Paper by Louise Mc Grath-Lone et al: “Administrative data are a valuable research resource, but are under-utilised in the UK due to governance, technical and other barriers (e.g., the time and effort taken to gain secure data access). In recent years, there has been considerable government investment in making administrative data “research-ready”, but there is no definition of what this term means. A common understanding of what constitutes research-ready administrative data is needed to establish clear principles and frameworks for their development and the realisation of their full research potential…Overall, we screened 2,375 records and identified 38 relevant studies published between 2012 and 2021. Most related to administrative data from the UK and US and particularly to health data. The term research-ready was used inconsistently in the literature and there was some conflation with the concept of data being ready for statistical analysis. From the thematic analysis, we identified five defining characteristics of research-ready administrative data: (a) accessible, (b) broad, (c) curated, (d) documented and (e) enhanced for research purposes…
Our proposed characteristics of research-ready administrative data could act as a starting point to help data owners and researchers develop common principles and standards. In the more immediate term, the proposed characteristics are a useful framework for cataloguing existing research-ready administrative databases and relevant resources that can support their development…(More)”.
Pandemic X Infodemic: How States Shaped Narratives During COVID-19
Report by Innovation for Change – East Asia (I4C-EA): “The COVID-19 pandemic has left many unprecedented records in the history of the world. The coronavirus crisis was the first large-scale pandemic that began in a time when the internet and social media connect people to each other. It provided the latest information to respond to the COVID-19 and the technology to ask about each other’s well-being. Yet, it spread and amplified disinformation and misinformation that made the situation worse in real-time.
In addition, some countries have had opaque communications with the public about the COVID-19, and some government officials have aided in the dissemination of unconfirmed information. Other countries also created their own narratives on the COVID-19 and were reluctant to disclose important information to the public. This has led to restrictions on freedom of expression. Activists and journalists who tell the different stories from the state-shaped narrative were arrested.
To strengthen civil society’s effort to empower the public with better access to the truth, the Innovation for Change – East Asia Hub initiated “Pandemic X Infodemic: How States Shaped Narratives During COVID-19“; a research to track East Asian governments’ information, disinformation, and misinformation efforts in their respective policy responses to the COVID-19 pandemic from 2020-21. This research covered four countries – China, Myanmar, Indonesia, and the Philippines – with one thematic focus on migrants in the receiving countries of Thailand and Singapore…(More)”.
How the Pandemic Made Algorithms Go Haywire
Article by Ravi Parikh and Amol Navathe: “Algorithms have always had some trouble getting things right—hence the fact that ads often follow you around the internet for something you’ve already purchased.
But since COVID upended our lives, more of these algorithms have misfired, harming millions of Americans and widening existing financial and health disparities facing marginalized groups. At times, this was because we humans weren’t using the algorithms correctly. More often it was because COVID changed life in a way that made the algorithms malfunction.
Take, for instance, an algorithm used by dozens of hospitals in the U.S. to identify patients with sepsis—a life-threatening consequence of infection. It was supposed to help doctors speed up transfer to the intensive care unit. But starting in spring of 2020, the patients that showed up to the hospital suddenly changed due to COVID. Many of the variables that went into the algorithm—oxygen levels, age, comorbid conditions—were completely different during the pandemic. So the algorithm couldn’t effectively discern sicker from healthier patients, and consequently it flagged more than twice as many patients as “sick” even though hospital capacity was 35 percent lower than normal. The result was presumably more instances of doctors and nurses being summoned to the patient bedside. It’s possible all of these alerts were necessary – after all, more patients were sick. However, it’s also possible that many of these alerts were false alarms because the type of patients showing up to the hospital were different. Either way, this threatened to overwhelm physicians and hospitals. This “alert overload” was discovered months into the pandemic and led the University of Michigan health system to shut down its use of the algorithm…(More)”.
Building a Data Infrastructure for the Bioeconomy
Article by Gopal P. Sarma and Melissa Haendel: “While the development of vaccines for COVID-19 has been widely lauded, other successful components of the national response to the pandemic have not received as much attention. The National COVID Cohort Collaborative (N3C), for example, flew under the public’s radar, even though it aggregated crucial US public health data about the new disease through cross-institutional collaborations among government, private, and nonprofit health and research organizations. These data, which were made available to researchers via cutting-edge software tools, have helped in myriad ways: they led to identification of the clinical characteristics of acute COVID-19 for risk prediction, assisted in providing clinical care for immunocompromised adults, revealed how COVID infection affects children, and documented that vaccines appear to reduce the risk of developing long COVID.
N3C has created the largest national, publicly available patient-level dataset in US history. Through a unique public-private partnership, over 300 participating organizations quickly overcame privacy concerns and data silos to include 13 million patient records in the project. More than 3,000 participating scientists are now working to overcome the particular challenge faced in the United States—the lack of a national healthcare data infrastructure available in many other countries—to support public health and medical responses. N3C shows great promise for unraveling answers to questions related to COVID, but it could easily be expanded for many areas of public health, including pandemic preparedness and monitoring disease status across the population.
