LakeHouse - MS Azure platform for data warehouse implementation and operation, including reporting

acterys, Data science a AI, microsoft fabric

Jihlava Hospital

Jihlava Hospital (Nemocnice Jihlava) is a state-funded institution that provides health care, which includes outpatient and inpatient basic and specialized diagnostic and therapeutic care, essential preventive care and pharmacy services. The organization carries out scientific work, educational activities and information provision in the health care sector, in particular clinical trials assessing the effects of pharmaceuticals and new medical technology, scientific research, undergraduate education of health care workers, postgraduate and continuing education of health care workers and maintaining a professional library.

Nemocnice Jihlava

Client’s requirements

Jihlava Hospital faces multiple challenges in managing and using the vast amount of data that relates to its operations. In response, the hospital sought a comprehensive solution that would enable it to efficiently store, process and analyze its data. The solution is intended to improve overall operational efficiency, enhance the quality of patient care and provide a modern foundation for future development.

Implementing a data warehouse solution is an essential step in modern data management and usage. Since Jihlava Hospital had already used the MS Azure cloud environment to implement projects, this environment was strongly preferred. The hospital required a solution that would allow it to store all its data in one place, support fast query processing and analysis, and provide advanced security and data protection. In addition, the solution needed to enable development of reports and dashboards to monitor the hospital’s activities and support data-driven decision making.

Jihlava Hospital anticipates that the implementation of the lake-house solution will bring significant benefits such as improved data accuracy, better insights into patient care, faster data processing and ad hoc analysis, more efficient resource allocation and increased revenue generation. The hospital also sought to reduce data management costs, improve compliance with regulatory requirements, and increase data sharing and collaboration between different departments and functions. The hospital also needed the solution to be a modern platform that can be scaled up in the future.

In summary, the hospital was looking for a robust and scalable data warehouse solution that would help it achieve its strategic goals, improve patient outcomes, and optimize its operational performance.

Solution

To fulfil the requirement, an analysis of user needs was carried out and a solution proposal was prepared and the solution was implemented.

  • overview map of source systems with their basic parameters;
  • selection of source systems for implementation in the initial phase;
  • design of data storage architecture;
  • analysis of selected reports currently in use;
  • parameterization of selected services in Azure and their basic operationalization;
  • design of the model and metadata sources;
  • integration of data from source systems;
  • transformation and storage of data into different architectural layers;
  • implementation of hourly incremental and daily data refreshing;
  • report design;
  • creation of sample reports for selected areas and a report management signpost;
  • training users to work with data in PowerBI;
  • training and workshops for solution administrators;

Benefits

  • Implementation of modern lakehouse architecture in a cloud environment;
  • Development of a centralized repository of data from multiple sources across the hospital;
  • Validation of the functionality of the proposed modern lakehouse architecture and the cloud services in use;
  • Implementation of data from the hospital’s core source systems;
  • Creation of a platform for using data according to current requirements – option of switching the current reporting;
  • Creation of a dimensional layer for future data consolidation between systems and advanced data processing at the data warehouse solution level;
  • Daily and hourly data updates from source systems
  • Leveraging metadata for faster implementation of sources
  • Developing skills to work with data through reports and analytical tools;
  • Understanding the data in primary systems and how it can be linked across systems;
  • Secure access to various parts of the overall solution, including reports;
  • Centralized data processing;
  • Minimization of manual data management;

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