Reducing Friction in High-Volume Claims Adjudication

Legacy proto-personas created by a previous UX team (2021) were used as a starting point for secondary research. Because FOX (claims application) had recently launched, their relevance to current user behaviors was uncertain.

To address this, I began validating the proto-personas by re-analyzing the original data and conducting interviews with product and business stakeholders. Gaps were identified where personas had been left incomplete, and additional persona definitions were developed to better reflect emerging user patterns.

This work established a foundation for evolving the proto-personas into fully validated user personas that more accurately represent current FOX users and inform ongoing design decisions.

Once the UX team developed a working understanding of the FOX claims processing workflow, we designed a mixed-method survey to validate emerging insights. The questionnaire included a balance of quantitative and qualitative questions, ranging from straightforward task-based prompts to open-ended questions intended to surface sentiment and emotional friction.

The 24-question survey was distributed to approximately 140–150 claims examiners on the AARP distribution list. Eleven participants responded (≈7.9% response rate), providing directional quantitative signals and rich qualitative feedback.

Based on these responses, a subset of participants was selected for follow-up interviews to deepen understanding of workflows, pain points, and context—allowing the team to validate findings beyond the survey data alone.

The UX team conducted 30-minute-long contextual (ethnographic) observation sessions with 17 users across multiple business areas to understand how claims processors interact with the FOX and COMPAS (admin & billing app) systems in real working conditions.

Using a “fly-on-the-wall” approach, we observed end-to-end task execution while asking targeted, in-context questions about specific workflows.

Application trainers shared established shortcuts, workarounds, and ideal “happy paths,” while also surfacing system limitations and recurring sources of frustration. These sessions provided critical insight into how work is actually performed—beyond documented processes or assumed behaviors.

Following the initial research phase, the team conducted in-depth interviews with three participants to clarify ambiguous findings and explore specific workflow challenges in greater detail. Interview scripts were developed based on insights from the earlier survey, allowing the team to probe areas that required deeper context.

Each 30-minute session was conducted over a two-week period using a collaborative interview model. With participant consent, sessions were recorded and analyzed using Dovetail to support accurate capture and synthesis of feedback.

I moderated the interviews, guiding the discussion toward actionable insights. A UX colleague supported the sessions with development and accessibility expertise, while the UX manager provided business-level context to ensure findings aligned with organizational priorities.

Insights were synthesized using task, thematic, and sentiment analysis, and affinity mapping—transforming qualitative data into design-ready insights that informed strategic hypotheses.

AI-assisted analysis was used selectively to accelerate synthesis while maintaining human judgment and research rigor.

To translate qualitative feedback into actionable insights, I used a structured analysis approach that combined task-level observation with thematic and sentiment-based synthesis.

Task Analysis
I examined the step-by-step workflows claims examiners followed to complete their work, identifying inefficiencies that directly affected productivity. This surfaced recurring friction points such as inconsistent tab navigation and redundant actions that disrupted focus and quota completion.

Thematic Analysis
User feedback was grouped into core themes that revealed root causes of frustration, including system reliability issues, cognitive overload from poorly structured layouts, and data inconsistencies. These themes helped prioritize UI and workflow improvements aligned with both user needs and business goals.

Sentiment Analysis
I assessed the emotional tone of feedback to understand how system behavior affected examiners beyond task completion. This highlighted elevated frustration and anxiety around errors and slowness, reinforcing the need to reduce cognitive strain and improve system confidence.

Affinity Mapping
Findings were clustered into visual groups to reveal patterns across roles and workflows. Issues were prioritized by frequency and severity, enabling the team to focus on changes with the highest operational and experiential impact.

Together, this synthesis transformed raw qualitative data into clear, design-ready priorities—balancing functional efficiency with the human realities of high-volume, high-stakes work.