Open health data: Mapping the ecosystem

Curated on Posted on by Stefaan Verhulst

Paper by Roel Heijlen and Joep Crompvoets: “Governments around the world own multiple datasets related to the policy domain of health. Datasets range from vaccination rates to the availability of health care practitioners in a region to the outcomes of certain surgeries. Health is believed to be a promising subject in the case of open government data policies. However, the specific properties of health data such as its sensibilities regarding privacy, ethics, and ownership encompass particular conditions either enabling or preventing datasets to become freely and easily accessible for everyone…

This paper aims to map the ecosystem of open health data. By analyzing the foundations of health data and the commonalities of open data ecosystems via literature analysis, the socio-technical environment in which health data managed by governments are opened up or potentially stay closed is created. After its theoretical development, the open health data ecosystem is tested via a case study concerning the Data for Better Health initiative from the government of Belgium…

The policy domain of health includes de-identification activities, bioethical assessments, and the specific role of data providers within its open data ecosystem. However, the concept of open data does not always fully apply to the topic of health. Such several health datasets may be findable via government portals but not directly accessible. Differentiation within types of health data and data user capacities are recommendable for future research….(More)”

Understanding Algorithmic Discrimination in Health Economics Through the Lens of Measurement Errors

Curated on Posted on by Stefaan Verhulst

Paper by Anirban Basu, Noah Hammarlund, Sara Khor & Aasthaa Bansal: “There is growing concern that the increasing use of machine learning and artificial intelligence-based systems may exacerbate health disparities through discrimination. We provide a hierarchical definition of discrimination consisting of algorithmic discrimination arising from predictive scores used for allocating resources and human discrimination arising from allocating resources by human decision-makers conditional on these predictive scores. We then offer an overarching statistical framework of algorithmic discrimination through the lens of measurement errors, which is familiar to the health economics audience. Specifically, we show that algorithmic discrimination exists when measurement errors exist in either the outcome or the predictors, and there is endogenous selection for participation in the observed data. The absence of any of these phenomena would eliminate algorithmic discrimination. We show that although equalized odds constraints can be employed as bias-mitigating strategies, such constraints may increase algorithmic discrimination when there is measurement error in the dependent variable….(More)”.

Evaluating the trade-off between privacy, public health safety, and digital security in a pandemic

Curated on Posted on by Stefaan Verhulst

Paper by Titi Akinsanmi and Aishat Salami: “COVID-19 has impacted all aspects of everyday normalcy globally. During the height of the pandemic, people shared their (PI) with one goal—to protect themselves from contracting an “unknown and rapidly mutating” virus. The technologies (from applications based on mobile devices to online platforms) collect (with or without informed consent) large amounts of PI including location, travel, and personal health information. These were deployed to monitor, track, and control the spread of the virus. However, many of these measures encouraged the trade-off on privacy for safety. In this paper, we reexamine the nature of privacy through the lens of safety focused on the health sector, digital security, and what constitutes an infraction or otherwise of the privacy rights of individuals in a pandemic as experienced in the past 18 months. This paper makes a case for maintaining a balance between the benefit, which the contact tracing apps offer in the containment of COVID-19 with the need to ensure end-user privacy and data security. Specifically, it strengthens the case for designing with transparency and accountability measures and safeguards in place as critical to protecting the privacy and digital security of users—in the use, collection, and retention of user data. We recommend oversight measures to ensure compliance with the principles of lawful processing, knowing that these, among others, would ensure the integration of privacy by design principles even in unforeseen crises like an ongoing pandemic; entrench public trust and acceptance, and protect the digital security of people…(More)”.

Can digital technologies improve health?

Curated on Posted on by Stefaan Verhulst

The Lancet: “If you have followed the news on digital technology and health in recent months, you will have read of a blockbuster fraud trial centred on a dubious blood-testing device, a controversial partnership between a telehealth company and a data analytics company, a social media company promising action to curb the spread of vaccine misinformation, and another addressing its role in the deteriorating mental health of young women. For proponents and critics alike, these stories encapsulate the health impact of many digital technologies, and the uncertain and often unsubstantiated position of digital technologies for health. The Lancet and Financial Times Commission on governing health futures 2030: growing up in a digital world, brings together diverse, independent experts to ask if this narrative can still be turned around? Can digital technologies deliver health benefits for all?

