Trends in Healthcare Innovation

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

You didn’t pursue a career in healthcare informatics just to chase outdated job titles. The world is changing. So are the roles. If you're still searching with 2015 job titles, you’ll miss the 2030 opportunities. Here’s the truth: The next decade will belong to those who understand not just healthcare, but data, automation, and digital systems together. And Healthcare Informatics is at that intersection. Top Hiring Trends for Healthcare Informatics (2024–2025): According to [HIMSS & BLS 2024 projections]: Healthcare Data Analyst roles grew by 18% last year. Clinical Decision Support & AI roles are emerging in major health systems. EHR System Support & Optimization remains the most in-demand skill. Population Health & Value-Based Care roles up by 11% due to Medicaid reforms. Clinical Research Informatics is growing in pharma/biotech. 2025–2035: What Roles Will Dominate? If you’re planning for long-term success, focus on roles that blend: Data + Outcomes AI + Patient Safety Compliance + Digital Health Here are the future-proof titles to track (and skill up for): Next-Gen Healthcare Informatics Roles: Healthcare Data Scientist (Python, SQL, predictive analytics) Clinical AI Analyst (ML models for outcomes + risk prediction) Digital Health Program Manager (mHealth, RPM, app-based care) Value-Based Care Analyst (Population health metrics, QI dashboards) Health Data Governance Specialist (HIPAA, HITECH, compliance) Clinical Informatics Consultant (Epic/Cerner + workflow redesign) Health Equity Data Analyst (DEI metrics, SDoH data) Telehealth Informatics Coordinator (virtual care workflows + UX design) Top Skills to Focus on (2025 and beyond): SQL, Python/R for health data Power BI / Tableau for dashboarding Epic or Cerner EHR optimization Clinical workflow mapping & UI/UX HL7, FHIR, interoperability knowledge Privacy regulations (HIPAA, GDPR) AI/ML foundations for clinical contexts Job Hunting Tip: Don’t search by degree. Search by outcome. Try: “Remote Patient Monitoring + Analyst” | “Epic + Optimization” | “Public Health + Data” These combos will open new doors. Tag a classmate, I’ll help you decode job titles, keywords, and roles that actually work in 2025. We rise faster when we learn together 💙 #HealthInformatics #HealthcareAnalytics #PublicHealthCareers #EntryLevelJobs #InternationalStudent #HealthTech

Did you see the recent news??? Microsoft recently unveiled its latest AI Diagnostic Orchestrator (MAI DxO), reporting an impressive 85.5% accuracy on 304 particularly complex cases from the New England Journal of Medicine, compared to just ~20% for physicians under controlled conditions . These results—quadrupling the diagnostic accuracy of human clinicians and more cost-effective than standard pathways — have gotten a lot of buzz. They may mark a significant milestone in clinical decision support and raise both enthusiasm but also caution. Some perspective as we continue to determine the role of AI in healthcare. 1. Validation Is Essential Promising results in controlled settings are just the beginning. We urge Microsoft and others to pursue transparent, peer reviewed clinical studies, including real-world trials comparing AI-assisted workflows against standard clinician performance—ideally published in clinical journals. 2. Recognize the value of Patient–Physician Relations Even the most advanced AI cannot replicate the human touch—listening, interpreting, and guiding patients through uncertainty. Physicians must retain control, using AI as a tool, not a crutch. 3. Acknowledge Potential Bias AI is only as strong as its training data. We must ensure representation across demographics and guard against replicating systemic biases. Transparency in model design and evaluation standards is non-negotiable. 4. Regulatory & Liability Frameworks As AI enters clinical care, we need clear pathways from FDA approval to liability guidelines. The AMA is actively engaging with regulators, insurers, and health systems to craft policies that ensure safety, data integrity, and professional accountability. 5. Prioritize Clinician Wellness Tools that reduce diagnostic uncertainty and documentation burden can strengthen clinician well-being. But meaningful adoption requires integration with workflow, training, and ongoing support. We need to look at this from a holistic perspective. We need to promote an environment where physicians, patients, and AI systems collaborate, Let’s convene cross sector partnerships across industry, academia, and government to champion AI that empowers clinicians, enhances patient care, and protects public health. Let’s embrace innovation—not as a replacement for human care, but as its greatest ally. #healthcare #ai #innovation #physicians https://lnkd.in/ew-j7yNS

