Overview
Coordinators track how their study is going. It just lives in spreadsheets, on paper, and in their heads.
Research studies across universities, hospital systems, and institutions like the VA are managed by clinical research coordinators or CRCs. They make sure participants show up for everything a study asks of them, from a check-up to every piece of follow-up paperwork, and they communicate with care at each step. They track participation to ensure that every participant is compensated fairly and has a comfortable experience. The work takes as much patience and care for the people involved as it does organization.
During my internship at Emory, I worked on Fabla, a clinical research platform where that tracking didn’t yet have a home. Coordinators had built their own: spreadsheets, paper, several separate tools, and a running mental picture of who was on track.
Solution
A dashboard that shows coordinators where each participant stands in a study, and how their payment adds up.
A note for context:
Adherence is whether participants are completing what a study asks of them. Compensation is what they’ve earned for it.
cohort overview
Every participant at a glance: entries completed against what’s expected, split by the study’s own phases, with a notify button on each row.
participant detail
One participant’s onboarding answers, notification settings, adherence, and compensation in a single record.
adherence
Daily activity across every phase a participant is in, with overlapping phases and fully missed days visible at a glance and a hover for detail.
compensation
Earnings broken down by the pay rules set for the study. Toggle phases on or off, and use the rate calculator to trace a number that looks off.
interested in the process behind this solution? keep scrolling :)
problem discovery
Knowing what a participant submitted isn’t the same as knowing whether it fits what the study asked for, or what they’re owed for it.
Fabla is a research platform for ecological momentary assessment (EMA) and daily diary studies, where participants record short entries and can respond to a range of survey questions. The team was building a researcher dashboard so coordinators and researchers could configure and launch studies and monitor participation without relying as heavily on the team that runs Fabla. At the time, that dashboard had no view of adherence or compensation. The closest thing was a standalone data toolkit that cleaned raw response exports, and it became my starting point.
user research
Landscape
I started with a competitive analysis of the tools researchers already use: REDCap, Centralive, m-Path, ilumivu, and ExpiWell. REDCap came up most. It’s highly customizable and carries deep institutional trust, but none of the tools had built-in guidance for adherence, compensation, or onboarding. Teams patch those gaps with other tools and workarounds.
I then mapped the coordinator journey twice: once as it works today, and once assuming the Fabla team is out of the loop, which is the goal of a self-serve dashboard. The second map showed where the friction sits. IRB and compliance work is heavy and manual, and it repeats every time a study changes.
talking to coordinators
I interviewed five coordinators, and most screen-shared to walk me through their process from start to finish, including their workarounds, their separate spreadsheets, and the issues they ran into. That let me see problems as they happened instead of relying on memory. I coded the interviews and mapped what came up into two themes:
Tool Sprawl:
Coordinators stitched together spreadsheets, calendars, other data collection platforms, and the existing toolkit to track a single study. The existing toolkit didn’t reach everyone - one coordinator’s institution even blocked it outright.
Scattered compensation:
Each coordinator’s picture of a study lived in their own files and their own head, which made it hard to hand off.
Each coordinator had built their own system to answer two questions:
Who is keeping up and what are they owed?
pain points
Scattered compensation:
Working out what each participant was owed meant pulling numbers from several tools.
Knowledge bottleneck:
Because the tracking lived in personal files, a study’s status was hard to hand off.
Design Process
mistakes I made
I started with two assumptions that didn’t hold. The first was that building this into the dashboard meant data could flow in automatically and the CSV upload could go. HIPAA and IRB rules keep CSV upload as the only way data comes in, for now, so the brief became an upload flow clear enough that a coordinator would choose it over their own spreadsheet. The second was that one way of showing adherence could fit every study.
reading seven studies
So I went back to the protocols - I compared how seven existing Fabla studies tracked adherence and paid participants, with each study’s length, tracking style, and full pay formula side by side. Adherence was count-based in every one: did they submit enough entries, never did they submit on time. Because each study’s phases and cadence differ, I organized adherence around the study’s own phases instead of a flat status list. The differences came down to four variables: how often entries are expected, how the rate changes over time, the entry type when a study has more than one, and time of day.
Coordinators also trust their own systems first, so a number only helps if they can see why it’s right. With too little detail they won’t trust it, and with too much it overwhelms them. That left me with a sharper problem statement:
How might we balance visual simplicity with the amount of detail CRCs actually need to trust a number?
insights → Design Decisions
The design had to fit how coordinators already think, not teach them a new system:
Adherence meant submitting enough entries, not just submitting on time.
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Adherence counts submissions with the additional context of a schedule.
A study’s rhythm, its phases and cadence, is what changes from one study to the next.
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Adherence is laid out across each study’s own phases, not as a flat status list.
No single equation covers how studies pay: rates scale, bonuses vary, thresholds differ by entry type.
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Compensation follows the pay rules set for each study, not a one-size calculator.
A total alone isn’t actionable - the context surrounding it is what matters.
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Compensation shows progress toward each threshold in plain language.
Coordinators trust their own systems first.
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Summary by default, phase toggles, hover detail, and a calculator to trace a number that looks off.
HIPAA and IRB rules keep CSV upload as the only input, and keep contact details off screen.
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A clear upload step up front, and a notify button on each row that goes through notification settings.
Final Design
