UX Design For Smart Home Devices

“I am CONCERNED about this patient.” 🔔🔔🔔 When I hear this comment from one of our experienced and knowledgeable Neonatal ICU nurses or NNPs, my ears perk up and I quickly move to the bedside to gather more information; vitals, labs, exam, images etc.. Critical care nursing INTUITION is real. Critical care nursing INTUITION is really valuable as insightful and as a warning system.  But we as busy, and sometimes aloof or deaf physicians, often do not take the time to listen, evaluate, and act on a nurses' intuition.  A new Nature Portfolio Medicine trial by Sarah Rossetti, RN, PhD, Kenrick D Cato (He, Him) PhD, RN, CPHIMS, FAAN, FACMI et al. from NewYork-Presbyterian Hospital, Brigham and Women's Hospital, University of Pennsylvania, and Children's Hospital of Philadelphia (https://lnkd.in/g2swDpGB) puts tangible data behind nursing intuition and concern. By harnessing real‑time (hourly) nursing documentation patterns using a machine‑learning model and natural language processing to screen nursing documentation for "concerns"—the authors created the CONCERN Early Warning System—that: 🟢 Reduced in‑hospital mortality by 36% 🟢 Shortened length of stay by 11 % 🟢 Decreased sepsis risk by 7% 🟢 Increased timely ICU transfers by 25 % This isn’t just another algorithm—it’s a nurse‑centered alert that recognizes and elevates the “gut instinct” nurses have relied on for decades. They used a unique and intriguing approach by identifying when an increased nursing frequency and unusual timing of documentation occurred (more frequent than standard etc.) or a missed medication--- as they may signs that a patients clinical status is concerning or worsening. In the current wave of AI, not a lot has been focused on improving nursing capabilities and documentation burden. 👏🏽 It is refreshing and important to see a study such as this one that puts nurses and patients at the center.👏🏽 🧏🏽♂️ We should learn to listen, to study, and to integrate nursing intuition into our care pathways—not as a checkbox, but as a trusted signal that drives earlier, life‑saving interventions. Nurses spend the most time with patients, every shift, in every hospital in the country, and they usually “know” when there is something different with their patient, but they do not always know why or what.  They just "feel something is off." Somethings are difficult to explain in words, but it may be easier and better to explain with computer models that can make the "subjective feeling" into something more objective, tangible, and actionable. I’m excited to see if  more hospitals could adopt CONCERN‑style tools,  partner with our nursing colleagues, and evaluate such models to refine how we capture invaluable nursing expertise. How have you or your team operationalized nursing intuition in your unit? Sara Deakyne Davies, CT Lin MD, FACP, FAMIA, George Ferzli, MD, MBOE, EMBA, Lindsey Knake, Brynne Sullivan   #UsingWhatWeHaveBetter

