While governments and tech titans are rushing to create contact-tracing apps to curb the spread of COVID-19, a new review published by the Ada Lovelace Institute in the United Kingdom says not so fast.  

“Based on the current evidence in this review, the significant technical limitations, and deep social risks, of digital contact tracing outweigh the value offered to the crisis response,” authors of the report write. “Overcoming these limitations and risks is not impossible but will require, at a minimum, that Government establishes a multidisciplinary Group of Advisors on Technology in Emergencies (GATE) to stand alongside the Scientific Advisory Group on Emergencies (SAGE) and act as gatekeepers of the deployment of technologies in support of a transition strategy."

Tracing has been a heavily debated topic in the UK and beyond. In fact, this report coincides with the news that the UK has decided not to use Apple and Google’s much anticipated contact-tracing tool, as originally planned, according to the BBC. Apple and Google’s model pitches a decentralized system for tracing, while the NHSX – which works on the UK’s digital health efforts – is proposing a centralized system.

NHS officials told the BBC that the centralized system will allow it to adapt more quickly as new advances in coronavirus information come to the surface.

In terms of all efforts in tracing, as well as symptom trackers and any digital immunity certificate, the Ada Lovelace institute urges the UK government to adopt legislation that supports companies creating such technologies insures that the tech isn’t “undermining public trust and confidence.” 

As part of the findings, researchers argued that there is a risk that technological interventions set up now could cause state intrusion into people’s lives after the pandemic. 

“Technical and legal infrastructure built during this pandemic may be difficult to dismantle once it is over unless proper safeguards are in place. The technology sector may bring cutting-edge innovation to solve difficult problems, but a democratic deficit emerges when private sector providers (alone or in partnership with the public sector) are deputized to implement public health policy during times of crisis.”

In order to remedy this, the organization recommends sunset clauses. 

“We call on Government to encourage privacy-by-design in technical implementations, and to advance primary legislation requiring symptom tracking and digital contact tracing apps to delete personal data after the crisis has subsided," authors of the report wrote. 

One of the overarching messages from this report is that government, and not tech, should hold the reins in developing these tools. 

“We recommend two accountability mechanisms to bookend Government decision making – the establishment of the Group of Advisors on Technology for Emergencies to act as gatekeeper for the deployment of technical measures, and the establishment of an independent oversight mechanism to conduct real-time scrutiny of Government policy formulation.”

WHY IT MATTERS

To date there are over 2.8 million reported coronavirus cases, according to the World health Organization. It also says that contact tracing is important because it helps identify individuals that are at risk of becoming infected, and who could then go on to spread the disease. 

In response, governments and technology companies have collaborated on efforts to build tools for tracking and tracing the virus. 

“I worked on the SARS epidemic in 2003 and testing/tracing is a cornerstone of how you stop a serious infection, and I do think that strategy, scaled up, is tremendously effective,” Professor Christophe Fraser from the Oxford Big Data Institute, said on BBC this morning. 

However, privacy has been a constant question in the debate over tracking and tracing apps, and each country has a different take on what that looks like. 

“Contact tracing apps collect and combine two highly sensitive categories of information: location and health status,”Ryan Calo, a professor of law at the University of Washington, and Kinsa CEO Inder Singh, said during a US Senate committee hearing on big data and privacy protections. “It seems fair to wonder whether these apps, developed by small teams, will be able to keep such sensitive information private and secure. To the extent digital contact tracing – or any private, technology-driven response to the pandemic – involves the sharing of healthcare data with private parties, there is also the specter of inadequate transparency or consent.”

THE LARGER TREND

In early April, Apple and Google announced that they were teaming up on a project to introduce health data-sharing and COVID-19 contact-tracing technologies to the lion's share of the smartphone market. 

The pair plan to launch APIs that will enable interoperability between iOS and Android products by way of official apps from public health authorities. The companies said these apps will be available for consumers to download from the Apple App Store and the Google Play Store starting in May. 

A few days after this announcement, the NHSX reported that they would be working with the companies on a tool for the UK, but as of today that plan is null. 

Tracing isn’t unique to the UK and the US. Several governments, including Singapore and South Korea, have developed tools to help trace the virus. In late March the Government Technology Agency of Singapore (GovTech), the in-house IT agency of the Singapore public service, in collaboration with the Ministry of Health (MOH), launched a mobile app called TraceTogether, to help support and supplement current contact-tracing efforts in the nation-state, in an effort to reduce the spread of COVID-19.

Perhaps first the first to come up with this kind of tool was the Chinese government, which also rolled out an app back in February that is intended to help citizens check whether they came into contact with the virus. App users are asked to register a phone number, name and ID number to see if they were in contact with someone infected. 

Implementation of either an automated or semi-automated deep learning system for diabetic retinopathy screening could lead to cost savings at the health-system level, according to an economic analysis modeling study recently published in The Lancet Digital Health.

Backed by Singapore's Ministry of Health, the investigation looked at data from a national diabetic retinopathy screening program conducted within the country in 2015, and modeled the simulated costs of substituting the human-led approach with artificial intelligence-augmented screening techniques.

TOPLINE DATA

Based on the study's models, diabetes patients would incur a 12-month total cost of $77 per patient when assessed by a human. Using a fully automated screening process would cut this price by 14.3%, to $66 per patient per year, while a semi-automated approach would increase savings by 19.5%, to $62 per patient per year.

Costs relating to the human graders, screening specificities and IT considerations had the greatest impact on these prices. For the former, the researchers highlighted the roughly two minutes a human grader would require to assess each image, which a deep learning system could cut down almost entirely.

Meanwhile, the major difference between the fully automated model and the semi-automated model, which only reduced human grading costs by 74%, was follow-up care driven by each screening method's specificity.

"The fully automated model ... yields greater savings," the researchers wrote. "This is because of a higher rate of false positives, and therefore more unnecessary specialist visits, under the fully automated model. The higher costs of graders in the semi-automated model is more than offset by the lower consultation costs. However, this is ... based on the wages in Singapore, and might not apply to other settings."

HOW IT WAS DONE

The study relied on a historical dataset of 39,006 diabetes patients screened through a tele-ophthalmology platform as part of the Singapore Integrated Diabetic Retinopathy Programme.

The recorded cost of screening these patients against the hypothetical two deep learning system-based approaches using a decision tree model developed by the research team. Parameters included in this model included diabetic retinopathy prevalence rates, screening costs of each approach, their sensitivity and specificity, and resulting medical consultation costs.

Diagnostic performance and disease prevalence values were collected from local sources or based on the researchers' prior work. The costs of goods and services were either obtained in 2015, or were adjusted for inflation to reflect their price in June 2015.

THE LARGER TREND

A number of academic teams and major tech providers have been developing algorithm-based approaches to diabetic retinopathy screening, some of which involve devices that are easily mounted onto a smartphone to encourage point-of-care diagnoses. Google in particular has been beating the drum of machine learning-based screening for the last few years, having published study data regarding their system in 2018 and announcing its first real-world clinical use in 2019.

IN CONCLUSION

"Our study shows that both the fully automated and semi-automated [deep learning systems] were less expensive than the current manual grading system for diabetic retinopathy screening in Singapore. By 2050, Singapore is projected to have close to 1 million people with diabetes; if a [deep learning system] is adopted, this could translate into savings of $15 million," the researchers concluded.