Digital technologies could improve health in many ways. For example, electronic health records can support clinical trials and provide large-scale observational data. These approaches have underpinned several high-profile research findings during the COVID-19 pandemic. Sequencing and genomics have been used to understand SARS-CoV-2 transmission and evolution. There is vast promise in digital technology, but the Commission argues that, overall, digital transformations will not deliver health benefits for all without fundamental and revolutionary realignment.

Globally, digital transformations are well underway and have had both direct and indirect health consequences. Direct effects can occur through, for example, the promotion of health information or propagating misinformation. Indirect ones can happen via effects on other determinants of health, including social, economic, commercial, and environmental factors, such as influencing people’s exposure to marketing or political messaging. Children and adolescents growing up in this digital world experience the extremes of digital access. Young people who spend large parts of their lives online may be protected or vulnerable to online harm. But many individuals remain digitally excluded, affecting their access to education and health information. Digital access, and the quality of that access, must be recognised as a key determinant of health. The Commission calls for connectivity to be recognised as a public good and human right.

Describing the accumulation of data and power by dominant actors, many of which are commercial, the Commissioners criticise business models based on the extraction of personal data, and those that benefit from the viral spread of misinformation. To redirect digital technologies to advance universal health coverage, the Commission invokes the guiding principles of democracy, equity, solidarity, inclusion, and human rights. Governments must protect individuals from emerging threats to their health, including bias, discrimination, and online harm to children. The Commission also calls for accountability and transparency in digital transformations, and for the governance of misinformation in health care—basic principles, but ones that have been overridden in a quest for freedom of expression and by the fear that innovation could be sidelined. Public participation and codesign of digital technologies, particularly including young people and those from affected communities, are fundamental.

The Commission also advocates for data solidarity, a radical new approach to health data in which both personal and collective interests and responsibilities are balanced. Rather than data being regarded as something to be owned or hoarded, it emphasises the social and relational nature of health data. Countries should develop data trusts that unlock potential health benefits in public data, while also safeguarding it.

Digital transformations cannot be reversed. But they must be rethought and changed. At its heart, this Commission is both an exposition of the health harms of digital technologies as they function now, and an optimistic vision of the potential alternatives. Calling for investigation and expansion of digital health technologies is not misplaced techno-optimism, but a serious opportunity to drive much needed change. Without new approaches, the world will not achieve the 2030 Sustainable Development Goals.

However, no amount of technical innovation or research will bring equitable health benefits from digital technologies without a fundamental redistribution of power and agency, achievable only through appropriate governance. There is a desperate need to reclaim digital technologies for the good of societies. Our future health depends on it….(More)”.

Open data in digital strategies against COVID-19: the case of Belgium

Curated on Posted on by Stefaan Verhulst

Paper by Robert Viseur: “COVID-19 has highlighted the importance of digital in the fight against the pandemic (control at the border, automated tracing, creation of databases…). In this research, we analyze the Belgian response in terms of open data. First, we examine the open data publication strategy in Belgium (a federal state with a sometimes complex functioning, especially in health), second, we conduct a case study (anatomy of the pandemic in Belgium) in order to better understand the strengths and weaknesses of the main COVID-19 open data repository. And third, we analyze the obstacles to open data publication. Finally, we discuss the Belgian COVID-19 open data strategy in terms of data availability, data relevance and knowledge management. In particular, we show how difficult it is to optimize the latter in order to make the best use of governmental, private and academic open data in a way that has a positive impact on public health policy….(More)”.

Building the Behavior Change Toolkit: Designing and Testing a Nudge and a Boost

Curated on Posted on by Stefaan Verhulst

Blog by Henrico van Roekel, Joanne Reinhard, and Stephan Grimmelikhuijsen: “Changing behavior is challenging, so behavioral scientists and designers better have a large toolkit. Nudges—subtle changes to the choice environment that don’t remove options or offer a financial incentive—are perhaps the most widely used tool. But they’re not the only tool.

More recently, researchers have advocated a different type of behavioral intervention: boosting. In contrast to nudges, which aim to change behavior through changing the environment, boosts aim to empower individuals to better exert their own agency.

Underpinning each approach are different perspectives on how humans deal with bounded rationality—the idea that we don’t always behave in a way that aligns with our intentions because our decision-making is subject to biases and flaws.

A nudge approach generally assumes that bounded rationality is a constant, a fact of life. Therefore, to change behavior we best change the decision environment (the so-called choice architecture) to gently guide people into the desired direction. Boosting holds that bounded rationality is malleable and people can learn how to overcome their cognitive pitfalls. Therefore, to change behavior we must focus on the decision maker and increasing their agency.

