Best of LinkedIn: Health Tech CW 50 - 01

00:00:00: This episode is provided by Thomas Allgaier and Frennus based on the most relevant LinkedIn posts on health tech in CW-Fifty and O-One.

00:00:07: Frennus equips product and strategy teams with market and competitive intelligence to navigate the health tech landscape.

00:00:15: Welcome back to the deep dive.

00:00:17: Our mission today is pretty laser focused.

00:00:19: We're synthesizing the top health tech insights from LinkedIn over the last few weeks calendar weeks.

00:00:24: fifty and oh one.

00:00:26: And if you're in product or strategy in this space what we're seeing is this really critical tangible shift.

00:00:32: The conversation is finally moving past all the hype.

00:00:35: Yeah past the what if and into the.

00:00:37: you know the greedy reality of implementation.

00:00:38: That is the key takeaway.

00:00:40: It's The whole energy has shifted.

00:00:42: It's gone from.

00:00:42: is AI even possible to this intense focus on what it actually takes to make it operational.

00:00:49: In real daily care settings.

00:00:50: Exactly.

00:00:51: We're tracking serious momentum around platform integration, imaging productivity, modernizing the EHR, but the old gatekeepers... They haven't gone anywhere.

00:01:00: Interoperability, governance, and the big one, trust.

00:01:03: They're still setting the speed limit for everything.

00:01:05: So let's unpack that first one because it's huge.

00:01:08: This move from successful pilots to real workflow grade deployment.

00:01:14: And the sources are all pointing to one major hurdle.

00:01:17: And it's not technical, is it?

00:01:19: It's this trust deficit.

00:01:21: Exactly.

00:01:21: And what's fascinating is that the trust gap... It's not just about if the algorithm is accurate.

00:01:26: It's deeply emotional.

00:01:27: It's structural.

00:01:28: Right.

00:01:29: Wolfgang Schleifer captured this so well, he observed that it's largely an emotional crisis.

00:01:35: It's caused by algorithmic opacity and this sort of fragmented accountability.

00:01:40: Meaning if you don't know why it made a decision?

00:01:42: Or who's responsible when it's wrong, then trust just evaporates instantly.

00:01:47: And we are seeing some countries trying to build that trust back, right?

00:01:50: structurally.

00:01:51: We are.

00:01:51: Singapore and Malaysia are focusing on what they're calling minimum viable assurance, MVA.

00:01:56: Okay.

00:01:56: It's about providing tangible, visible metrics.

00:01:59: Things like tracking how often a clinician actually overrides an AI suggestion.

00:02:03: You're building concrete confidence, making accountability measurable.

00:02:07: That

00:02:07: transparency seems absolutely vital, especially when you look at how physicians actually feel using these tools.

00:02:14: Oh, yeah.

00:02:14: Menal Shah summarized the off-gall study and the findings create this this real tension.

00:02:20: On one hand, you have eighty-four percent of physicians feeling that AI makes them better at their job.

00:02:26: which is a phenomenal signal.

00:02:28: It's huge, but that's where the problem is, the strategic problem.

00:02:31: That same study showed, eighty-one percent are unhappy with how their health systems are deploying the tools.

00:02:36: So not the tool itself, but the rollout.

00:02:38: Precisely.

00:02:39: And sixty-seven percent want more direct control over the AI.

00:02:43: So it tells us the core tech is validated, but the implementation strategy, the product integration, the training, It's fundamentally broken for the end user.

00:02:52: That friction has to be a major challenge for every vendor right now, and it links directly to sustained use, which is where the value is.

00:02:59: It is.

00:02:59: Asin Khan made this point.

00:03:01: He said the value of digital health hinges entirely on sustained use, not just a download or a pilot.

00:03:06: So what's stopping them?

00:03:07: He found the biggest barriers were often personal, things like digital literacy or the patient's health status.

00:03:13: I mean, if a patient stops using an app after three weeks, the clinical outcome just disappears.

00:03:18: So how do you get them to stick with it?

