CASE STUDY / FIREFLY HEALTH

AI Self-Service for Members

Timeline: Q1, 2025

Challenge: Design self-service AI tools that reduce operational costs while improving member experience for routine healthcare tasks

“Quick Links” were added to the app home screen for the most routine and automate-able member needs. Each quick link launches a simple, AI-based questionnaire or workflow.

The insight

In early 2024, I identified a significant business opportunity: many routine member requests were consuming expensive clinical resources when members would actually prefer handling these tasks through automated tools. After presenting a formal business case to the leadership team, the project received a green light and roadmap prioritization for its ROI potential.

Before: A routine request (e.g. med refill request) could take an hour or more to resolve

Phase 1

Research & Discovery

I conducted analysis to validate my hypothesis and identify which routine tasks offered the highest impact opportunities for automation.

Critical insights:

  • Hidden operational costs: Routine requests like prescription refills required multiple clinical touchpoints, consuming expensive RN and NP time

  • Member frustration with async delays: What leadership perceived as "convenient chat" was actually creating friction for simple tasks that members expected to complete immediately

  • High-volume, low-complexity patterns: Prescription refills and basic symptom inquiries represented a disproportionate amount of routine clinical interactions

  • ROI opportunity: Automating top routine tasks could redirect clinical resources to complex care while improving member satisfaction

  • Technical feasibility: Existing clinical protocols could be translated into guided self-service flows without compromising care quality

Illustrative concept from initial business case presentation

Phase 2

Design

Based on research insights, I developed a phased self-service strategy that would deliver immediate ROI while establishing patterns for future automation.

Key design decisions:

  • Prescription refill automation with smart routing based on medication type, refill history, and clinical flags

  • Symptom checker with guided triage helping members understand next steps while capturing clinical context

  • Contextual escalation points throughout flows to clinical team when complexity increases

  • Progress tracking and status updates keeping members informed during automated processes

Phased rollout strategy:

  • Phase 1: RX Refills MVP

  • Phase 2: New Symptom Checker alongside Rx Refills improvements

  • Phase 3: Provider Search and Care Navigation (complex but meaningful strategic value)

User Flow: Get help with symptoms

User Flow: Refill a medication

User flow: Request a referral

User flow: Status tracking

Phase 3

Validation & Testing

Before population-wide rollout, I conducted extensive validation through usertesting.com and A/B testing to ensure self-service tools would meet member needs, safety requirements, and existing clinical workflows.

Key findings:

  • Prescription refill automation tested very well - member adoption was fast and operational efficiency jumped

  • Clinical escalation points worked seamlessly when triggered by smart routing algorithms

  • Staff workflow improvements emerged as clinical team could focus on complex cases rather than routine requests

Refinements made:

  • Refined symptom checker through multiple iterations to balance clinical thoroughness with user experience simplicity

  • Enhanced status tracking and notifications after users expressed anxiety about lack of system feedback

Learning & Iteration

Designing for “Red Flag” moments

What we missed:
First version focused on the happy path. We hadn't designed for "red flag" symptoms.

What clinicians flagged:
Inputs like ‘chest pain’ or ‘heart palpitations’ shouldn't just route to self-service options—they need immediate escalation.

What we added:
Triggers that bypass automation and surface emergency guidance and notify on-call staff to begin outreach protocols.

Learning & Iteration

Recognizing Intent

What we noticed:
Most members adopted the new flows quickly but some still went straight to chat out of habit.

The opportunity:
Instead of forcing behavior change, meet them where they are—and test something we'd been wanting to try.

What we added:
Intent recognition that catches common requests and redirects to self-service.

70%

of refill requests automated

3x

reduction in clinical touchpoints for Rx

5x

reduction in clinical touchpoints for new symptoms

Business insight drives design strategy

Identifying operational inefficiencies created a compelling business case that secured leadership buy-in and resources. By framing self-service as both cost savings and member experience improvement, design became a strategic business driver rather than just interface optimization.

Member preferences can evolve faster than business assumptions

What leadership perceived as "convenient chat" was actually creating friction for routine tasks. Regularly challenging internal assumptions about member preferences through research prevents well-intentioned features from becoming user frustrations.

Key project insights

Phased rollout enables learning and refinement

Launching prescription refills first provided immediate ROI and validated our approach, while the more complex symptom checker benefited from lessons learned. Sequential releases allowed us to refine patterns and build confidence before tackling higher-risk automation.

Clinical safety and efficiency can coexist

Automated self-service doesn't compromise care quality when properly designed with clinical input and smart escalation triggers. The key is transparent handoffs between automated and human assistance that maintain care continuity.

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