AI-Driven Healthcare Analysis & Reporting

Overview

Project work combining Python and MySQL anomaly detection with HIPAA-conscious BI dashboards and role-based access. Public product framing also appears on the Hello Doctor project site. Presented as educational/experimental decision support—not a clinically validated diagnostic system.

Business problem

Which health-metric patterns should receive additional review, how can anomaly detection reduce time-to-insight, and what access controls are required for trustworthy monitoring?

Analysis and approach

• Build anomaly detection workflows in Python with MySQL-backed data
• Design BI dashboards with role-based access controls
• Validate data accuracy across user roles in a small team
• Translate model outputs into monitoring and review questions

Skills: Anomaly detection · Model evaluation concepts · Dashboard design · Data validation

Evidence and visuals

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  Evidence placeholder — add evaluation charts or cleared dashboard crops.

Findings and outcome

• Anomaly detection is most useful when paired with clear review ownership and access controls.
• Dashboard speed and entitlement design affect whether monitoring insights are used in time.
• Cross-role validation is required before treating flagged patterns as decision-ready.

Outcome: Built an AI-powered anomaly detection and reporting workflow for critical health-metric patterns, paired with role-based BI dashboards for faster monitoring.

Recommendations

1. 1

   Report precision/recall-style trade-offs beside any anomaly threshold before operational use.

2. 2

   Keep role-based access tests in the release checklist for every dashboard change.

3. 3

   Treat the system as decision support for review prioritization, not diagnosis.