Future Of Work Technologies

5 HealthTech CEO Imperatives for the Next 24 Months 1. AI is no longer a tech initiative. It’s a clinical one. The AI wave is here, but most HealthTech leaders still treat it as a back-office efficiency play. Wrong lens IMO. Generative AI will directly shape care protocols, triage flows, and patient trust. If your Chief Medical Officer isn’t AI-literate, you’re already behind. By 2026, AI will write discharge summaries and co-diagnose in live consults. It’s not a tool. It’s a co-pilot. Get your team aligned. 2. Data isn’t your moat - interoperability is. Everyone talks about “owning” data. But hoarded data is dead data. The real advantage is in how well your data moves - across partners, platforms, regulators, and patient hands. APIs, federated learning, synthetic data - these aren’t IT terms. They’re survival tools for value-based care. Health systems are only as smart as the worst data they can’t access. 3. Personalised medicine is coming - one use case at a time “Digital twins” sound like sci-fi. Until your largest client asks why you can’t simulate disease progression in silico. The question is no longer “Will this scale?” It’s “Which patients do we start with Watch genomics + wearables + real-time monitoring - this is the care stack of the future. The hardest part Operationalising it in messy clinical settings. 4. Virtual care isn’t telehealth anymore. It’s infrastructure. Post-COVID, many HealthTech firms de-invested in remote models. That’s shortsighted. Virtual consults are now just one node in a larger remote ecosystem: Asynchronous triage, at-home diagnostics, smart monitoring. The real play is integrating these into care pathways without fragmenting continuity. Remote-first care delivery models are emerging - and they’re crushing CAC (sorry for the SaaS reference) and improving outcomes. 5. Cybersecurity is not a cost centre. It’s a competitive advantage. A single breach will kill patient trust - and your next partnership. But the best firms are going further: Embedding compliance by design, automating audit trails, and using security posture as a BD asset. Trust is the new UX. Especially in multi-jurisdictional deployments and sensitive care domains like fertility, mental health, and paediatrics. This isn’t a roadmap. It’s a decision filter. The next 24 months will break companies with beautiful product vision but brittle strategy IMO. You don’t need to do everything. You need to know what not to ignore. If you’re a HealthTech CEO navigating these realities, what are you doubling down on right now?

In recent years, no technology has impacted HR or accelerated its digital transformation as much as Generative AI (Gen AI). Its conversational capabilities make technology more accessible than ever, overcoming historical challenges. ✨ This intuitive and highly efficient AI is enabling task automation, saving time, and significantly enhancing the employee experience. AI has revolutionized HR, presenting a unique opportunity to free up time, increase strategic value, and redefine the HR function in 2025. At íncipy and Advantage Consultores, we have identified the key trends that will shape the future of HR in 2025 and the challenges companies must embrace to maximize HR's strategic impact: 📌 The 13 Key HR Trends for 2025 1️⃣ AI’s Role in HR: HR will lead the adoption of Gen AI within organizations and its own processes. 2️⃣ Digital-First Culture: Creating a collaborative and agile environment that embraces technological innovation. 3️⃣ Training, Discovery & Roadmap: Raising awareness and training leaders and teams to integrate AI into corporate strategies. 4️⃣ Digital Tools Adoption: Maximizing the use of underutilized digital tools. 5️⃣ New Ways of Working: Enhancing productivity with AI assistants like ChatGPT and Copilot. 6️⃣ Employee Experience Journey Automation: Automating and personalizing the employee journey. 7️⃣ AI Agents for Employee Support: AI-driven agents autonomously handling employee requests. 8️⃣ AI Digital Workplace Evolution: Transforming intranets into interactive and AI-powered spaces. 9️⃣ AI in Internal Communications: Automating content creation, streamlining communication, and analyzing employee sentiment. 🔟 AI in Learning & Development: Personalizing training pathways and predicting skill gaps. 1️⃣1️⃣ AI in Recruiting & Onboarding: Automating selection processes, reducing bias, and optimizing onboarding. 1️⃣2️⃣ AI-Driven Organization: Enabling data-driven decision-making with predictive algorithms. 1️⃣3️⃣ AI Adoption & Change Management: Supporting AI implementation projects and managing change effectively. ⚡ 2025 will bring profound changes, challenging companies to innovate, accelerate, and move beyond traditional practices. This is a pivotal year for HR, providing a unique opportunity to lead AI-driven transformation and reinforce its critical role in business strategy. 🚀 Let’s make 2025 a year of innovation, leadership, and impact! 🙌 Excited to hear your thoughts! How is your company preparing for AI in HR? Drop your comments below! Post by: Mireia Ranera, Digital HR & AI Transformation Director, INCIPY Sylvia Taudien CEO, Advantage Consultores #HRTrends2025 #AIinHR #DigitalTransformation #FutureOfWork #Innovation #Leadership #AI

