Understanding User Experience

⏳ Designing Better Loading and Progress Indicators UX. Practical UX guidelines to reduce the impact of waiting and choose the right loading indicator based on anticipated wait time ↓ ✅ Perception of wait time is more important than its duration. 🤔 Users overestimate passive waiting (standing still) by 36%. ✅ Active waiting (walking, interacting) feels much shorter. ✅ 20% rule: users only notice speed changes of at least 20%. 🤔 Small optimizations (e.g. shaving 0.2s off 5s) go unnoticed. ✅ 2 questions: "How much longer?" and "Is it working?" 🚫 Don’t use any loading indicators for waiting times < 1s. ✅ Short wait times (1–3s): use skeleton screens or spinners. ✅ Medium wait times (3–10s): use progress bars or indicators. ✅ Long wait time (10+s): show progress and allow interaction. 🤔 Uncertainty makes waiting feel significantly longer. ✅ Explain to users what’s happening in the background. ✅ Optimistic UI: ask for next steps while procees is running. ✅ The more valuable the reward, the longer tolerance to wait. ✅ Aim for improving perceived speed with reduced passive wait. Often we can’t speed up interactions for technical reasons. But we can reduce the perceived waiting time, which is often way more important than the actual duration. When a UI visualizes progress, users accept longer waits because they have right expectations and can track progress ((Buell & Norton, 2011). People are impatient if they don’t know how long to wait. Waiting without any explanation (spinning circle) feels longer than one where the product says why it’s busy. Also, waiting to START a task feels longer than waiting for a task to FINISH, so early start helps reduce frustrations as well. Users also tend to be highly sensitive to “queue jumping”. If a process they started later finishes earlier than a previous one, it creates significant frustration and abandonment. In the end, it’s all about setting right expectations, explaining what happens frequently and keeping people busy when waiting. It might not necessarily help make the application faster, but it will make it feel faster — and it could be enough to keep users on the page for just a little bit longer, and drive them to success from there. – ✤ Useful resources: Perceived Performance (Series), by Denys Mishunov https://lnkd.in/dvVkt3r3 Loading and Progress Indicators UX, by Taras Bakusevych https://lnkd.in/e5KFPiiq ↓

The most fascinating piece of research I've read this week: Anthropic's study on "disempowerment patterns" in AI—cases where AI distorts users' beliefs, values, or actions rather than informing them. Two findings stand out: 1️⃣ Users actively seek these outcomes—asking "what should I do?" and accepting answers without pushback. The disempowerment comes not from AI overriding human agency, but from people voluntarily ceding it 🤯 2️⃣ Users rate these harmful interactions more favorably in the moment. Satisfaction only drops after they've acted on the advice 🤦♀️ We've seen 1️⃣ in productivity software before. Features that empower sophisticated users can create mindless dependency in others. The difference lies in whether the tool teaches you the "why" or just handles the "what". The most empowering products build capability over time, not just provide quick hacks for grunt work. We've also seen 2️⃣ before. For a decade, social platforms optimized for engagement that users validated through clicks and shares. Content that felt compelling in the moment—validation, tribal reinforcement—often left people worse off. The gap between what users wanted and what was good for them became a central tension. Designing AI products and experiences continues to be an incredibly creative and human endeavor... https://lnkd.in/g8K7A_4P

Have you ever abandoned a purchase because the button was too small? Neither have your prospects. People only ever drop out for 3 reasons: 1. They don’t understand what you do – Comprehension 2. They understand but don’t care – Urgency 3. They get it, they care, but they don’t believe you – Trust In that order. Here’s how to diagnose and fix each of those 3 gaps: 𝗚𝗮𝗽 𝟭: 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝗼𝗻 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲: Show your headline to a prospect for 5 seconds. Ask them “What does that mean?” Repeat 5 times. If nobody gets it, rewrite. 𝗙𝗶𝘅: You’ve got 5 words + one image to demonstrate how you can help them. If your headline talks about your product, you’re asking them to guess. If your headline talks about their challenges and goals, you’re on the right track. 𝗚𝗮𝗽 𝟮: 𝗨𝗿𝗴𝗲𝗻𝗰𝘆 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲: This usually shows up as poor sales conversion, or an activation problem. Prospects will say they’re “too busy,” which means you’re not a top-3 priority. 𝗙𝗶𝘅: You can boost urgency with simple tactics like time-limited discounts, or poking at their pain. But you might have a chance to deepen product/market fit: Ask prospects what 𝘪𝘴 on their top-3 list, and reposition or pivot to address a top 3 priority. 𝗚𝗮𝗽 𝟯: 𝗧𝗿𝘂𝘀𝘁 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗲: Talk to recent signups, and ask them “what made you almost not sign up?” What specific questions or worries did they have? 𝗙𝗶𝘅: Some marketers see trust as an amorphous concept and throw money at “brand building.” But trust is rooted in specific concerns like “What will my team think?” “Will I actually use it?” or “Are your providers vetted?” 𝗦𝗶𝗺𝗽𝗹𝗲 𝗻𝗲𝘅𝘁 𝘀𝘁𝗲𝗽 If you’re stuck in the land of marginal gains, look beyond simple UX tweaks. Discover the root cause of dropoff and address it: Improve comprehension, find urgency and build trust – in that order. .

