Azure-Samples/healthcare-agent-orchestrator
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
AI IntegrationMedical Data ProcessingPrompt Engineering
jspreston
PROFILE
Jspreston
Developed a Molecular Tumor Board Timeline Prompt Enhancement for the Azure-Samples/healthcare-agent-orchestrator repository, focusing on refining AI-driven patient timeline generation for medical data analysis. Leveraging Python and prompt engineering, the work centered on integrating MTB-specific details into system messages to guide the AI in producing more comprehensive and relevant timelines for multidisciplinary team discussions. This enhancement improved the accuracy and speed of patient data analysis by aligning prompts with established MTB workflows. The project emphasized AI integration and medical data processing, addressing the need for higher-quality, context-aware timelines to support decision-making in tumor board case reviews without introducing new bugs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
19
Activity Months1
Your Network
8 peopleShared Repositories
8
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Delivered Molecular Tumor Board Timeline Prompt Enhancement for Azure-Samples/healthcare-agent-orchestrator, refining the AI timeline generation to include MTB-specific details and a more comprehensive medical timeline for MTB analyses. This improvement enhances decision support for MDT discussions, reduces time to insights, and improves data quality for patient case reviews. No major bugs were reported this period; focus was on feature delivery and prompt engineering.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary: Delivered Molecular Tumor Board Timeline Prompt Enhancement for Azure-Samples/healthcare-agent-orchestrator, refining the AI timeline generation to include MTB-specific details and a more comprehensive medical timeline for MTB analyses. This improvement enhances decision support for MDT discussions, reduces time to insights, and improves data quality for patient case reviews. No major bugs were reported this period; focus was on feature delivery and prompt engineering.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance60.0%
AI Usage60.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AI IntegrationMedical Data ProcessingPrompt Engineering
Repositories Contributed To
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