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Best of LinkedIn: Health Tech CW 50 - 01 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 1: 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.
• 0:07
  Speaker 1: Frennus equips product and strategy teams with market and competitive intelligence to navigate the health tech landscape.
• 0:15
  Speaker 1: Welcome back to the deep dive.
• 0:17
  Speaker 1: Our mission today is pretty laser focused.
• 0:19
  Speaker 1: We're synthesizing the top health tech insights from LinkedIn over the last few weeks calendar weeks.
• 0:24
  Speaker 1: fifty and oh one.
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  Speaker 1: And if you're in product or strategy in this space what we're seeing is this really critical tangible shift.
• 0:32
  Speaker 1: The conversation is finally moving past all the hype.
• 0:35
  Speaker 1: Yeah past the what if and into the.
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  Speaker 1: you know the greedy reality of implementation.
• 0:38
  Speaker 0: That is the key takeaway.
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  Speaker 0: It's The whole energy has shifted.
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  Speaker 0: It's gone from.
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  Speaker 0: is AI even possible to this intense focus on what it actually takes to make it operational.
• 0:49
  Speaker 1: In real daily care settings.
• 0:50
  Speaker 0: Exactly.
• 0:51
  Speaker 0: We're tracking serious momentum around platform integration, imaging productivity, modernizing the EHR, but the old gatekeepers... They haven't gone anywhere.
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  Speaker 0: Interoperability, governance, and the big one, trust.
• 1:03
  Speaker 0: They're still setting the speed limit for everything.
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  Speaker 1: So let's unpack that first one because it's huge.
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  Speaker 1: This move from successful pilots to real workflow grade deployment.
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  Speaker 1: And the sources are all pointing to one major hurdle.
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  Speaker 1: And it's not technical, is it?
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  Speaker 1: It's this trust deficit.
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  Speaker 0: Exactly.
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  Speaker 0: And what's fascinating is that the trust gap... It's not just about if the algorithm is accurate.
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  Speaker 0: It's deeply emotional.
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  Speaker 0: It's structural.
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  Speaker 0: Right.
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  Speaker 0: Wolfgang Schleifer captured this so well, he observed that it's largely an emotional crisis.
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  Speaker 0: It's caused by algorithmic opacity and this sort of fragmented accountability.
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  Speaker 1: Meaning if you don't know why it made a decision?
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  Speaker 0: Or who's responsible when it's wrong, then trust just evaporates instantly.
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  Speaker 1: And we are seeing some countries trying to build that trust back, right?
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  Speaker 0: structurally.
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  Speaker 0: We are.
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  Speaker 0: Singapore and Malaysia are focusing on what they're calling minimum viable assurance, MVA.
• 1:56
  Speaker 0: Okay.
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  Speaker 0: It's about providing tangible, visible metrics.
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  Speaker 0: Things like tracking how often a clinician actually overrides an AI suggestion.
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  Speaker 0: You're building concrete confidence, making accountability measurable.
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  Speaker 0: That
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  Speaker 1: transparency seems absolutely vital, especially when you look at how physicians actually feel using these tools.
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  Speaker 1: Oh, yeah.
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  Speaker 1: Menal Shah summarized the off-gall study and the findings create this this real tension.
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  Speaker 1: On one hand, you have eighty-four percent of physicians feeling that AI makes them better at their job.
• 2:26
  Speaker 1: which is a phenomenal signal.
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  Speaker 0: It's huge, but that's where the problem is, the strategic problem.
• 2:31
  Speaker 0: That same study showed, eighty-one percent are unhappy with how their health systems are deploying the tools.
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  Speaker 1: So not the tool itself, but the rollout.
• 2:38
  Speaker 1: Precisely.
• 2:39
  Speaker 0: And sixty-seven percent want more direct control over the AI.
• 2:43
  Speaker 0: 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.
• 2:52
  Speaker 1: 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.
• 2:59
  Speaker 0: It is.
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  Speaker 0: Asin Khan made this point.
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  Speaker 0: He said the value of digital health hinges entirely on sustained use, not just a download or a pilot.
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  Speaker 1: So what's stopping them?
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  Speaker 0: He found the biggest barriers were often personal, things like digital literacy or the patient's health status.
• 3:13
  Speaker 0: I mean, if a patient stops using an app after three weeks, the clinical outcome just disappears.
• 3:18
  Speaker 1: So how do you get them to stick with it?
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  Speaker 0: The consensus seems to be professional endorsement.
• 3:23
  Speaker 0: adherence is way higher when healthcare professionals are actually trained to actively endorse these digital health tools.
• 3:30
  Speaker 0: The doctor has to be the champion.
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  Speaker 1: Not just the IT department.
• 3:32
  Speaker 1: Exactly.
• 3:33
  Speaker 1: And we saw a great use case of that in teledermatology.
