AI Workflow · UX Research · Data Viz
Designing a 0 to 1 feature analytics dashboard for full spending visibility
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- Designed a 0-to-1 analytics dashboard, Covered Entity Insights, giving pharmacy teams their first consolidated view of what and how much they purchase across three pricing channels (340B, WAC, and GPO).
- Partnered with UX Research on a customer survey that validated appetite for four candidate spend metrics, confirming the business already had the right instincts before design began.
- Gave each channel a fixed color that’s consistent in all data visualizations and graphs, allowing users to isolate specific channels without losing context.
- Built one interface for two depths: a sorted bar chart for a quick read, backed by a comprehensive data table with different hierarchy levels that mirrors how customers actually drill into their own spend.
- Made account selection hierarchical, so a hospital system can pull in every site under a parent or cherry-pick individual accounts, paired with a 3-to-24-month date range filter.
- Shipped three of four validated metrics in the MVP to keep scope lean, with the designs now in development toward a late-2026 release.
Background
Covered entities are hospitals and clinics serving underserved patients, which qualifies them for federal pricing on outpatient drugs. They buy through three channels at different prices: WAC (the manufacturer’s list price), GPO (negotiated by hospital buying groups), and 340B (the federal program with the deepest discounts). Most use all three, and the same drug can move through any channel depending on the patient.
Pharmacy teams manage enormous purchasing volume across these channels with limited visibility into their own data. They consistently describe gaps between what they could see and what they needed to know — and a single missed contract or unnoticed spending shift can mean six figures in lost savings.
Research Validation
Letting customers rank their own needs
The business had four ideas to validate with hospital pharmacy customers, drawn from previous user feedback and the overall sentiment of the spend analytics space. I partnered with UX Research to conduct a survey on all four: High-Cost Drug Focus, Purchase History, Total Spend Analysis, and Year-over-Year Comparison.
We asked customers to select and rank what they would actually use and what would deliver immediate value, with the option to select none or free-text their own.
Which metrics would customers use?
% of customers who selected each metric (n=46)
Which metric delivers the most immediate value?
% who ranked each metric #1 for immediacy (n=46)
Internal Audit
Checking our own shelves first
Before designing, I audited our internal tools and dashboards to see what could be carried into this project to ensure pattern and brand consistency across products, where applicable.
Many of these customers already use our other dashboards, so reusing established patterns meant they would not have to relearn how things like filters and tables behave, and it gave engineering less to build from scratch. The audit also showed me where this dashboard genuinely needed to diverge from those patterns.
The opportunity
How might we give pharmacy teams a clear view of what and how much they are purchasing across their spending channels?
Goals
The survey validated appetite for the metrics, but it also made clear that shipping the right data was only half the problem. Pharmacy teams still needed to answer three questions once they had it: how do I read it, how deep can I go, how do I narrow it. Each one became a design goal.
Readability
Multiple data points should be visually distinct and consistent everywhere they appear, so a user can isolate one without losing their place in the rest.
Serves Decision 1Depth
The same interface should serve a user who needs a high-level picture and a user who needs line-item details, each at the level of depth that matches their task.
Serves Decision 2Scope
Users should be able to narrow what they see to match how they actually work, without losing the broader context they started from.
Serves Decision 3Early Iteration
Iterating quickly in Figma Make
Before diving into detailed wireframes and mockups, I utilized Figma Make and Figma Agent to quickly put various working concepts in front of key stakeholders for early feedback.
This allowed me to swiftly make updates after each round of iteration in just a few hours, a process that previously would’ve likely taken days.
Key design decisions
Key Decision #1 · Readability
How might a pharmacy team narrow to a single channel without losing context?
Each channel keeps the same color everywhere it appears in the dashboard for consistency: 340B in purple, WAC in blue, and GPO in pink. By default, users can view the total spend across all three channels. The Filter by Spend toggle narrows the graph to a single channel to allow users to focus on specific channel spending.
Key Decision #2 · Depth
How might a pharmacy team be able to move through their own analysis and tailor to their level of detail?
The Spend by therapeutic class chart ranks spend for easy scanning and comparison. The table below expands from therapeutic class to product to account and sorts by spending column.
Key Decision #3 · Scope
How might a pharmacy team with dozens of sites narrow down to the accounts they actually manage?
Account selection is hierarchical rather than a flat list. A large hospital system can select a parent and pull in every site beneath it, or expand and pick individual accounts. The date range filter sits alongside it, covering 3 to 24 months of purchase history.
Reflection
Of the four validated metrics, the three that users felt would provide the most immediate value ultimately made the MVP, in order to keep the scope lean.
The final designs for MVP have been handed off and are currently in development, with a target release in late 2026. Post launch, I would like to explore and include the last metric, the Year-over-Year period comparison and variance callouts, as there is value in allowing users to understand purchasing trends and where they deviate.
