GoodRx
Pricing Clarity System
Insurance coverage for consumers and clinicians
Insurance coverage for consumers and clinicians
Insurance coverage data is fragmented, inconsistent, and difficult to interpret at the moment it matters most, when someone is trying to understand what a medication will cost. The challenge was designing a single, clear coverage system that could stay simple for consumers and structured for clinicians without increasing effort for either.
Insurance coverage data is fragmented, inconsistent, and difficult to interpret at the moment it matters most, when someone is trying to understand what a medication will cost. The challenge was designing a single, clear coverage system that could stay simple for consumers and structured for clinicians without increasing effort for either.
ROLE
Lead Product Designer
DURATION
6-month MVP build
SURFACES
iOS / Android / Web
SCOPE
Consumer coverage flow
Clinician coverage experience
Coverage system architecture
OUTCOME
Shared coverage system
Predictable coverage logic
Faster decisions
ROLE
Lead Product Designer
DURATION
6-month MVP build
SURFACES
iOS / Android / Web
SCOPE
Consumer coverage flow
Clinician coverage experience
Coverage system architecture
OUTCOME
Shared coverage system
Predictable coverage logic
Faster decisions
ROLE
Lead Product Designer
DURATION
6-month MVP build
SURFACES
iOS / Android / Web
SCOPE
Consumer coverage flow
Clinician coverage experience
Coverage system architecture
OUTCOME
Shared coverage system
Predictable coverage logic
Faster decisions
The Forces That Shaped the Design
Insurance is complex, inconsistent, and hard to interpret. To make it understandable, the experience needed structure shaped by the realities of the domain. Four forces grounded the design and set the direction for how coverage could be presented.
The Forces That Shaped the Design
Insurance is complex, inconsistent, and hard to interpret. To make it understandable, the experience needed structure shaped by the realities of the domain. Four forces grounded the design and set the direction for how coverage could be presented.
Fragmented insurance data
Multiple sources, formats, and confidence levels needed to resolve into a single interpretable signal.
Fragmented insurance data
Multiple sources, formats, and confidence levels needed to resolve into a single interpretable signal.
Two mental models
Consumers optimize for cost; clinicians optimize for requirements.
Two mental models
Consumers optimize for cost; clinicians optimize for requirements.
Confusing terminology
Clinical terms had to remain precise for professionals while being readable and non-threatening for consumers.
Confusing terminology
Clinical terms had to remain precise for professionals while being readable and non-threatening for consumers.
Crowded, high-stakes surfaces
Coverage had to fit without disrupting price or prescribing workflows.
Crowded, high-stakes surfaces
Coverage had to fit without disrupting price or prescribing workflows.
What the First Version Revealed
The only existing design was an early clinician prototype. While it surfaced the right data, coverage appeared in an order that conflicted with real decision flow, requiring vertical scanning before tier or cost was clear. The layout also disrupted key price surfaces, adding friction in a revenue-critical context.
There was no consumer experience in place, which made it clear the issue wasn’t isolated to a single surface but to the underlying structure. Both experiences were redesigned together around a shared coverage model aligned to real decision-making.
What the First Version Revealed
The only existing design was an early clinician prototype. While it surfaced the right data, coverage appeared in an order that conflicted with real decision flow, requiring vertical scanning before tier or cost was clear. The layout also disrupted key price surfaces, adding friction in a revenue-critical context.
There was no consumer experience in place, which made it clear the issue wasn’t isolated to a single surface but to the underlying structure. Both experiences were redesigned together around a shared coverage model aligned to real decision-making.
A System That Scales Across Users
I redesigned the coverage model so a single system could support both consumers and clinicians. Inputs were normalized and plan selection simplified, reducing noise so coverage could be interpreted quickly and consistently. From that foundation, each audience gets an experience shaped to their needs while the underlying structure stays shared.
A System That Scales Across Users
I redesigned the coverage model so a single system could support both consumers and clinicians. Inputs were normalized and plan selection simplified, reducing noise so coverage could be interpreted quickly and consistently. From that foundation, each audience gets an experience shaped to their needs while the underlying structure stays shared.
One normalized coverage model supports different experiences through hierarchy and language, not data.