Reflecting on Five Years of Transformation: The Rise of Connected Care in Healthcare As we mark five years since the onset of the COVID-19 pandemic, it’s crucial to reflect on the profound changes that have reshaped our healthcare landscape. A recent article by Fierce Healthcare delves into these transformations, highlighting key shifts in the U.S. healthcare system. One of the most significant evolutions has been the accelerated adoption of connected care models. The pandemic necessitated rapid innovation, leading to the widespread implementation of telehealth services and virtual hospitals. For instance, virtual hospitals have emerged to provide remote medical care to patients using video consultations and monitoring devices, addressing challenges of geographical access and specialized resources. This shift towards digital health has not only enhanced patient engagement but also improved access to care, especially for those in remote or underserved areas. However, as we integrate these technologies, it’s imperative to address challenges such as ensuring data security, maintaining the quality of care, and bridging the digital divide to prevent disparities in access. The journey towards a more connected and resilient healthcare system is ongoing. By embracing innovation and prioritizing patient-centric approaches, we can build a future where quality care is accessible to all, regardless of location. #ConnectedCare #DigitalHealth #Telemedicine #HealthcareInnovation #PatientEngagement #VirtualHospitals #HealthTech #COVID19Lessons #HealthcareTransformation #AccessToCare https://lnkd.in/eGHVMuA3

Telehealth attitudes have changed. Patients are now expecting more. The newest Rock Health Consumer Adoption Survey tells us what some of those changes are. Much of it that virtual care is an expected option: 📊 76% of Americans have used virtual care 📱 83% of them used it in the past year Amazing. Telehealth isn't an option but a core part of how healthcare is accessed. So it’s time our strategy, funding, and workflows reflected that. 💡 And by the way, all of the #AI hype, much of its in healthcare can used in telemedicine so it can be part of a smart strategy. 💡 We’ve entered a new phase, however, one defined by consumer expectations, more selections, and trust. Also, patients have gotten used to telemedicine as an option so expect more. Human nature really is predictable in some ways, huh? 😂 Here’s what really stood out to me in this year's report: 1️⃣ Virtual care is now a baseline expectation. Patients expect it to be offered. Instead they want to know how it works for their specific needs. Some patients still prefer in-person, and some have trust or access concerns. That’s fine. What this calls for is a truly omnichannel model where patients choose what works for them. 2️⃣ Convenience is no longer a competitive edge. It is expected. Yes, patients use virtual care for convenience, speed, and availability but it's no longer enough. There is now a stress on value: culturally competent care, lower costs, seamless communication, and yes, options. And competition is no longer just other health system but grocery-store clinics, retail chains, and tech-backed hybrid models. 3️⃣ At-home testing has promise but hasn’t expanded beyond COVID. Meaning, 72% of people have used at-home testing, but the majority have only done a COVID test. There is potential for many other options since options have expanded such as chronic care, women’s health, or new therapeutic areas. Point is, Convenience alone won’t drive sustained adoption. 4️⃣ Consumers are becoming more selective about data sharing even with clinicians. Only 64% of respondents were comfortable sharing health data with a clinician which is a 6-point drop in one year. Wow. For younger adults, patients of color, and those without a PCP, we’re seeing clear signs that people don’t feel safe, seen, or understood by the system. Fixing that means going beyond compliance checkboxes. We have to rebuild trust from the ground up, especially in how we use and explain data. And yes, I realize today's climate makes it extra hard. But that is what we all doing here, right? In summary: 🔹 Design care around people’s lives not our assumptions 🔹 Stop thinking digital = disconnected 🔹 Prioritize trust, usability, and equity in every single decision Telehealth continues to be a part of our healthcare system, even if flattened. & Patients are telling us what they expect from it next. Link to article: https://lnkd.in/ePKeeJRM #Telehealth #DigitalHealth #HealthEquity

