Acquire and expand customers quicker, cheaper, and for longer | Coaches B2B scale-ups scale more efficiently and effectively through proven frameworks | Organizing the MarTech World Forum London 2024

If your board or investors ask simple revenue questions and three people disappear into Excel, we need to talk.

Your forecast sounds confident on Monday. By Thursday, reality disagrees, and no one can explain why.
You spend more time arguing about stages than coaching deals. 
Deals do not die in your CRM. They just quietly age out while everyone watches.

The heroics that got you here are now the bottleneck. You have 15 dashboards, forecast theatre instead of truth, and a revenue engine leaking cash at invisible handoff points.

You are not bad at this. The system is.

I help €20 to 100M B2B SaaS scale-ups turn scattered data and forecast theatre into a revenue engine you can actually steer. Within 90 days. Without a reorg, new tools, or hiring a full-time VP RevOps.

Your RevOps team owns the system when I leave. I bring the pattern. They keep it running.

We start with a fast 4-part maturity scan to find the real constraint. Then we install three structures:
→ One-page strategy linking goals to the programs that actually move pipeline, win rates, and NRR.
→ One Revenue Room where metrics, risks, and decisions live in one view. No more "marketing numbers" vs "sales numbers." Just the number.
→ One operating cadence that replaces updates with decisions. If we are not making decisions, we are just having expensive coffee.

When it works, you explain where pipeline is healthy, where it is leaking, and how you are fixing it in 10 minutes flat. You get the headspace to think 3 years ahead, not 3 hours behind.

I am Rutger Katz, Fractional VP of RevOps.
Most RevOps people grew up in the growth bubble. They know how to spend money. I spent 15 years architecting efficient growth, so I know how to stop wasting money.

At one tech company, I uncovered €1M in unseen upsell opportunities. 
At Relay42, I rebuilt onboarding and adoption to move NRR.

Stop drifting. Start steering.
Revenue Engine Checkup in Featured. 10 minutes.
Or DM me "CHECKUP" for the questions directly.

I also speak/podcast guest on strategic RevOps, neurodivergence in leadership, and scaling to 100M ARR. 
Check www.neontriforce.com/speaking for more

Rutger Katz

Helping healthcare use the power of AI⚕️

With a childhood filled with hospital visits, I knew I would be a part of improving healthcare one day.

From seeing the fragile human life, to how much the treatment will influence the future.

My dad's car accident marked my life, not physically, but mentally.

As a witness to my dad's struggle, I saw the realities of healthcare. Its triumphs and its failures.

My glimmer of hope for healthcare lies in innovation, specifically the transformative power of AI.

The hope has evolved to become my passion.

A passion that makes sure:
Patients get the best care out there
Clinicians can focus on their patients
Peace of mind for caregivers

At the bottom line, is dignity for every individual.

In order to do so, healthcare as is needs to change.

Technology is the only feasible way to truly change healthcare as is.

A future where AI in healthcare is not just a possibility.

It's a necessity.

Knowing what to change though, can be difficult.

Should you be following the old ways of:
Prioritizing aesthetics over functionality
Making small tweaks ignoring core issues
Inefficient exchange of information

AI can create true value and impact in healthcare.

Interested in working towards a clear vision of AI for your organization?

Contact me to plan a call (sigrid@eirhealth.ai)

Sigrid Berge van Rooijen

New year, new conferences. 
And I created a short list of conferences for innovation in healthcare. 

I wish I had endless time and money to go to conferences, 
Especially about innovation in healthcare,
And how to make innovation equitable (though let’s face it, is not a hot topic). 

The truth is, I don’t have endless resources, so I have to prioritize. 

But if I did have, these are some European and Middle Eastern conferences I would consider for 2026. 

What I do look for is:
Relevant and interesting topics.
Mix of attendees and networking.
Action, and real world impact. Not just talk.
(and as a bonus: good food). 

I know very few conferences has it all, 
So sometimes you need to choose a mix. 

