Cera Care: Designing AI that helps prevent falls.
Falls are one of the leading causes of emergency hospital admissions for older adults, placing significant pressure on individuals, carers and the NHS.
Cera's ambition was to move beyond reacting to falls and instead identify service users whose risk was increasing before an incident occurred.
Working alongside the Data Science team, we had access to a predictive model capable of analysing care data, medication, mobility and behavioural trends to predict falls up to seven days in advance.
The challenge wasn't building the AI. It was designing a product that helped carers and branch teams understand those predictions, trust them and take action before a fall happened.
Falls are one of the leading causes of emergency hospital admissions for older adults, placing significant pressure on individuals, carers and the NHS.
Cera's ambition was to move beyond reacting to falls and instead identify service users whose risk was increasing before an incident occurred.
Working alongside the Data Science team, we had access to a predictive model capable of analysing care data, medication, mobility and behavioural trends to predict falls up to seven days in advance.
The challenge wasn't building the AI.
It was designing a product that helped carers and branch teams understand those predictions, trust them and take action before a fall happened.
Understanding the Problem
Before investing in a fully integrated experience, the team validated the concept using a lightweight pilot.
High-risk service users were identified through manual reports, allowing branch teams to intervene while the Data Science team validated the prediction model.
This reduced engineering effort while proving the value of proactive intervention before committing to a larger build.
Proving the Concept
One of the biggest design challenges was presenting complex AI predictions without overwhelming users.
Rather than exposing confidence scores or technical probabilities, I designed a simple three-tier risk system using familiar traffic-light colours:
Low risk = Green
Medium risk = Amber
High risk = Red
This allowed branch teams to understand the level of urgency at a glance while keeping the interface accessible for users with varying levels of technical confidence.
Alongside the overall risk level, the interface surfaced the factors contributing to the prediction, helping carers understand why someone had been flagged and supporting informed decision making rather than blind trust in the AI.
The experience was designed to fit naturally into existing workflows, allowing teams to prioritise visits and intervene earlier without introducing unnecessary complexity.
Designing the Solution
As the designs evolved, it became clear that improving the quality of data entering the system would strengthen future predictions.
I identified an opportunity within the existing visit report to capture more consistent information about falls. Rather than creating additional workflows, I introduced a dedicated falls-related question that carers could answer during every visit.
This small addition required very little extra effort from carers while creating a continuous feedback loop that improved the quality of data available to the AI model over time.
The feature wasn't just displaying predictions. It was also helping make future predictions more accurate.
Improving the Quality of Data
Throughout the project, designs were reviewed collaboratively with Product Managers, clinicians, engineers and the Data Science team to ensure the experience balanced usability with clinical accuracy.
Testing focused on questions such as:
Could users immediately understand the different risk levels?
Did the interface explain enough without becoming overwhelming?
Would carers know what action to take after viewing a risk alert?
Did the additional visit report question fit naturally into existing workflows?
Feedback helped refine the hierarchy, simplify supporting information and ensure the feature felt like an extension of existing care processes rather than a separate AI product.
Testing
The completed feature provided branch teams with a clear overview of people at increased risk of falling, allowing them to prioritise interventions before incidents occurred.
Key improvements included:
AI-powered falls risk prediction up to seven days in advance.
Simple traffic-light risk indicators.
Clear explanations supporting each prediction.
New falls-related question within the visit report to improve future predictions.
Flexible design that could support future predictive health features beyond falls.
Rather than replacing professional judgement, the feature supported better decision making by giving carers earlier visibility of changing risk.
Final Product
The Falls Risk feature helped shift Cera from reactive care towards preventative care.
Following rollout:
AI predictions identified increased falls risk up to 7 days earlier.
Prediction accuracy reached up to 97%.
Falls were reduced by approximately 20%.
Hospital admissions reduced by up to 70%.
Branch teams gained earlier visibility of vulnerable service users, enabling proactive interventions before incidents occurred.
The project demonstrated how thoughtful product design can make advanced AI understandable, trustworthy and genuinely useful in everyday clinical workflows.
Measuring Success
This project reinforced that designing AI isn't about exposing more data—it's about helping people make better decisions with confidence.
The biggest challenge wasn't visualising machine learning models. It was designing an experience that carers could immediately understand and trust while working in fast-paced environments.
I'm particularly proud of identifying the opportunity to improve the quality of incoming data through a simple addition to the visit report. By thinking beyond the interface itself, the product not only delivered better insights today but also created stronger predictions for the future.
Reflection
