Leading pharmacy services provider: Using AI to translate prescription Sig Codes

26 September 2025 — 1 min read

Healthcare & Pharmaceuticals

AI & Machine Learning

Digital Strategy

Generative AI

Our client, a leading provider of pharmacy services, processes thousands of prescriptions daily. A large proportion of these prescriptions require manual intervention to convert natural language instructions into standardised Sig Codes (codes indicating medication dosages, measurements, dosage times or frequency and routes of administration to a pharmacist) for accurate fulfilment. They engaged us to streamline the process through implementation of AI-powered automation.

Outcome 1: Realised more than $500K in annual savings due to the reduction in the number of staff needed to manually translate prescriptions
Outcome 2: Reduced the number of prescriptions requiring manual translation significantly, allowing technicians to focus on quality control
Outcome 3: Enhanced the accuracy of translated prescriptions using the AI model
Outcome 4: Automated processing led to faster turnaround times, enabling the client to scale its operations efficiently

The challenge

Translating diverse prescription instructions into standardised Sig Codes is a time-consuming and complex task. Manual processing consumes significant employee capacity and carries a high risk of human error. With technicians averaging just 30 prescriptions per hour, scaling operations would require additional headcount: an ultimately unsustainable approach.

The approach

We designed and developed an AI system to automatically convert prescription directions from natural language into standardised Sig Codes, reducing reliance on manual intervention and improving efficiency. The system was built to parse instructions across a wide range of formats and structures, ensuring consistent and accurate conversion.

Our approach integrated these translations directly into the order management system, enabling streamlined adoption within existing processes. To support continuous improvement, we embedded a feedback mechanism that allowed end-users to review and correct AI-generated outputs.

This closed-loop framework enabled the AI to become self-learning, improving translation accuracy over time while driving both operational efficiency and user trust.

Impact

Reduced manual effort and improved accuracy meant a significant boost in turnaround times. These operational efficiency gains enabled the client to scale operations effectively and sustainably, while also achieving substantial cost savings.