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  • NEW FEATURE

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Medikou

Category

Payment processing

Year

2022

Location

San Francisco, CA

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🌐 Overview

🌐 Context

In the context of a daunting Japanese health system offering 180,000 clinics, our task was to develop Medikou to assist young Japanese citizens in finding the most suitable clinics. Leveraging data-driven user interviews, prioritization mapping, rapid prototyping, and A/B testing, our aim was to enhance user satisfaction, targeting an NPS of 7. We measured the impact of our designs on clinic discovery, focusing on improving user interaction and satisfaction.

The overwelming Japanese health system offers 180,000 clinics that cater to local Japanese residents and expats, our task was to assist young Japanese citizens in finding the most suitable clinics for their unique needs. Leveraging data-driven user interviews, prioritization mapping, rapid prototyping, and A/B testing, our aim was to build a solution from 0 that enhances user satisfaction, using NPS 7 as a benchmark. We measured the impact of our designs on clinic discovery, focusing on improving user interaction and satisfaction.

⚠️ Challenge

Time & Stress involved in finding clinics in Japan

In 2021, users had access to over 180k clinic offering diverse healthcare services. However, despite this abundance, the average 12-minute engagement on clinic websites often coincided with a notable churn rate, indicating users' inclination towards exploring alternative options amidst the extensive choices available.

Key Takeaway

Patients expressed frustration with the current clinic discovery process. As the user, they feel physically burdened by the task of manually filtering through 1M+ search results to find a suitable clinic that can address their needs.

📑 Research

Understanding Clinic Search Patterns in Japan

To identify target users and their pain points, we structured research interviews after reviewing a medical publication that surveyed 1000 participants nationwide regarding existing patient-client experiences. Our task encompassed understanding user behavior through 20 interviews, understanding their mental models for search flow, information prioritization, and decision-making during clinic discovery. This enabled us to discern patterns and pain points crucial for effective solution design. Consequently, this approach facilitated a deeper understanding of our target users and their needs, enabling tailored design solutions to address their specific challenges effectively.

Key Insights

Young adults (age 22-35) were the ideal user segment due to their digital savviness and willingness to be early adopters of technology. They valued convenience and efficiency, making them prime candidates for digital solutions. As active healthcare consumers, they seek tailored experiences, making them strategic targets for digital product design in technology-driven contexts.

How might we relieve our patients of the mental burden of navigating clinic search?

🧠 Ideation

Translating our findings into opportunities

Using our collected insights, we aimed to design and test features for our MVP with mid-fi prototypes. To enhance user experience in clinic search and selection, we prioritized core features.

Based on data showing the importance of hospital reputation, we created two variations to represent credibility and increased visibility of well-reviewed clinics to boost user confidence. Research on user search patterns led us to design a home-screen with selectable preferences (clinic type, location, availability) and a standard search bar for symptoms and location input. Insights on clinic availability drove the design to highlight essential booking information (services, address, websites) within the appointment booking flow.

These prioritized features, tested through A/B testing, informed iterative design improvements, ensuring the MVP met user needs effectively.

🔁 Iteration

Using Data to lead our Design Decisions

In order to quickly evaluate high priority deliverables within a short timeframe, we went through 4 prototyping iterations and 12 A/B testing sessions.


Testing the limits of information capacity
Iterating on clinic cards were prioritized as they reflect key details such as rating, location and availability that guide users to appointment booking. After user testing, we found 80% of users weren’t convinced by rating score alone and sought out review counts to support the rating validity.

Factoring the situation at hand when designing
We deprioritized presenting clinic cards early in the search flow as “recommended clinics” to increase booking rates because 90% of users did not trust the suggestions due to the timing within the task flow.

Booking with clinics was not feasible
Based on feedback from 8 clinics in Tokyo, we deprioritized the appointment booking flow. Recognizing the varying appointment management software across Japanese medical facilities, we focused on designing solutions that provided users with the necessary information to contact and book directly with clinics for the development phase.

🎯 Outcomes

Goals were met for MVP

The success indicator exceeded our goal, achieving an NPS of 8 versus the initial target of 7.

Results showed a 20% increase in onboarding completion, a 40% reduction in clinic search time, and higher engagement with filter criteria addressing user needs

Want to get in touch?

Aaron

Portfolio 2024