One normalized coverage model supports different experiences through hierarchy and language, not data.
Consumer Experience
Consumer Experience
Consumers approach insurance with uncertainty. The experience needed to reduce effort and make cost easy to interpret.
Consumers approach insurance with uncertainty. The experience needed to reduce effort and make cost easy to interpret.
Designed for low-effort interpretation
Designed for low-effort interpretation
The system requests only essential inputs and absorbs missing details, allowing users to reach interpretable results without upfront complexity.
The system requests only essential inputs and absorbs missing details, allowing users to reach interpretable results without upfront complexity.
Coverage results built for clarity
Coverage results built for clarity
Results follow a cost-first hierarchy, surfacing coverage state and estimated copay first. Restrictions appear only when relevant, with alternatives available when coverage is limited or expensive.
Results follow a cost-first hierarchy, surfacing coverage state and estimated copay first. Restrictions appear only when relevant, with alternatives available when coverage is limited or expensive.
Clinician Experience
Clinician Experience
Clinicians move quickly and rely on clear signals. The experience needed to support decision-making without disrupting workflow.
Clinicians move quickly and rely on clear signals. The experience needed to support decision-making without disrupting workflow.
Built to support workflow
Built to support workflow
Coverage opens without displacing the price page, allowing clinicians to assess insurance details while staying oriented.
Coverage opens without displacing the price page, allowing clinicians to assess insurance details while staying oriented.
Results aligned to prescribing
Results aligned to prescribing
Formulary tier leads, followed by estimated copay. Coverage requirements scan quickly and remain collapsible to keep the experience fast.
Formulary tier leads, followed by estimated copay. Coverage requirements scan quickly and remain collapsible to keep the experience fast.
Impact
By separating coverage logic from presentation, the redesigned system reduced decision friction and made insurance information easier to interpret when it mattered most.
What changed
•
Consumers could quickly understand expected cost and next steps
•
Clinicians could assess coverage requirements in-flow
•
Coverage signals followed a predictable, decision-aligned hierarchy
What scaled
•
A single coverage model supports multiple audiences
•
Shared logic remains consistent across surfaces
•
New coverage data can be added without redesigning flows
Impact
By separating coverage logic from presentation, the redesigned system reduced decision friction and made insurance information easier to interpret when it mattered most.
What changed
•
Consumers could quickly understand expected cost and next steps
•
Clinicians could assess coverage requirements in-flow
•
Coverage signals followed a predictable, decision-aligned hierarchy
What scaled
•
A single coverage model supports multiple audiences
•
Shared logic remains consistent across surfaces
•
New coverage data can be added without redesigning flows
What Designing for Price Clarity Taught Me
Principles that bring order to complexity
•
Strong models matter. Clarity starts upstream, not at the UI.
•
Language shapes trust. Precision and approachability must coexist.
•
Hierarchy guides interpretation. The right order reduces cognitive load
•
One system can serve many users. A shared model can flex.
Reflections
Designing for price clarity reinforced how heavily decision-making depends on information structure. People move quickly, look for the strongest signal, and trust what feels well ordered. Labels, spacing, and hierarchy mattered as much as the data itself.
This work reshaped how I think about multi-audience systems. Consumers and clinicians need different things, but clarity doesn’t require separate solutions. When complexity is absorbed by the system, people can make decisions faster and with confidence.
What Designing for Price Clarity Taught Me
Principles that bring order to complexity
•
Strong models matter. Clarity starts upstream, not at the UI.
•
Language shapes trust. Precision and approachability must coexist.
•
Hierarchy guides interpretation. The right order reduces cognitive load
•
One system can serve many users. A shared model can flex.
Reflections
Designing for price clarity reinforced how heavily decision-making depends on information structure. People move quickly, look for the strongest signal, and trust what feels well ordered. Labels, spacing, and hierarchy mattered as much as the data itself.
This work reshaped how I think about multi-audience systems. Consumers and clinicians need different things, but clarity doesn’t require separate solutions. When complexity is absorbed by the system, people can make decisions faster and with confidence.
More case studies
More case studies
Made with love ♡
Made with love ♡
© 2025 Michael Fofrich
© 2025 Michael Fofrich