Test and Trace (2020-2022)
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The government’s response to the Covid-19 pandemic offered the full sweep of civil service performance: the good, the bad, and the ugly.
The pandemic temporarily suspended the ‘normal rules of play’ in government. Suddenly, there was a clear, shared, cross-government outcome to aim for, and the imperative to act quickly. Out of necessity, Whitehall was forced to abandon many of its standard ways of working, as these simply could not deliver the pace of decision-making demanded by a crisis.
In some instances, such as the procurement of personal protective equipment (PPE), abandoning normal governance led to egregious failures and wasted public money. But there were several other cases where the opportunity to innovate led to positive outcomes that would have been virtually unimaginable for those organisations using their pre-pandemic working practices.
NHS Test and Trace is one example. While Test and Trace was clearly not perfect, it nonetheless demonstrated impressive agility in innovating and scaling despite a constantly evolving set of policy requirements in a highly complex and uncertain context.
Test and Trace benefited from having a clear high-level mission and dedicated multidisciplinary teams designing and running experiments that could be scaled, once they’d learned “what works” to drive up COVID testing amongst specific groups of people. These included offering anonymous testing for marginalised groups and community-led testing in areas of enduring transmission. Rapid experiments were set up in days, sometimes in just 24 hours. Then they were adjusted, abandoned, or scaled up over a bigger area. Each experiment team included its own “silo-busters”, people from operations, clinicians, policy, legal, and so on. Ministerial approval was same-day. These were “experiments” rather than “pilots”; they tested a specific hypothesis, such as “If we set up a walk-in clinic, will we reach people who haven’t used our online service?” Scientific method, but for service design. Test and Trace was the Radical How, in action, at pace.
This approach allowed for rapid experimentation to coexist alongside high levels of pressure and scrutiny. Combining a clear focus on outcomes from the top with empowering teams on the ground to figure out how to achieve them is what created conditions for rapid delivery impossible using more typical Whitehall processes.
Test and Trace also countered the enduring government premise that you need a single, monolithic service offering in order to deliver equality of experience across the country. Test and Trace proved that different experiments worked better to deliver the same outcome for different use cases. For example: the team needed to find out what worked for speakers of different languages, people with low literacy, or people with disabilities. The team noticed that COVID was rife among food factory workers—and that they weren’t getting tested that much—so proactively worked with employers to establish on-site workplace testing.
Scaling meant driving greater adoption of a service geared towards a single outcome; to do that, Test and Trace needed to create additional channels to reach all the target groups. As a programme, Test and Trace exemplified how taking an experimental approach can also help drive accessibility and inclusivity—when being inclusive around service quality wasn’t optional.
All this was possible because working during a pandemic placed a premium on factors not always typical of national-scale government programmes. Test and Trace had very high observability—short, data-led feedback loops on the metrics that mattered (e.g. testing rates, COVID infection rates). Rapid experimentation was made possible because the technical infrastructure to deliver this data, and a culture that put it at the heart of rapid decision-making, were in place. Testing rates and openly published COVID incidence data (e.g. via the Covid-19 dashboard) also enabled innovation and iteration by others working outside Whitehall; leaders in local health ecosystems had access to the same data and used it to target their own efforts.
This was far from perfect.
There was significant local government frustration that central government decided not to make use of local capabilities and deliberately went outside existing structures. Careful thought is needed into how test-and-learn approaches can be reconciled with devolution. In theory, the principle of incrementally scaling experiments should align with engaging local, regional, and national levels of government more effectively than ‘big bang’ launches, but politics and practice may make this harder than it looks.
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