DHCS California Healthcare Data Visualization

Government healthcare data visualization is the practice of transforming complex public health datasets into interactive dashboards and visual interfaces that allow policy makers, program managers, and public health administrators to identify patterns, disparities, and trends without technical training. It differs from commercial data visualization because the audience is non-technical, the data is often multi-year and multi-dimensional, and the decisions informed by it affect large populations. For DHCS, the visualization system covers beneficiary data spanning 2017 through 2021 across ethnicity, language, age group, county, and delivery system. Each dimension required a different chart type chosen specifically to match how that data is structured and how stakeholders actually need to read it.

A choropleth map is a geographic visualization that uses color gradients to show how a variable changes across defined regions, such as counties or states. It is the right choice when the question being asked is inherently geographic: where are the highest concentrations of beneficiaries, which counties have seen the largest changes over time, and how does distribution vary across the state. For the DHCS beneficiaries by demographics dashboard, a choropleth map of California’s counties was selected because geographic spread is immediately readable from color density. A stakeholder can change the timeframe and watch the color distribution shift across the map, making trend analysis visual rather than numerical. No other chart type answers the question of where as clearly.

A Sankey diagram is a flow visualization that shows how quantities are distributed across multiple categories, with the width of each band representing the proportional size of that group. It is particularly useful when data has multiple simultaneous dimensions that need to be shown together without losing the relationships between them. For the DHCS age group dashboard, a Sankey diagram was chosen because the dataset covers multiple age groups across multiple years, and a standard bar chart would require the user to compare bars mentally rather than seeing the flow directly. The Sankey allows a stakeholder to see both the total size of each age group and how those proportions have shifted over the five-year period in a single view. Fuselab calculated and added an average data point to the chart to give stakeholders a reference baseline for comparison.

Designing for non-technical government stakeholders means the interface must answer a policy question, not present data for analysis. A public health administrator or a program manager comes to the platform with a specific question in mind: which counties have the highest concentration of Spanish-speaking beneficiaries, how has dual eligibility changed over five years, where are the largest gaps in LTSS delivery. The dashboard needs to answer that question without requiring the user to understand the underlying data structure. For DHCS, this shaped every design decision. Hover tooltips add context without cluttering the view. Autofill search, calendar pickers, and pre-selected dropdowns let each user type start working immediately. The interface was designed to require no training, which for a government platform serving hundreds of stakeholders across different departments is a functional requirement, not a preference.

When a design agency handles only the design of a government data platform, there is always a translation risk between what was designed and what gets built. Developers interpreting design specifications without the designer present often make small decisions that cumulatively change the user experience in ways that were not intended. When the same team handles research, design, and development under one engagement, that risk disappears. For DHCS, Fuselab’s scope covers the complete project lifecycle: user research including in-person and remote stakeholder interviews, UX/UI design, and full development and deployment of every dashboard. This means every decision from the initial research finding through to the deployed interface was made by the same team with the same understanding of the stakeholder needs. The result is a platform where the design intent is preserved in the built product.

An ongoing government retainer means the agency is not delivering a single product and stepping away. It is a structured long-term engagement where new dashboards are researched, designed, developed, and deployed on a continuous basis as the client’s data and reporting needs evolve. For DHCS, Fuselab’s retainer is now in its second two-year contract. Several dashboards have been designed, developed, and deployed since the engagement began, and additional dashboards are currently in production. Each new dashboard follows the same research, design, test, and refine process as the first, and uses the same design language and interaction patterns as existing dashboards so the platform grows consistently. For a government agency, a retainer model also means the design team has accumulated deep knowledge of the data, the users, and the reporting requirements over time, which produces better outcomes than starting a new engagement from scratch each time a new dashboard is needed.

User research on a government data project involves understanding how different stakeholder types think about the data, not just how they use the interface. A policy analyst, a program manager, and a public health administrator all access the same DHCS dataset but with different questions and different levels of data familiarity. Research for DHCS included in-person interviews, remote interviews, group interviews, and online surveys across multiple stakeholder types. The findings shaped not only the interface design but also the choice of visualization types for each dashboard. A choropleth map was chosen for geographic data because research showed stakeholders think about beneficiary distribution in terms of counties, not in terms of raw numbers. A Sankey diagram was chosen for age group data because research showed stakeholders needed to see proportional flow across years, not just point-in-time comparisons. The research findings are the reason the visualization choices are specific rather than generic.