In today’s always-on world, downtime isn’t just an inconvenience — it’s a liability. One missed alert, one overlooked spike, and suddenly your users are staring at error pages and your credibility is on the line. System reliability is the foundation of trust and business continuity and it starts with proactive monitoring and smart alerting. 📊 𝐊𝐞𝐲 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐌𝐞𝐭𝐫𝐢𝐜𝐬: 💻 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: 📌CPU, memory, disk usage: Think of these as your system’s vital signs. If they’re maxing out, trouble is likely around the corner. 📌Network traffic and errors: Sudden spikes or drops could mean a misbehaving service or something more malicious. 🌐 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧: 📌Request/response counts: Gauge system load and user engagement. 📌Latency (P50, P95, P99):  These help you understand not just the average experience, but the worst ones too. 📌Error rates: Your first hint that something in the code, config, or connection just broke. 📌Queue length and lag: Delayed processing? Might be a jam in the pipeline. 📦 𝐒𝐞𝐫𝐯𝐢𝐜𝐞 (𝐌𝐢𝐜𝐫𝐨𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 𝐨𝐫 𝐀𝐏𝐈𝐬): 📌Inter-service call latency: Detect bottlenecks between services. 📌Retry/failure counts: Spot instability in downstream service interactions. 📌Circuit breaker state: Watch for degraded service states due to repeated failures. 📂 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞: 📌Query latency: Identify slow queries that impact performance. 📌Connection pool usage: Monitor database connection limits and contention. 📌Cache hit/miss ratio: Ensure caching is reducing DB load effectively. 📌Slow queries: Flag expensive operations for optimization. 🔄 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐉𝐨𝐛/𝐐𝐮𝐞𝐮𝐞: 📌Job success/failure rates: Failed jobs are often silent killers of user experience. 📌Processing latency: Measure how long jobs take to complete. 📌Queue length: Watch for backlogs that could impact system performance. 🔒 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: 📌Unauthorized access attempts: Don’t wait until a breach to care about this. 📌Unusual login activity: Catch compromised credentials early. 📌TLS cert expiry: Avoid outages and insecure connections due to expired certificates. ✅𝐁𝐞𝐬𝐭 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐥𝐞𝐫𝐭𝐬: 📌Alert on symptoms, not causes. 📌Trigger alerts on significant deviations or trends, not only fixed metric limits. 📌Avoid alert flapping with buffers and stability checks to reduce noise. 📌Classify alerts by severity levels – Not everything is a page. Reserve those for critical issues. Slack or email can handle the rest. 📌Alerts should tell a story : what’s broken, where, and what to check next. Include links to dashboards, logs, and deploy history. 🛠 𝐓𝐨𝐨𝐥𝐬 𝐔𝐬𝐞𝐝: 📌 Metrics collection: Prometheus, Datadog, CloudWatch etc. 📌Alerting: PagerDuty, Opsgenie etc. 📌Visualization: Grafana, Kibana etc. 📌Log monitoring: Splunk, Loki etc. #tech #blog #devops #observability #monitoring #alerts

The night we got 50 PagerDuty alerts at 3 AM was the moment I knew IT monitoring was broken. False alerts → alert fatigue → burnout. The problem is too many alerts… but even more important it’s alerts without context. For 25 years, I lived with this frustration. That’s why at Eyer we built something different: time series anomaly detection + Claude + our own IT knowledge docs = alerts that make sense and guide action. I wrote a short free guide that shares: ✅ How to go from raw data → early anomaly alerts → actionable remediation ✅ A recorded demo of an example setup ✅ Practical lessons learned from 25 years of frustration 👉 If you’d like access, just comment “YES” below, connect with me and I’ll send it over. And if this resonates, please repost so more in the IT community can benefit. #AIOps #Observability #ITOps #Automation #ModernOps #NoMorePagerDuty

Everyone talks about what you should do before you push to production, but software engineers, what about after? The job doesn’t end once you’ve deployed; you must monitor, log, and alert. ♠ 1. Logging Logging captures and records events, activities, and data generated by your system, applications, or services. This includes everything from user interactions to system errors. ◄Why do you need it? To capture crucial data that provides insight into system health user behavior and aids in debugging. ◄Best practices • Structured Logging: Use a consistent format for your logs to make it easier to parse and analyze. • Log Levels: Utilize different log levels (info, warning, error, etc.) to differentiate the importance and urgency of logged events. • Sensitive Data: Avoid logging sensitive information like passwords or personal data to maintain security and privacy. • Retention Policy: Implement a log retention policy to manage the storage of logs, ensuring old logs are archived or deleted as needed. ♠ 2.Monitoring It’s observing and analyzing system performance, behavior, and health using the data collected from logs. It involves tracking key metrics and generating insights from real-time and historical data. ◄Why do you need it? To detect real-time issues, monitor trends, and ensure your system runs smoothly. ◄Best practices: • Dashboard Visualization: Use monitoring tools that offer dashboards to present data in a clear, human-readable format, making it easier to spot trends and issues. • Key Metrics: Monitor critical metrics like response times, error rates, CPU/memory usage, and request throughput to ensure overall system health. • Automated Analysis: Implement automated systems to analyze logs and metrics, alerting you to potential issues without constant manual checks. 3. Alerting It’s all about notifying relevant stakeholders when certain conditions or thresholds are met within the monitored system. This ensures that critical issues are addressed as soon as they arise. ◄Why do you need it? To promptly address critical issues like high latency or system failures, preventing downtime. ◄Best practices: •Thresholds: Set clear thresholds for alerts based on what’s acceptable for your system’s performance. For instance, set an alert if latency exceeds 500ms or if error rates rise above 2%. • Alert Fatigue: To prevent desensitization, avoid setting too many alerts. Focus on the most critical metrics to ensure that alerts are meaningful and actionable. • Escalation Policies: Define an escalation path for alerts so that if an issue isn’t resolved promptly, it is automatically escalated to higher levels of support. Without these 3, no one would know there’s a problem until the user calls you themselves. 

