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Potential Coronavirus (COVID-19) symptoms reported through NHS Pathways and 111 online

Summary Data published on potential COVID-19 symptoms reported through NHS Pathways and 111 online Dashboard shows the total number of NHS Pathways triages through 111 and 999, and online assessments in 111 online which have received a potential COVID-19 final disposition. This data is based on potential COVID-19 symptoms reported by members of the public to NHS Pathways through NHS 111 or 999 and 111 online,  and is not based on the outcomes of tests for coronavirus. This is not a count of people.

ISB1523: Anonymisation Standard for Publishing Health and Social Care Data This process standard for publishing health and social care data provides an agreed and standardised approach to anonymisation.

Although the law makes a clear distinction between identifying and non-identifying data, where that line should be drawn may be far from clear in practice. That is why this anonymisation standard for publishing health and social care data is needed. This process standard provides an agreed and standardised approach, grounded in the law, enabling organisations to: distinguish between identifying and non-identifying information deploy a standard approach and a set of standard tools to anonymise information to ensure that, as far as it is reasonably practicable to do so, information published does not identify individuals.

Now, as the covid-19 pandemic rages, several independent sources say that Palantir has started work with a new client: Britain’s National Health Service.

Health chiefs in the UK have tasked a team of software developers to “investigate” switching its unique contact-tracing app to the global standard proposed by Apple and Google, signalling a potential about-turn just days after the NHS launched its new coronavirus app. 

One moment the prime minister, Boris Johnson, was asking people with symptoms to stay home for seven days; a few days later, he had ordered a lockdown. What changed was data from Italy’s experience of the pandemic, in which more people were critically ill than anticipated, and from the NHS about its inability to cope if the same should happen in the UK.

Data companies are offering to mine troves of personal and public information to help local officials in the UK identify people who are struggling in the aftermath of the coronavirus crisis.

The aim is to move beyond assigning risk just based on an individual’s health and also include those who might be at greater risk of domestic violence, marital breakdown and financial difficulties, said Xantura’s chief executive Wajid Shafiq.

Xantura’s software runs the data against a set of risk factors and demographic data, as well as the NHS’s “shielded list” of individuals believed to be most at risk from Covid-19 complications, scoring households according to their risk profile.

The current epidemic is a classic application of what economists call “radical uncertainty” (most recently explored by John Kay and Mervyn King in their brilliant book of that title, which came out last month): in a world that has inevitably become too complex to be adequately captured in models, a world of both “known unknowns” and “unknown unknowns”, the most sensible response to the question “what should we do?” is “I don’t know”. At the onset of this crisis, we could not put probabilities on which forms of social distancing would best limit its spread because we’d never done it before. We didn’t know how people would alter their behaviour in response to the appeal to “save the NHS”. We didn’t even know whether reducing the spread was desirable: perhaps fewer deaths now would come at the cost of more next winter. And these were just the known unknowns. With a disruption as big as this, unknown unknowns are also lurking. We have no experience of the material and economic repercussions from shutdowns of this nature and their aftermath in a modern economy, and no meaningful way of assigning probabilities; nor of how people’s behaviour will evolve.

What the modellers should have said, right from the beginning, was that it was vital to establish two fundamental parameters: the incidence and the rate of contagion, both of which require mass testing, and without which mortality rates are impossible to decipher and hence sensible policy impossible to implement. It is frankly astounding that four months into this new virus such tests are only now being instigated.

. Shifting responsibilities down the system not only enables rapid scale-up, it has a further huge advantage: the power of decision is closer to the coalface of practitioner experience. We learn not just from accumulating and analysing codifiable knowledge – the domain of the expert. We learn by doing, or by trying to do things that we can’t do and that force us to experiment. A decentralized system learns from a litany of failed experiments running in parallel, and so it learns fast: teams copy other teams that have hit on something that works well enough to get the job done.