• 3:36
  Speaker 1: Laura's Shuline highlighted a successful application using smartphone imaging and AI tools for chronic spontaneous heredicaria patients.
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  Speaker 1: CSU.
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  Speaker 1: It's a perfect example.
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  Speaker 1: Moving, monitoring, and self-care outside the clinic, but supported by the specialist's AI tool set.
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  Speaker 1: It addresses those access and adherence gaps.
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  Speaker 0: And that connects us directly to the foundational work.
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  Speaker 0: Our second theme... interoperability, fragmentation, and what people are calling the plumbing work.
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  Speaker 1: Ah, the plumbing.
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  Speaker 0: Yeah, before AL can really perform, the data pipes have to be clean and connected.
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  Speaker 1: It's a perfect analogy.
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  Speaker 1: It's messy, it's expensive, and it's absolutely essential.
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  Speaker 1: I mean, fragmentation is cited over and over as the root cause of stalled population health outcomes.
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  Speaker 0: And Anna Forsberg raised a really sharp question about this.
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  Speaker 0: She said, look, if data fragmentation is basically a political problem.
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  Speaker 1: Different decision makers, silos, budgets.
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  Speaker 1: Right.
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  Speaker 0: If it's political, why are we only talking about AI being hampered by bad data?
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  Speaker 0: Why aren't we exploring if AI itself could be engineered to fix the data, to clean it
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  Speaker 1: up?
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  Speaker 1: That completely flips the script.
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  Speaker 1: AI is the cleanup crew, not the victim.
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  Speaker 0: Exactly.
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  Speaker 0: And on the technical side, the focus is still on enablers like FHIR.
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  Speaker 0: Santosh Dasani explored using FHIR and the model context protocol, MCP, to create an AI that can instantly synthesize a patient's history.
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  Speaker 0: Across
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  Speaker 1: different systems.
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  Speaker 0: Yeah, and prepare a concise visit brief.
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  Speaker 0: It's a tool designed to handle the cognitive load that doctors are currently carrying.
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  Speaker 1: But all of this advanced engineering is what's meaningless if the architecture can't handle it.
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  Speaker 0: That's Bob Farlander's core message.
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  Speaker 0: He stressed that health systems need modern cloud architecture.
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  Speaker 0: Without it, you just can't scale real-time data management.
• 5:20
  Speaker 0: You hit structural limits immediately.
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  Speaker 1: And speaking of foundations, we have to talk about trust and execution, which brings up compliance.
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  Speaker 1: Yeah.
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  Speaker 1: Larry Trotter made a really sharp point here.
• 5:30
  Speaker 1: Oh, this was good.
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  Speaker 1: He said vendors rarely lose deals over high-pay compliance.
• 5:35
  Speaker 1: Everyone claims to be compliant.
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  Speaker 1: They lose them over a lack of hyper-credibility.
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  Speaker 0: It's such a crucial distinction.
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  Speaker 0: Credibility is about the demonstrable execution, the approach, not just checking a box.
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  Speaker 1: Right, it's how you do it that matters.
• 5:48
  Speaker 1: Okay, let's move on to where that execution translates into immediate results.
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  Speaker 1: Theme three, productivity, reimbursement, and sustainability.
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  Speaker 1: Especially in imaging, the energy is all about measurable workflow efficiency and speed.
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  Speaker 0: This is a huge area for AI.
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  Speaker 0: The impact is instant.
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  Speaker 0: Katharine Schmidler summarized discussions showing that workflow adaptation is just as important as the algorithms.
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  Speaker 0: The best algorithm fails if it adds clicks for the radiologist.
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  Speaker 1: Totally.
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  Speaker 1: We saw two fantastic examples.
• 6:16
  Speaker 1: Scott Miller introduced Genesis View, an AI-driven viewer that helps reduce the radiologist's cognitive load.
• 6:22
  Speaker 1: And then Jody Spakes highlighted this dual AI reconstruction.
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  Speaker 0: Smart speed, precise.
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  Speaker 0: That's
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  Speaker 1: it.
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  Speaker 1: It makes cardiac MRI up to three times faster, which is just... It means more throughput and a better patient experience.
• 6:34
  Speaker 0: And critically, those gains are starting to get funded.
• 6:37
  Speaker 0: Guido Matthews shared a massive milestone out of Germany.
• 6:41
  Speaker 0: The Kwamadi program in Schleswig Holstein is now integrating, and this is the key part, reimbursing AI and routine mammography.
• 6:48
  Speaker 1: Wow.
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  Speaker 1: That sets a powerful precedent for the whole market.
• 6:51
  Speaker 0: It's huge.
• 6:52
  Speaker 0: It basically says if AI demonstrably improves quality, it belongs in routine care.
• 6:57
  Speaker 0: It's not a luxury anymore.
