👁️ Glaucoma Decision Support Dashboard
Research and information architecture project for a tool that helps non-specialists assess glaucoma risk and reduce unnecessary referrals.
Overview
Glaucoma is a leading cause of irreversible blindness, yet non-expert clinicians often refer patients to specialists unnecessarily, overloading them and delaying care for high-risk cases. This project shaped the Glaucoma Decision Support Dashboard, a tool embedded in existing products to help non-specialists make accurate, data-driven referral decisions.
I led research and information architecture, mapping workflows, defining decision logic, and structuring clinical data to support fast, confident assessments. This work was part of the global Shared Care initiative to improve data exchange and co-management across diverse eye-care settings.
Project Goals
Equip non-specialists with clear, data-driven glaucoma risk insights after a single patient visit.
Shorten decision-making time when assessing glaucoma risk.
Boost clinician confidence in risk assessment outcomes.
Standardize referral criteria and decision processes across providers.
Improve accuracy of referrals to glaucoma specialists, reducing unnecessary escalations.
Research Objectives
Map current glaucoma assessment workflows and referral practices.
Identify and categorize key user types in the referral process.
Uncover pain points, goals, and motivations for each user group.
Synthesize user feedback into actionable insights for product direction.
Process
Participants
I iterviewed ODs and MDs from diverse clinical settings, including private optometric centers, retail vision chains (e.g., Costco), colleges of optometry, and specialty hospitals to capture a broad range of perspectives.
Interviews
I conducted remote, two-week interview series with open-ended questions on workflows, referral patterns, challenges, and success stories. Explored each participant’s approach to screening, diagnosing, managing, and treating glaucoma.
Analysis
I captured and documented all responses, then applied affinity diagramming to group insights into thematic clusters. Translated each finding into clear conclusions and actionable recommendations for the product team.
Research Findings
Progression Is Essential
Across all participants, one point was unanimous: except in severe cases, glaucoma diagnosis requires multiple visits to confirm findings and rule out errors.
A single visit often lacks diagnostic reliability due to:
Poor image quality
Artifacts (e.g., dust, eyelashes)
Outlier results relative to reference databases
Eye movement during acquisition
Patient errors during initial Visual Field testing
OCT Images Are Crucial
Participants agreed that high-quality OCT images are essential for accurately assessing glaucoma risk.
Key points from interviews:
Clinicians prefer testing on devices they know well to avoid interpretation errors from unfamiliar report formats.
Half noted a gap in educational resources from OCT manufacturers, particularly guidance on reading reports and interpreting images.
Irrelevant Referrals
Specialists reported that at least half of glaucoma referrals to surgeons could have been managed in optometry practices. Common drivers of unnecessary referrals include:
Stable glaucoma patients already managed on medication.
Early-stage cases referred by less experienced clinicians seeking confirmation.
Benign anomalies or poor-quality scans misinterpreted as pathology.
Providers lacking time or interest in glaucoma management.
A cultural tendency to default to specialist referral.
Relevant Referrals
Specialists agreed that a strong glaucoma referral pairs OCT evidence of nerve damage with a matching Visual Field defect. Appropriate cases include:
Advanced disease not manageable with noninvasive treatment.
Patients on noninvasive therapy who may require laser or surgery.
High-probability glaucoma cases (90%+) beyond the referrer’s expertise.
Patients already blind in one eye.
User Categories
Analysis of interview data revealed three key user groups driving glaucoma referrals. Each group has distinct motivations, pain points and goals, as well as varying levels of technical capability to screen and manage glaucoma patients.
Journey Mapping
Current Referral Flow
To better understand referral patterns, I mapped the current glaucoma referral flow for each user category. This visualization revealed key decision points, bottlenecks, and inconsistencies, highlighting where the Glaucoma Decision Dashboard could streamline workflows, reduce unnecessary referrals, and improve diagnostic confidence.
Improved Referral Flow
Next, I mapped an optimized referral journey, reimagining how decisions could be made if every user had access to a shared decision-support tool. For each decision point, we identified the minimum clinical data needed to reduce uncertainty, support accurate diagnosis, and prevent unnecessary specialist referrals.
Feature Ideas
To bridge research insights into actionable design, I defined a set of high-level feature concepts, each mapped to the specific user group it would serve. This ensured early alignment between potential solutions and the distinct needs, goals, and workflows uncovered during research.
Referral Flow
For the first design iteration, I mapped a user flow for GL Non-Experts integrating the Glaucoma Decision Dashboard into their screening and management process.
In the legacy workflow, doctors could either close the patient chart or refer to a specialist immediately after evaluation, often leading to premature referrals. To address this, I introduced decision checkpoints that prompt doctors to:
Schedule a follow-up to capture disease progression data
Seek a second opinion from another practitioner before referring
These guided steps aim to reduce unnecessary referrals while preserving clinical confidence.
Next Steps
Before moving into design, we met with key opinion leaders to define which data points should be collected during glaucoma evaluations. While factors like age, family history, and related conditions were clear, the exact combination and presentation needed validation.
With their input, we turned research insights into low-fidelity prototypes exploring how the dashboard could collect, analyze, and visualize data in real time. These concepts are now in iteration, informed by direct user feedback.
Reflection
This project showed how upfront research and solid information architecture can shape high-impact clinical tools. Mapping referral workflows, pinpointing pain points, and tying features directly to user needs gave us a clear path to cut unnecessary referrals and boost diagnostic accuracy.
The work is already shaping product strategy within the “Shared Care” initiative. Once live, the Glaucoma Decision Dashboard has the potential to be a go-to resource for clinicians, helping them make faster, more confident calls and improving outcomes for patients.