D597: Relational Database Design and Implementation D597 Scenario 1
HealthFit Innovations is a rapidly growing healthcare technology company that specializes in developing innovative solutions for improving patient care and healthcare management. They are working on the development of a new platform called “HealthTrack,” which aims to revolutionize the way healthcare data is collected, analyzed, and utilized to provide personalized health recommendations.
HealthTrack is designed to integrate data from various sources including wearable devices, electronic health records (EHRs), medical imaging systems, and patient-reported outcomes. The platform offers personalized health insights, real-time monitoring, and predictive analytics to help individuals manage their health proactively and healthcare providers deliver better care.
As the user base of HealthTrack continues to expand, the volume and variety of health-related data being generated are increasing exponentially. Users are inputting data from a wide range of wearables such as fitness trackers, smartwatches, and medical devices, capturing information like heart rate, sleep patterns, activity levels, blood glucose levels, and more.
The current database infrastructure at HealthFit is struggling to cope with the diverse and dynamic nature of this health-related data. The existing database management system (DBMS) is not scalable enough to handle the massive influx of data in real time, leading to performance bottlenecks, data silos, and challenges in data integration and analysis.
To address these issues and ensure the scalability, flexibility, and efficiency of their data management system, HealthFit has decided to seek the expertise of a database consultant to recommend and present the logical design of a database that can efficiently handle the diverse and dynamic nature of health-related data. The database consultant’s role is to understand the specific requirements and challenges of HealthTrack and propose a logical design for a relational database that can efficiently handle the diverse and dynamic nature of health-related data. This includes defining the
· data model,
· entities,
· attributes,
· data organization,
· scalability,
· security, and
· integration capabilities of the new database system.
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