MedTech Masters Q&A

In this MedTech Masters Q&A, Etiometry’s VP of Marketing, Maayan Wenderow, shares insights from her experience leading marketing for a fast-growing clinical decision-support company. She discusses the challenges of positioning complex ICU analytic solutions, aligning messaging with real clinical needs, and supporting long hospital sales cycles. Drawing from her work at Etiometry, she highlights the importance of clearly communicating both clinical and economic value in today’s data-driven healthcare environment.

Read on to learn more about Maayan’s experience, how AI is shaping Etiometry’s work in healthcare and other insights from this smart, strategic MedTech Master:

1. What is your background in medtech? How did you get started? 

My career actually began outside of medtech, in telecommunications. My first role was as a product manager at a large company, but early on, I found myself drawn to building something new rather than just managing what already existed. I helped launch a new business within the company to support growth and ultimately prepare it for going public. It was a capability our competitors already had, and we didn’t, so it was clear it needed to be built. That experience really shaped my entrepreneurial mindset.

After about five years in telecom, I transitioned into medtech. My husband had started a robotics company in cardiology, and seeing firsthand how technology could directly impact patient care really sparked my interest. That was the moment I caught the “medtech bug.”

My first role in the industry was with a small investment group focused on taking early-stage companies to market, primarily in radiology and imaging technologies. It was an incredible learning experience, working across multiple innovations and seeing what it takes to move technology from concept to clinical adoption.

Over time, I became especially drawn to patient monitoring and patient safety, where technology has an immediate and meaningful impact on outcomes. I’ve now been in that space for about 15 years, focused on advancing solutions that improve care delivery and protect patients at their most vulnerable moments.

2. As we move into a new calendar year, AI continues to be a hot topic – especially in healthcare. Where does Etiometry fit in the AI landscape and why is this unique?

AI adoption in healthcare has reached a critical inflection point. Many of today’s most widely adopted solutions focus on reducing administrative burden, automating documentation, streamlining workflows and easing time spent with electronic health records (EHR). While important for addressing burnout, these tools largely operate around the periphery of care delivery.

Etiometry is different. We apply AI directly to clinical decision support in critical care, where decisions are complex, time-sensitive and directly impact patient outcomes. Our platform transforms existing EHR and monitoring data into actionable insights. Rather than adding more data or alerts, Etiometry provides a standardized framework for understanding patient physiology while still enabling individualized decision-making at the bedside.

What sets Etiometry apart is that we are the only clinical decision support system that enables both standardization and personalization of care escalation and de-escalation. By leveraging hospitals’ existing EHR and monitoring investments, we use AI to continuously assess, learn and improve care delivery, not just for individual patients, but across populations and over time.

3. Tell us about your role at Etiometry – what are you working on these days and what energizes you? 

Etiometry began in pediatrics, with a strong focus on cardiac critical care, and over time has demonstrated meaningful clinical outcomes, such as reduced time on mechanical ventilation, decreased use of vasoactive medications and shorter length of stay. In recent years, the company has expanded into additional critical care settings and into the adult market, marking an exciting phase of growth.

In my role, I focus on supporting and accelerating that expansion. This includes helping translate clinical evidence into clear, compelling messaging, guiding product enhancements that better support adult critical care environments, and contributing to the company’s growing footprint in markets outside the U.S.

Marketing plays a central role in this work. We act as both the connective tissue and the quarterback, bringing together voice-of-customer insights, market feedback and clinical perspectives to inform product strategy, positioning and go-to-market execution. We maintain a continuous feedback loop with clinicians and customers to ensure what we deliver aligns with real-world needs and demonstrates measurable value.

What energizes me most is working at the intersection of clinical impact and innovation, helping connect sophisticated technology with the clinicians its designed to support. It’s incredibly motivating to see our platform scale into new patient populations, care settings and geographies, and knowing it’s improving how critical care is delivered.

4. Do you have any predictions about AI’s impact on the healthcare industry in 2026? What will it look like? How will it evolve, etc.?

In 2026, I see AI in healthcare continuing to mature, moving beyond early experimentation toward more practical, trusted use. Many administrative and workflow-focused tools will become more embedded in everyday systems and feel increasingly routine. While these solutions can improve efficiency and help address burnout, they will remain largely supportive rather than transformational.

The more interesting evolution will be the gradual expansion of AI into clinical decision support, particularly in high-acuity settings like critical care. We see a growing focus on tools that can synthesize complex physiologic and clinical data into clearer, more actionable insights, rather than adding additional data or alerts.

We’re also likely going to see higher expectations around evidence, trust and measurable impact. Health systems will increasingly look for AI that supports consistent, standardized care while still allowing for individualization, and that can contribute to learning and improvement over time across patient populations.