Red text everywhere. I opened a medtech app during my audit. Every screen was screaming at me. The doctor using it didn't even notice anymore. That's when I knew they had a problem ↓ 𝐓𝐇𝐄 𝐀𝐋𝐄𝐑𝐓 𝐂𝐇𝐀𝐎𝐒: This app had 7 different error styles: • Red banners • Yellow tooltips • Orange pop-ups • Bold red text • Flashing notifications • Inline warnings • Modal alerts Everything looked urgent. So nothing felt urgent. I watched a doctor dismiss a critical alert without reading it. "I just click through them now. There are too many." A life-saving warning looked exactly like "username too short." 𝐓𝐇𝐄 𝐑𝐄𝐀𝐋 𝐃𝐀𝐍𝐆𝐄𝐑: When everything is urgent, nothing is. Users develop alert blindness. They stop reading. They auto-dismiss. They ignore what matters. One support ticket said: "I lost patient data because I didn't see the warning." The warning was there. Buried among 12 other "urgent" messages. 𝐓𝐇𝐄 𝐅𝐈𝐗: We built a 3-level alert system: 🔴 Critical (Red) System errors. Data loss. Stop everything. 🟡 Warning (Yellow)   Action needed soon. But not urgent. 🔵 Info (Blue) Nice to know. Totally optional. Simple rules: → One color = one meaning → Same position every time → Clear next steps always 𝟔 𝐖𝐄𝐄𝐊𝐒 𝐋𝐀𝐓𝐄𝐑: 📈 Critical alert response: Up 36% 📉 Support tickets: Down significantly   💙 User trust: Restored A doctor messaged: "I finally know what actually needs my attention. Thank you." How many alert styles does YOUR product have? #ProductDesign #HealthTech #UXDesign #MedTech #AlertDesign

Most SaaS companies still rely on static health scores. The problem? By the time they fire an alert, the customer is already halfway out the door. Instead of static scores, you need a health system — a framework that tracks signals, triggers alerts, and connects to action playbooks, in real time. A score tells you what. A system tells you when and how to act. When alerts are tied to signals and playbooks, your team moves from reactive firefighting to proactive engagement. That’s the difference between waiting for churn… and staying one step ahead of it. So how do you actually build one? It comes down to 5 practical steps. 1️⃣ Map the customer journey -> Define the key checkpoints: onboarding, first value, adoption, renewal prep, expansion. -> Write down what “healthy” looks like at each stage. 2️⃣ Define the right signals -> Leading indicators (daily usage, exec engagement, QBR attendance) → trigger early. -> Lagging indicators (NPS, renewal outcome) → track for context, not action. 3️⃣ Set up two types of alerts -> ✅ Milestone alerts – pre-scheduled based on the journey (e.g. Month 6 QBR, Year 1 ROI review). They keep customers moving forward. -> ⚠️ Risk alerts – event-driven, triggered by negative signals (e.g. drop in adoption, sponsor silence, high support escalations). They help you act before churn. 4️⃣ Link every alert to a playbook -> An alert without a clear next step is just noise. -> Decide: who acts, what they do, and by when. 5️⃣ Close the loop -> Track which alerts triggered, which actions were taken, and what changed. -> Refine thresholds and signals over time — let data make the system smarter. What’s the most valuable alert you’ve built into your CS process? I’m building a library of best-practice alerts to share in a future post. Drop your most valuable one below 👇 #CustomerSuccess #CustomerHealth #SaaS #AIinCustomerSuccess #ProactiveCS

