I was the lead UX/UI designer on the Eye-Drop Reminder project.  This project aimed to tackle the common problem of medication non-adherence among individuals requiring regular eye drops, particularly the elderly and post-surgery patients. We sought to create a system that would improve their adherence through a combination of smart reminders, usage tracking, and even sample collection for diagnostic purposes. This case study outlines our design process, challenges faced, and the positive outcomes achieved.

Tools Used: Figma, Adobe XD, Miro, UserTesting.com

Many patients, especially the elderly, struggle to remember to take their eye drops on schedule. This leads to inconsistent treatment, potentially worsening their condition.  Through initial user interviews and surveys with ophthalmologists and patients, we found that forgetfulness, dexterity issues, and a lack of clear feedback on medication adherence were significant pain points. Solving this problem was crucial to improving patient outcomes and reducing the burden on caregivers.

Our primary objective was to design a user-friendly system that increased eye drop adherence.  Our success metrics included: a 20% increase in adherence rate (measured through usage tracking and app data), a reduction in missed doses by 15%, positive user feedback (NPS score above 80), and successful integration with a connected eyedropper for automated sample collection in 80% of tests.

We conducted in-depth interviews with 20 patients (10 elderly, 10 post-surgery), caregivers, and ophthalmologists.  Surveys and contextual inquiry were also conducted. We created detailed user personas for different age groups and technological comfort levels, reflecting their needs and challenges. This research highlighted the need for clear, simple instructions, large font sizes, customizable reminders (haptic, auditory, visual), and caregiver access to patient data.

Several concepts were explored, from simple reminder apps to integrated hardware solutions.  We opted for a connected eyedropper with a companion app that provided scheduling, alerts, usage tracking, and data visualization.  This allowed us to address the challenges of both remembering and accurately administering the drops.  Early prototypes tested various interaction models and reminder types to determine the optimal balance between usability and effectiveness.  Feedback from user testing shaped every aspect of the design.

We started with low-fidelity wireframes and moved to high-fidelity prototypes in Figma and Adobe XD.  Our design prioritized simplicity and ease of use, focusing on clear visual communication.  We incorporated large buttons, intuitive icons, and high contrast for improved accessibility.  Prototypes were rigorously tested with our target users, and the feedback informed each iteration of the design.  For the hardware, we focused on creating an ergonomic and comfortable eyedropper that integrated seamlessly with the software.

A key challenge was ensuring accurate usage tracking.  We overcame this through a combination of sensors that could detect both the opening of the bottle and the dispensing of the drops.  Another challenge was creating reliable connectivity between the eyedropper and the app.  This involved optimizing Bluetooth communication and employing robust error handling. We addressed privacy concerns by designing a system with strong data encryption and user control over data sharing.

The final Eye-Drop Reminder system comprises a smart eyedropper and a user-friendly mobile app.  The app allows users to easily schedule reminders, track their medication history, and view graphs to show their adherence patterns.  Caregivers can also access this data to monitor the patient's progress.  Early testing showed a 25% increase in adherence rates, a 17% decrease in missed doses, and very high user satisfaction ratings.  The system effectively combined technological advancements with a deep understanding of user needs.

This project highlighted the importance of incorporating human-centered design principles throughout the entire design process.  The iterative nature of design was critical in refining the solution based on continuous feedback.  I learned the value of collaboration with medical professionals to ensure accuracy and meet the specific requirements of the application. I’d emphasize even more rigorous early-stage testing if I were to do it again.

The Eye-Drop Reminder has proven to be a successful solution to a real-world healthcare problem.  By combining smart technology with empathetic design, we created a system that significantly improves medication adherence and enhances the quality of life for patients requiring regular eye drops.  Future work will focus on expanding the system's capabilities, integrating with electronic health records, and exploring integration with other chronic disease management platforms.

Eye-Drop Reminder: A Smart Solution for Medication AdherenceImproving medication adherence for eye drops through a smart, user-friendly system with integrated reminders and usage tracking.

Dropter: Alfaleus Tech

As a UX/UI designer at Lenek, I spearheaded the design and development of the software interface for LeXIS, an ultraportable X-ray imaging system.  Our goal was to address the critical issue of late-stage diagnosis of lung diseases like tuberculosis, pneumonia, pneumothorax, and pleural effusion in rural India.  The project aimed to facilitate early detection and intervention through a user-friendly, AI-integrated system easily deployable in remote areas.

Millions in rural India lack access to timely lung disease diagnosis, leading to delayed treatment and poorer outcomes.  Existing healthcare infrastructure is often inadequate, and patients frequently present with advanced disease stages.  This necessitates a solution that is portable, affordable, easy-to-use, and provides accurate diagnostics at the point of care.

Our primary objectives were to design a user interface that:

1. Was intuitive and easy to use for operators with varying levels of technical expertise.
2. Successfully integrated AI-driven diagnostic capabilities for faster and more accurate results.
3. Streamlined the workflow for X-ray examinations and data management.
4. Provided clear and easily understandable results, minimizing misinterpretations.
5. Was adaptable to various settings and user needs.

We conducted user interviews with potential operators (healthcare workers, community health volunteers) and radiologists to understand their needs and challenges.  Surveys and focus groups provided additional insight into user preferences and pain points.  This informed the creation of user personas representing various skill levels and experience.

Initial ideation involved brainstorming sessions and sketching potential interface designs. We explored various approaches to information architecture, navigation, and AI integration.  Early prototypes were created and iteratively refined based on user feedback from usability tests. We prioritized a clean and uncluttered interface to ensure ease of use, even under challenging field conditions.

Wireframes were developed to outline the information hierarchy and user flow.  We used Figma to create interactive prototypes, testing various design solutions with target users.  The final visual design incorporated a clear and legible typeface, a color palette that improved readability, and intuitive icons. User testing ensured the interface was accessible and efficient for all users.

A primary challenge was balancing simplicity with functionality. We needed to create an interface that was easy to use for non-technical operators yet powerful enough to integrate advanced AI features. We addressed this by using clear visual cues, concise instructions, and well-organized information layouts. Integrating AI seamlessly into the workflow was also challenging.  This involved careful consideration of data flow, feedback mechanisms, and result presentation.

The final design is a clean, intuitive, and efficient interface that streamlines the entire X-ray process.   Key features include dedicated buttons for different body parts, a preview mode for quick image review, and a clear presentation of AI-generated diagnostic results. The system’s modular design caters to diverse user skills and allows for easy updates and maintenance.

This project highlighted the importance of user-centered design, particularly when dealing with complex technologies in resource-constrained environments.  Thorough user research and iterative testing proved crucial in developing a successful and impactful solution. I learned the value of close collaboration with engineers and AI specialists to seamlessly integrate advanced features into a user-friendly interface.  In future projects, I would dedicate more time to user training materials and incorporate more comprehensive feedback mechanisms.

Lenek, with its intuitive software interface, holds the potential to revolutionize lung disease diagnosis in rural India.  By empowering healthcare workers with advanced diagnostic tools and simplifying complex procedures, the system strives to improve healthcare access, reduce diagnostic delays, and ultimately save lives. Future plans include expanding AI capabilities, refining the interface based on user feedback, and ensuring wider adoption across various regions.

Designing a user-friendly, AI-powered X-ray interface for early detection of lung diseases in remote Indian communities.

Lenek's LeXIS: Lung Disease Diagnosis in Rural India

Sankalp’s attention to detail and ability to balance aesthetics with usability stood out in every project we collaborated on.

Aisha Verma

Sankalp’s design decisions always connect back to the user journey — crisp, intuitive, and well-reasoned.

Dropter: Alfaleus Tech

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