Nagging works in healthcare, but it may have a hefty cost. The views are mine. The success of value-based care may be about how effectively we can "nag" patients and doctors. For sure, I oversimplified the nature of the business. However, our day-to-day work consists of communicating various reminders to providers and patients to take specific actions to improve their health and lower overall costs. The truth is that many preventive interventions do not provide short-term gratifications (or quick revenues); we all know that most people prefer short-term gains. With many proven track records, text message reminders are the most common form of "nagging" in the industry. For example, Clara Chow et al. sent four lifestyle-focused text messages per week for six months to patients with coronary heart disease [1]. After the test period, the test group that received the reminders showed significant reductions in LCL-C, systolic blood pressure, and BMI. The applications of text reminders go beyond motivating healthier lifestyles and focus on specific clinical interventions, such as medication adherence [2], transitional care management [3], and reducing no-shows for appointments [4]. For example, Jay Thakkar et al. analyzed sixteen clinical trials about sending text reminders for medication adherence and concluded that text reminders generally improve the odds of medication adherence [2]. Unfortunately, the effectiveness of the text reminder strategy has been over-utilized over the past years. John Steiner et al. showed that patients who receive a high volume of reminders tend to opt out of receiving future reminders [6]. The alert fatigue also spills over to doctors - patients sometimes reply to auto-reminders, and doctors often need to respond. Tracy Lieu et al. discussed that managing patient communications has become a new stressor in many practices and is associated with burnout symptoms [7]. Nagging works in healthcare, but it may have a hefty cost. Rather than repeatedly sending the same messages over and over again, we would need an innovative strategy that can achieve the same goal without annoying patients. For example, Amay Parikh, MD,MBA,FACP, et al. found that staff-written messages are more effective than templated ones [4]. As another example, Ernesto Ulloa et al. demonstrated an approach of selectively sending reminders by predicting behaviors to reduce the message volume. It is very amusing that I am fascinated by finding effective ways to nag people, while I really hate being nagged. :) I am curious about what others have tried in their work. What would be an effective way to remind patients without making them annoyed? [1] https://lnkd.in/gDgPsect [2] https://lnkd.in/gphW3VDs [3] https://lnkd.in/gMVAUr6v [4] https://lnkd.in/ggrRRRYw [5] https://lnkd.in/gRmRQEYJ [6] https://lnkd.in/g-UkP3v9 [7] https://lnkd.in/gMVAUr6v #aireminders #healthcareinnovations #healthtech

AI Dominates Digital Health Funding in H1 2025 as IPOs Return and Provider Adoption Ramps Up: 🔘Digital health funding reached $6.4B across 245 US deals in H1 2025, holding steady with 2023–2024 levels but with bigger average deal sizes and fewer total deals 🔘AI is now driving the sector: 62% of all funding went to AI-enabled startups, which raised an average of $34.4M per round, an 83% premium over non-AI peers 🔘Top funded areas were non-clinical and clinical workflow tools and data infrastructure, together drawing 55% of funding, all being transformed by AI and automation 🔘Abridge and OpenEvidence are leading examples of provider-facing AI tools gaining real traction, with Abridge now embedded into Epic and OpenEvidence passing 100K clinician users 🔘H1 saw 11 mega deals over $100M, 9 of them AI-led, including back-to-back rounds for Abridge and rising interest in rumored deals like OpenEvidence 🔘Two major digital health IPOs broke the exit drought: Hinge Health and Omada Health both went public, showing that long-term resilience and AI integration are now rewarded 🔘Hinge and Omada have each embedded AI into their care delivery, with Hinge pushing automation and Omada releasing a consumer-facing AI for nutrition support 🔘M&A is hot: 107 deals closed already this year, with digital health companies driving most activity and private equity firms rolling up AI startups into legacy healthcare platforms 🔘Policy turbulence looms: new Medicaid work requirements and ACA changes may shrink the insured population, creating downstream pressure on startups and systems 🔘Amid this uncertainty, federal agencies are actively soliciting input on digital health and AI, offering innovators a rare chance to shape policy and future reimbursement models 👇See links to Rock Health report and other commentary below #DigitalHealth #AI

