Medication Adherence : A Solution for tracking medication adherence during Clinical Trials

Clinical trials suffer from unreliable medication adherence data, particularly for eyedrop medications. Current manual tracking methods are inefficient and error-prone, creating incomplete trial data and adding significant administrative burden to Clinical Research Coordinators (CRCs). The goal of the project was to design a connected eyedrop tracking system for an elderly user base (65+) who are not tech-savvy, while meeting the stringent data requirements of clinical trial protocols.

Project Summary

Context

10XBETA

Timeline

8 weeks (Research & Early Design Phase)

Collaborators

Product Manager, Software Engineer, Creative Technologist

User research, stakeholder interviews, information architecture, early wireframes

Responsibilities

Contributions

I joined the project during the foundational research phase to:

  • Conduct user research with clinical stakeholders and patients

  • Map the clinical trial ecosystem and identify key pain points

  • Define information architecture for patient app and CRC dashboard

  • Create early wireframes establishing product structure

The company used these deliverables as the foundation for their MVP hardware device.

50-60% of patients fail to adhere correctly to eyedrop regimens

Manual data entry consumes 50% of CRC administrative time

23% of trial data is incomplete or inaccurate

Overview

Research Methodologies

Subject Matter Expert Interviews

2 in-depth interviews with clinical research professionals to understand the ecosystem and validate assumptions:

  • Clinical Research Coordinator (CRC): Explored daily workflows, manual data entry processes, patient management challenges across multiple concurrent trials, and observations of common patient adherence issues

  • Principal Investigator (PI): Discussed trial integrity requirements, data quality standards, regulatory compliance needs (HIPAA, FDA 21 CFR Part 11), and decision-making criteria for trial technology adoption

These SME interviews provided critical insights into:

  • Current manual tracking methods and their inefficiencies

  • CRC administrative burden (estimated 2-3 hours daily on data entry)

  • Regulatory and compliance requirements

  • Inconsistent Electronic Data Capture (EDC) system integration needs

User Research & Patient Understanding

Due to regulatory constraints preventing direct patient contact during early research phase, I employed alternative methods to build patient understanding:

  • Clinical studies on medication adherence rates in elderly populations (65+)

  • Usability research on medical devices and IoT products for seniors

  • Accessibility guidelines and age-related capability changes (motor skills, vision, cognition)

  • Statistics on smartphone ownership and internet access in 65+ demographic

SME-Informed Assumptions:

Based on the CRC interview and demographic research, developed validated assumptions about the patient population:

  • Primary demographic: 65+ with varying technology literacy

  • Physical constraints: Mild arthritis affecting bottle handling, vision impairment

  • Cognitive challenges: Short-term memory uncertainty about dose timing

  • Technology access: Limited smartphone ownership (~18%), unreliable WiFi (~35%)

  • Motivations: Strong desire to complete trial successfully, fear of "letting down" research team

Research Synthesis & Key Insights

  1. Memory is the primary barrier - Memory uncertainty drives missed doses and inaccurate self-reporting

  2. Technology must be passive - 70%+ of patients express anxiety about "complicated" devices

  3. Connectivity cannot be assumed - 35% unreliable WiFi, 18% no smartphone ownership

  4. Real-time data is essential - Monthly check-ins too infrequent for timely intervention

  5. Manual data entry is the bottleneck - 2-3 hours daily impacts CRC capacity

Design Recommendations

Based on these insights, I developed recommendations that directly addressed each pain point:

For Patients:

  • Automated tracking eliminating need for manual logging

  • Multi-modal reminders (device + app notifications)

  • Simple visual feedback confirming dose recorded

  • Offline-capable device with local data storage

For Clinical Research Coordinators:

  • Real-time adherence dashboard for all trial participants

  • Alert system for missed doses requiring intervention

  • Automated data export to EDC systems

  • Device health monitoring for proactive troubleshooting

Technical Requirements:

  • Dual connectivity: Primary WiFi + cellular data backup

  • Connection monitoring with patient reminders when offline

  • Local buffering to store dose events when disconnected

  • Automatic sync when connection restored

Design Principles

From research insights, I established core principles to guide interface design:

  1. Invisible Technology - System requires zero active management from users

  2. Graceful Degradation - Must work even when connectivity fails

  3. Accessibility First - Design for limited dexterity and vision

  4. Device-First Design - Core functionality without requiring app

  5. Trust Through Transparency - Clear feedback about what's being tracked

Impact & Outcomes

Research Impact:

  • Validated product-market fit with clinical stakeholders

  • Identified critical design constraints (elderly UX, offline-first architecture)

  • De-risked major assumptions about technology adoption and connectivity

Service Design Impact:

  • Service blueprint became critical alignment artifact for engineering team

  • Defined technical requirements for connectivity, reminders, and data sync

  • Provided clear vision for how frontstage/backstage/support systems integrate

IA & Design Impact:

  • Created scalable structure supporting future features

  • Established clear separation of patient vs. CRC needs

  • Defined MVP scope based on IA complexity analysis

  • Wireframes guided development team and enabled early stakeholder buy-in

The company moved forward with hardware development and higher-fidelity UI design based on these deliverables. Later patient testing validated the assumption-based personas and design decisions.

Key Learnings

What Worked Well

1.Resourceful research within constraints - Using SME expertise and video ethnography as patient proxies yielded actionable insights despite access limitations

2. Service blueprint as alignment tool - Mapping frontstage/backstage/support systems helped engineering understand UX requirements and made connectivity decisions more user-centered

3. Alert-first IA - Structure emerged directly from understanding CRC exception-driven workflow, validated through testing

4. Progressive disclosure strategy - Assumption-based persona development successfully guided interface complexity decisions

What I'd Do Differently

1. Direct patient research earlier - While SME insights were valuable, direct patient validation would have reduced assumptions and uncovered edge cases sooner. I would advocate strongly for at least 5-6 patient interviews even in constrained environments.

2. More diverse SME perspectives - 2 interviews provided solid foundation, but adding a data manager and regulatory specialist would have enriched understanding of backend requirements

3. Prototype testing with proxies - Could have recruited elderly users outside clinical trials (friends/family of team members) for early wireframe feedback to validate assumptions before company testing phase

Want to Learn More?

This summary covers the highlights of a complex, multi-phase design process. Reach out to discuss the full case study, including detailed research findings, design iterations, and lessons learned from bringing hospital-grade care to the road.