𝐎𝐧𝐥𝐲 12% 𝐨𝐟 𝐇𝐑 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐝𝐨 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐰𝐨𝐫𝐤𝐟𝐨𝐫𝐜𝐞 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐚 𝐭𝐡𝐫𝐞𝐞-𝐲𝐞𝐚𝐫 𝐟𝐨𝐜𝐮𝐬. 73% 𝐬𝐭𝐢𝐜𝐤 𝐭𝐨 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐬. - 𝐌𝐜𝐊𝐢𝐧𝐬𝐞𝐲’𝐬 𝐇𝐑 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐫𝐞𝐩𝐨𝐫𝐭 The gap between having data and making decisions is where most organizations fail. HR teams are sitting on goldmines of workforce intelligence. Dashboards are built. Metrics are tracked. Reports are generated monthly. But here's the uncomfortable truth: most of this data never influences a single strategic decision. 𝐓𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐢𝐬𝐧'𝐭 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐢𝐭𝐬𝐞𝐥𝐟. 𝐈𝐭'𝐬 𝐰𝐡𝐚𝐭 𝐰𝐞 𝐝𝐨 𝐰𝐢𝐭𝐡 𝐢𝐭. 𝐖𝐡𝐚𝐭 𝐰𝐞 𝐦𝐚𝐲 𝐛𝐞 𝐦𝐢𝐬𝐬𝐢𝐧𝐠 - You know your turnover rate. But can you predict which critical talent will leave next quarter? - You track engagement scores. But do you know which teams are at risk of performance decline? - You measure time-to-hire. But can you forecast where capability gaps will bottleneck your growth strategy? 𝐖𝐡𝐚𝐭’𝐬 𝐞𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐢𝐧 2025: Leading organizations are moving from descriptive to predictive analytics and seeing real impact. The shift is clear: reactive HR is becoming obsolete. A recent example from a client story -  One business unit had "acceptable" retention numbers on paper. But deeper analysis revealed high performers leaving strategic roles, creating a capability gap that would derail execution within months. And also the reason behind it came across to us so clearly. That insight changed everything. Not because the data was new, but because it answered a question leadership was asking: "What could derail our strategy?" What shifted: - From reporting to forecasting - From metrics to narratives that connect to business outcomes - From dashboards to decisions with clear actions attached The real power of people analytics isn't in sophisticated tools or data volume. It's in connecting workforce insights directly to enterprise strategy, before problems become crises. After reading this, ask yourself: → When was the last time your people data changed a strategic decision? → Can you identify which workforce trends will impact your next fiscal year's goals? → Does your leadership team see HR analytics as insight or just information? What will you adapt in your approach to make your people analytics truly strategic? #StrategicHR #PeopleAnalytics #DataDrivenHR #Leadership #FutureOfWork

If you ask most data leaders, “What drives your users to take the highest value actions in your product?”, they’ll gaze back at you with a pained look on their face. They’ll probably respond through gritted teeth, “That’s a hard question.” And they’re right. It’s not that they don't care; the opposite in fact. But it’s an incredibly complex puzzle, and they wish they had better answers. Throughout my career, understanding the drivers of high value actions has been *the* burning analytics question. At Wealthfront, we obsessed over what led customers to transfer their other investment accounts to us. At Uber, it was the factors behind frequent trips and subscription sign-ups. At Gopuff, it was what drove large orders and purchases of high-margin products. The problem is, traditional analytics tools like BI dashboards and spreadsheets can’t untangle the web of factors that lead to high value actions. Answering these questions requires high-dimensional causal factor analysis, decomposing outcomes across dozens, or hundreds, or even thousands of input variables. In other words, they require machine learning. This is what the most advanced analytics teams are doing — using ML to find the needles in the haystack and unveil unexpected relationships between behaviors and outcomes. The good news: upgrading your product analytics with ML is within reach. In our latest article, Jeremy and I break down three core techniques you can use today. The topic is on our mind because we’re coming across it frequently at Delphina. We're eager as always for feedback and reactions, and if you’re tackling a similar problem and want to brainstorm, reach out! #datascience #analytics #machinelearning #artificialintelligence

🚀 How Machine Learning Helps Telecom Networks Self-Optimize What if your network could predict traffic surges and adjust its own resources before users even notice a slowdown? With AI and machine learning, that’s exactly what’s happening in telecom today. Let’s break down how it works: 1️⃣ Data Collection: The Foundation Telecom operators continuously gather network data across: ✔ Different regions ✔ Cities & neighborhoods ✔ Individual cell towers This helps track traffic flow and identify normal usage patterns. 2️⃣ Detecting Anomalies in Real Time ML models compare live data against historical trends. A sudden spike in usage? → Could be a major event, festival, or unexpected demand. → The system flags it before performance drops. 3️⃣ Smart, Automated Adjustments Once an anomaly is detected, the system recommends (or even automates) actions like: 📶 Adding bandwidth ⚙️ Optimizing software resources 🔧 Tweaking network settings 4️⃣ Continuous Learning = Smarter Networks The system learns from every event: ✔ Were predictions accurate? ✔ Did adjustments work? ✔ How can it improve next time? The result? A proactive network that: ✅ Prevents congestion ✅ Enhances user experience ✅ Optimizes costs & efficiency Key Takeaways 🔹 ML turns raw data into actionable insights 🔹 AI-driven recommendations reduce downtime 🔹 Self-improving systems = future-proof networks To learn about AI & 5G, visit - https://lnkd.in/eT-ZZyrP #AI #Telecom #MachineLearning #Networks #Innovation #Tech

