Building the Data Foundation for AI
Outcome dataset grew from ~400 inconsistent records to 3,800 standardised within 12 months; AI tool deployment hit vendor benchmarks.
12-month programme with milestone-gated progress. Photography protocol live in month 2. Platform consolidation complete by month 6. Historical digitisation complete by month 10.
The challenge
Every AI tool the group evaluated underperformed vendor benchmarks — not because the tools were wrong, but because the data infrastructure wasn't ready for them.
The group had invested in two AI-powered tools over the previous 18 months — a consultation simulation platform and a retention prediction tool — and had seen neither deliver the commercial improvement the vendors had projected. Investigation identified the cause: outcome photography was captured inconsistently across practitioners and locations (different lighting, positioning, and equipment), making the simulation tool unreliable. Patient history data was fragmented across a legacy CRM, a practice management platform, and paper records that had never been digitised. The retention prediction model was working with data that covered only 14 months and had significant gaps in treatment history. The tools weren't the problem. The data was.
What we did
The approach
We designed and implemented a 12-month data infrastructure programme covering outcome photography standardisation, patient record consolidation, historical data migration, and the governance frameworks to keep data quality high once established. The programme was sequenced to deliver immediate operational value while building the long-term data asset that would make AI tools perform as advertised.
Key findings & actions
Outcome photography protocol
standardised camera settings, lighting rigs, patient positioning guides, and image naming conventions implemented across all locations — with practitioner training and compliance monitoring
Patient record consolidation
data migration from legacy CRM and paper records into a single integrated practice management platform, with data quality validation at each stage
Historical data digitisation
structured retrospective capture of treatment histories from paper records covering approximately 2,400 patients, with clinical team involvement to ensure accuracy
Data governance framework
ongoing data quality standards, ownership roles, and monthly audit process to prevent regression to previous inconsistency levels
AI readiness scoring
quarterly assessment of dataset quality against the specific requirements of each AI tool in the group's deployment roadmap, with clear go/no-go criteria for each tool activation
How we worked
Scope
Photography protocol design, data migration, historical digitisation, governance framework, and AI readiness assessment across a multi-site group.
Timeline
12-month programme with milestone-gated progress. Photography protocol live in month 2. Platform consolidation complete by month 6. Historical digitisation complete by month 10.
Operating model
Clinical lead owned photography protocol compliance. Operations manager owned platform migration. Governance framework embedded in monthly management reporting from month 7.
Outcomes
What changed
Outcome dataset grew from ~400 inconsistent records to 3,800 standardised within 12 months; AI tool deployment hit vendor benchmarks.
Structured outcome dataset grew from approximately 400 usable records to 3,800 standardised records within 12 months, creating a clinical photography asset with genuine AI training value
Subsequent deployment of consultation simulation tool, with the improved data foundation, delivered conversion improvement of 28%
within 15% of the vendor's benchmark projection
Retention prediction model accuracy improved from 61% to 84% once trained on consolidated, complete patient history data
making it actionable for the first time
Group's Series A investor specifically identified the quality and depth of the patient outcome dataset as a key diligence finding, citing it as a structural competitive asset that independent practices at the same revenue scale rarely possess
Governance
Trust, collaboration & governance
Data quality validation methodology shared with the group's management team and investors — no inflated record counts
Historical data digitisation performed with clinical oversight — no retrospective assumptions made without practitioner confirmation
AI readiness criteria agreed jointly with the group before programme start — go/no-go for each tool was an objective assessment, not a commercial decision
Patient data handling throughout the consolidation programme reviewed against applicable regulatory requirements
Reframe
AI tool selection is not the primary investment — data infrastructure is. It doesn't sort itself out.
Across every engagement, the goal is the same: engineer a system that makes better decisions — faster, more consistently, and at scale — than the process it replaces.
Next steps
Related services
Start a discovery
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We do not send a proposal before we understand the problem. Start by telling us about your decision context — we will identify the highest-leverage intervention areas before any scope is agreed.