In practice, a nudge and a boost can look quite similar, as we describe below. But their theoretical distinctions are important and useful for behavioral scientists and designers working on behavior change interventions, as each approach has pros and cons. For instance, one criticism of nudging is the paternalism part of Thaler and Sunstein’s “libertarian paternalism,” as some worry nudges remove autonomy of decision makers (though the extent to which nudges are paternalistic, and the extent to which this is solvable, are debated). Additionally, if the goal of an intervention isn’t just to change behavior but to change the cognitive process of the individual, then nudges aren’t likely to be the best tool. Boosts, in contrast, require some motivation and teachability on the part of the boostee, so there may well be contexts unfit for boosting interventions where nudges come in handy….(More)”.

Mobile Big Data in the fight against COVID-19

Curated on Posted on by Stefaan Verhulst

Editorial to Special Collection of Data&Policy by Richard Benjamins, Jeanine Vos, and Stefaan Verhulst: “Almost two years into the COVID-19 pandemic, parts of the world feel like they may slowly be getting back to (a new) normal. Nevertheless, we know that the damage is still unfolding, and that much of the developing world Southeast Asia and Africa in particular — remain in a state of crisis. Given the global nature of this disease and the potential for mutant versions to develop and spread, a crisis anywhere is cause for concern everywhere. The world remains very much in the grip of this public health crisis.

From the beginning, there has been hope that data and technology could offer solutions to help inform governments’ response strategy and decision-making. Many of the expectations have been focused on mobile data analytics, and in particular the possibility of mobile network operators creating mobility insights and decision-making tools generated from anonymized and aggregated telco data. This hoped-for capability results from a growing group of mobile network operators investing in systems and capabilities to develop such decision-support products and services for public and private sector customers. The value of having such tools has been demonstrated in addressing different global challenges, ranging from the possibilities offered by models to better understand the spread of Zika in Brazil to interactive dashboards that aided emergency services during earthquakes and floods in Japan. Yet despite these experiences, many governments across the world still have limited awareness, capabilities, budgets and resources to leverage such tools in their efforts to limit the spread of COVID-19 using non-pharmaceutical interventions (NPI).

This special collection of papers we launched in Data & Policy examines both the potential of mobile data, as well as the challenges faced in delivering these tools to inform government decision-making. To date, the collection

Consisting of 11 papers from 71 researchers and experts from academia, industry, and government, the articles cover a wide range of geographies, including Argentina, Austria, Belgium, Brazil, Ecuador, Estonia, Europe (as a whole), France, Gambia, Germany, Ghana, Italy, Malawi, Nigeria, Nordics, and Spain. Responding to our call for case studies to illustrate the opportunities (and challenges) offered by mobile big data in the fight against COVID-19, the authors of these papers describe a number of examples of how mobile and mobile-related data have been used to address the medical, economic, socio-cultural and political aspects of the pandemic….(More)”.

Volunteers Sped Up Alzheimer’s Research

Curated on Posted on by Stefaan Verhulst

Article by SciStarter: “Across the United States, 5.7 million people are living with Alzheimer’s disease, the seventh leading cause of death in America. But there is still no treatment or cure. Alzheimer’s hits close to home for many of us who have seen loved ones suffer and who feel hopeless in the face of this disease. With Stall Catchers, an online citizen science project, joining the fight against Alzheimer’s is as easy as playing an online computer game…

Scientists at Cornell University found a link between “stalled” blood vessels in the brain and the symptoms of Alzheimer’s. These stalled vessels limit blood flow to the brain by up to 30 percent. In experiments with laboratory mice, when the blood cells causing the stalls were removed, the mice performed better on memory tests.about:blankabout:blank

The researchers are working to develop Alzheimer’s treatments that remove the stalls in mice in the hope they can apply these methods to humans. But analyzing the brain images to find the stalled capillaries is hard and time consuming. It could take a trained laboratory technician six to 12 months to analyze each week’s worth of data collection.

So, Cornell researchers created Stall Catchers to make finding the stalled blood vessels into a game that anyone can play. The game relies on the power of the crowd — multiple confirmed answers — before determining whether a vessel is stalled or flowing…

Since its inception is 2016, he project has grown steadily, addressing various datasets and uncovering new insights about Alzheimer’s disease. Citizen scientists who play the game identify blood vessels as “flowing” or “stalled,” earning points for their classifications.

