In health and social care, what we choose to measure shapes what we value, how we work, and ultimately, the outcomes people experience. Nowhere is this more important than in emergency and acute hospital services where success has often been defined narrowly in terms of waiting times and speed of throughput (e.g. delayed discharges)
But if we want to truly meet people’s needs, we need to look deeper. Below, I’ll explore how services can rethink their data – both at the point of request and at the point of treatment – to better capture impact, wellbeing, and real-life change.
1. Making Sense of Requests for Help
The first moment of contact – when someone requests help or is referred – is rich with insight. Tracking what happens here tells us whether services are reaching the right people and whether early conversations are meaningful.
The most useful indicators include:
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Volume of requests/referrals – not as a measure of success in itself, but as context. Demand often rises as awareness grows.
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Conversion to assessment and treatment – ideally, fewer assessments and treatments are needed if people are being supported earlier and more effectively.
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Who is requesting – over time, we’d hope to see more requests from those who can genuinely benefit, and fewer from those who send people to receive help.
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Primary vs. secondary requesters – are people asking for themselves, or simply passing on someone else’s concern? A shift towards more primary requesters suggests better public health education and empowerment.
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What people are asking for – ideally, more requests for personal outcomes, not just interventions that they have come to believe they need.
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Decision outcomes at the point of request – whether the result is reassurance, education, signposting, investigation, or treatment.
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Repeat requests – these tell a story. If the same person keeps asking with the same concern, the initial response hasn’t met their needs. If they return with new understanding (e.g. spotting red flags), it shows effective education.
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Community patterns – when different people request help for the same individual, it reveals whether reassurance and education are spreading through the person’s “village.”
And finally, we must not overlook qualitative feedback: did the requester feel their needs were met, their wellbeing supported, and their voice heard?
2. From Assessment to Intervention
Once a service moves from listening to acting, a different set of measures come into play. A crucial distinction here is between the intention to assess (investigating need) and the intention to treat (active intervention). This avoids penalising services with unrealistic expectations (for example, applying a weight-loss target to someone who isn’t yet receiving treatment).
When treatment does begin, three dimensions matter most:
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Any change in the condition or functional ability e.g. BMI or % weight loss and/or mobility
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Wellbeing and participation outcomes (how the patient’s life has improved – what mattered most to them)
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Number of episodes of care or length if treatment required to achieve this change
Together, these allow for richer analysis. Using the above weight-loss example:
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If weight loss occurs, has it also improve wellbeing – or not?
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If no weight loss occurs, can we show improved quality of life, better well-being, prevention of decline, or stabilisation of weight gain?
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How long does it take to achieve meaningful change – and are pathways flexible enough to support motivation and adjustment, rather than rushing towards weight targets?
Tracking these questions not only shows outcomes, but also highlights where pathways may be too rigid, relapse rates too high, or services misaligned with what people really need.
3. Aligning Purpose with People’s Needs
Sometimes, treatment data and wellbeing data don’t match – they can even move in opposite directions. When this happens, it may suggest the service’s purpose has drifted away from people’s lived realities.
For example, a service focused narrowly on reducing BMI may miss opportunities to address the broader causes of obesity – from lifestyle and environment to mental health and social participation.
By rebalancing the data we collect, we can reframe success: not just pounds lost, but confidence gained, independence maintained, relationships improved and well-being enriched.
Final Thoughts
Data isn’t just numbers on a dashboard – it’s a mirror of what we think matters. If services only measure treatment goals, they risk missing the bigger picture. By expanding our focus to include requests, experiences, wellbeing outcomes, and pathways of change, we can build services that are more responsive, humane, and effective.
In short: when we measure what matters, we create the conditions for care that truly makes a difference.