In the healthcare-diagnostic sector, the ability to monitor operational and clinical performance in real time is essential to ensure service quality and management efficiency. When an important medical-diagnostic center approached us, the challenge was ambitious: to develop a complete system of key performance indicators to transform dispersed data into informed strategic decisions.
Our customer, a medical-diagnostic center of reference in the area, had expressed the need, both in the past and with renewed urgency recently, to develop a set of key performance indicators (KPIs) to monitor different metrics related to diagnostic, reporting and logistics activities.
The required KPIs were divided into two main categories: Health KPIs to monitor clinical performance and Administrative KPIs to track management efficiency. The challenge was not only to collect this data, but to create automated processes for transferring information from their original sources to a system where they could be organized in structured tables and where the logic of KPI could be defined and applied consistently.
The choice of Azure Synapse Analytics and Kusto Query Language (KQL) for this project it was not random. In the healthcare sector, where data is fragmented between clinical, administrative and operational systems, a platform capable of unify, process and analyze information heterogeneous while maintaining compliance and security.
Azure Synapse Analytics is much more than just a data warehouse. It is a unified analytics platform that combines big data and data warehousing, allowing you to create intelligent pipelines that orchestrate the entire flow of data. In our healthcare context, Synapse manages the acquisition from clinical databases, administrative Excel files and operational SharePoint lists, applying transformations and validations specific to each source. Its serverless architecture allows it to scale automatically based on load, a crucial aspect when processing peaks of data such as those generated during screening campaigns or periods of high turnout.
Kusto Query Language (KQL) is the real analytical engine of the solution. Similar to SQL but designed specifically for analyzing time-series and semi-structured data, KQL excels at processing large volumes of healthcare data. Its intuitive syntax allows you to express complex logic - such as the calculation of average reporting times excluding holidays and weekends, or the analysis of trends in the use of diagnostic equipment - with concise and efficient queries. KQL's ability to natively manage temporal data is fundamental in healthcare, where every metric has a critical time dimension.
The native integration with Power BI completes the picture: KQL queries directly become live datasets in reports, ensuring that the dashboards always show updated data without the need for intermediate ETL processes. This real-time approach is essential for healthcare management, which must make immediate operational decisions based on reliable data.
Learn more Azure Synapse Analytics here.
We have designed a solution that takes full advantage of the capabilities of Azure Synapse Analytics and the Kusto KQL environment to create a robust and scalable analytics ecosystem.
The process is divided into well-defined phases. The first phase consists ofdata acquisition through Synapse pipeline that query databases, process CSV and XLSX files, and connect to SharePoint lists. Each source is treated with specific logic to ensure correct extraction.
This is followed by the phase of validation and ingestion in the Kusto KQL environment. The data undergoes strict quality controls before importing, ensuring the reliability of subsequent analyses.
The third phase concerns the creating KQL queries and importing them directly into Power BI, where data is manipulated to create visualizations that meet the specifications of each KPI. Throughout the entire process, Azure Storage acts as a repository for archiving files in the various phases of copying and validation.
During the implementation, we faced several significant technical challenges. THEanalysis and creation of KQL queries to obtain the precise tabular output requested by the customer proved to be particularly complex, with specific logics for each KPI that had to consider exceptions and articulated business rules.
La understanding and implementing pipelines within Azure Synapse required an in-depth analysis of existing data flows, mapping all dependencies and ensuring resilience to errors.
La validation of complex data, with datasets that had irregular structures or complex relationships, required the development of custom validation routines for each type of data.
The technology stack is completely based on the Azure and Microsoft ecosystem:
The use of Azure Functions in C# has made it possible to extend the native capabilities of Synapse with custom logic for complex transformations.
The implementation of the solution has brought results that testify to the scalability and effectiveness of the chosen architecture.
We have generated more than 30 fully automated Power BI reports and developed more than 150 KPI functions, all in full compliance with specific customer requirements. Each report offers a specialized view of different aspects of the center's performance.
The ability to manage big data has been demonstrated with the efficient execution of imports that have processed volumes of up to 130 million records, maintaining consistent performance thanks to query optimization and Azure's scalable architecture.
The implementation of numerous optimized pipelines has transformed fragmented manual processes into reliable automated flows, while the development of 4 dedicated Azure Functions has made it possible to implement complex business logic impossible with only Synapse's native tools.
The Modern Apps team responds swiftly to IT needs where software development is the core component, including solutions that integrate artificial intelligence. The technical staff is trained specifically in delivering software projects based on Microsoft technology stacks and has expertise in managing both agile and long-term projects.