One way Stall Catchers makes this research fun is by allowing volunteers to form teams and engage in friendly competition…(More)”.

Secondary use of health data in Europe

Curated on Posted on by Stefaan Verhulst

Report by Mark Boyd, Dr Milly Zimeta, Dr Jeni Tennison and Mahad Alassow: “Open and trusted health data systems can help Europe respond to the many urgent challenges facing its society and economy today. The global pandemic has already altered many of our societal and economic systems, and data has played a key role in enabling cross-border and cross-sector collaboration in public health responses.

Even before the pandemic, there was an urgent need to optimise healthcare systems and manage limited resources more effectively, to meet the needs of growing, and often ageing, populations. Now, there is a heightened need to develop early-diagnostic and health-surveillance systems, and more willingness to adopt digital healthcare solutions…

By reusing health data in different ways, we can increase the value of this data and help to enable these improvements. Clinical data, such as incidences of healthcare and clinical trials data, can be combined with data collected from other sources, such as sickness and insurance claims records, and from devices and wearable technologies. This data can then be anonymised and aggregated to generate new insights and optimise population health, improve patients’ health and experiences, create more efficient healthcare systems, and foster innovation.

This secondary use of health data can enable a wide range of benefits across the entire healthcare system. These include opportunities to optimise service, reduce health inequalities by better allocating resources, and enhance personalised healthcare –for example, by comparing treatments for people with similar characteristics. It can also help encourage innovation by extending research data to assess whether new therapies would work for a broader population….(More)”.

Greece used AI to curb COVID: what other nations can learn

Curated on Posted on by Stefaan Verhulst

Editorial at Nature: “A few months into the COVID-19 pandemic, operations researcher Kimon Drakopoulos e-mailed both the Greek prime minister and the head of the country’s COVID-19 scientific task force to ask if they needed any extra advice.

Drakopoulos works in data science at the University of Southern California in Los Angeles, and is originally from Greece. To his surprise, he received a reply from Prime Minister Kyriakos Mitsotakis within hours. The European Union was asking member states, many of which had implemented widespread lockdowns in March, to allow non-essential travel to recommence from July 2020, and the Greek government needed help in deciding when and how to reopen borders.

Greece, like many other countries, lacked the capacity to test all travellers, particularly those not displaying symptoms. One option was to test a sample of visitors, but Greece opted to trial an approach rooted in artificial intelligence (AI).

Between August and November 2020 — with input from Drakopoulos and his colleagues — the authorities launched a system that uses a machine-learning algorithm to determine which travellers entering the country should be tested for COVID-19. The authors found machine learning to be more effective at identifying asymptomatic people than was random testing or testing based on a traveller’s country of origin. According to the researchers’ analysis, during the peak tourist season, the system detected two to four times more infected travellers than did random testing.

The machine-learning system, which is among the first of its kind, is called Eva and is described in Nature this week (H. Bastani et al. Nature https://doi.org/10.1038/s41586-021-04014-z; 2021). It’s an example of how data analysis can contribute to effective COVID-19 policies. But it also presents challenges, from ensuring that individuals’ privacy is protected to the need to independently verify its accuracy. Moreover, Eva is a reminder of why proposals for a pandemic treaty (see Nature 594, 8; 2021) must consider rules and protocols on the proper use of AI and big data. These need to be drawn up in advance so that such analyses can be used quickly and safely in an emergency.

In many countries, travellers are chosen for COVID-19 testing at random or according to risk categories. For example, a person coming from a region with a high rate of infections might be prioritized for testing over someone travelling from a region with a lower rate.

By contrast, Eva collected not only travel history, but also demographic data such as age and sex from the passenger information forms required for entry to Greece. It then matched those characteristics with data from previously tested passengers and used the results to estimate an individual’s risk of infection. COVID-19 tests were targeted to travellers calculated to be at highest risk. The algorithm also issued tests to allow it to fill data gaps, ensuring that it remained up to date as the situation unfolded.

During the pandemic, there has been no shortage of ideas on how to deploy big data and AI to improve public health or assess the pandemic’s economic impact. However, relatively few of these ideas have made it into practice. This is partly because companies and governments that hold relevant data — such as mobile-phone records or details of financial transactions — need agreed systems to be in place before they can share the data with researchers. It’s also not clear how consent can be obtained to use such personal data, or how to ensure that these data are stored safely and securely…(More)”.