"Start with why". Some of my users recently texted me to say they preferred using our app to a larger, more established competitor. The reasons they gave surprised me. I previously shared how we took an unconventional approach to onboarding each of our customers at Peek individually. It's slow, cumbersome, and uncomfortable. But what we learned from that process has been invaluable. People told us that other portfolio tracking and personal finance apps felt like they were designed for CFOs, or portfolio managers at asset management companies. Sure, the graphs and charts were comprehensive and pretty, but they didn't have the slightest clue about what to do next. It also helped us understand the social context around investing. People invest to make money, but the real "jobs-to-be-done" are concrete life goals. Most apps also weren't designed to help them visualize their progress towards personal goals; like saving for a retirement number, or a house! To build what people want, don't start from thinking about cool features, designing an impressive tech stack, or even how to get eyeballs on the product. Talk to users. Every single one of them. For as many and as long as you can. You'll be surprised how much of an edge it could give you over the competition!

When I say, ‘See you at 7’, do I mean 7 AM or 7 PM? ⏰ This is what we call, an interoperability problem - the inability for systems to exchange data and understand it in the same way. Why is interoperability in healthcare so hard? Because it’s not just a tech issue. It’s a stack of challenges And the hardest part isn’t connection, it's understanding. Let’s break this down. 1️⃣ Technical Interoperability - can systems connect and exchange data? Sounds simple, until: 🔸One system uses CSV, another wants XML 🔸Dates are DD/MM/YYYY vs MM/DD/YYYY 🔸Fields don’t match or exist Without standard formats, even basic connections break. Challenging - yes. But ironically, the easiest layer to fix. (Most teams stop here. That’s the issue.) 2️⃣ Semantic Interoperability - Can systems understand the data? Take “discharge date” as an example: 🔸One system uses the paperwork date 🔸Another, the bed exit time 🔸A third, the billing date Same label, different meanings. Now try running a report across all three. This is where projects quietly fail. Semantics needs shared meaning, clinical context, and governance. (And that’s just admin data, imagine lab values, diagnoses, or clinical notes. Get it wrong and it’s not just inefficiency, it’s a safety issue!) 3️⃣ Workflow Interoperability - do systems fit real care delivery? 🔸A patient sees a doctor in the morning, does a lab test in the afternoon 🔸Lab results are ready but not visible till the next day 🔸Why? The EHR and lab system don’t sync in real time, and no one flagged it. Digital isn’t fast if the workflow stays broken. 4️⃣ Organizational Interoperability - do institutions even want to collaborate? 🔸Hospitals, clinics, insurers, labs etc. have different systems, incentives, and vendors 🔸Even if tech and semantics align, nothing moves without shared ownership The real question isn’t “Can systems talk?” It’s “Do they understand each other and act together?” And more importantly - who’s responsible for making that happen? Because in healthcare, everyone is in charge, yet no one really is. Let’s stop treating interoperability like a checkbox and start treating it as a system-wide commitment: to shared meaning, coordinated action, and patient-centered design. What’s one interoperability headache you’ve seen that should’ve been solved by now? #Interoperability #SemanticStandards #SystemThinking #HealthData 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡The ongoing series and additional resources are available at http://www.enabler.xyz 💡Repost if this message resonates with you!

