Building AI that adapts to mission-critical complexity
The team at Breast Cancer Now were sceptical about AI's ability to accurately transcribe complex patient surveys across 20+ different NHS form structures. Would it be accurate enough for the researchers to feel confident?
When Breast Cancer Now approached us about their Service Pledge Programme, they faced a constraint that threatened to limit their impact across the NHS: the manual transcription bottleneck. Their 18-month projects with NHS hospitals generated enormous amounts of patient and staff feedback through paper surveys, but processing this data consumed valuable time that the team wanted to dedicate to strategic conversations and relationship building.
The team were fairly sceptical about AI's ability to handle this work accurately. They'd seen AI tools struggle with simpler tasks, and their surveys involved complex layouts, varied question formats, and nuanced patient responses. The risk of errors was unacceptable - these were insights that directly influenced patient care.
The technical challenge was to create AI that could achieve the transcription accuracy needed to overcome this well-founded scepticism, whilst enabling cross-institutional analysis that hadn't been possible before.
When simplification means mission compromise
Each NHS trust had evolved slightly different survey formats. Patient advocates had input into question wording and layout. Each form contained approximately 400 data elements across different question types, often spanning multiple pages.
We knew we couldn't take the conventional approach of standardising forms or forcing digital collection. The patient cohort, often older and dealing with serious illness, consistently preferred paper to digital. Standardisation would have disrupted collaborative relationships with NHS trusts and digital surveys would have excluded or burdened patients who preferred paper-based feedback. Instead, we built AI sophisticated enough to handle their reality - processing over 20 different form structures across multiple institutions and patient populations.
Engineering for organisational reality
Each NHS trust uses slightly different question wording or sequencing to meet their specific needs. We created comprehensive mapping files so the system could recognise equivalent data points and ensure consistent categorisation.
We built dual-schema systems - per-page schemas to process individual form pages, and per-question schemas to reconstruct complete responses across multi-page questions. Extensive mock generation capabilities enabled rigorous testing without compromising sensitive healthcare information. The result was a processor maintaining high levels of accuracy whilst preserving the nuance of Breast Cancer Now's collaborative approach. The team's initial scepticism was overcome through demonstrable accuracy on their actual data.
From operational efficiency to strategic capability
The system transformed more than transcription speed - it created new analytical capabilities. The team can now conduct meaningful cross-institutional comparisons and identify factors that correlate with improved patient outcomes. The implementation preserved the aspects that mattered - relationships, collaborative form development, trust-building processes, and patient preference for paper-based feedback - whilst eliminating only the manual transcription work. They gained new analytical capabilities whilst maintaining every aspect of their approach that contributed to improved patient outcomes.
For healthcare charities working within complex institutional networks like the NHS, this represents a model for responsible AI implementation - technology that serves mission requirements rather than imposing technological constraints on mission-critical work.
Technological change continues to accelerate but only a quarter of charities say they feel prepared to respond to the opportunities and challenges. Let's close the opportunity gap together.
