules for clinical trial data-sharing
Paper by Claude Pellen et al: “Clinical trial data-sharing is seen as an imperative for research integrity and is becoming increasingly encouraged or even required by funders, journals, and other stakeholders. However, early experiences with data-sharing have been disappointing because they are not always conducted properly. Health data is indeed sensitive and not always easy to share in a responsible way. We propose 10 rules for researchers wishing to share their data. These rules cover the majority of elements to be considered in order to start the commendable process of clinical trial data-sharing:
- Rule 1: Abide by local legal and regulatory data protection requirements
- Rule 2: Anticipate the possibility of clinical trial data-sharing before obtaining funding
- Rule 3: Declare your intent to share data in the registration step
- Rule 4: Involve research participants
- Rule 5: Determine the method of data access
- Rule 6: Remember there are several other elements to share
- Rule 7: Do not proceed alone
- Rule 8: Deploy optimal data management to ensure that the data shared is useful
- Rule 9: Minimize risks
- Rule 10: Strive for excellence…(More)”
Nudging: A Tool to Influence Human Behavior in Health Policy
Book by František Ochrana and Radek Kovács: “Behavioral economics sees “nudges” as ways to encourage people to re-evaluate their priorities in such a way that they voluntarily change their behavior, leading to personal and social benefits. This book examines nudging as a tool for influencing human behavior in health policy. The authors investigate the contemporary scientific discourse on nudging and enrich it with an ontological, epistemological, and praxeological analysis of human behavior. Based on analyses of the literature and a systemic review, the book defines nudging tools within the paradigm of prospect theory. In addition to the theoretical contribution, Nudging also examines and offers suggestions on the practice of health policy regarding obesity, malnutrition, and especially type 2 diabetes mellitus…(More)”.
Examining public views on decentralised health data sharing
Paper by Victoria Neumann et al: “In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining ‘proof’ of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems…(More)”.
Health data justice: building new norms for health data governance
Paper by James Shaw & Sharifah Sekalala: “The retention and use of health-related data by government, corporate, and health professional actors risk exacerbating the harms of colonial systems of inequality in which health care and public health are situated, regardless of the intentions about how those data are used. In this context, a data justice perspective presents opportunities to develop new norms of health-related data governance that hold health justice as the primary objective. In this perspective, we define the concept of health data justice, outline urgent issues informed by this approach, and propose five calls to action from a health data justice perspective…(More)”.
Data sharing during coronavirus: lessons for government
Report by Gavin Freeguard and Paul Shepley: “This report synthesises the lessons from six case studies and other research on government data sharing during the pandemic. It finds that current legislation, such as the Digital Economy Act and UK General Data Protection Regulation (GDPR), does not constitute a barrier to data sharing and that while technical barriers – incompatible IT systems, for example – can slow data sharing, they do not prevent it.
Instead, the pandemic forced changes to standard working practice that enabled new data sharing agreements to be created quickly. This report focuses on what these changes were and how they can lead to improvements in future practice.
The report recommends:
- The government should retain data protection officers and data protection impact assessments within the Data Protection and Digital Information Bill, and consider strengthening provisions around citizen engagement and how to ensure data flows during emergency response.
- The Department for Levelling Up, Housing and Communities should consult on how to improve working around data between central and local government in England. This should include the role of the proposed Office for Local Government, data skills and capabilities at the local level, reform of the Single Data List and the creation of a data brokering function to facilitate two-way data sharing between national and local government.
- The Central Digital and Data Office (CDDO) should create a data sharing ‘playbook’ to support public servants building new services founded on data. The playbook should contain templates for standard documents, links to relevant legislation and codes of practice (like those from the Information Commissioner’s Office), guidance on public engagement and case studies covering who to engage and when whilst setting up a new service.
- The Centre for Data Ethics and Innovation, working with CDDO, should take the lead on guidance and resources on how to engage the public at every stage of data sharing…(More)”.
COVID isn’t going anywhere, neither should our efforts to increase responsible access to data
Article by Andrew J. Zahuranec, Hannah Chafetz and Stefaan Verhulst: “..Moving forward, institutions will need to consider how to embed non-traditional data capacity into their decision-making to better understand the world around them and respond to it.
