Data saves lives: reshaping health and social care with data

Curated on Posted on by Stefaan Verhulst

UK Government Policy Paper: “…Up-to-date information about our health and care is critical to ensuring we can:

  • plan and commission services that provide what each local area needs and support effective integrated care systems
  • develop new diagnostics, treatments and insights from analysing information so the public have the best possible care and can improve their overall wellbeing
  • stop asking the public to repeat their information unnecessarily by having it available at the right time
  • assess the safety and quality of care to keep the public safe, both for their individual care and to improve guidance and regulations
  • better manage public health issues such as COVID-19, health and care disparities, and sexual health
  • help the public make informed decisions about their care, including choosing clinicians, such as through patient-reported outcome measures (PROMs) that assess the quality of care delivered from a patient’s perspective

When it comes to handling personal data, the NHS has become one of the most trusted organisations in the UK by using strict legal, privacy and security controls. Partly as a consequence of this track record, the National Data Guardian’s recent report Putting Good Into Practice found that participants were supportive of health and social care data being used for public benefit. This reflects previous polls, which show most respondents would trust the NHS with data about them (57% in July 2020 and 59% in February 2020).

During the pandemic, we made further strides in harnessing the power of data:

However, we cannot take the trust of the public for granted. In the summer of 2021, we made a mistake and did not do enough to explain the improvements needed to the way we collect general practice data. The reasons for these changes are to improve data quality, and improve the understanding of the health and care system so it can plan better and provide more targeted services. We also need to do this in a more cost-effective way as the current system using ad hoc collection processes is more expensive and inefficient, and has been criticised by the National Audit Office and the House of Commons Public Accounts Committee.

Not only did we insufficiently explain, we also did not listen and engage well enough. This led to confusion and anxiety, and created a perception that we were willing to press ahead regardless. This had the unfortunate consequence of leading to an increase in the rate of individuals opting out of sharing their data. Of course, individual members of the public have the right to opt out and always will. But the more people who opt out, the greater the risk that the quality of the data is compromised….

In this data strategy, which differs from the draft we published last year, we are putting public trust and confidence front and centre of the safe use and access to health and social care data. The data we talk about is not an abstract thing: there is an individual, a person, a name behind each piece of data. That demands the highest level of confidence. It is their data that we hold in trust and, in return, promise to use safely to provide high-quality care, help improve our NHS and adult social care, develop new treatments, and, as a result, save lives…(More)”

Prediction machines, insurance, and protection: An alternative perspective on AI’s role in production

Curated on Posted on by Stefaan Verhulst

Paper by Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb: “Recent advances in AI represent improvements in prediction. We examine how decisionmaking and risk management strategies change when prediction improves. The adoption of AI may cause substitution away from risk management activities used when rules are applied (rules require always taking the same action), instead allowing for decisionmaking (choosing actions based on the predicted state). We provide a formal model evaluating the impact of AI and how risk management, stakes, and interrelated tasks affect AI adoption. The broad conclusion is that AI adoption can be stymied by existing processes designed to address uncertainty. In particular, many processes are designed to enable coordinated decisionmaking among different actors in an organization. AI can make coordination even more challenging. However, when the cost of changing such processes falls, then the returns from AI adoption increase….(More)”.

A Future Built on Data: Data Strategies, Competitive Advantage and Trust

Curated on Posted on by Stefaan Verhulst

Paper by Susan Ariel Aaronson: “In the twenty-first century, data became the subject of national strategy. This paper examines these visions and strategies to better understand what policy makers hope to achieve. Data is different from other inputs: it is plentiful, easy to use and can be utilized and shared by many different people without being used up. Moreover, data can be simultaneously a commercial asset and a public good. Various types of data can be analyzed to create new products and services or to mitigate complex “wicked” problems that transcend generations and nations (a public good function). However, an economy built on data analysis also brings problems — firms and governments can manipulate or misuse personal data, and in so doing undermine human autonomy and human rights. Given the complicated nature of data and its various types (for example, personal, proprietary, public, and so on), a growing number of governments have decided to outline how they see data’s role in the economy and polity. While it is too early to evaluate the effectiveness of these strategies, policy makers increasingly recognize that if they want to build their country’s future on data, they must also focus on trust….(More)”.

AI Ethics: Global Perspectives

Curated on Posted on by Stefaan Verhulst

New Course Modules: “A Cybernetics Approach to Ethical AI Design” explores the relationship between cybernetics and AI ethics, and looks at how cybernetics can be leveraged to reframe how we think about and how we undertake ethical AI design. This module, by Ellen Broad, Associate Professor and Associate Director at the Australian National University’s School of Cybernetics, is divided into three sections, beginning with an introduction to cybernetics. Following that, we explore different ways of thinking about AI ethics, before concluding by bringing the two concepts together to understand a new approach to ethical AI design.