Can we ever be sure that our #ML #models perform well? Ultimately, it's about more than the overall #performance #metrics. With experience, many #datascientists develop an intuition about our models' performance. We know the underlying #data and understand where the performance problems might be located. We start evaluating subgroups, using #explainability techniques to spot irregularities in feature contributions and model behavior. We talk to our end users and stakeholders, discuss the specifics of the business process and discover more possible sources of performance drops. ➕ The upside is we are often correct in our assumptions and can fix future performance issues while looking deeper into the model. ➖ The downside is that we cannot possibly think about everything in the data. Besides, it takes time to get to know the data well enough to do this in the first place. We often need expert knowledge about the business process and get feedback on our assumptions from the business audience. 🚀 I've been looking for a more structured approach for some time and want to share a method called #PERFEX that I discovered at the conference on #explainableAI. PERFEX is a surprisingly straightforward approach that beautifully combines the intuition that many data scientists, myself included, currently follow while building their ML solutions. I work with classification models for business processes a lot and usually can identify some bigger subgroups with possible performance differences relatively early in the process. While training the models, I then focus on reaching similar performance levels across all groups. PERFEX helps to extend this approach to the entire feature space. It creates clusters of performance differences and provides descriptions of such clusters. While there is no guaranteed cure for variations in model performance, this is a great tool to highlight potential problems and address them before model productization. It's also a perfect discussion starter to illustrate the challenges of ML development to the business stakeholders and explain why we need to dig deeper when the overall performance seems good. Do you have a preferred approach to spotting uneven model performance?

Patients loved the convenience of Telehealth. So why are they flooding back to clinics? During COVID, Telehealth became a lifeline, offering care without commutes or waiting rooms. However, as this convenience became permanent, its limitations surfaced. While it solved access and immediacy, it didn't fully address the deeper need for continuous, personalized care. Recent data from the National Center for Health Statistics indicates Telehealth usage declined from 37% to roughly 30%, and some reports quote a 45% drop since the pandemic peak. Patients aren't rejecting it outright. Though it’s becoming clear they're seeking the continuity and personal connection that some forms of virtual visits fail to deliver. This shift has strained already overworked reception teams and increased patient frustration, highlighting that convenience alone isn't enough. Telehealth was an important first step, but it was never designed to stand alone. Today, some experiences feel like watching a TV series out of sequence - disconnected, isolated, and difficult to follow. This fragmentation is especially problematic for patients with chronic or complex health needs, who require coherent, ongoing understanding that picks up from where they left off, rather than disconnected appointments. The next wave in healthcare is not systems overhaul, it’s smarter integration. Intelligent voice AI and autonomous operational systems, like Reggie Health, can bridge this continuity gap by proactively managing patient journeys. These solutions remember patient histories, facilitate scheduling, and handle follow-ups efficiently, freeing staff to focus on meaningful interactions rather than repetitive tasks. Regulatory shifts reflect this need. Medicare and private insurers are increasingly emphasizing continuity and patient outcomes, making the integration of continuous, personalized care a financial and clinical imperative. Ultimately, effective healthcare requires coherence, not just connectivity. The future won't be defined by more isolated technological solutions, but by care systems that genuinely understand and respond to patient needs across every encounter. If your practice struggles to balance convenience with meaningful connection, let's explore how Voice AI can help bridge this critical gap. #Health #Healthcare #HealthTech #BostonHealth

AI just became the most valuable member of your staffing team. Here's why healthcare operations managers are paying attention. The global healthcare workforce shortage is projected to reach 10 million workers by 2030. That's roughly the entire population of Sweden missing from hospital floors and care facilities. Traditional staffing methods react to problems after they happen. AI predicts them before they start. Predictive workflows analyze historical patterns, seasonal fluctuations, and patient admission trends to forecast exactly when and where you'll need staff. This means fewer last-minute scrambles for coverage and better allocation of your existing resources. The operational impact is substantial. Managers can now plan shifts weeks in advance with actual data backing their decisions. They can identify bottlenecks before they cripple departments. They can redistribute teams based on predicted demand rather than gut feeling. This changes HR strategy at a fundamental level. Recruitment cycles become more strategic. Training programs align with forecasted needs. Budget planning gains precision that was previously impossible. The technology addresses a real crisis with practical solutions. Every hospital administrator knows the cost of understaffing. Patient care suffers. Staff burnout accelerates. Emergency coverage eats budgets. AI provides the foresight to tackle these challenges systematically. The question for healthcare leaders is simple: will you continue reacting to staffing crises or start preventing them? The tools exist today. The workforce shortage will only intensify tomorrow.