PIP Call Audio Analysis
Department: DWP
Problem to Solve
PIP assessment call recordings contain crucial information that needs to be extracted and analysed, but manually reviewing hours of audio is time-consuming and inconsistent.
Proposed Solution
Use AI transcription and analysis to automatically extract key information from PIP assessment call recordings, enabling faster processing and consistent data capture.
Where This Challenge Fits
This challenge focuses on the health assessment call stage of the PIP assessment journey
Start the claim
Claimant
Claim registration
DWP
PIP2 form issued
DWP
PIP2 form completed
Claimant
Form reviewed
DWP
Health assessment
Claimant/DWP
Decision made
DWP
Decision received
Claimant
Start the claim
Claimant
Claim registration
DWP
PIP2 form issued
DWP
PIP2 form completed
Claimant
Decision received
Claimant
Decision made
DWP
Health assessment
Claimant/DWP
Form reviewed
DWP
See it in Action
Resources
What we did
- ✓Researched publicly available information about PIP assessment criteria and processes
- ✓Scripted and recorded realistic sample PIP assessment calls following documented assessment frameworks
- ✓Built an AI pipeline to transcribe assessment calls and identify key information automatically
- ✓Developed extraction logic to pull out relevant medical conditions, daily living impacts, and mobility limitations
- ✓Tested the system's ability to structure unstructured conversation data into assessment-ready formats
- ✓Validated accuracy of extraction against the PIP assessment framework
Outcome
The system successfully transcribed conversational audio and accurately extracted structured assessment data, demonstrating that modern speech-to-text and LLM technology can meaningfully reduce the manual note-taking burden on assessors.
We achieved high accuracy in identifying relevant medical information, daily living challenges, and mobility issues mentioned during calls—automatically mapping these to the appropriate PIP assessment categories and descriptors.
This proof-of-concept showed that AI can reliably support assessment processes in real-time, allowing assessors to focus on empathetic conversation and clinical judgement rather than simultaneous documentation.
Technical Insights
Key Technologies
- • Real-time speech transcription
- • Large language models for extraction
- • Structured output parsing
- • Medical terminology recognition
- • Category mapping algorithms
Challenges Solved
- • Handling conversational language patterns
- • Extracting clinical details from dialogue
- • Maintaining context across long calls
- • Mapping to PIP assessment criteria
- • Real-time processing and structuring
Testing Methodology
By creating scripted yet natural-sounding assessment calls based on publicly available PIP criteria, we ensured:
- •Realistic conversation flow: Natural dialogue patterns including clarifications, pauses, and conversational elements
- •Assessment framework coverage: Testing extraction across all PIP assessment categories and descriptors
- •Known ground truth: Controlled content for accuracy validation against expected outputs
- •Edge case testing: Handling ambiguous statements, interruptions, and non-linear conversation patterns