As public servants dedicated to improving public health and equity, we believe that to unite the nation’s fragmented public health system, the United States should establish a standing capacity to collect, harmonize, and sustain a wide range of data types and sources. The public health data collected by N3C would ultimately be but one component of a rich landscape of interoperable data systems that can guide public policy in an era of rapid environmental change, sophisticated biological threats, and an economy enabled by biotechnology. Such an effort will require new thinking about data collection, infrastructure, and regulation, but its benefits could be enormous—enabling policymakers to make decisions in an increasingly complex world. And as the interconnections between society, industry, and government continue to intensify, decisionmaking of all types and scales will be more efficient and responsive if it can rely on significantly expanded data collection and analysis capabilities…(More)”.
The quantified self
Special issue by The Economist: “Bryan Johnson has just spent another weekend being examined. “On Saturday the sonographer was measuring…my ankles and knees and hips and shoulders and elbows, assessing what is the age of my tendons and ligaments,” he says. It is part of a mission to have all 70-plus organs of his body measured in exhaustive detail so he can see whether, and to what extent, his healthy lifestyle is rejuvenating them.
Mr Johnson, a tech entrepreneur in California, says he has undergone more than 300 tests of various sorts to that end. At one point he had one to check for damage to his arteries from all the blood drawn for other tests. His diet is also entirely determined by tests which have looked at how his body reacts to some 150 foods. “My conscious mind never decides what to eat,” he says. The main meal every day is the same green veggie mush, with a side of strictly regimented sleep, exercise and meditation….”
More:
- The quantified self: Wearable devices are connecting health care to daily life
- One ring to rule them all: Wearable devices measure a growing array of health indicators
- Killer apps, saving lives: Apps interpreting data from wearable devices are helping people to live better
- Digital therapeutics: Some health apps are able not just to diagnose diseases, but also to treat them
- The pulse of the people: Data from wearable devices are changing disease surveillance and medical research
- Sources and acknowledgments
Roe draft raises concerns data could be used to identify abortion seekers, providers
Article by Chris Mills Rodrigo: “Concerns that data gathered from peoples’ interactions with their digital devices could potentially be used to identify individuals seeking or performing abortions have come into the spotlight with the news that pregnancy termination services could soon be severely restricted or banned in much of the United States.
Following the leak of a draft majority opinion indicating that the Supreme Court is poised to overturn Roe v. Wade, the landmark 1973 decision that established the federal right to abortion, privacy advocates are raising alarms about the ways law enforcement officials or anti-abortion activists could make such identifications using data available on the open market, obtained from companies or extracted from devices.
“The dangers of unfettered access to Americans’ personal information have never been more obvious. Researching birth control online, updating a period-tracking app or bringing a phone to the doctor’s office could be used to track and prosecute women across the U.S.,” Sen. Ron Wyden (D-Ore.) said in a statement to The Hill.
Data from web searches, smartphone location pings and online purchases can all be easily obtained with little to no safeguards.
“Almost everything that you do … data can be captured about it and can be fed into a larger model that can help somebody or some entity infer whether or not you may be pregnant and whether or not you may be someone who’s planning to have an abortion or has had one,” Nathalie Maréchal, senior policy manager at Ranking Digital Rights, explained.
There are three primary ways that data could travel from individuals’ devices to law enforcement or other groups, according to experts who spoke with The Hill.
The first is via third party data brokers, which make up a shadowy multibillion dollar industry dedicated to collecting, aggregating and selling location data harvested from individuals’ mobile phones that has provided unprecedented access to the daily movements of Americans for advertisers, or virtually anyone willing to pay…(More)”.
European Health Union: A European Health Data Space for people and science
Press Release: “Today, the European Commission launched the European Health Data Space (EHDS), one of the central building blocks of a strong European Health Union. The EHDS will help the EU to achieve a quantum leap forward in the way healthcare is provided to people across Europe. It will empower people to control and utilise their health data in their home country or in other Member States. It fosters a genuine single market for digital health services and products. And it offers a consistent, trustworthy and efficient framework to use health data for research, innovation, policy-making and regulatory activities, while ensuring full compliance with the EU’s high data protection standards…
Putting people in control of their own health data, in their country and cross-border
- Thanks to the EHDS, people will have immediate, and easy access to the data in electronic form, free of charge. They can easily share these data with other health professionals in and across Member States to improve health care delivery. Citizens will be in full control of their data and will be able to add information, rectify wrong data, restrict access to others and obtain information on how their data are used and for which purpose.
- Member States will ensure that patient summaries, ePrescriptions, images and image reports, laboratory results, discharge reports are issued and accepted in a common European format.