For example, wastewater surveillance programmes that emerged during the pandemic continue to provide valuable insights about outbreaks before they are reported by clinical testing and have the potential to be used for other emerging diseases.
We need these and other programmes now more than ever. Governments and their partners need to maintain and, in many cases, strengthen the collaborations they established through the pandemic.
To address future crises, we need to institutionalize new data capacities – particularly those involving non-traditional datasets that may capture digital information that traditional health surveys and statistical methods often miss.
The types and sources of non-traditional data sources that stood out most during the COVID-19 response. Image: The GovLab
In our report, we suggest four pathways to advance the responsible access to non-traditional data during future health crises…(More)”.
Innovative informatics interventions to improve health and health care
Editorial by Suzanne Bakken: “In this editorial, I highlight 5 papers that address innovative informatics interventions—3 research studies and 2 reviews. The papers reflect a variety of information technologies and processes including mobile health (mHealth), behavioral nudges in the electronic health record (EHR), adaptive intervention framework, predictive models, and artificial intelligence (eg, machine learning, data mining, natural language processing). The interventions were designed to address important clinical and public health problems such as adherence to antiretroviral therapy for persons living with HIV (PLWH), opioid use disorder, and pain assessment and management, as well as aspects of healthcare quality including no-show rates for appointments and erroneous decisions, waste, and misuse of resources due to EHR choice architecture for clinician orders…(More)”.
Orchestrating distributed data governance in open social innovation
Paper by Thomas Gegenhuber et al: “Open Social Innovation (OSI) involves the collaboration of multiple stakeholders to generate ideas, and develop and scale solutions to make progress on societal challenges. In an OSI project, stakeholders share data and information, utilize it to better understand a problem, and combine data with digital technologies to create digitally-enabled solutions. Consequently, data governance is essential for orchestrating an OSI project to facilitate the coordination of innovation. Because OSI brings multiple stakeholders together, and each stakeholder participates voluntarily, data governance in OSI has a distributed nature. In this essay we put forward a framework consisting of three dimensions allowing an inquiry into the effectiveness of such distributed data governance: (1) openness (i.e., freely sharing data and information), (2) accountability (i.e., willingness to be held responsible and provide justifications for one’s conduct) and (3) power (i.e., resourceful actors’ ability to impact other stakeholder’s actions). We apply this framework to reflect on the OSI project #WirVsVirus (“We versus virus” in English), to illustrate the challenges in organizing effective distributed data governance, and derive implications for research and practice….(More)”.
Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups.
Paper by Elizabeth Ford et al: “Digital programmes in the newly created NHS integrated care boards (ICBs) in the United Kingdom mean that curation and linkage of anonymised patient data is underway in many areas for the first time. In Kent, Surrey and Sussex (KSS), in Southeast England, public health teams want to use these datasets to answer strategic population health questions, but public expectations around use of patient data are unknown….We aimed to engage with citizens of KSS to gather their views and expectations of data linkage and re-use, through deliberative discussions…
We held five 3-hour deliberative focus groups with 79 citizens of KSS, presenting information about potential uses of data, safeguards, and mechanisms for public involvement in governance and decision making about datasets. After each presentation, participants discussed their views in facilitated small groups which were recorded, transcribed and analysed thematically…
The focus groups generated 15 themes representing participants’ views on the benefits, risks and values for safeguarding linked data. Participants largely supported use of patient data to improve health service efficiency and resource management, preventative services and out of hospital care, joined-up services and information flows. Most participants expressed concerns about data accuracy, breaches and hacking, and worried about commercial use of data. They suggested that transparency of data usage through audit trails and clear information about accountability, ensuring data re-use does not perpetuate stigma and discrimination, ongoing, inclusive and valued involvement of the public in dataset decision-making, and a commitment to building trust, would meet their expectations for responsible data use…
Participants were largely favourable about the proposed uses of patient linked datasets but expected a commitment to transparency and public involvement. Findings were mapped to previous tenets of social license and can be used to inform ICB digital programme teams on how to proceed with use of linked datasets in a trustworthy and socially acceptable way…(More)”.