How should organizations put AI ethics and responsible AI into practice? Is the answer AI ethics principles and AI ethics boards or should everyone developing AI systems become experts in ethics? In “An Ethics Model for Innovation: The PiE (Puzzle-solving in Ethics) Model”, Cansu Canca, Founder and Director of the AI Ethics Lab, presents the model developed and employed at AI Ethics Lab: The Puzzle-solving in Ethics (PiE) Model. The PiE Model is a comprehensive and structured practice framework for organizations to integrate ethics into their operations as they develop and deploy AI systems. The PiE Model aims to make ethics a robust and integral part of innovation and enhance innovation through ethical puzzle-solving.

Nuria Oliver, Co-Founder and Scientific Director of the ELLIS Alicante Unit, presents “Data Science against COVID-19: The Valencian Experience”. In this module, we explore the ELLIS Alicante Foundation’s Data-Science for COVID-19 team’s work in the Valencian region of Spain. The team was founded in response to the pandemic in March 2020 to assist policymakers in making informed, evidence-based decisions. The team tackles four different work areas: modeling human mobility, building computational epidemiological models, predictive models on the prevalence of the disease, and operating one of the largest online citizen surveys related to COVID-19 in the world. This lecture explains the four work streams and shares lessons learned from their work at the intersection between data, AI, and the pandemic…(More)”.

Dynamic World

Curated on Posted on by Stefaan Verhulst

About: “The real world is as dynamic as the people and natural processes that shape it. Dynamic World is a near realtime 10m resolution global land use land cover dataset, produced using deep learning, freely available and openly licensed. It is the result of a partnership between Google and the World Resources Institute, to produce a dynamic dataset of the physical material on the surface of the Earth. Dynamic World is intended to be used as a data product for users to add custom rules with which to assign final class values, producing derivative land cover maps.

Key innovations of Dynamic World

  1. Near realtime data. Over 5000 Dynamic World image are produced every day, whereas traditional approaches to building land cover data can take months or years to produce. As a result of leveraging a novel deep learning approach, based on Sentinel-2 Top of Atmosphere, Dynamic World offers global land cover updating every 2-5 days depending on location.
  2. Per-pixel probabilities across 9 land cover classes. A major benefit of an AI-powered approach is the model looks at an incoming Sentinel-2 satellite image and, for every pixel in the image, estimates the degree of tree cover, how built up a particular area is, or snow coverage if there’s been a recent snowstorm, for example.
  3. Ten meter resolution. As a result of the European Commission’s Copernicus Programme making European Space Agency Sentinel data freely and openly available, products like Dynamic World are able to offer 10m resolution land cover data. This is important because quantifying data in higher resolution produces more accurate results for what’s really on the surface of the Earth…(More)”.

Global Struggle Over AI Surveillance

Curated on Posted on by Stefaan Verhulst

Report by the National Endowment for Democracy: “From cameras that identify the faces of passersby to algorithms that keep tabs on public sentiment online, artificial intelligence (AI)-powered tools are opening new frontiers in state surveillance around the world. Law enforcement, national security, criminal justice, and border management organizations in every region are relying on these technologies—which use statistical pattern recognition, machine learning, and big data analytics—to monitor citizens.

What are the governance implications of these enhanced surveillance capabilities?

This report explores the challenge of safeguarding democratic principles and processes as AI technologies enable governments to collect, process, and integrate unprecedented quantities of data about the online and offline activities of individual citizens. Three complementary essays examine the spread of AI surveillance systems, their impact, and the transnational struggle to erect guardrails that uphold democratic values.

In the lead essay, Steven Feldstein, a senior fellow at the Carnegie Endowment for International Peace, assesses the global spread of AI surveillance tools and ongoing efforts at the local, national, and multilateral levels to set rules for their design, deployment, and use. It gives particular attention to the dynamics in young or fragile democracies and hybrid regimes, where checks on surveillance powers may be weakened but civil society still has space to investigate and challenge surveillance deployments.

Two case studies provide more granular depictions of how civil society can influence this norm-shaping process: In the first, Eduardo Ferreyra of Argentina’s Asociación por los Derechos Civiles discusses strategies for overcoming common obstacles to research and debate on surveillance systems. In the second, Danilo Krivokapic of Serbia’s SHARE Foundation describes how his organization drew national and global attention to the deployment of Huawei smart cameras in Belgrade…(More)”.

Americans’ Views of Government: Decades of Distrust, Enduring Support for Its Role

Curated on Posted on by Stefaan Verhulst

Pew Research: “Americans remain deeply distrustful of and dissatisfied with their government. Just 20% say they trust the government in Washington to do the right thing just about always or most of the time – a sentiment that has changed very little since former President George W. Bush’s second term in office.

Chart shows low public trust in federal government has persisted for nearly two decades

The public’s criticisms of the federal government are many and varied. Some are familiar: Just 6% say the phrase “careful with taxpayer money” describes the federal government extremely or very well; another 21% say this describes the government somewhat well. A comparably small share (only 8%) describes the government as being responsive to the needs of ordinary Americans.

The federal government gets mixed ratings for its handling of specific issues. Evaluations are highly positive in some respects, including for responding to natural disasters (70% say the government does a good job of this) and keeping the country safe from terrorism (68%). However, only about a quarter of Americans say the government has done a good job managing the immigration system and helping people get out of poverty (24% each). And the share giving the government a positive rating for strengthening the economy has declined 17 percentage points since 2020, from 54% to 37%.

