Unlock the Power of Azure OpenAI Integration with FhirBlaze

Table of Contents

1. Introduction
2. Background of Fireblaze
3. Problem with querying HL7 Fire data
4. Solution: Implementing Azure open AI service
5. Demo of Azure open AI service integration with Fireblaze
6. Choosing the appropriate model in Azure open AI service
7. Benefits of using natural language search in Fireblaze
8. Challenges and limitations of Azure open AI service
9. Documentation and access to Azure open AI service
10. Conclusion

Introduction

Welcome everyone! Today, in this article, we will discuss the implementation of the Azure open AI service with the Fireblaze project. We will explore the background of Fireblaze, the challenges in querying HL7 Fire data, and how the Azure open AI service can provide a solution. Additionally, we will provide a demo of the integration and discuss the benefits, challenges, and documentation of this service.

Background of Fireblaze

Fireblaze is an open-source Blazor web-assembly application developed by the emerging opportunities team. It facilitates interaction with HL7 Fire data through RESTful APIs, allowing users to view patient data and perform basic filtering. However, writing queries for HL7 Fire data can be complex and time-consuming.

Problem with querying HL7 Fire data

Interacting with HL7 Fire data format poses challenges, particularly when writing queries. Building a user interface that covers every possible Type of patient query is impractical, especially considering the variability in query requirements. Simplifying the query process while maintaining accuracy and efficiency is essential for effective data retrieval.

Solution: Implementing Azure open AI service

To address the challenges of writing queries for HL7 Fire data, the team considered integrating the Azure open AI service. By leveraging the service's capabilities, the team aimed to automate and streamline the query process. The Azure open AI service is an implementation of GPT-3 with various deployment options.

Demo of Azure open AI service integration with Fireblaze

In the Fireblaze user interface, a natural language search feature was implemented using the Azure open AI service. Users can input their query in plain English, and the service generates a valid HL7 Fire query. The resulting query is displayed for review and can be manually edited if necessary. Upon executing the query, the Relevant patient data is retrieved and displayed.

Choosing the appropriate model in Azure open AI service

The Azure open AI service offers a range of models for natural language processing. The team selected the "text DaVinci zero zero two" model for their implementation. This model showed promising results during testing and was readily available in their region. While the Current model may not perfectly generate queries every time, continuous improvements and updates are expected.

Benefits of using natural language search in Fireblaze

By incorporating natural language search in the Fireblaze project, the team achieved significant time savings and improved developer efficiency. Instead of manually coding various query scenarios, users can express their search criteria in plain language. This approach simplifies the querying process and reduces the barrier to accessing patient data.

Challenges and limitations of Azure open AI service

Although the Azure open AI service offers valuable capabilities, there are some limitations to consider. The service may not always generate perfect queries, and the results can vary. Tweaking certain parameters and configurations can improve the consistency of query results. Additionally, the available AI models may not cover all possible combinations due to training data limitations. However, as the service evolves, the range of supported combinations is expected to expand.

Documentation and access to Azure open AI service

Detailed documentation for interacting with the Azure open AI service and accessing the RESTful APIs is available. To use the service, users must Apply for access, which typically takes a few days to a week. The documentation provides insights into best practices, troubleshooting tips, and information on available models and their deployment options.

Conclusion

In conclusion, the integration of the Azure open AI service with Fireblaze has enabled natural language search for HL7 Fire data. Despite some limitations, this implementation saves valuable developer time and simplifies the querying process. As the Azure open AI service continues to evolve, the accuracy and consistency of generated queries are expected to improve, further enhancing the user experience in Fireblaze.