About the brand
This organisation is a large-scale pharmacy services provider, processing thousands of prescriptions daily across a broad network. With efficiency and accuracy at the heart of its operations, it’s continually exploring innovative ways to scale safely and sustainably.
The challenge
Manual, error-prone workflows
Prescription instructions came in a wide variety of formats and phrasings. Translating these into standardised Sig Codes required manual interpretation, which is a time-consuming process that’s prone to inconsistencies and human error.
Limited throughput and scalability
With each technician only able to process around 30 prescriptions per hour, scaling the service meant proportionally increasing headcount, an ultimately unsustainable approach in the long term.
The solution
Automated Sig Code translation
We developed an AI-powered translation engine to convert natural language prescription directions into standardised Sig Codes. This reduced the need for human input and improved processing speed.
LLM-powered interpretation
The solution used large language models (LLMs) to accurately interpret structured prescription instructions from printed scripts, enabling consistent translation into Sig Codes.
Order system integration
The AI outputs were fed directly into the provider’s order management system, creating a seamless workflow from translation right through to fulfilment.
Built-in feedback loop
Technicians could review and correct AI-generated translations, allowing the system to self-learn and improve its accuracy over time.
The impact
- Reduction in manual effort: AI automation significantly cut the number of prescriptions needing human translation. Technicians were freed up to focus on quality control.
- Improved accuracy: Translation quality increased, with fewer errors and improved compliance.
- Operational efficiency gains: Faster processing enabled the provider to scale without increasing staff.
- Cost savings: >$500K in annual savings by reducing the need for manual translation labour.
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in annual savings
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