Owning the Default Option

Default options are powerful, because 60% of people perceive them as an implied recommendation. In software that translates to 66-95% of people never opening the settings menu. But who is people? Part of being a professional is about staying on top of new developments and improving your craft. In

Best vs Most Integrated Product, Heidi’s Achilles Heel

Last week I summarised the Heidi Interview on LinkedIn: 1. Heidi doesn't try to do documentation for diagnostic purposes, which is already a very saturated and regulatory-heavy space. Instead, they make a complete transcript of what happened and use that to improve budget planning on the government/

Documentation Purposes, Cost and Product Moats, Heidi Health Interview

Heidi Health recently closed their Series C round. Digital Health Inside Out had a great interview with Heidi CEO Thomas Kelly and CMO Simon Kos, where they shared a bunch of interesting insights about their approach. Well worth a watch:

Dedalus Wants to Orchestrate, Keeping Microsoft Out, Corti’s Playbook

I didn’t have to wait long to get an answer to my question from Monday. kma.Online reported today (translated, emphasis mine): At this year's DMEA, Dedalus and Corti presented for the first time how their future collaboration is intended to work: "We are positioning Orbis

Prof. Valmed Gets Telekom Distribution, Differentiation Difficulties and Data Moats

From kma.Online (translated): To support doctors in diagnostic and treatment decisions using artificial intelligence, Telekom has partnered with the Langen-based start-up Prof. Valmed. Its AI solution of the same name makes medical knowledge from millions of documents, such as studies and guidelines, directly available within the treatment

Agentic AI in ORBIS, Corti Agentic Framework, Dedalus Existential Risks

Last week Dedalus announced that it is stepping up on the agentic HIS arena, from E-HEALTH.COM (translated): Dedalus HealthCare has made this approach an integral part of ORBIS. As a result, ORBIS is evolving from a documentation system into an intelligent clinical partner – with AI that is not

Charité’s Launches Clé, Research Hospitals, the Cosmos Data Moat

From ai.berlin (emphasis mine): Charité – Universitätsmedizin Berlin has launched its own AI platform. "Clé" is available to staff in patient care, research, teaching and administration, and runs entirely on Charité's own servers. According to the hospital, that setup is so far unique among Germany'

Redrawing Germany’s Healthcare

Germany’s healthcare market is going through a complete makeover. Drastic changes in how hospitals work will cause drastic changes in how out-patient practices work. The former is funded by 50 billion EUR, the latter is not. Thank the KHVVG. Hospitals are being regrouped: they need to scale their

Multi-Model Agents, Harness as a Platform (HaaP), Microsoft Autopilot

Few weeks ago I wrote about how using a single AI model for diagnostics is fundamentally biased: It is a well established fact that a single-person view is inherently subjective. We all have our blind spots, no matter how much of an expert somebody might be. The solution is

DiGA Goes Class IIb, DiGA Wearables, Oura’s Opportunity

Starting with some “old” news, in March BfArM started reimbursing Class IIb DiGA apps, from reuschlaw: Prior to the Digital Act, DiGAs had been limited to the lower risk classes (I and IIa). This has now also been extended to the higher risk class IIb within the meaning of Art.

AI Agents On Premise vs In The Cloud, Part 3

This is a continuation of Part 1 and Part 2. All that being said, there is a really good reason why hospitals and other care institutions should be thinkering with AI in-house, even if they shouldn’t necessarily be using it in production, and that has everything to do

AI Agents On Premise vs In The Cloud, Part 2

You can read Part 1 here. When considering the cases in which AI could run on-premise, we need to understand the clinical workflow from the perspective of computing intensity, speed requirements and collaboration needs. Here is an easy example: An MRI or CT scan requires a massive computing power