00:03:20: The consensus seems to be professional endorsement.

00:03:23: adherence is way higher when healthcare professionals are actually trained to actively endorse these digital health tools.

00:03:30: The doctor has to be the champion.

00:03:31: Not just the IT department.

00:03:32: Exactly.

00:03:33: And we saw a great use case of that in teledermatology.

00:03:36: Laura's Shuline highlighted a successful application using smartphone imaging and AI tools for chronic spontaneous heredicaria patients.

00:03:46: CSU.

00:03:47: It's a perfect example.

00:03:48: Moving, monitoring, and self-care outside the clinic, but supported by the specialist's AI tool set.

00:03:53: It addresses those access and adherence gaps.

00:03:56: And that connects us directly to the foundational work.

00:03:58: Our second theme... interoperability, fragmentation, and what people are calling the plumbing work.

00:04:04: Ah, the plumbing.

00:04:05: Yeah, before AL can really perform, the data pipes have to be clean and connected.

00:04:09: It's a perfect analogy.

00:04:10: It's messy, it's expensive, and it's absolutely essential.

00:04:14: I mean, fragmentation is cited over and over as the root cause of stalled population health outcomes.

00:04:20: And Anna Forsberg raised a really sharp question about this.

00:04:23: She said, look, if data fragmentation is basically a political problem.

00:04:27: Different decision makers, silos, budgets.

00:04:29: Right.

00:04:30: If it's political, why are we only talking about AI being hampered by bad data?

00:04:35: Why aren't we exploring if AI itself could be engineered to fix the data, to clean it

00:04:40: up?

00:04:40: That completely flips the script.

00:04:42: AI is the cleanup crew, not the victim.

00:04:44: Exactly.

00:04:45: And on the technical side, the focus is still on enablers like FHIR.

00:04:49: Santosh Dasani explored using FHIR and the model context protocol, MCP, to create an AI that can instantly synthesize a patient's history.

00:04:58: Across

00:04:58: different systems.

00:04:59: Yeah, and prepare a concise visit brief.

00:05:01: It's a tool designed to handle the cognitive load that doctors are currently carrying.

00:05:05: But all of this advanced engineering is what's meaningless if the architecture can't handle it.

00:05:10: That's Bob Farlander's core message.

00:05:13: He stressed that health systems need modern cloud architecture.

00:05:17: Without it, you just can't scale real-time data management.

00:05:20: You hit structural limits immediately.

00:05:22: And speaking of foundations, we have to talk about trust and execution, which brings up compliance.

00:05:28: Yeah.

00:05:28: Larry Trotter made a really sharp point here.

00:05:30: Oh, this was good.

00:05:31: He said vendors rarely lose deals over high-pay compliance.

00:05:35: Everyone claims to be compliant.

00:05:37: They lose them over a lack of hyper-credibility.

00:05:39: It's such a crucial distinction.

00:05:41: Credibility is about the demonstrable execution, the approach, not just checking a box.

00:05:45: Right, it's how you do it that matters.

00:05:48: Okay, let's move on to where that execution translates into immediate results.

00:05:52: Theme three, productivity, reimbursement, and sustainability.

00:05:56: Especially in imaging, the energy is all about measurable workflow efficiency and speed.

00:06:01: This is a huge area for AI.

00:06:03: The impact is instant.

00:06:04: Katharine Schmidler summarized discussions showing that workflow adaptation is just as important as the algorithms.

00:06:10: The best algorithm fails if it adds clicks for the radiologist.

00:06:13: Totally.

00:06:14: We saw two fantastic examples.

00:06:16: Scott Miller introduced Genesis View, an AI-driven viewer that helps reduce the radiologist's cognitive load.

00:06:22: And then Jody Spakes highlighted this dual AI reconstruction.

00:06:26: Smart speed, precise.

00:06:27: That's

00:06:28: it.

00:06:28: It makes cardiac MRI up to three times faster, which is just... It means more throughput and a better patient experience.

00:06:34: And critically, those gains are starting to get funded.