• 6:58
  Speaker 1: And when we talk value, we have to mention sustainability.
• 7:01
  Speaker 1: Nancy Kaspozvi detailed Phillips' launch of the first three-T virtually helium-free MR system.
• 7:07
  Speaker 0: Blue Seal.
• 7:08
  Speaker 0: This is so smart.
• 7:09
  Speaker 1: It's innovation driven by scarcity.
• 7:11
  Speaker 1: A traditional system needs, what, fifteen hundred liters of liquid helium?
• 7:14
  Speaker 1: And
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  Speaker 0: Blue Seal uses just half a liter.
• 7:17
  Speaker 1: It's incredible.
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  Speaker 1: That dramatically reduces reliance on a volatile commodity, which means a lower total cost of ownership for hospitals.
• 7:25
  Speaker 1: It's just smart business.
• 7:26
  Speaker 0: It really is the ultimate strategic intersection.
• 7:29
  Speaker 1: Okay, let's shift focus to the surgical suite for theme four.
• 7:33
  Speaker 1: Robotics is moving closer to the mainstream.
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  Speaker 0: The conversation here has shifted so much.
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  Speaker 0: It's not about technical novelty anymore.
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  Speaker 0: It's about scalable, flexible choice for hospitals.
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  Speaker 1: And the big regulatory news came from Medtronic.
• 7:46
  Speaker 1: Their Hugo robotic system got FDA clearance for urologic procedures.
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  Speaker 1: Achilles Young, Austin Chiang, and Rajit Kamal all noted this.
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  Speaker 1: It's a huge signal.
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  Speaker 0: It is.
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  Speaker 0: It says the competitive field is widening.
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  Speaker 0: And the strategy behind Hugo's design is very telling.
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  Speaker 0: Billy Zhao noted that the system's modularity and digital ecosystem are all about scalability.
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  Speaker 1: So the narrative is changing.
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  Speaker 1: It's
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  Speaker 0: not by the best machine anymore.
• 8:11
  Speaker 0: It's by a system that can evolve with your operating room.
• 8:14
  Speaker 0: That modular approach lowers the barrier to entry.
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  Speaker 1: And the clinical validation is backing that up.
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  Speaker 1: Jane Zhao referenced a groundbreaking achievement.
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  Speaker 1: a fully robotic heart transplant at Baylor St.
• 8:26
  Speaker 1: Luke's.
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  Speaker 0: I saw that.
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  Speaker 0: They did it through tiny incisions, completely avoiding cracking the sternum.
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  Speaker 0: The
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  Speaker 1: clinical takeaway is immediate.
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  Speaker 1: Less trauma, faster recovery.
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  Speaker 1: It's the ultimate proof point.
• 8:37
  Speaker 0: But the best tech is useless without the human expertise.
• 8:41
  Speaker 0: And that's a point that came up again and again.
• 8:44
  Speaker 1: The training investment.
• 8:45
  Speaker 0: Exactly.
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  Speaker 0: Narratives reinforce that specialized training and collaboration are paramount.
• 8:51
  Speaker 0: We saw mentions of refresh sessions on the Hugo system for GYN oncology teams.
• 8:56
  Speaker 0: Investing in the people is just as critical as the tech.
• 8:59
  Speaker 1: Absolutely essential.
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  Speaker 1: Okay, let's pivot to theme five.
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  Speaker 1: Platform integration and utilization management.
• 9:05
  Speaker 1: This is where AI gets absorbed into the core systems.
• 9:08
  Speaker 1: And this is
• 9:08
  Speaker 0: driven by necessity.
• 9:10
  Speaker 0: Specifically, clinician overload.
• 9:12
  Speaker 0: Summer Siddiqui raised what he calls the signal problem.
• 9:15
  Speaker 1: The signal problem.
• 9:15
  Speaker 0: Yeah.
• 9:16
  Speaker 0: Physicians are just bombarded by pagers, EHR alerts, messages, and every signal is treated as equally urgent.
• 9:23
  Speaker 0: It creates this massive alert fatigue.
• 9:25
  Speaker 0: If everything is urgent, nothing is.
• 9:27
  Speaker 1: So the system has to manage the complexity, not the clinician.
• 9:30
  Speaker 0: Yes.
• 9:31
  Speaker 0: Scale comes from orchestrating the systems.
• 9:34
  Speaker 0: And we're starting to see results.
• 9:36
  Speaker 0: Oracle Health Narratives emphasized phase rollouts and user feedback.
• 9:40
  Speaker 0: A KLAS survey confirmed it.
• 9:42
  Speaker 0: The majority of their clinical AI agent users are seeing reduced documentation time.
• 9:47
  Speaker 0: Which
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  Speaker 1: is a direct, measurable win against burnout.
• 9:50
  Speaker 1: A huge challenge for every system.