In the fast-paced world of cybersecurity, alert storms can overwhelm Security Operations Centres (SOCs), causing analyst fatigue and increasing the risk of critical threats slipping through unnoticed. Managing these storms effectively is crucial to maintaining operational stability and protecting sensitive data. 5 WAYS TO AVOID ALERT STORMS IN SECURITY OPERATION CENTRE (SOC) 1. UNIFY THREAT MONITORING Fragmented security tools generate isolated alerts, leading to duplicate notifications and poor threat correlation. By unifying threat monitoring across systems, you can: • Centralise all alerts from firewalls, SIEMs, EDR and other tools in a single platform. • Streamline threat visibility to identify patterns across multiple attack vectors. • Reduce manual effort and improve incident prioritisation. Example: Use a well-integrated SIEM solution to ingest and correlate logs from multiple sources, reducing noise from disparate systems. 2. FINE-TUNE DETECTION RULES Default detection rules often generate excessive false positives. Analysts can avoid unnecessary alerts by fine-tuning detection mechanisms to: • Set specific thresholds based on the environment and use case. • Reduce false positives by excluding benign behaviour patterns. • Update rules regularly to reflect evolving threats. Tip: Regularly review and customise detection rules in your SIEM or EDR tool based on your organisation’s risk profile. 3. GROUP ALERTS INTELLIGENTLY Alert storms often occur when multiple alerts are triggered for a single incident. Intelligent grouping helps analysts focus on the bigger picture by: • Aggregating alerts related to the same event or threat. • Using correlation rules to identify connections between logs and alerts. • Reducing the number of tickets created for similar incidents. Example: Implement alert deduplication and correlation logic in your SOC tools to group login attempts from the same source IP into a single incident. 4. PRACTICE GOOD ALERT HYGIENE Poorly managed alerts can clog the system, overwhelming analysts. Practising alert hygiene ensures that: • Old, irrelevant or low-priority alerts are reviewed and resolved promptly. • Alerts with no actionable outcomes are tuned or suppressed. • Historical alert data is archived but accessible for compliance and review. Tip: Conduct regular alert reviews to identify noisy rules and disable alerts that do not add value. 5. AUTOMATE REPETITIVE TASKS Manual alert triaging during a storm is time-consuming and error-prone. Automation can help SOC teams handle large volumes efficiently by: • Automating triage processes for known low-risk events. • Using SOAR tools to investigate and respond to alerts without human intervention. • Deploying playbooks for common incidents to reduce response time. Example: Configure your SOAR tool to automatically resolve low-risk phishing alerts by blocking the sender and tagging the email for further review. For more details, please refer to the attached PDF.