ICT&Health World Conference, Maastricht, 27-29 January https://lnkd.in/e6YuBG6Z 
Health.Tech Global Summit, Basel, 3-5 March https://www.health.tech/
European Digital HealthTech Conference, Luxembourg City, 26-27 March https://lnkd.in/eNAf7VqY
WIRED Health, London, 16 April https://health.wired.com/
Vitalis, Gothenburg, 4-7 May https://en.vitalis.nu/
The MedTech Forum, Stockholm, 11-13 May https://lnkd.in/e9KDsv8G
Digital Health World Congress, London, 26-27 May https://lnkd.in/e9uZwgNx
HLTH, Amsterdam, 15-18 June https://europe.hlth.com/
WHX Tech, Dubai, 14-16 September https://lnkd.in/e3tR_JSi
HETT, London, 29-30 September https://lnkd.in/e5bNSYuH
Global Summit on Healthcare Innovation, Lisbon, 7-8 October https://lnkd.in/eD4F2CAJ
World Health Summit, Berlin, 11-13 October https://lnkd.in/exDCK5iV
Frontiers Health, Berlin, October, https://lnkd.in/e6tdbAVR
Digital Health Conference, Barcelona, 25-26 November https://lnkd.in/eXVURZqG
Future Health, London, November https://lnkd.in/eEPxwmsy
GIANT Health, London, 7-8 December https://www.giant.health/
Health 2.0, Dubai, 8-10 December https://lnkd.in/eGg65BB2

What conference would you recommend for anyone interested in innovation in healthcare? 
And which ones have you decided to go to?

We cannot ban ChatGPT in healthcare, 
But will consumer laws protect the patients?

For the one’s who haven’t followed the conversation last week, here is a short summary:
ChatGPT will have a part of the chatbot dedicated to health and wellness conversations. 
People can connect their medical records and wellness apps to ChatGPT. 
ChatGPT Health will not be used to foundation models. 
Has been evaluated through the HealthBench framework (OpenAI developed framework). 
Currently available outside the European Economic Area, Swizerland and UK. 

The move doesn’t come from nowhere. 

ChatGPT have seen a growing demand on health and wellness related questions. 

People asking to support in medical cases. 
Whether or not to go to the doctor. 
What diagnosis they could have.
What treatment they should start. 
How to improve vitals. 

Many startups, and even European governments, 
Have tried to connect health and medical data, 
But without big success. 

Now, a player makes it possible to see this data together, 
With a large reach. 

But, OpenAI is not a healthcare provider. 
It is not responsible for your health. 
Nor will it take responsibility if anything gone wrong. 
It is a "consumer product". 

Focusing on “supporting” not replacing medical care. 

People google symptoms, 
But Google never connected your medical record, 
Nor gave personalized recommendations. 

People are not critical enough to answers from ChatGPT. 
Most people don’t understand how ChatGPT functions or how it resonates.

If the ChatGPT Health was used with cautiousness, the risk would be smaller.
And it could help people get access to health advice, 
Perhaps even people who normally wouldn’t be able to easy access a doctor. 

What worries me is the following:
- Personalized advice without going to regulatory route (read, who is responsible for adverse outcomes)
- Biased answers, perpetuating inequalities in care (gender, race, socioeconomic status)
- Lack of nuanced and context-specific reasoning

And how will the platform be monitored and evaluated?
What about safety incidences? 
The nearly missed?
Or the fully missed?

Will they be reported? 
Improved on? 

And what will happen if clinicians upload patient medical records into ChatGPT Health? 
Do patients consent?
Clinicians relying on an available platform. 
As a first or second opinion.
Risking biased answers. 

How does OpenAI make sure that people are only connecting their own medical record?

Delegating judgment to AI does not shift healthcare professionals’ duty of care.

If ChatGPT Health will be used in a more extensive way by clinicians, 
And without their organization's knowledge, 
How can patterns of harm, inequity, and safety incidences with the tool be detected? 

ChatGPT has become too far to ban it for being used for health. 
Patients need to understand the risks. 
And organizations need to be aware of how, and for what it is being used. 

What do you think about the connecting health records to ChatGPT?

Are you ignoring a crucial factor for trust during AI implementation?

Risking low adoption and ineffective use of AI tools you purchase. 

Ethics in AI implementation makes sure that technologies align with human rights, avoiding harm, and promoting equitable care for everyone. 

Essential for building trust among patients and healthcare professionals. 

Here are 12 important factors to consider to increase trust in AI: 

Governance: 
Establish ethics committees to oversee AI deployment and auditing. 

Regulatory:
Stay compliant with relevant laws and regulations for AI in healthcare. 

Bias Auditing:
Collaborate with vendors to audit tools for bias. 

Monitoring:
Implement ongoing monitoring of AI performance, safety, and ethical compliance. 

Privacy & Data Security:
Enforce strict data protection measures, and limit data retention by vendors. 

Explainability & Transparency:
Require vendors to provide transparent AI models or explanations of decision-making processes. 