92% of healthtech founders make the same mistake: They wait until their product is perfect before launching. Founders spend months building - refining features, fixing bugs, polishing UX. But when they finally launch? – No users – No feedback – No market pull Because they were optimizing for perfection - not market validation. The best founders don't wait to sell. They start before they're "ready." Here's the exact playbook that works: ▶︎ 1. Build your target list first Identify 100 specific people who feel your problem daily. Whether its a diagnostic tool or a workflow software, be as specific as you can. ▶︎ 2. Find them where they already socialise Join medical/health groups on LinkedIn, attend conferences, follow their publications. Don't cold email - engage with their content first. Comment thoughtfully on their posts about industry challenges. ▶︎ 3. Share one painful problem you've discovered each week Example - "I noticed ICU nurses spend 40% of their shift on documentation instead of patient care." Ask if others see this too. You'll get replies from people living this problem daily. ▶︎ 4. Turn conversations into 15-minute calls When someone engages, offer: "I'm exploring solutions to this exact problem - would you spare 15 minutes to share what you've tried?" Most say yes because you're asking for expertise, not selling. ▶︎ 5. Test demand before building Mock up a landing page. Show what the product might do. Then ask: “If this existed, would you pilot it for 30 days?” Real demand = budget, pilot interest, usage. Founders who do this aren’t waiting to get “fundable.” They’re testing their demand and product from day 1. Because your goal isn't to impress investors. It's to find 100 people who can't live without what you're building. So if you are still in the pre-launch stage, DM me what you’re building and I’ll send a few ways to test it fast. #entrepreneurship #startup #funding

Since every day in healthcare seems like something new, it can take time to keep up as a startup. I wanted to address some things I think startups should be thinking about if you are operating in healthcare following the Chevron ruling because it could have some significant impacts on your business. Here are a few areas that I think we should all be looking at: Regulatory Uncertainty: Startups should prepare to adapt to a changing regulatory landscape. Agencies like the FDA and CMS, which used to have the final say on ambiguous laws, will now face more challenges in court. This means startups might have to deal with a patchwork of rules that vary by region, making it harder to know exactly what’s required to stay compliant. If your business is taking advantage of a space where there was a regulatory shift in your favor, you need to be really careful that you are safeguarding the business for potential changes. Slower Approvals: Expect things to slow down. With agencies being more cautious to avoid lawsuits, the approval process for new products and services might take longer. This could slow your sales cycle for startups (which seems impossible but is likely). It is easier for a health plan or provider to not act until they have certainty than to take on risk. Make sure you are considering this when you go to market. Higher Legal Costs and Compliance Costs: Legal bills are likely to go up. Startups will need to invest more in legal support to keep up with regulatory changes and defend against potential court cases. Most importantly, just making sure your product stays compliant with changing rules. As an aside, in the post-Change Healthcare world, there is also going to be no wiggle room around security certifications. Advocacy Opportunities: Here’s a silver lining: the new legal landscape could give startups more chances to influence how laws are interpreted. By getting involved in legal advocacy, startups might be able to shape regulations in ways that benefit them and the broader industry. It is obviously easier for startups that are further along to do this, but even in our earliest stages we were adding our voice. Important: you can respond when CMS asks for comments on a proposed rule. Add your voice! Strategic Planning: It’s going to be important to plan strategically. Startups might need to adjust their operations to fit different legal interpretations in various regions. This approach can help mitigate risks and even take advantage of regulatory differences, making startups feel prepared for the changes ahead. Room for Competition: Less red tape could mean more room for competition and potentially innovation, depending on what you are doing. Again, depending on what changes are made, startups might find it easier to introduce disruptive technologies and practices, leveling the playing field against established companies. #healthcare #startups #regulations #chevron #themoreyouknow

Beth Kutscher's latest article excellently captures the evolving landscape of healthcare leadership. In the healthcare strategy class I teach at the Stanford University Graduate School of Business, about a third of the students are from the medical school. Their discussions with business-school peers (experienced in finance, consulting, and entrepreneurship) enhances the learning of both. For med students, these interactions also enhance their ability to lead transformative changes in healthcare. This type of interdisciplinary collaboration is crucial, and I advocate for its broader adoption to bridge the gaps between clinical practice and business strategy. About 8 years ago, I wrote about why medical students should take business school electives, citing the need for strategic thinking, operational excellence and teamwork as we shift from "pay-for-volume" to "pay-for-value." I'm glad to see this type of learning is taking place more frequently.

