This chapter examines the challenges of the revised opt out system and the secondary use of health data in England. The analysis of this data could be very valuable for science and medical treatment as well as for the discovery of new drugs. For this reason, the UK government established the care.data program in 2013. The aim of the project was to build a central nationwide database for research and policy planning. However, the processing of personal data was planned without proper public engagement. Research has suggested that IT companies, such as in the Google DeepMind deal case, had access to other kinds of sensitive data and failed to comply with data protection law. Since May 2018, the government has launched the national data opt out system with the hope of regaining public trust. Nevertheless, there are no evidence of significant changes in the ND opt out, compared to the previous opt out system. Neither in the use of secondary data, nor in the choices that patients can make. The only notorious difference seems to be in the way that these options are communicated and framed to the patients. Most importantly, according to the new ND opt out, the type 1 opt out option, which is the only choice that truly stops data from being shared outside direct care, will be removed in 2020. According to the Behavioral Law and Economics literature (Nudge Theory), default rules, such as the revised opt out system in England, are very powerful, because people tend to stick to the default choices made readily available to them. The crucial question analyzed in this chapter is whether it is desirable for the UK government to stop promoting the type 1 opt outs, and whether this could be seen as a kind of hard paternalism.

It is true that artificial intelligence (AI) will come to influence almost every aspect of our lives. In the scramble to realize the potential economic and societal benefits promised by AI, the ready availability of massive, complex, and assumed-to-be generalizable datasets with which to train and test new algorithms is vital. The interaction of governments with their citizens throughout their lives generates huge volumes of diverse information, and these continuously expanding repositories of data are now seen as a public good, providing the raw material for AI industries. In passing the National Artificial Intelligence Initiative Act of 2020 (NAIIA), the United States has adopted a path similar to that of the European Union, as defined within the European Commission's Coordinated Plan on Artificial Intelligence 2021 Review. Under the provisions of the NAIIA, the National Artificial Intelligence Research Resource Task Force (NAIRRTF) has been constituted to make recommendations to Congress on, among other things, the capabilities necessary to create shared computing infrastructure for use by AI researchers and potential solutions in respect to "barriers to the dissemination and use of high-quality government data sets."

Patients are set to get easier access to their medication lists and care plans through the NHS App under the government's new data strategy. New requirements for data sharing across the entire health and care system are also set to come into place, with new legislation to be introduced to require all adult social care providers to provide information about the services they fund. Published today (June 22), the NHSX draft strategy'Data Saves Lives: Reshaping health and social care with data', aims to capitalise on the work undertaken using data during the pandemic to improve health and care services. In a bid to establish openness, the government committed to publishing the first transparency statement setting out how health and care data has been used across the sector by 2022. Under the proposals, patients are set to gain more control over their health data, while data will also be used to improve care and treatment.

The NHS has started trials of a machine-learning system designed to help hospitals in England anticipate the demand on resources caused by COVID-19. The COVID-19 Capacity Planning and System (CPAS) is being piloted at four acute hospitals in England to demonstrate whether it can help the NHS predict the demand for equipment like ICU beds and ventilators. If successful, CPAS will be rolled out nationally. From cancelled conferences to disrupted supply chains, not a corner of the global economy is immune to the spread of COVID-19. NHS Digital told ZDNet the initiative marked the "first time any project of this scale and scope using machine learning has been rolled out in the NHS."

Trials have begun of a system that will use machine learning to help predict the upcoming demand for intensive care (ICU) beds and ventilators needed to treat patients with COVID-19 at individual hospitals and across regions in England. The COVID 19 Capacity Planning and Analysis System (CPAS), developed by NHS Digital data scientists and a team of researchers from the University of Cambridge, and using data from Public Health England, will support hospitals to plan more accurately and help ensure that resources are deployed to best effect to support COVID-19 throughout the NHS. The first stage alpha trials began this week at four hospitals, aiming to demonstrate the relative accuracy of the system and fine tune it to best meet the needs of hospitals. "With the pressure being placed on intensive care by the current coronavirus pandemic it is essential to be able to predict demand for critical care beds, equipment and staff,"says NHS Digital Chief Medical Officer Professor Jonathan Benger. "CPAS allows individual hospitals to plan ahead, ensuring they can give the best care to every patient. At the same time, the wider NHS can ensure that the ventilators, other equipment and drugs that each intensive care unit will need are in place at exactly the time they are required. In the longer term, it is hoped that CPAS can be used to predict hospital length of hospital stay, discharge planning and wider intensive care demand in the time that will come after the pandemic."

Trials have begun of a system using machine learning to predict the approaching demand across England for intensive care beds and ventilators for patients with Covid-19. NHS Digital said the Covid-19 Capacity Planning and Analysis System (CPAS) has been developed by its data scientists and researchers from the University of Cambridge, using data from Public Health England (PHE) and aimed at supporting hospitals in their planning. It has been built on the Cambridge Adjutorium machine learning engine, developed by a Cambridge team led by Professor Mihaela van de Schaar and which has already been used to obtain insights on cardiovascular disease and cystic fibrosis. It is using data collected by PHE's 19 Covid-19 Hospitalisation in England Surveillance System (CHESS). Alpha stage trials have begun at four hospitals.

The NHS is turning to artificial intelligence (AI) to help predict upcoming demand for intensive care beds and ventilators during the coronavirus pandemic across England. Trials of the predictive system, known as the COVID 19 Capacity Planning and Analysis System (CPAS), began today at four hospitals. It harnesses the principles of machine learning – algorithms that find and apply patterns in data – to provide statistics, forecasts and simulation environments to the NHS to better plan resources during the pandemic. For example, predictions made by the machine learning system could inform a hospital that capacity will be reached in advance, giving it time to bring in extra resources or share capacity with neighbouring hospitals. If CPAS proves to be accurate, the NHS will look to roll it out across the rest of the country.

NHS Digital wanted to make it easier for users to research and access published NHS health data. To achieve that, the agency partnered with IPsoft to provide users with their own data concierge whom they call ViDA (or Virtual Digital Assistant). ViDA is an always-on conversational agent based on our industry-leading digital colleague, Amelia. Users simply tell ViDA what information they are attempting to locate using everyday language, and ViDA can take it from there. You can read more about the project in detail here from our Cognitive Project Lead for UK Healthcare, David King.

Tens of millions of chronically ill people around the world rely on repeat prescriptions, but getting the drugs they need can be a time-consuming and frustrating process. Now apps, automation and home delivery services are making their lives easier. When Melissa Fehr came home from hospital after undergoing a major bone marrow procedure, she says she was carrying "two enormous bags of medication". There were drugs to guard against infection, and other drugs to combat the side effects of other drugs, she says. A bout of shingles was "horrible ... it was just constant pain, so I was prescribed five or six different pain killers", she says.

One small mistake might have allowed the hack that brought chaos to the NHS. Health trusts across England were sent details of a patch that would have kept them safe from the ransomware used in the huge cyber attack, according to NHS Digital. The attack brought problems for huge swathes of the NHS, leaving almost 50 trusts without proper access to their computers. The problems led to appointments being cancelled, doctors unable to work and may even have caused deaths within affected hospitals. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.