Risk Management:
Use risk management frameworks to map,prioritize and mitigate AI-related risks. 

Education & Literacy:
Provide ongoing education for healthcare professionals about ethical use of AI. 

Informed Consent:
Inform patients about the use of AI in their care, and rights to opt out. 

Clinical Oversight:
Review and approve all AI-generated outputs before action. 

AI Policies:
Develop and maintain clear, robust policies regarding AI use. 

Due Diligence:
Evaluate vendors’ ethical practices, data security measures, transparency, and regulatory compliance. 

Trust is essential if we want AI to be adopted in healthcare. 

What are you doing to increase trust in AI tools in your organization, for patients and healthcare professionals alike?

If I still worked in healthcare, I would probably risk patient safety and data security,
Without knowing, I would be risking it. 

Testing “innocent” tools that could support my clinical decisions. 
Especially if I got a rare condition on my table. 

Which, back then, I regularly got. 

Most healthcare professionals are not aware of the risks. 
And I don’t blame them. 

More worryingly, most healthcare organizations are not aware of Shadow AI
And it’s a bigger problem than they’d ever think. 

AI has become so common in healthcare, 
And people are not understanding what appropriate use is. 
While more people have recognized the possibilities of AI, 
More people have also started to explore AI. In different contexts. 
One of which is health-related. 

Patients see added value by asking AI about their symptoms and potential treatment. 
Healthcare professionals see added value in AI for second opinions, summaries, and decision support. 

But most available AI tools are not for clinical purposes. 

Since AI is so widely known and available, 
People can access it from wherever. 
No additional resources needed other than your personal phone. 

Quick clicks and you have your personal guide. 

Organizations can try to stop this. 
They can write guidelines. 
They can block certain tools from their networks. 
They can provide approved tools. 

But if the approved tools are not supporting the healthcare professionals in the way they are used to, 
They will find other solutions.
Use the tool of their choice. 

ChatGPT and other tools are rapidly integrating into clinical practice. 
But their organizations are not aware of the full usage. 
Nor are they aware of how the tools are used.

Organizations can warn as much as they can about using GenAI tools with identifiable patient information, 
But if these warnings, or the full understanding of the implications, don’t reach the healthcare professionals, 
They will continue to use the tools that simplify some of their work. 

Surveys show that 20-25% of healthcare professionals regularly use GenAI in their clinical practice. 
I think the number is higher and growing every month. 

Surveys have also shown that few have received guidelines (or are aware of the guidelines) about the use of these tools. 

The fact is, these tools are not made for clinical use. 
It is so easy to accidentally add personally identifiable information, 
And GenAI can be convincing. So convincing that one can start doubting their own knowledge. 

Healthcare organizations need to be aware of the use of Shadow AI tools in their organizations. 
And instead of writing guidelines that end up in a dusty corner, 
They should open up the conversation with their employees. 
Understand which tools are used, 
For what they are used, 
The risks and how to minimize them. 

What is your organization doing to increase knowledge around the use and risks of unauthorized tools?

Think all medical data is good data? Here’s why that assumption could be a costly mistake.

Everyone who is serious about data for AI development and AI training, should take note. 

Because there are some (overlooked) aspects of how data quality could be sabotaging your AI's performance and patient outcomes.

The METRIC-framework has highlighted five aspects that will challenge how you think about data quality used for medical training/testing data:

1. Measurement Process

Emphasizes the importance of understanding how data is collected and processed. It distinguishes between device errors and human-induced errors, highlighting the need for accurate and reliable data acquisition methods in healthcare AI.

2. Timeliness

Medical knowledge evolves rapidly, and it is important to use up-to-date data for AI applications, ensuring that the information reflects current medical understanding and practices.

3. Representativeness

How well does the dataset reflect the target population? We need to ensure our AI is trained on data that covers a wide range of demographics, data sources, and clinical scenarios, promoting fairness and generalizability.

4. Informativeness

The data should be clear, concise, and beneficial, and we should focus on the relevance and value of features, rather than just accumulating large amounts of data.

5. Consistency

There is a need for uniform data presentation across different aspects. This includes rule-based, logical, and distribution consistency, ensuring that the data is reliable, free of contradictions, and maintains its integrity over time.

We need to have a conversation about how the medical data impacts the performance of the AI tools, including its impact on patient outcomes.

Have you already reviewed this framework? Is it sufficient, or is it missing some dimensions?

Link to article here: https://lnkd.in/epKaGqQq

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