HEALTHCARE
AI PRODUCT DEVELOPMENT
INTELLIGENT SYSTEMS
INTELLIGENT SYSTEMS
AI PRODUCT DEVELOPMENT
INTELLIGENT SYSTEMS
AI PRODUCT DEVELOPMENT
AI PRODUCT DEVELOPMENT
AI PRODUCT DEVELOPMENT
Augmenting Clinical Coders with AI Document Intelligence
Clinical coding is one of healthcare's most demanding knowledge tasks, skilled staff manually classifying thousands of diagnoses from dense medical records. We led development of a deep learning platform that reduced manual coding workload by 70%, achieved 89% model accuracy on code suggestions, and delivered approximately 3× faster time-to-code, freeing clinicians to focus on cases that genuinely need human expertise.
70%
70%
70%
reduction in manual clinical coding workload
89%
70%
70%
model accuracy on clinical code suggestions
~3×
70%
~3×
faster time-to-code vs. fully manual review
CLIENT
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
Healthcare Organisation
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
ENGAGEMENT FOCUS
Intelligent Systems · AI Product Development
INDUSTRY
WHO THIS IS FOR
WHO THIS IS FOR
Healthcare · Clinical Operations
WHO THIS IS FOR
WHO THIS IS FOR
WHO THIS IS FOR
Healthcare teams managing clinical documentation, coding, and medical records workflows
The Challenge
A highly skilled, time-intensive process with no room to scale
Clinical coding sits at the intersection of medicine, administration, and compliance. Trained coders analyse complex medical records, discharge summaries, procedure notes, diagnostic reports, and assign standardised codes that determine funding, planning, and reporting for the organisation.
Clinical coding is not a task that can simply be delegated or simplified. It requires deep medical knowledge, careful interpretation of clinical language, and consistent application of classification standards. The challenge was never finding people willing to do the work, it was that there weren't enough hours in the day to do it at the volume required.
The organisation faced a growing backlog with limited options for relief:
- Large volumes of medical documentation arriving daily, each requiring individual review
- Experienced clinical coders spending significant time on routine, lower-complexity cases
- Inconsistent coding quality across staff, leading to review and rework cycles
- Difficulty recruiting and retaining specialist coding staff in a tight labour market
- No scalable path forward without a fundamentally different approach to the workflow
The organisation wanted to explore whether AI could augment their coding team, handling routine cases automatically while surfacing complex ones for expert review.
Our Approach
A multidisciplinary team, built around clinical domain expertise
Clinical AI is a domain where technical competence alone is insufficient. We assembled a multidisciplinary delivery team that brought together deep learning engineers, clinical informatics expertise, and direct input from practising coders throughout the development process.
The engagement was structured to validate assumptions early and avoid the common failure mode of building a technically impressive model that doesn't reflect real-world clinical language or coding conventions.The engagement included:
- Literature research and technical evaluation of NLP architectures suited to medical text
- Clinical domain analysis, understanding coding standards, edge cases, and failure modes
- Co-design sessions with experienced clinical coders to define acceptable confidence thresholds
- AI solution architecture designed for explainability, not just accuracy
- Iterative model development and testing against real de-identified patient records
- Stakeholder engagement across clinical, operations, and IT teams to align on deployment
The Solution
An intelligent platform that works alongside clinical coders
We designed and delivered a deep learning platform capable of analysing medical documentation and suggesting the most probable clinical codes, with confidence scores that allow coders to quickly accept, modify, or override suggestions.
Critically, the system was designed as an augmentation tool, not an autonomous one. Human coders remain in control; the AI handles the volume, they handle the judgement.
01 Medical text analysis engine
01 Medical text analysis engine
01 Medical text analysis engine
Processes discharge summaries, clinical notes, and procedure records using deep learning architectures optimised for medical language, including abbreviations, clinical shorthand, and specialist terminology.
02 Automated code suggestion
01 Medical text analysis engine
01 Medical text analysis engine
Generates ranked clinical code suggestions with associated confidence scores, allowing coders to accept high-confidence suggestions instantly and focus attention on ambiguous or low-confidence cases.
03 Complexity-based case routing
04 Audit trail and explainability
04 Audit trail and explainability
Automatically classifies incoming cases by complexity, routing straightforward records to assisted review and flagging complex or multi-condition cases for direct expert attention.
04 Audit trail and explainability
04 Audit trail and explainability
04 Audit trail and explainability
Every code suggestion is accompanied by the source text that informed it, giving coders the evidence they need to review quickly and maintaining a full audit trail for compliance purposes.
The Outcome
Skilled coders doing more of what only they can do
The platform gave the organisation a meaningful increase in coding throughput without adding headcount. More importantly, it shifted how experienced coders spent their time, from routine processing toward complex case review and quality assurance.
70%
70%
70%
reduction in manual clinical coding workload
89%
70%
70%
model accuracy on clinical code suggestions
~3×
70%
~3×
faster time-to-code vs. fully manual review
BEFORE
BEFORE
BEFORE
- Every record reviewed manually from start to finish
- Coders spending hours on routine, low-complexity cases
- Inconsistent review quality across the team
- Growing backlog with no scalable path forward
AFTER
BEFORE
BEFORE
- High-confidence cases completed in a fraction of the time
- Expert attention directed to complex and ambiguous cases
- Consistent code suggestion quality across all records
- Throughput increased without adding to headcount
- Clinical coders report spending more time on meaningful, complex work
- Full audit trail maintained for every AI-assisted coding decision
- Model accuracy of 89% on code suggestions, conservative and production-credible
- Prototype validated for scale-up across additional documentation types
- Organisational confidence built for broader AI adoption in clinical operations
What Made This Work
Clinical AI that earned trust before it earned scale
Healthcare is a domain where AI adoption fails most often, not because the technology isn't capable, but because trust hasn't been built. Three things made this engagement different.
Coders helped design the system
Explainability built in from day one
Coders helped design the system
Experienced clinical coders were involved throughout development, defining what a good suggestion looked like, where the model needed to defer to humans, and what the interface needed to show.
Augmentation, not replacement
Explainability built in from day one
Coders helped design the system
The system was explicitly designed to keep humans in control. Coders can accept, modify, or override every suggestion, which made adoption faster and reduced resistance to the change.
Explainability built in from day one
Explainability built in from day one
Explainability built in from day one
Every suggestion links to the source text that generated it. Coders can see the AI's reasoning, making it possible to verify quickly rather than just accept or reject blindly.
Let's explore how AI can support your clinical workflows
Whether you're managing coding backlogs, clinical documentation, or complex health data workflows, we can help you identify where AI augmentation delivers the most value without compromising clinical standards.
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