We’ve chosen not to name specific organisations, but these examples reflect real scenarios where better analytics in adult social care have revealed deeper truths; truths that headline metrics alone can’t capture.
The Hidden Costs of Misclassified Care
Take reablement, for example; a short-term, intensive form of adult social care designed to help individuals regain independence after a period of illness or crisis. It’s not intended to be ongoing support; it’s a temporary boost of acute care, with the goal of enabling people to return to living independently.
Performance indicators may suggest that services are being provided efficiently. But when we dig deeper, patterns in the data reveal something more complex. We’ve seen cases where clients receiving reablement have their care recorded as maintenance care, which is not reflective of the service being delivered. Social workers, understandably trying to manage budgets, are sometimes allocating activity under job types that incur lower costs to the local authority, even when the activity should be classified differently.
This misclassification can lead to problems when teams are reviewing capacity, as clients may remain on waiting lists unnecessarily, or declined access to the service due to perceived capacity constraints. The longer individuals wait on these lists, or are denied timely support, the greater the risk that their needs escalate, resulting in a requirement for longer-term and more expensive care sooner than might have been necessary. This practice, while potentially well-intentioned, can mask bottlenecks in the system.
The result? Increased pressure on budgets, delayed outcomes for service users, and difficulty predicting future demand.
Scheduling That Doesn’t Match Reality
Another insight we’ve uncovered relates to operational planning, and timetabling of care visits. Planned visit durations rarely match reality, and the differences vary significantly by job type. Review and assessment visits typically take longer than support visits, yet admin teams aren’t always factoring this in when scheduling a carer’s day.
This means carers are frequently running late, not because they’re inefficient, but because the system hasn’t accounted for the true nature of the work. This not only affects service quality but makes it nearly impossible to plan capacity effectively.
These are just a few examples that reflect some of the insights we’ve uncovered. Superficially, they may not seem that important, but in reality, they can make a huge difference to customer experience and costs. By addressing these underlying issues, local authorities can not only improve operational efficiency but also ensure that individuals receive the right support at the right time.
This approach ultimately leads to better outcomes for residents and helps councils make the most of their available resources.
What This Means for Decision-Makers
These insights aren’t visible in basic dashboards or statutory returns. They require layered, contextual analytics that reflect the reality on the ground. And that’s exactly what itelligent-i delivers.
Local authorities need to understand the full end-to-end picture; how services interact, where demand and capacity issues really lie, and how operational decisions impact both finances and outcomes.
That’s where itelligent-i’s Adults Social Care Analytics Accelerator come in. They’re not just about automating reports. They’re about delivering meaningful analysis that reflects your local context. We combine technical proficiency with deep understanding of business processes and data quality to help you uncover patterns and exceptions that matter to you, helping you to:
- Identify where demand and capacity issues really lie
- Understand how service interdependencies impact budgets and outcomes
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Make informed decisions that improve quality of life for residents
What Sets itelligent-i Apart?
Our approach to delivering analytics involves decision makers at each stage of the process, ensuring that these early insights are uncovered throughout the development phase and shaping the final reports and outcomes as soon as possible.
By engaging stakeholders from the outset, we foster a collaborative environment where valuable operational knowledge can be integrated with data analysis, leading to more relevant and actionable findings. This ensures that the solutions we deliver are not only technically robust, but also aligned with the practical realities and priorities of those who use them.
