Firstlook AI is a healthcare company that specializes in providing advanced imaging services to patients. With a growing patient base and a need to streamline their scheduling process, Firstlook AI approached Seven Billion Analytics for help in building a data platform that would assist their team with three main tasks: visualization of the schedule, modification of the schedule, and creation and modification of simple rules using drop-downs.
The project began with a thorough analysis of Firstlook AI’s current scheduling process and identifying the pain points and areas for improvement. After conducting a series of workshops and meetings with the Firstlook AI team, Seven Billion Analytics proposed a solution that would leverage the power of big data and analytics to streamline the scheduling process and provide real-time insights.
The data platform that I built for Firstlook AI was designed to be highly scalable and secure to meet the strict HIPAA compliance requirements. The platform was built on top of a modern data architecture that included a data lake, a data warehouse, and a set of data pipelines for data ingestion, processing, and visualization.
The platform integrated PowerBI as a data visualization tool, which allowed the Firstlook AI team to easily create and share interactive reports and dashboards. The platform also had the capability of data writing back and can be accessed from mobile phones too. The platform was built in a way that it can be integrated with other systems and data sources in the future, making it a flexible and adaptable solution for Firstlook AI’s evolving needs.
The new data platform has helped Firstlook AI achieve a number of benefits, including:
- Improved visibility and control over the scheduling process.
- Real-time insights into patient scheduling and capacity management.
- Increased efficiency and reduced errors in the scheduling process.
- The ability to make data-driven decisions and improve overall operations.
Azure: Azure would be used as the foundation for the data platform, providing the necessary storage, computing, and security capabilities. Azure Data Factory would be used for data integration, and Azure Data Lake Storage would be used for data storage.
Power BI: Power BI would be used for data visualization and reporting. Power BI would be connected to the data lake through the Azure Data Factory, allowing for real-time data visualization and reporting.
Power Platform: The Power Platform (specifically PowerApps and Power Automate) would be used to build the custom schedule modification and rule creation capabilities. PowerApps would be used to create a mobile-friendly interface for modifying the schedule, and Power Automate would be used to create the rules and automate the schedule modification process.
Azure Cosmos DB: Azure Cosmos DB would be used as a data store for the scheduled data, providing a globally distributed, low-latency, highly available, and scalable database.
Azure Logic Apps: Azure Logic Apps would be used to integrate the schedule modification app with the scheduling system. Logic apps would manage the flow of data between systems and execute the schedule modification rules.