{"id":627,"date":"2026-08-03T13:56:52","date_gmt":"2026-08-03T13:56:52","guid":{"rendered":"https:\/\/torreyowens.com\/staging\/?page_id=627"},"modified":"2026-08-04T01:07:43","modified_gmt":"2026-08-04T01:07:43","slug":"accurate","status":"publish","type":"page","link":"https:\/\/torreyowens.com\/staging\/accurate\/","title":{"rendered":"Accurate"},"content":{"rendered":"\n
\n
\"\"<\/figure>\n\n\n\n
\n
\n
\n

Public Health<\/p>\n<\/div>\n\n\n\n

\n

Service Design<\/p>\n<\/div>\n\n\n\n

\n

Enterprise UX<\/p>\n<\/div>\n<\/div>\n\n\n\n

Modernizing Public Health Data Reconciliation<\/h3>\n\n\n\n

Designed a modern reconciliation platform for the CDC that transformed spreadsheet-driven workflows into a clearer, more scalable experience for state and federal public health teams.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

\n
\n
\n
\n
\n

The Problem<\/h2>\n<\/div>\n\n\n\n

Public health data is only useful if people can trust it.<\/p>\n<\/div>\n\n\n\n

Every day, state public health agencies send disease surveillance data to the CDC. In theory, those records should match. In reality, they often don\u2019t.<\/p>\n\n\n\n

An investigation might begin with something as simple as a case count being off by one. From there, epidemiologists would manually compare spreadsheets, search through multiple systems, and rely on years of institutional knowledge to determine whether the problem was a reporting error, a missing record, or simply two systems interpreting the same data differently.<\/p>\n\n\n\n

The work wasn\u2019t difficult because people lacked expertise. It was difficult because the tools never gave them a clear place to start. Our goal wasn\u2019t simply to build another application. It was to make finding the truth easier.<\/p>\n\n\n\n

\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n\n\n
\n
\n
\n
\n

Why It Was Hard<\/h2>\n<\/div>\n\n\n\n

This wasn\u2019t a software problem. It was an ecosystem problem.<\/p>\n<\/div>\n\n\n\n

One of the biggest lessons I learned on ACCURATE is that complex systems rarely have a single source of truth. Every participating state had slightly different workflows, data quality, reporting schedules, and operational constraints. What looked like \u201cbad data\u201d was often the result of different business rules or legitimate differences in how systems stored information. That meant we couldn\u2019t design for one workflow. We had to design for many.

The challenge wasn\u2019t deciding where to place a button or simplify a screen. It was creating a process that gave users confidence regardless of which state they represented. That required spending far more time understanding how people worked than deciding what the interface should look like.<\/p>\n<\/div>\n\n\n\n

\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n\n\n
\n
\n
\n
\n

My Role<\/h2>\n<\/div>\n\n\n\n

I led UX from discovery through MVP.<\/p>\n<\/div>\n\n\n\n

From the beginning, my responsibility wasn\u2019t just designing screens.

I partnered with CDC stakeholders, epidemiologists, engineers, product managers, and participating states to understand the reconciliation process, identify pain points, and translate complex operational workflows into a product that felt structured and approachable.

My work included:

\u2022 <\/span>\u00a0Planning and facilitating discovery sessions
\u2022 <\/span>\u00a0Interviewing users and subject matter experts
\u2022 <\/span>\u00a0Mapping reconciliation workflows and service blueprints
\u2022 <\/span>\u00a0Creating wireframes and interactive prototypes
\u2022 <\/span>\u00a0Validating designs with CDC and state partners
\u2022 <\/span>\u00a0Working closely with engineering throughout implementation<\/mark>

One thing I\u2019ve learned over the years is that good UX isn\u2019t created in Figma. It\u2019s created through conversations. By the time I started designing interfaces, most of the difficult design decisions had already been made through discovery. The work wasn\u2019t difficult because people lacked expertise. It was difficult because the tools never gave them a clear place to start. Our goal wasn\u2019t simply to build another application. It was to make finding the truth easier.<\/p>\n<\/div>\n<\/div>\n\n\n\n\n\n

\n
\n
\n
\n

How We Got There<\/h2>\n<\/div>\n\n\n\n

We resisted the urge to build first<\/p>\n<\/div>\n\n\n\n

Instead of immediately designing a complete solution, we focused on understanding the entire reconciliation journey. We observed how epidemiologists compared records, documented decision points, identified where uncertainty entered the process, and mapped how information moved between state systems and the CDC.<\/p>\n\n\n\n

One insight quickly became clear. Users didn\u2019t need more information. They needed better context. That realization shaped everything that followed.<\/p>\n\n\n\n


<\/p>\n<\/div>\n\n\n\n

\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n

Rather than designing isolated screens, we designed an end-to-end workflow that guided users from importing data through reviewing discrepancies and ultimately resolving them with confidence. Discovery didn\u2019t validate our assumptions. It replaced them.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n\n\n
\n
\n
\n
\n

Key Design Decisions<\/h2>\n<\/div>\n\n\n\n

Every major decision reduced uncertainty.<\/p>\n<\/div>\n\n\n\n

One idea continued to surface during interviews\u2026

People weren\u2019t asking for more features. They wanted to know where to begin.<\/strong><\/em>

That changed how we approached the product.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Instead of presenting large tables of data immediately, we broke reconciliation into a guided workflow.

\u2022\u00a0\u00a0Users first understood the health of the report
\u2022\u00a0\u00a0Then reviewed discrepancies
\u2022\u00a0\u00a0Then investigated individual cases
\u2022\u00a0\u00a0Then resolved them<\/mark>
<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Wireframes Lo-Fi<\/em><\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Schedule Reports Hi-Fi<\/em><\/p>\n\n\n\n

Every screen answered one question before introducing the next. Looking back, that was probably the most important design decision we made. Not because it simplified the interface. Because it simplified the thinking required to use it.<\/p>\n\n\n<\/div>\n<\/div>\n\n\n\n

\n
\n
\n
\n
\n
\n

The Product<\/h2>\n<\/div>\n\n\n\n

Transforming reconciliation into a guided experience.<\/p>\n<\/div>\n\n\n\n

The final product replaced disconnected spreadsheets with a structured workflow that helped epidemiologists focus on investigating discrepancies instead of finding them. Rather than overwhelming users with thousands of records at once, the experience guided them step by step\u2014from creating a reconciliation report, to identifying discrepancies, to comparing case details, and ultimately documenting a resolution. Each stage provided the right information at the right time, reducing cognitive load and giving users confidence in the decisions they were making.
<\/p>\n<\/div>\n\n\n\n

\n
\n