
Developed the foundational Virtual Check-In System for the umgc/2025_fall repository, focusing on backend architecture and robust data persistence. Designed and implemented a comprehensive data model to capture structured patient-reported information, including entities for questions, answers, check-ins, and question types. Leveraged Java, SQL, and Spring Data JPA to establish repository patterns and cross-file JPA mappings, supporting caregiver-selected check-in flows through a dedicated table. The work emphasized database design best practices, enabling reliable data capture and future analytics. Maintained clear traceability with descriptive commit messages, delivering an end-to-end solution that reduces manual entry and supports scalable virtual care workflows.
October 2025: Delivered the foundational Virtual Check-In System and the associated data persistence layer in the umgc/2025_fall project. Implemented a complete data model for virtual patient check-ins (Question, CheckIn, Answer, QuestionType) with repositories, enabling reliable capture of structured patient-reported data. Established caregiver-selected check-in support via the check_in_questions table and cross-file JPA mappings. This work lays the groundwork for scalable data analytics, improved care coordination, and faster onboarding of virtual care workflows. Key business impact includes reduced manual data entry, standardized data for analytics, and a flexible schema for future questions and check-in flows. Technologies demonstrated: Java, JPA/Hibernate, repository pattern, and database design. No major bugs reported; all changes focused on feature development and data model robustness.
October 2025: Delivered the foundational Virtual Check-In System and the associated data persistence layer in the umgc/2025_fall project. Implemented a complete data model for virtual patient check-ins (Question, CheckIn, Answer, QuestionType) with repositories, enabling reliable capture of structured patient-reported data. Established caregiver-selected check-in support via the check_in_questions table and cross-file JPA mappings. This work lays the groundwork for scalable data analytics, improved care coordination, and faster onboarding of virtual care workflows. Key business impact includes reduced manual data entry, standardized data for analytics, and a flexible schema for future questions and check-in flows. Technologies demonstrated: Java, JPA/Hibernate, repository pattern, and database design. No major bugs reported; all changes focused on feature development and data model robustness.

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