About Us
A Learning Resource for Understanding Brain Monitoring Data and Its Role in Clinical Decision-Making
Understand what this site is, why it exists, and how to engage with it.
About the Neuromonitoring Analytics Network (NAN)
Helping People Make Sense of Neuromonitoring Data in Practice
The Neuromonitoring Analytics Network is an open educational resource focused on neuromonitoring data interpretation, brain monitoring analytics, and data driven clinical decision making.
It brings together webinars, case discussions, publications, technical resources, and community conversations that explore how multimodal brain monitoring data is understood and applied in real clinical environments. The focus is on practical interpretation: how signals behave, how analytics are used, and how clinicians integrate complex physiologic data into real-world decisions.
The goal is to create a shared learning environment where clinicians, researchers, engineers, and trainees can better understand the evolving role of data in brain monitoring and clinical care.
This site is created and maintained by Moberg Analytics. Click the link below to learn more about the company.
FAQs
This site exists to support clearer understanding of how brain monitoring data is used in clinical decision-making.
Data-driven care only becomes meaningful when there is shared understanding of:
- what the data represents
- how it is generated
- how it should (and should not) influence decisions
The goal is to make that understanding more accessible through practical resources, real-world examples, and ongoing discussion.
No. While this site is created by Moberg Analytics, it is intentionally separate from product promotion.
The focus is on education and shared understanding of data-driven brain monitoring, not sales or product positioning. Content is designed to be useful regardless of the systems or tools being used.
No. You do not need to use the Moberg Clinical Platform or have any affiliation with Moberg Analytics to engage with these resources.
Everything here is intended to be system-agnostic and applicable across different clinical and research environments.
This site is for anyone working with or interested in brain monitoring data and its role in clinical decision-making, including clinicians, researchers, engineers, data scientists, and trainees.
It is especially relevant for those interpreting continuous physiologic signals, working with multimodal datasets, or exploring how data is integrated into bedside decisions.
This site brings together a range of educational and community-driven resources, including:
- webinars and case-based discussions
- newsletters and field updates
- interviews, podcasts, and publications
- community discussions and shared insights
All content focuses on how brain monitoring data is interpreted and applied in real-world clinical decision-making.
At Moberg Analytics, our work focuses on developing tools that support understanding and interpretation of brain monitoring data in acute care settings.
But advancing the field is not only about building better tools—it is also about improving how data is understood and used in practice.
In brain monitoring, a central challenge remains: how to translate complex, continuous physiologic data into decisions that are consistent, meaningful, and clinically useful. These questions extend beyond any single technology and are still being actively defined across the field.
This site was created to support that process.
It serves as an open space for shared learning, where clinical experience, research insight, and technical development can come together to better define what data-driven care in brain monitoring looks like in practice.
This site focuses on interpretation, context, and applied understanding of brain monitoring data.
Other resources may focus on implementation (such as code repositories) or real-time discussion (such as forums), but this platform is designed to provide structured educational context that supports both.
Together, these resources help connect learning, interpretation, and application into a continuous cycle of improvement in data-driven clinical practice.