- Interoperability and security will become mandatory requirements. Manufacturers of electronic health record systems will need to certify compliance with these standards.
- To ensure that citizens’ rights are safeguarded, all Member States have to appoint digital health authorities. These authorities will participate in the cross-border digital infrastructure (MyHealth@EU) that will support patients to share their data across borders.
Improving the use of health data for research, innovation and policymaking
- The EHDS creates a strong legal framework for the use of health data for research, innovation, public health, policy-making and regulatory purposes. Under strict conditions, researchers, innovators, public institutions or industry will have access to large amounts of high-quality health data, crucial to develop life-saving treatments, vaccines or medical devices and ensuring better access to healthcare and more resilient health systems.
- The access to such data by researchers, companies or institutions will require a permit from a health data access body, to be set up in all Member States. Access will only be granted if the requested data is used for specific purposes, in closed, secure environments and without revealing the identity of the individual. It is also strictly prohibited to use the data for decisions, which are detrimental to citizens such as designing harmful products or services or increasing an insurance premium.
- The health data access bodies will be connected to the new decentralised EU-infrastructure for secondary use (HealthData@EU) which will be set up to support cross-border projects…(More)”
Better, broader, safer: using health data for research and analysis
The Goldacre Review: “This review was tasked with finding ways to deliver better, broader, safer use of NHS data for analysis and research: more specifically, it was asked to identify the strategic or technical blockers to such work, and how they can be practically overcome. It was commissioned to inform, and sit alongside, the NHS Data Strategy. The recommendations are derived from extensive engagement with over 300 individuals, 8 focus groups, 100 written submissions, substantial desk research, and detailed discussion with our SSG….
In the past ‘data infrastructure’ meant beige boxes in large buildings. In the 21st century, data infrastructure is code, and people with skills. As noted in previous reviews, many shortcomings in the system have been driven by a ‘destructive impatience’: constantly chasing small, isolated, short-term projects at the expense of building a coherent system that can deliver faster, better, safer outputs for all users of data.
If we invest in platforms and curation – at less than the cost of digitising one hospital – and engage robustly with the technical challenges, then we can rapidly capitalise on our skills and data. New analysts, academics and innovators will arrive to find accessible platforms, with well curated data and accessible technical documentation. The start-up time for each new project will shrink, productivity will rocket, and lives will be saved.
Seventy-three years of complete NHS patient records contain all the noise from millions of lifetimes. Perfect, subtle signals can be coaxed from this data, and those signals go far beyond mere academic curiosity. They represent deeply buried treasure that can help prevent suffering and death around the planet on a biblical scale. It is our collective duty to make this work…(More)”.
Responsiveness of open innovation to COVID-19 pandemic: The case of data for good
Paper by Francesco Scotti, Francesco Pierri, Giovanni Bonaccorsi, and Andrea Flori: “Due to the COVID-19 pandemic, countries around the world are facing one of the most severe health and economic crises of recent history and human society is called to figure out effective responses. However, as current measures have not produced valuable solutions, a multidisciplinary and open approach, enabling collaborations across private and public organizations, is crucial to unleash successful contributions against the disease. Indeed, the COVID-19 represents a Grand Challenge to which joint forces and extension of disciplinary boundaries have been recognized as main imperatives. As a consequence, Open Innovation represents a promising solution to provide a fast recovery. In this paper we present a practical application of this approach, showing how knowledge sharing constitutes one of the main drivers to tackle pressing social needs. To demonstrate this, we propose a case study regarding a data sharing initiative promoted by Facebook, the Data For Good program. We leverage a large-scale dataset provided by Facebook to the research community to offer a representation of the evolution of the Italian mobility during the lockdown. We show that this repository allows to capture different patterns of movements on the territory with increasing levels of detail. We integrate this information with Open Data provided by the Lombardy region to illustrate how data sharing can also provide insights for private businesses and local authorities. Finally, we show how to interpret Data For Good initiatives in light of the Open Innovation Framework and discuss the barriers to adoption faced by public administrations regarding these practices…(More)”.
Why AI Failed to Live Up to Its Potential During the Pandemic
Essay by Bhaskar Chakravorti: “The pandemic could have been the moment when AI made good on its promising potential. There was an unprecedented convergence of the need for fast, evidence-based decisions and large-scale problem-solving with datasets spilling out of every country in the world. Instead, AI failed in myriad, specific ways that underscore where this technology is still weak: Bad datasets, embedded bias and discrimination, susceptibility to human error, and a complex, uneven global context all caused critical failures. But, these failures also offer lessons on how we can make AI better: 1) we need to find new ways to assemble comprehensive datasets and merge data from multiple sources, 2) there needs to be more diversity in data sources, 3) incentives must be aligned to ensure greater cooperation across teams and systems, and 4) we need international rules for sharing data…(More)”.