The Practicality of AI in Prescription Processing

The Challenge of Handwriting in Medical Prescriptions

In the realm of healthcare automation, we often encounter complex challenges that require balancing accuracy, efficiency, and practicality. One such challenge is the interpretation of handwritten prescriptions. While electronic prescription systems exist, many doctors still rely on handwritten notes, leading to potential misinterpretations and errors in medication processing.

To address this, we designed a solution leveraging AI—specifically Claude—to analyze prescriptions received via email. The AI attempts to extract the medication information and matches it against a list of legal medications. If it finds a match, the prescription is sent to the pharmacy. However, a key challenge arises: sometimes, the AI cannot confidently determine what the doctor has written due to poor handwriting.

The False Negative Approach

In response, we implemented a process where AI makes an educated guess and then generates a verification request for the doctor. If the doctor does not explicitly confirm the AI’s interpretation within 24 hours, the request is canceled. This ensures that no incorrect prescriptions are fulfilled while still enabling automation in a field where errors can have life-threatening consequences.

This approach follows a false-negative model rather than a false-positive one. In other industries, such as retail, a false-positive approach might be acceptable—if a customer orders potatoes and they’re out of stock, the system might suggest an alternative. However, when it comes to automation in healthcare, prescribing the wrong medication is not an option. Ensuring accuracy, even at the expense of some efficiency, is paramount.

The Alternative Perspective: Why Not Just Use a Digital System?

A reasonable counterpoint to this approach is: Why use AI for handwriting analysis at all? Wouldn’t it be better to implement a structured digital input system for prescriptions, eliminating errors from handwriting altogether?

While this is an ideal solution, the reality of healthcare systems complicates matters. Implementing a new UI for prescription entry would require extensive stakeholder buy-in, approvals, and training, which could take six months or more. During this time, the existing manual process—with all its inefficiencies—would continue.

Instead, by leveraging AI alongside workflow automation tools like Camunda, Intelligent Document Processing (IDP), and Robotic Process Automation (RPA), we built a functional prototype in just six weeks. This solution automates six integrations, freeing up three full-time employees from tedious manual work such as managing emails, entering data into Excel, and making follow-up calls.

The Concept of Temporal Architecture

This approach aligns with the principle of temporal architecture—solving the problem as it exists today rather than waiting for an ideal future state. While a structured prescription entry system is the right long-term solution, the AI-based approach provides immediate value, improving efficiency and reducing errors in the interim.

A pragmatic engineer focuses on solving real-world problems with immediate, actionable solutions, while a scientist strives for the perfect theoretical solution. There is an intersection where practical solutions can be built elegantly, and that’s where technologies like Camunda shine. They allow rapid development, iteration, and continuous optimization, ensuring that solutions evolve based on real-world feedback.

Engineering vs. Science: The Iterative Path to Perfection

The beauty of process automation is in its iterative nature. By deploying a working solution quickly, we gain valuable insights and user feedback that guide future improvements. This kinetic approach—building, delivering, and refining—ensures that users see the solution in motion and can provide informed suggestions.

In the end, the structured digital system for prescription entry is where we should be headed. However, until we reach that point, an AI-driven approach provides significant benefits today. It’s a step toward the ideal, implemented in a way that is both practical and impactful.

This discussion is open-ended—there are always improvements to be made and perspectives to consider. What do you think? How would you approach this challenge and provide top-notch healthcare automation solutions?

Want to learn more? Head to CapBPM.com/Contact to talk with our team of Camunda Experts about solutions like this!