Enhancing User Experience in Health Research Platforms

Boomers can’t navigate patient portals. Gen Z won’t tolerate outdated tech. Healthcare leaders: When was the last time you experienced your patient portal through the eyes of a confused 70-year-old or an impatient 20-year-old? The digital divide isn’t a patient problem – it’s a UX design problem. Here’s why: 𝗕𝗼𝗼𝗺𝗲𝗿𝘀: • Less than 30% actually use portal features, despite 90% of providers offering them.[1,2] • Clunky interfaces, tiny fonts, confusing menus that no glasses can translate. • Digital confusion and trust issues prevent seniors from accessing critical care. 𝗚𝗲𝗻 𝗭: • Zero tolerance for slow, outdated UX. • Quick to abandon clumsy apps and portals. • Expect seamless experiences like Instagram or Netflix. Anything less pushes them away. The good news? We can bridge the gap for both groups. Here’s how: ✅ 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀: Mobile-first designs, minimal clutter, clear language, intuitive navigation. ✅ 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Multi-modal UX + AI (voice, video, chat) that meets users where they are – whether 25 or 75 years old. ✅ 𝗥𝗲𝗮𝗹 𝗨𝘀𝗲𝗿 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Involve Boomers, Gen Z, and everyone in between to co-design solutions that actually work. ✅ 𝗔𝗜 𝗗𝗼𝗻𝗲 𝗥𝗶𝗴𝗵𝘁: Use AI to personalize and simplify tasks, not complicate them. Make it a UX ally, not a gimmick. The bottom line? Great UX bridges generational divides. Bad UX creates them. – What’s your best (or worst!) example of generational UX in healthcare? Let’s design digital healthcare that works for everyone – not just some imaginary “average” user. - Sources: (1) Pymnts https://lnkd.in/gUuQKkZj (2) Medsphere https://lnkd.in/gBWvRGFm

Traditional UX Analytics tell us what happened - users clicked here, spent X minutes, and fell somewhere on the way. But they do not tell us why. Why did a user leave a process? Why did he hesitate before completing the action? This is where the hidden Markov model (HMM) comes. Instead of tracking only surface-level metrics, HMMs expose hidden users, showing how people infection between engagement, hesitation and frustration. With this, we can predict the drop -off before it is - a game changer for UX optimization. Take a health-tracking app. Standard analytics may show: - Some users log smooth data. - Browse without completing other tasks. - Repeat the data again and again before leaving anything. Standard matrix cannot tell us what users are experiencing. HMMs fill the difference that shows how users infection between states over time. By monitoring sessions, clicks and drop-offs, classify HMM users: - Moving → Smarting through tasks. - Search → Click around but not to complete the actions. - Disappointed → hesitation, possibility of repeating steps, leaving. Instead of reacting to the drop-off, teams may see the initial signals of disappointment and intervention. HMMs predict behavior, making UX research active: - Personal onboarding → finds out that users require help. - Hoosier A/B test → explains why a design works better. - Preemptive UI fix → identifies friction before leaving users. Blending qualitative insights with HMM-driven modeling gives a fuller picture of user experience. Traditional UX reacts to problems after research problems. HMM estimates issues, helping teams to customize experiences before despair set. As UX becomes more complex, tracking click is not enough - we need to understand the behavior pattern

💬 Last November I had a call with the CEO of an emerging health platform. She sounded very concerned -- "Our growth's hit a wall. We've put so much into this site, but we're running out of money and time. A big makeover isn’t an option, we need smart, quick fixes." Looking at the numbers, I noticed: ✅ Strong interest during initial signups. ❌ Many users gave up after trying it just a few times. ❌ Users reported that the site was too complicated. ❌ Some of the key features weren’t getting used at all. Operating within the startup’s tight constraints of time and budget, we decided on the immediate plan of actions-- 👉 Prioritized impactful features: We spotlighted "the best parts". Pushed secondary features to the backdrop. 👉  Rethought onboarding: Incorporated principles from Fogg's behavioral model: • Highlighted immediate benefits and rewards of using the platform (motivation) • Simplified tasks, breaking down the onboarding into easy steps (ability) • Nudged users with timely prompts to explore key features right off the bat (triggers)    👉 Pushed for community-driven growth: With budget constraints in mind, we prioritized building an organic community hub. Real stories, shared challenges, and peer-to-peer support turned users into brand evangelists, driving word-of-mouth growth. 👉  Started treating feedback as "currency": In a tight budget scenario, user feedback was gold. An iterative approach was adopted where user suggestions were rapidly integrated, amplifying trust and making users feel an important part of the platform's journey. In a few months time, the transformation was evident. The startup, once fighting for user retention, now had a dedicated user base, championing its vision and propelling its growth! 🛠  In the startup world, it's not just about quick fixes, but finding the right ones. ↳ A good UXer can show where to look. #ux #startupux #designforbehaviorchange