• 9:52
  Speaker 0: And we also saw strategic partnerships tackling administrative bottlenecks.
• 9:56
  Speaker 0: Chuck Ferrick highlighted one between latitude health and health edge guiding care.
• 10:00
  Speaker 0: They're integrating an AI-driven utilization management platform directly into the workflow.
• 10:05
  Speaker 1: And for anyone unfamiliar, utilization management.
• 10:08
  Speaker 1: That's the process of figuring out if care is medically necessary.
• 10:11
  Speaker 1: It's a major choke
• 10:12
  Speaker 0: point.
• 10:12
  Speaker 0: A huge choke point.
• 10:13
  Speaker 0: So this integration promises faster determinations and more consistency.
• 10:17
  Speaker 0: It's using AI to streamline a core administrative function that causes a ton of friction.
• 10:22
  Speaker 0: OK,
• 10:23
  Speaker 1: finally, let's wrap with theme six, regulation, evidence, and market signals.
• 10:27
  Speaker 1: It feels like regulators and investors are finally starting to adapt.
• 10:31
  Speaker 0: The FDA is definitely leading on this.
• 10:34
  Speaker 0: Rui Zhu detailed the Tempo pilot.
• 10:36
  Speaker 0: It offers a reduced regulatory burden for digital health devices.
• 10:40
  Speaker 1: In exchange for what?
• 10:41
  Speaker 0: In exchange for a data-driven... risk management approach in collecting real-world data, RWD.
• 10:47
  Speaker 0: Essentially, they're trading some upfront burden for continuous monitoring once the product is in the wild.
• 10:53
  Speaker 1: That makes a lot of sense.
• 10:54
  Speaker 1: And
• 10:54
  Speaker 0: Kelny Birchuk added that the FDA is now accepting de-identified aggregate real-world evidence submissions.
• 11:02
  Speaker 0: This just speeds up the validation friction so much.
• 11:05
  Speaker 1: But the European reality is... bit more complex.
• 11:09
  Speaker 0: It is.
• 11:09
  Speaker 0: Rudolph Wagner cautioned that the proposed EU simplification of medical software regulation, it doesn't actually change that much for therapeutic or diagnostic software.
• 11:19
  Speaker 1: So what's the real problem there?
• 11:21
  Speaker 0: His argument is that enforcement capacity is the real bottleneck in the EU market, not regulatory complexity.
• 11:27
  Speaker 0: So while the US is speeding up, the EU is still struggling with implementation.
• 11:31
  Speaker 1: And what does this all mean for where the money is going?
• 11:33
  Speaker 0: Blake Tindall's market analysis was great on this.
• 11:36
  Speaker 0: Yes, capital is tilting toward AI-enabled startups.
• 11:39
  Speaker 0: Sixty-two percent of funds raised in H-One twenty twenty-five went to them.
• 11:42
  Speaker 1: But.
• 11:43
  Speaker 1: There's a but.
• 11:43
  Speaker 0: There's a but.
• 11:44
  Speaker 0: His analysis showed that non-AI med tech is still highly fundable if the clinical outcome is crystal clear.
• 11:51
  Speaker 0: That
• 11:51
  Speaker 1: the takeaway is.
• 11:52
  Speaker 0: AI has to be an implementation detail that moves the needle.
• 11:55
  Speaker 0: Fewer clicks, faster imaging, lower cost per outcome.
• 11:58
  Speaker 0: Not just a label to attract capital, the market is demanding measurable impact
• 12:03
  Speaker 1: now.
• 12:03
  Speaker 1: So if we synthesize all of this from the trust deficit to the plumbing work, robotics to regulation.
• 12:11
  Speaker 1: What's the central mandate for strategists in twenty-twenty-six?
• 12:14
  Speaker 0: It's one word, intentionality.
• 12:16
  Speaker 0: Meenal Shah's data showed us the technical capability is proven.
• 12:19
  Speaker 0: The success of health tech now depends entirely on moving from chasing what's possible to strategically implementing what an organization actually needs.
• 12:26
  Speaker 1: For measurable benefit.
• 12:27
  Speaker 0: For tangible, measurable patient and clinician benefit.
• 12:30
  Speaker 0: The focus has to be on the deployment strategy, the adherence pathway, the ROI.
• 12:34
  Speaker 0: not the buzzword.
• 12:35
  Speaker 1: That's a powerful synthesis.
• 12:37
  Speaker 1: The industry is maturing and the focus is on results.
• 12:40
  Speaker 1: If you enjoyed this deep dive, new episodes drop every two weeks.
• 12:43
  Speaker 1: 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.
• 12:53
  Speaker 1: Thank you so much for joining us.
• 12:55
  Speaker 1: We hope you feel thoroughly well informed and ready to tackle the future of help tech implementation.
• 13:00
  Speaker 1: We'll talk to you next time.