How does a fuzzy sensor reading become an emergency shutdown, a maintenance ticket, or a personalized automation? Here’s the 12-step flow I think about when designing AIoT systems that actually work in the real world. ➞ Sensor Activation Environmental signals get captured in real time - temperature, motion, vibration, GPS coordinates. ➞ Data Acquisition & Filtering Raw signals get cleaned, normalized, and timestamped. Garbage in still equals garbage out, even with AI. ➞ Edge Preprocessing Local devices apply basic rules and anomaly detection before anything hits the network. This saves bandwidth and enables offline operation. ➞ AI Inference at the Edge TinyML models run directly on-device to classify behavior, detect patterns, or make urgent decisions in milliseconds. ➞ Local Action Triggers Critical conditions trigger immediate responses - shut off valves, sound alarms, adjust HVAC - without waiting for cloud approval. ➞ Secure Data Transmission Summarized, encrypted data flows to the cloud via MQTT, CoAP, or HTTP protocols optimized for IoT constraints. ➞ Cloud Storage & Structuring Data gets organized by device ID, timestamp, and type in time-series databases designed for IoT scale and query speed. ➞ Heavy AI Processing Cloud-based models handle complex pattern recognition, forecasting, and cross-system analysis that edge devices can't manage. ➞ Cross-Device Correlation The system connects signals across your entire device fleet to spot system-wide trends, optimization opportunities, or security threats. ➞ Intelligent Insights Generation Real-time recommendations emerge - predictive maintenance alerts, energy optimization suggestions, security notifications. ➞ Continuous Learning Loop User interactions, device outcomes, and environmental changes feed back to improve both edge and cloud models over time. ➞ Human Interface Layer Dashboards, mobile apps, and APIs surface actionable insights while letting users set automation rules and thresholds. This isn't just connected devices anymore. It's distributed intelligence making autonomous decisions while keeping humans in control of what matters. The magic happens in the orchestration between edge and cloud, not just the individual components. ♻️ Repost if you're building intelligent systems, not just connected ones ➕ Follow me, Nick Tudor, for more real-world insights on AI + IoT architectures that actually work.

Alert on user impact (symptoms), not on possible issues (causes). Example: Data processing batch jobs should be running once a day. It's common to alert on the failed process of the batch job. But what if there is retry logic on that batch job (and there likely is or should be)? The process failed, so you got alerted. But on the retry it succeeded. Now the on-call engineer has been woken up to only see that it actually ran and there is nothing to do (an alert with no action means that there should be one new action: Preventing that alert from happening again). You don't really care that the process failed. You care that the data was processed. Instead you should be alerting on data ingestion rate. What your users care about is that there is data processed every day. Alert on that. And make it quick and easy for the on-call engineer to diagnose and troubleshoot potential causes (dashboards, scripts, runbooks, etc.).

Too many alerts can be just as dangerous as too few. A few years ago, I worked with a client named Daniel who was leading engineering operations for a fast-growing technology company. On paper, their monitoring setup looked excellent. ✅ Alerts everywhere ✅ Dashboards everywhere ✅ Notifications everywhere ✅ Monitoring for every service But there was one problem. The teams were drowning in alert noise. Critical incidents were buried under thousands of repetitive notifications. Engineers spent hours checking inboxes, dashboards, logs, and monitoring tools, yet still struggled to identify what actually required attention. Everything looked urgent. Which meant nothing truly was. When I started analyzing the environment, I reviewed alert patterns, dashboards, repositories, deployment pipelines, and application behavior across multiple teams. The goal was simple: Determine which alerts actually mattered. After studying alert trends and working closely with development teams, the root cause became clear. Many alerts were providing little operational value. They generated noise without driving action. We categorized alerts into two groups: 🔴 Critical alerts requiring immediate attention ⚪ Redundant alerts that could be safely removed or consolidated From there, we decommissioned unnecessary alert rules and redesigned the alerting strategy. But reducing noise wasn't enough. We also implemented automated threshold-based alerting that triggered only when meaningful conditions were detected. To accelerate response times, automated ticket creation was added so incidents were immediately routed to the right teams. The results were significant: ✅ Massive reduction in alert volume ✅ Faster incident identification ✅ Better operational visibility ✅ Improved response times ✅ Higher trust in monitoring systems But the biggest improvement wasn't technical. Engineers felt less overwhelmed. Operations teams regained focus. And critical issues became much easier to detect and resolve. The lesson? More monitoring doesn't automatically create better observability. Sometimes the fastest way to improve visibility is to remove the noise. #DevOps #SRE #Observability #Monitoring #CloudEngineering #PlatformEngineering #IncidentManagement #SiteReliabilityEngineering