Healthcare Analytics refers to the use of statistical models and advanced analysis tools to extract useful information from data collected in the healthcare field. The objective is to improve internal processes and therefore the services provided to medical organizations or directly to patients. A customer turned to us to obtain a customized solution, through which to monitor and improve their clinical and administrative activities. Here's how it went.
A medical center needed to monitor its clinical and administrative activities through a series of KPIs (key performance indicators), to be identified specifically for evaluate the efficiency of diagnostic processes and internal logistics within the company.
Since the center's activities include both the diagnosis and treatment of patients, and the management of resources and operating times, we were required to design a new system capable of accurately monitoring the performance of individual activities, based on criteria such as:
To ensure the quality of the services provided, the customer needed to integrate administrative KPIs. These are a type of indicators useful for identifying any inefficiencies in terms of:
Our experts had therefore been asked to Identify KPIs most relevant clinical and administrative offices, but not only.
It was necessary implement a dual solution, through which:
The core of the problem presented by our customer therefore resided in creation of an automated and structured system, which would allow us to manage large volumes of data and, at the same time, obtain valuable insights to improve productivity and the quality of the services provided.
For those who do not know the term “Healthcare Analysis”, here is a brief overview that will serve to better understand how we have faced our client's challenge.
“Healthcare Analysis” indicates the use of analytical tools and statistical models for extract value from health information, with the aim of improving the performance of a company and the quality of the services provided to patients.
This approach is based on the management of a significant amount of data, which must be reprocessed based on specific KPIs for each organization. In fact, the indicators to be taken into consideration vary depending on the type of company (hospital, private clinic, medical-diagnostic center, etc.) and, of course, on the operations that are carried out within it.
Nevertheless, the Healthcare Analytics process is divided into:
Given this brief introduction, it is clear that the adoption of customized software can help companies interested in Healthcare Analytics.
First, customized software makes it possible to integrate all the data sources present in the company and obtain a unified view of information related to productivity and services.
But it's not just about integration: the software is designed to be scalable. This means that a company can always adapt the implemented system to its needs (for example by integrating new data sources or selecting information through new KPIs) without having to constantly replace parts of its IT infrastructure.
A second advantage to consider then concerns the phases underlying the Healthcare Analytics process, listed above, which would come automated Let it be for Break down the times that exist between data collection and the implementation of insight-based strategies, both for Curb the risk of errors that inevitably grows with the amount of data to be reprocessed.
The solution developed for the customer is based on an infrastructure integrated into the Microsoft ecosystem and, in particular, on Microsoft Azure. Let's see the details of our solution below, delving into the individual components of the technological stack involved.
Azure Synapse Analytics represents the heart of our solution, since it allows us to orchestrate, transform and analyze large volumes of data efficiently.
The use of Azure Synapse has also allowed us to create the data pipeline through which it is possible to transfer and validate data from corporate databases, .CSV files, .XLSX files and SharePoint lists. The pipelines, composed of functional and logical blocks, also ensure that the data goes through the two cleaning and validation phases prior to real time processing.
To automate the process of creating and managing KPIs, C# was used within Azure Functions. The serverless functions of Microsoft Azure have made it possible toautomation KPI calculations and workflow management, thus allowing code to be executed in response to specific events, such as the processing of new data.
Azure Functions offers the flexibility to scale automatically on a per-request basis, reducing costs and simplifying code implementation. On the other hand, the use of C# has made it possible to customize the application logic, making the system even more agile.
Kusto Query Language (KQL) was used for data manipulation in the Azure environment.
This language, similar to SQL, was used to:
KQL is also optimized for real-time analysis and for managing large datasets, offering the speed and accuracy required to provide timely and valuable insights. Finally, KQL's ability to extract and manipulate complex data has simplified the process of validating and transforming the data needed for Power BI reports.
Once the data has been processed and validated, Power BI was chosen to view KPIs.
The interactive and personalized dashboards typical of the platform represent, in fact, the ideal means to provide the customer with a clear and unified view of business performance.
Thanks then to the integration with Azure Synapse Analytics and KQL, Power BI is able to automatically update data and thus allow continuous monitoring of the selected KPIs.
For the management and storage of data during the copy and validation phases, Azure Storage Explorer was used.
This tool allows you to easily interact with the data stored on Azure Storage, facilitating the file management process necessary for the processing of KPIs.
Thanks to the intuitive interface and the ability to access and manipulate data in different formats, Azure Storage Explorer has allowed us to optimize the management of import files and guarantee their secure temporary storage.
Despite the considerable complexity of the project, we were able to successfully address the most important obstacles. Among these, we could mention:
After the production, together with the customer, we tracked the performance of the new infrastructure for personalized KPI tracking. The goal was to understand if the solution was really the right one to monitor and improve clinical and administrative activities. The answer? Let's see some facts together:
The Modern Work team effectively and swiftly addresses IT needs, primarily focusing on software development. The technical staff is well-trained in implementing software projects using Microsoft technology stacks and is skilled in managing both agile and long-term projects.