Yet Americans’ unhappiness with government has long coexisted with their continued support for government having a substantial role in many realms. And when asked how much the federal government does to address the concerns of various groups in the United States, there is a widespread belief that it does too little on issues affecting many of the groups asked about, including middle-income people (69%), those with lower incomes (66%) and retired people (65%)…(More)”.

How can data stop homelessness before it starts?

Curated on Posted on by Stefaan Verhulst

Article by Andrea Danes and Jessica Chamba: “When homelessness in Maidstone, England, soared by 58% over just five years, the Borough Council sought to shift its focus from crisis response to building early-intervention and prevention capacity. Working with EY teams and our UK technology partner, Xantura, the council created and implemented a data-focused tool — called OneView — that enabled the council to tackle their challenges in a new way.

Specifically, OneView’s predictive analytic and natural language generation capabilities enabled participating agencies in Maidstone to bring together their data to identify residents who were at risk of homelessness, and then to intervene before they were actually living on the street. In the initial pilot year, almost 100 households were prevented from becoming homeless, even as the COVID-19 pandemic took hold and grew. And, overall, the rate of homelessness fell by 40%. 

As evidenced by the Maidstone model, data analytics and predictive modeling will play an indispensable role in enabling us to realize a very big vision — a world in which everyone has a reliable roof over their heads.

Against that backdrop, it’s important to stress that the roadmap for preventing homelessness has to contain components beyond just better avenues for using data. It must also include shrewd approaches for dealing with complex issues such as funding, standards, governance, cultural differences and informed consent to permit the exchange of personal information, among others. Perhaps most importantly, the work needs to be championed by organizational and governmental leaders who believe transformative, systemic change is possible and are committed to achieving it.

Introducing the Smart Safety Net

To move forward, human services organizations need to look beyond modernizing service delivery to transforming it, and to evolve from integration to intuitive design. New technologies provide opportunities to truly rethink and redesign in ways that would have been impossible in the past.

A Smart Safety Net can shape a bold new future for social care. Doing so will require broad, fundamental changes at an organizational level, more collaboration across agencies, data integration and greater care co-ordination. At its heart, a Smart Safety Net entails:

  • A system-wide approach to addressing the needs of each individual and family, including pooled funding that supports coordination so that, for example, users in one program are automatically enrolled in other programs for which they are eligible.
  • Human-centered design that genuinely integrates the recipients of services (patients, clients, customers, etc.), as well as their experiences and insights, into the creation and implementation of policies, systems and services that affect them.
  • Data-driven policy, services, workflows, automation and security to improve processes, save money and facilitate accurate, real-time decision-making, especially to advance the overarching priority of nearly every program and service; that is, early intervention and prevention.
  • Frontline case workers who are supported and empowered to focus on their core purpose. With a lower administrative burden, they are able to invest more time in building relationships with vulnerable constituents and act as “coaches” to improve people’s lives.
  • Outcomes-based commissioning of services, measured against a more holistic wellbeing framework, from an ecosystem of public, private and not-for-profit providers, with government acting as system stewards and service integrators…(More)”.

Serving citizens: measuring the performance of services for a better user experience

Curated on Posted on by Stefaan Verhulst

OECD Report: “Measuring the performance of services and making effective use of the results are critical for designing and delivering policies to improve people’s lives. Improving user satisfaction with public services is an objective in many OECD countries and is one of the indicators in the 2030 Sustainable Development Goal 16 of “Building effective, accountable and inclusive institutions at all levels”. This paper explores the use of satisfaction indicators to monitor citizens’ and users’ experience with public services. It finds that satisfaction indicators provide an accurate aggregate account of the factors driving service performance. At the same time, it shows that additional measures are needed to monitor the access, responsiveness and quality of public services, as well as to identify concrete areas of improvement. This paper provides examples of how countries use performance data in decision making (both subjective users’ experience and objective service outputs). It also highlights common challenges and good practices to strengthen performance measurement and management…(More)”.

Toolkit on Digital Transformation for People-Oriented Cities and Communities

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Toolkit by the ITU: “The Toolkit on Digital Transformation for People-Oriented Cities and Communities supports strategizing and planning the digital transformation of cities and communities to promote sustainable, inclusive, resilient and improved quality of life for residents in cities and communities.

The resources contained in this Toolkit include international standards and guidance, the latest research and projections, and cutting-edge reports on a variety of timely topics relevant to the digital transformation of cities and communities. The Toolkit can universally benefit cities and communities, as well as regions and countries regardless of their level of smart or digital development, or their geographical or economic status. ​

The Toolkit is:​

  • A one-stop guide containing latest international standards and other ITU and UN resources, publications and reports.​
  • An endeavour to identify the challenges faced by cities as well as potential solutions that they can leverage for maximum positive impact.​
  • A comprehensive, yet non-exhaustive collation of information that is meant to inspire and support progress toward the SDGs, especially SDG 11, at the local level.​..(More)”