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I’ve been inside control rooms for more than 35 years. Working with police, fire, ambulance and coastguard services all around the world, I’ve managed technical installations, delivered training and even fitted cables under floors. And I’ve yet to see any TV drama capture the pressure on operators adequately.
Most callers to 999 are in distress. They can be terrified, desperate, and see the person answering the call as their lifeline. The operator must extract accurate information quickly, picking up as much as they can from someone who is likely in a state of panic, whilst also having to manage a wave of other inputs and make potentially life-changing decisions simultaneously.
More information is not the answer
Validating the severity of an incident can be hard. Is the caller known to the service? Have they made a similar call before, and when? Is what they’re calling about the same incident that two units were dispatched to 20 minutes earlier? What type of response is normally provided in that area? During a major incident, when five operators might be seeing 50 calls come in and everything’s flashing, making the right decisions can feel next to impossible.
Operators don’t just need data, they need context. To do this demanding job well and avoid cognitive overload, they need the right information at the right time. Our approach is built on context sensitive displays using progressive disclosure; to present operators with exactly what they need, when they need it.
More people can’t always be the answer, either
Add in the budget pressures public services are facing, and it’s clear that a hiring spree won’t reduce the load on operators in the long term. Sometimes, locating a caller might require a colleague and maybe also a supervisor to help – essential support but an inefficient use of skilled resource. To reduce the burden to a more manageable level, we need to use technology in the right places.
Technology can help by running all kinds of checks in the background. AI-enabled tools can not only trigger records searches, like addresses and previous contact, but can also summarise the information it finds. Using historical data, the system can inform the operator that when a unit is dispatched to this location, additional resources are required in 80% of cases. This insight enables the operator to adjust the initial response proactively. If no relevant predictive data exists, the search remains hidden.
Seeking feedback on our latest thinking
I was in a major London control room recently, showing some of our users the latest prototypes from our development work. The results were telling. Operators with decades of experience were surprised by what at first seems like an absence of information. Colleagues that were newer to the control room thought it looked pretty normal. But everyone agreed that not having to perform manual searches – and not having to review multiple fields just to check whether any relevant information is there or not – would take some of the pressure away.
Listening for verbal cues
As well as helping the operator to keep their focus on the caller, technology can also act as an extra pair of ears. Large language models can provide contextual understanding and any nuances in the callers description of the situation, additionally identifying tone or sentiment shifts that could signify levels of stress or duress enabling operators to better evaluate the required response. Models like these can’t replace the human touch, but they can help operators to stay in active listening mode and supplement THRIVE (Threat, Harm, Risk, Investigation, Vulnerability, Engagement) assessments to support decision-making.
Control room operators are quick-thinkers and effective communicators. They are used to busy environments and a demanding workload. But as call volumes keep rising, and staff retention and cost of re-training are growing concern, technology partners like us need to play our part in relieving the pressure. So instead of telling them everything we know, we should instead focus on telling them only what matters.
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