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camera-webTeleCare Triage Workflow (AI-Assisted Analysis)

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TeleCare Triage Workflow (AI-Assisted Use Case)

This use case demonstrates how I used AI to analyze and improve a TeleCare triage system. By combining workflow analysis, prompt engineering, and strategic AI guidance, I evaluated the triage process from multiple viewpoints — clinical, operational, and executive.

The goal was to create a more efficient, patient-centered, and compliant intake process that reduces delays and improves clinical workload management.

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What I Asked AI To Do

I used a series of structured prompts to help evaluate the triage system from different decision-maker perspectives. For example:

Prompts used included:

  • “Act as a hospital CEO evaluating a telehealth triage model…”

  • “Identify operational risks and patient safety concerns.”

  • “Suggest improvements using CDC/AHRQ triage guidelines.”

  • “Map the workflow and highlight automation opportunities.”

  • “Explain where human oversight is required and why.”

The AI responded with analyses that helped refine both the workflow and the long-term strategy for TeleCare.

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What AI Helped Me Discover

AI elevated the project from simple intake automation to a deeper systems-level perspective:

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1. Patient Safety & Compliance

AI highlighted:

  • Required clinician oversight

  • Risks around symptom misclassification

  • HIPAA & documentation considerations

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2. Digital Equity

AI identified:

  • Barriers for patients without stable technology

  • Accessibility gaps for multilingual or low-health-literacy patients

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3. Operational Bottlenecks

AI identified:

  • Overreliance on manual review

  • Inefficient branching logic

  • Missing feedback loops

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4. Opportunities for Automation

AI suggested:

  • Automated symptom intake

  • Preliminary severity scoring before human review

  • Integration with scheduling and EMR endpoints

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AI-Supported Workflow Sketch

Using AI’s guidance, I created a Mermaid diagram representing:

  • Intake form

  • Triage scoring

  • API validation steps

  • Nurse review checkpoints

  • Routing for urgent vs. non-urgent cases

This helped clarify:

  • Workflow timing

  • Decision points

  • Technical integration points

  • Where human judgment must remain in the loop

Evidence of Work:

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Why This Use Case Matters

This project helped me:

  • Develop AI-assisted system design skills

  • Connect triage workflows to organizational strategy

  • Understand clinical risk, compliance, and scalability

  • Improve my ability to prompt AI for multi-layered analysis

It also demonstrated how AI can accelerate early ideation, while still requiring human expertise for final decisions.

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