The power in the ability to have a compelling conversation with a healthcare stakeholder cannot be overstated. Two things to stay focused on during your conversations: 1. Talk about something THEY care deeply about (which means uncovering what that is in the beginning of the conversation) 2. Make the conversation all about THEM, not your product (don't worry, they'll be asking you to tell them about your product when the time is right...and you'll have their undivided attention) "Dr. Smith, it seems like most [specialists like them] believe that [key issue or opinion]. What are your thoughts about this?" Stop talking. Listen. When they stop talking, or at the right moment, ask another question—to learn more about their thoughts and beliefs. Ask questions to guide the conversation. Only when appropriate, based on the preceding conversation, if your product may be able to provide a benefit, ask, "If you were able to [benefit related to the conversation], would it be worth discussing? Notice how it's possible to have a productive conversation without mentioning your product until the best possible moment? Simple. Not easy, but simple. And with some training and practice, it becomes a part of you. And you're no longer a product-pusher, but perceived as a person of value. How often have you been sold to this way?

AI in healthcare is useless without one thing: Data. Everyone’s talking about AI revolutionizing healthcare. What they’re not talking about? AI is only as good as the data it learns from. Garbage in, garbage out. 🚨 Bad data = Bad AI decisions. 🚨 Fragmented data = Half-baked AI insights. 🚨 Delayed data = AI that reacts too late. The real transformation in healthcare isn’t just AI. It’s how we collect, structure, and use data to make AI actually useful. The Data crisis in Healthcare is real: 🏥 80% of healthcare data is unstructured. 🩺 Medical records are siloed across EHRs, wearables, and provider systems. ⏳ Care teams waste hours manually entering data instead of using it. And here’s what no one admits: AI isn’t the problem. The data mess is. We expect AI to predict patient deterioration, optimize staffing, and reduce hospitalizations. But without clean, real-time data? AI is just guessing. Where AI + Data is quietly changing Healthcare 1️⃣ Real-time patient monitoring → AI predicting sepsis hours before symptoms appear. 📉 31% fewer ICU admissions. 2️⃣ Automated documentation → AI reducing charting time from 50+ minutes to 10-12 minutes. ⚡ More time with patients, less time on admin work. 3️⃣ Predictive analytics → AI flagging at-risk seniors before a crisis hits. 🏥 26% reduction in ER visits. 4️⃣ Smart patient-caregiver matching → AI optimizing schedules and workload balancing. 🤝 Fewer burnout cases, higher patient satisfaction. The future of AI in Healthcare is data-first. At Inferenz, we focus on AI that actually solves the data problem first: 🔹 AI that connects fragmented data—turning scattered records into real-time insights. 🔹 AI that strengthens decision-making—empowering care teams, not replacing them. 🔹 AI that adapts, learns, and evolves—making healthcare more predictive, precise, and personal. Because AI without good data is like medicine without a diagnosis—dangerous and ineffective. The question isn’t whether AI belongs in healthcare. It’s whether we’re ready to fix data so AI can actually work. Let’s build data-first, human-first AI. Gayatri Akhani Yash Thakkar James Gardner Brendon Buthello Kishan Pujara Trupti Thakar Amisha Rodrigues Priyanka Sabharwal Prachi Shah Jalindar Karande Mitul Panchal 🇮🇳 Patrick Kovalik Joe Warbington 📊 Julie Dugum Perulli Chris Mate Ananth Mohan Michael Johnson Marek Bako Dustin Wyman, CISSP Rushik Patel #AI #Healthcare #DataMatters #HealthTech #HumanizingAI #PatientCare #Inferenz