Designing for Clinical Clarity- Patient Signals
Healthtech 1 is a fast-growing SaaS company that automates patient registrations for NHS clinics, helping practices reduce administrative workload and save thousands of staff hours each week. Within this platform, I designed the Patient Signals feature to surface clinically relevant or sensitive information shared during registration, such as safeguarding concerns or accessibility needs. Drawing on behavioural principles around cognitive load and attention, the design focused on how NHS staff prioritise and act on information under pressure, improving signal salience, triage accuracy, and trust in the system from the start of care.
Ottawa, Ontario, Canada
2006
E-commerce
$1.578 billion (2019)
5,000+
Challenge
Although the feature was intended to help staff act quickly on critical information, usage data showed that signals were often missed or inconsistently handled. Interviews revealed that staff struggled to distinguish between high and low priority alerts and were uncertain about accountability for follow-up. These behavioural and workflow barriers limited trust in the system and led to duplicated work, missed actions, and inconsistent patient experiences.
Results
Follow-through on patient signals increased from 34% to 71%, reflecting stronger trust and clearer accountability.
Duplication of work decreased by around 30%, as teams gained greater confidence in when and how to act.
User engagement heatmaps showed concentrated attention in the redesigned Signals area, confirming its integration into daily workflows.
Personalisation options were introduced, allowing clinics to adapt signal visibility and priority rules to their local safeguarding protocols.
+109%
improvement in follow-through on patient signals
30%
reduction in duplicated work across staff workflows
100%
pilot practices adopted personalised signal settings
Process
Conducted user interviews and journey mapping with NHS administrative and clinical staff to understand how people interpret urgency cues, visual hierarchies, and shared responsibility under time pressure.
Identified behavioural barriers such as hesitation, alert fatigue, and unclear accountability that led to missed or duplicated actions.
Designed a more predictable and cognitively lighter interface, using clear categorisation, progressive disclosure, and intuitive filtering by signal type.
Refined visual and linguistic framing of alerts to reduce cognitive load, improve clarity, and encourage consistent follow-through on high-risk cases.
Collaborated with a full stack engineer to align system logic with user workflows and ensure a smooth, trustworthy triage experience.
“ The filtering setting has significantly saved the time for Freddie- our safeguarding lead, who is now able to much more effeciently deal with all safeguarding flags”
Outcome
The redesigned Patient Signals feature evolved into a trusted and behaviourally informed workflow tool that helped NHS staff make safer, faster, and more consistent decisions. It demonstrated how small design change, rooted in human factors and behavioural scienc, can meaningfully improve trust, attention, and coordination within healthcare systems.
Reflections
Conducting usability testing in medical products remains challenging due to patient privacy constraints. Future iterations should explore alternative methods such as anonymised prototypes or moderated task walkthroughs to gather richer behavioural data.
Working with a limited sample of NHS staff introduced potential self-selection bias, highlighting the need for broader participation to capture a more representative range of experiences.
Collaborating closely with an engineer who had UX research experience reinforced the importance of technical alignment throughout the design process. This improved my ability to balance user insight with implementation feasibility and strengthened cross-functional communication.
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