00:06:37: Guido Matthews shared a massive milestone out of Germany.

00:06:41: The Kwamadi program in Schleswig Holstein is now integrating, and this is the key part, reimbursing AI and routine mammography.

00:06:48: Wow.

00:06:49: That sets a powerful precedent for the whole market.

00:06:51: It's huge.

00:06:52: It basically says if AI demonstrably improves quality, it belongs in routine care.

00:06:57: It's not a luxury anymore.

00:06:58: And when we talk value, we have to mention sustainability.

00:07:01: Nancy Kaspozvi detailed Phillips' launch of the first three-T virtually helium-free MR system.

00:07:07: Blue Seal.

00:07:08: This is so smart.

00:07:09: It's innovation driven by scarcity.

00:07:11: A traditional system needs, what, fifteen hundred liters of liquid helium?

00:07:14: And

00:07:14: Blue Seal uses just half a liter.

00:07:17: It's incredible.

00:07:18: That dramatically reduces reliance on a volatile commodity, which means a lower total cost of ownership for hospitals.

00:07:25: It's just smart business.

00:07:26: It really is the ultimate strategic intersection.

00:07:29: Okay, let's shift focus to the surgical suite for theme four.

00:07:33: Robotics is moving closer to the mainstream.

00:07:36: The conversation here has shifted so much.

00:07:38: It's not about technical novelty anymore.

00:07:40: It's about scalable, flexible choice for hospitals.

00:07:44: And the big regulatory news came from Medtronic.

00:07:46: Their Hugo robotic system got FDA clearance for urologic procedures.

00:07:51: Achilles Young, Austin Chiang, and Rajit Kamal all noted this.

00:07:55: It's a huge signal.

00:07:56: It is.

00:07:57: It says the competitive field is widening.

00:07:59: And the strategy behind Hugo's design is very telling.

00:08:03: Billy Zhao noted that the system's modularity and digital ecosystem are all about scalability.

00:08:08: So the narrative is changing.

00:08:09: It's

00:08:09: not by the best machine anymore.

00:08:11: It's by a system that can evolve with your operating room.

00:08:14: That modular approach lowers the barrier to entry.

00:08:18: And the clinical validation is backing that up.

00:08:20: Jane Zhao referenced a groundbreaking achievement.

00:08:23: a fully robotic heart transplant at Baylor St.

00:08:26: Luke's.

00:08:26: I saw that.

00:08:27: They did it through tiny incisions, completely avoiding cracking the sternum.

00:08:31: The

00:08:31: clinical takeaway is immediate.

00:08:33: Less trauma, faster recovery.

00:08:35: It's the ultimate proof point.

00:08:37: But the best tech is useless without the human expertise.

00:08:41: And that's a point that came up again and again.

00:08:44: The training investment.

00:08:45: Exactly.

00:08:46: Narratives reinforce that specialized training and collaboration are paramount.

00:08:51: We saw mentions of refresh sessions on the Hugo system for GYN oncology teams.

00:08:56: Investing in the people is just as critical as the tech.

00:08:59: Absolutely essential.

00:09:00: Okay, let's pivot to theme five.

00:09:02: Platform integration and utilization management.

00:09:05: This is where AI gets absorbed into the core systems.

00:09:08: And this is

00:09:08: driven by necessity.

00:09:10: Specifically, clinician overload.

00:09:12: Summer Siddiqui raised what he calls the signal problem.

00:09:15: The signal problem.

00:09:15: Yeah.

00:09:16: Physicians are just bombarded by pagers, EHR alerts, messages, and every signal is treated as equally urgent.

00:09:23: It creates this massive alert fatigue.

00:09:25: If everything is urgent, nothing is.

00:09:27: So the system has to manage the complexity, not the clinician.

00:09:30: Yes.

00:09:31: Scale comes from orchestrating the systems.

00:09:34: And we're starting to see results.

00:09:36: Oracle Health Narratives emphasized phase rollouts and user feedback.

00:09:40: A KLAS survey confirmed it.