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

As we approach the holiday season, I find myself reflecting on the systemic challenges in healthcare—challenges that affect patients and healthcare workers alike. Here are some of the most pressing issues I am currently seeing as an oncologist: 💻 1. Telehealth at Risk for Medicare, Medicaid, United Health, and VA Patients: If Congress doesn’t act, millions of patients relying on these programs could lose access to telehealth. For cancer patients or those in rural areas, telehealth is a lifeline. Losing it would increase healthcare disparities, delay critical care, and harm patient outcomes. ⏳ 2. Prior Authorizations We waste countless hours on prior authorizations and peer-to-peer reviews, fighting for treatments we know our patients need. These delays create unnecessary stress and, in oncology, can mean life-or-death consequences. The administrative burden on healthcare workers also contributes significantly to burnout. 💔 3. Physician Shortages and Burnout Burnout, frustration with inefficiencies, and a lack of systemic support are driving clinicians out of medicine. This shortage leads to delays in care, later diagnoses, and poorer outcomes for patients. To fix this, we must focus on retaining and supporting healthcare workers. ⚖️ 4. Criminalization of Reproductive Healthcare Healthcare decisions are increasingly being politicized, with women and their physicians treated like criminals for accessing or providing necessary care. This creates a chilling effect on care, forcing healthcare workers to navigate legal fears instead of prioritizing patient needs. 💸 5. Physicians Are NOT Driving Healthcare Costs Doctors’ salaries are often scapegoated for rising healthcare costs, but the truth is that physicians’ pay accounts for <10% of total healthcare spending. The real cost drivers include administrative overhead, pharmaceutical pricing, and insurance inefficiencies. Physicians are not the problem; they’re advocates for patients navigating a broken system. 🚨 6. The Mental Health Crisis Among Healthcare Workers The toll of navigating these systemic barriers is immense. Clinicians often neglect their own mental health while balancing heavy workloads and systemic inefficiencies. Addressing burnout is not just about personal wellness—it’s a public health imperative. 📉 7. The Dangers of Prioritizing Cost Over Patient Safety The recent allegations against Amazon One highlight the risks of prioritizing cost-cutting and productivity over quality care. Reducing clinical staff and increased pressure to meet productivity targets are a stark reminder that healthcare isn’t like other industries. When efficiency and profit are prioritized over safety and good patient care, lives are at stake. As we move into 2025, I hope we can focus on building a healthcare system that prioritizes patients and supports the clinicians and staff who care for them. Change is possible, but it will require systemic transformation and collective advocacy.

When someone lands on your site, every extra word, button, or menu is a cognitive tax. Take this landing page comparison: Attio - keeps the load light • One navigation bar • 12 words in total for the header + sub-header • 9 clickable exits above the fold • Lots of whitespace • Sneak peak at product imagery The result = focus 🧘♀️ HubSpot - seems to have many cooks in the kitchen • Two navigation bars at the top • 50% more words (24 words in the header + subheader) • 13 clickable exits above the fold • Bigger chat widgets • Lifestyle imagery instead of whitespace The result = distraction 🐿️ With busier pages comes higher cognitive load, the paradox of choice, and decision paralysis 🧠 In real terms: if someone pauses even a split second more and doesn’t act, they’re more likely to bounce. And this isn’t just true for landing pages - it applies to pricing pages, homepages, dashboards… anywhere with competing priorities 👩🍳 👩🍳 👩🍳 It’s easy to add, hard to cut. ✂️ Good design isn’t what you add, it’s what you remove (or don't add in the first place). So ask yourself: What's the 30% you can remove from your page? 🗑️

No doubt about it, customer insights and customer understanding are vital to #customerexperience. But I think that we would do well as #CX practitioners to also focus on what we don't know about our customers. Ask yourself: 💠 What are you uncertain about? 💠 What are your customers unable to tell you? 💠 What are your customers refusing to reveal? 💠 What is unknowable about their experience? ✍ Then, make a list of that anti-knowledge. A list of what you do not know about what customers want, expect, and need from your experience. I'd argue, the longer, this list is the better. Why? Two reasons.  1️⃣ It means there's so much more for you to discover about your customers, it's a to-do list in other words. 2️⃣ The list is a lesson in humility. Remind yourself that there is still so much you do not know about your customers. So much you may never know if you're honest. Focus on your list of customer anti-knowledge, customer misunderstanding, customer blindspots. Call it whatever you want. It will keep you curious, and humble.