REDCap Database Design & Management

What is REDCap?

REDCap (Research Electronic Data Capture) is a secure, HIPAA-compliant data management platform used worldwide for clinical and longitudinal research.

It supports form design, workflow automation, survey logic, audit trails, multi-site collaboration, and complex data capture needs across regulated environments.

Most teams use it at a basic level.

I specialize in pushing REDCap far beyond its defaults—treating it like a true product environment.

How I Approach Database Design

I treat every REDCap build like a product design problem. I start with user research (coordinators, analysts, clinicians), map real workflows, and translate them into intuitive data entry experiences.

I design systems that reduce cognitive load, prevent errors, and scale across multiple sites and years of follow-up.


My goal is always the same: build tools that feel effortless to use and resilient behind the scenes.

Case Study: Multi-Site Cohort Database

SEARCH CVD | SEARCH for Diabetes in Youth

Problem
Teams across three states needed a single, secure platform to collect and integrate longitudinal clinical, survey, and biospecimen data.

My Role
Lead systems architect, UX designer, and data operations coordinator.

What I Built

  • 70+ instruments

  • Longitudinal event structure with automated windows

  • Integrated contact logs, alerts, and QA workflows

  • Custom HTML tables for lab values

  • Cross-site permission layers

  • QC and discrepancy reporting

  • Import frameworks for legacy Access data

Impact

  • Reduced data errors by 30%

  • Cut coordinator time per participant by 25%

  • Enabled automated reporting to PI teams

  • Improved consistency across three states

Code Snippet 

<table style="border-collapse: collapse; width: 99.9489%; height: 34px;" border="1"><colgroup><col style="width: 26.6809%;"><col style="width: 9.79244%;"><col style="width: 22.2822%;"><col style="width: 41.1558%;"></colgroup>
<tbody>
<tr style="height: 24px;">
<td style="text-align: center; background-color: #d4c6e6; border-style: hidden;"><span style="font-family: georgia, palatino; font-weight: normal;">BMI (<em>optional)</em></span></td>
<td style="text-align: center; background-color: #d4c6e6; border-style: hidden;" colspan="2"><span style="font-family: georgia, palatino; font-weight: normal;">Calculated BMI</span></td>
<td style="text-align: center; background-color: #f8cac6; border-style: hidden;"><span style="font-family: georgia, palatino; font-weight: normal;"><em>BMI flag<span style="font-size: 8pt;"> </span></em><span style="font-size: 8pt;"><em>(overweight </em>≥<em>25-29.9, obese </em>≥30)</span></span></td>
</tr>
<tr style="height: 10px;">
<td style="text-align: center; border-style: hidden;">
<div style="display: inline-flex; align-items: center;"><span style="display: inline-block;">{cvd_mra_bmi_1}</span> kg/m<sup>2</sup></div>
</td>
<td style="background-color: #afaab5; border-style: hidden; text-align: left;">BMI (US)</td>
<td style="background-color: #afaab5; border-style: hidden; text-align: left;">{cvd_mra_ibmi_calc}</td>
<td style="text-align: center; border-style: hidden;">
<p>{cvd_mra_bmiflag}{cvd_mra_bmiflag_3}</p>
</td>
</tr>
</tbody>
</table>

What I Build

  • End-to-end research databases for multi-year cohort studies

  • Complex form logic (branching, validation, calculated fields)

  • Automated workflows for recruitment, scheduling, and follow-up

  • APIs and integrations to move data between systems securely

  • Interactive dashboards for teams across sites and roles

  • UX-optimized layouts in a tool that is famously rigid

  • Data quality rules for multi-step validation and consistency

  • Longitudinal event structures spanning clinic visits, surveys, and biospecimen tracking

  • Custom HTML/CSS enhancements to improve clarity and usability

  • Multi-site permissions architecture for users, coordinators, and analysts

  • Data dictionaries, SOPs, and governance models

  • Migration frameworks to bring legacy Access or Excel data into REDCap

Logo Design

Data Dictionary

No PHI is present in the above materials*

Data Management & Quality Engineering

I oversee the entire lifecycle of research data across complex, multi-site environments by modeling structures, enforcing data quality, and ensuring every user has reliable, analysis-ready information.

Beyond form-building, I design scalable data architectures, maintain production systems, and run validation pipelines aligned with modern data engineering standards.

What This Looks Like in Practice

  • Data Model Architecture

    • Designing relational data structures across longitudinal events, linked instruments, and multi-system workflows. Ensures consistency, scalability, and clean downstream analytics.

  • QA/QC Pipelines

    • Writing SAS programs to automate discrepancy checks, validate derived variables, detect drift or inconsistencies, and generate automated exception reports.

  • System Maintenance & Governance

    • Performing routine database reviews, resolving errors, maintaining dictionaries, and ensuring compliance with regulatory standards.

  • Integration & Interoperability

    • Supporting API connections, synthetic data imports, and secure export pipelines for analysts and collaborators.

  • Documentation & Reproducibility

    • Creating data dictionaries, SOPs, codebooks, and reproducible scripts for transparent, scalable use across teams.

Data Visualization & Operational Tracking

What This Includes

  • Operational Dashboards

    • Building dashboards (in REDCap, R, SAS, Notion, or custom formats) that track recruitment, retention, biospecimen workflows, protocol adherence, and progress against milestones.
      (Translates to: KPI dashboards, BI tools, performance tracking.)

  • Real-Time Monitoring

    • Creating systems that automatically flag delays, missing data, follow-up requirements, or protocol deviations—helping teams stay ahead of problems instead of reacting to them.
      (Translates to: alerting systems, pipeline monitoring, proactive risk management.)

  • Analytics for Decision-Making

    • Using SAS, R, SQL, and visualization techniques to translate raw data into reports that inform planning, resource allocation, and strategic adjustments.
      (Translates to: product analytics, operational analytics, performance insights.)

  • Progress Tracking & Milestone Forecasting

    • Developing tools to track multi-site timelines, estimate completion trajectories, and ensure deliverables remain on schedule.
      (Translates to: roadmap tracking, delivery management, technical PM skills.)

  • Stakeholder Communication

    • Distilling complex datasets into clean, intuitive visuals that support PI teams, coordinators, leadership groups, and external partners.
      (Translates to: executive reporting, cross-functional communication, data storytelling.)

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Photography