00:09:42: The majority of their clinical AI agent users are seeing reduced documentation time.

00:09:47: Which

00:09:47: is a direct, measurable win against burnout.

00:09:50: A huge challenge for every system.

00:09:52: And we also saw strategic partnerships tackling administrative bottlenecks.

00:09:56: Chuck Ferrick highlighted one between latitude health and health edge guiding care.

00:10:00: They're integrating an AI-driven utilization management platform directly into the workflow.

00:10:05: And for anyone unfamiliar, utilization management.

00:10:08: That's the process of figuring out if care is medically necessary.

00:10:11: It's a major choke

00:10:12: point.

00:10:12: A huge choke point.

00:10:13: So this integration promises faster determinations and more consistency.

00:10:17: It's using AI to streamline a core administrative function that causes a ton of friction.

00:10:22: OK,

00:10:23: finally, let's wrap with theme six, regulation, evidence, and market signals.

00:10:27: It feels like regulators and investors are finally starting to adapt.

00:10:31: The FDA is definitely leading on this.

00:10:34: Rui Zhu detailed the Tempo pilot.

00:10:36: It offers a reduced regulatory burden for digital health devices.

00:10:40: In exchange for what?

00:10:41: In exchange for a data-driven... risk management approach in collecting real-world data, RWD.

00:10:47: Essentially, they're trading some upfront burden for continuous monitoring once the product is in the wild.

00:10:53: That makes a lot of sense.

00:10:54: And

00:10:54: Kelny Birchuk added that the FDA is now accepting de-identified aggregate real-world evidence submissions.

00:11:02: This just speeds up the validation friction so much.

00:11:05: But the European reality is... bit more complex.

00:11:09: It is.

00:11:09: Rudolph Wagner cautioned that the proposed EU simplification of medical software regulation, it doesn't actually change that much for therapeutic or diagnostic software.

00:11:19: So what's the real problem there?

00:11:21: His argument is that enforcement capacity is the real bottleneck in the EU market, not regulatory complexity.

00:11:27: So while the US is speeding up, the EU is still struggling with implementation.

00:11:31: And what does this all mean for where the money is going?

00:11:33: Blake Tindall's market analysis was great on this.

00:11:36: Yes, capital is tilting toward AI-enabled startups.

00:11:39: Sixty-two percent of funds raised in H-One twenty twenty-five went to them.

00:11:42: But.

00:11:43: There's a but.

00:11:43: There's a but.

00:11:44: His analysis showed that non-AI med tech is still highly fundable if the clinical outcome is crystal clear.

00:11:51: That

00:11:51: the takeaway is.

00:11:52: AI has to be an implementation detail that moves the needle.

00:11:55: Fewer clicks, faster imaging, lower cost per outcome.

00:11:58: Not just a label to attract capital, the market is demanding measurable impact

00:12:03: now.

00:12:03: So if we synthesize all of this from the trust deficit to the plumbing work, robotics to regulation.

00:12:11: What's the central mandate for strategists in twenty-twenty-six?

00:12:14: It's one word, intentionality.

00:12:16: Meenal Shah's data showed us the technical capability is proven.

00:12:19: The success of health tech now depends entirely on moving from chasing what's possible to strategically implementing what an organization actually needs.

00:12:26: For measurable benefit.

00:12:27: For tangible, measurable patient and clinician benefit.

00:12:30: The focus has to be on the deployment strategy, the adherence pathway, the ROI.

00:12:34: not the buzzword.

00:12:35: That's a powerful synthesis.

00:12:37: The industry is maturing and the focus is on results.

00:12:40: If you enjoyed this deep dive, new episodes drop every two weeks.

00:12:43: Also check out our other editions on ICT and tech insights, defense tech, cloud, digital products and services, artificial intelligence and sustainability and green ICT.

00:12:53: Thank you so much for joining us.

00:12:55: We hope you feel thoroughly well informed and ready to tackle the future of help tech implementation.

00:13:00: We'll talk to you next time.