From Protocols to Networks: A New Way to Think About Patient Care

A new framework for understanding why patients plateau and how clinicians can think beyond protocols to unlock better outcomes.

Last week’s TruNeura Mastermind introduced a concept that immediately shifted how many clinicians in the room think about chronic disease:

What if the problem isn’t what you’re treating… but how you’re thinking about it?

This week, James Maskell led a session exploring a simple but powerful idea:

Chronic illness may not be a single dysfunction. It may be the result of network insufficiency.

A Different Lens on Chronic Disease

The insight originated from a moment many in functional medicine would recognize.

Hearing Dr. Bredesen describe Alzheimer’s as “network insufficiency” sparked a deeper question:

If cognition breaks down when networks fail…

What if the same is true across all chronic diseases?

Instead of isolated root causes, we may be looking at systems losing coherence over time.

Not one pathway. Not one deficiency. But multiple networks falling below the threshold required for health.

Why Great Protocols Still Fall Short

Every clinician has experienced this:

You run the labs. You identify the imbalances. You build a precise, personalized protocol.

And still… the patient plateaus.

This session challenged why that happens.

Because even when biology improves, other layers may remain untouched:

• Chronic stress with no regulation strategy

• Isolation or lack of community support

• Emotional patterns that keep physiology dysregulated

• Lack of purpose or motivation to sustain change

• Resource constraints that limit follow-through

Biology isn’t failing. The system is incomplete.

The 8 Networks of Human Health

To make this practical, James introduced a working model clinicians could begin to apply immediately.

Health is not just biological. It emerges from multiple interacting networks, including:

• Physiological (metabolism, inflammation, hormones)

• Regulatory (nervous system, stress resilience)

• Emotional (processing, stored stress, reactivity)

• Relational (close relationships, support systems)

• Social (community, belonging)

• Purpose (motivation, meaning)

• Resource (financial and environmental stability)

• Learning (adaptability, behavior change)

Patients don’t typically fail across all areas.

They fail in clusters.

And often, progress comes not from doing more… but from identifying which network is holding everything else back.

A Case That Changes How You Think

One story shared during the session captured this perfectly.

A patient with a severe neurological condition experienced a full remission after committing to a daily Qigong practice.

No new medication. No advanced intervention.

Just a shift in one domain.

From a traditional lens, this looks like an outlier.

From a network lens, it raises a different question:

What system was restored that allowed everything else to recover?

From Protocols to Network Thinking

This is where the conversation becomes actionable.

Instead of asking: “What protocol does this patient need?”

The better question becomes: “Which networks are insufficient, and where is the highest leverage point?”

Because when one network strengthens, others often follow:

• Community improves adherence

• Emotional regulation improves inflammation

• Purpose improves consistency

• Learning improves long-term outcomes

Health is not linear. It’s interconnected.

Why This Matters Now

Functional medicine has already moved beyond one-size-fits-all care.

But this session made something clear:

Precision alone is not enough.

The future belongs to clinicians who can:

• See patterns across systems

• Prioritize interventions beyond labs

• Integrate care across multiple domains

• And support patients in ways that extend beyond the visit itself

Inside the TruNeura Mastermind

This is exactly the type of thinking being developed and refined inside the TruNeura Mastermind each week.

Not just new information, but new frameworks for how to think, assess, and treat complex cases.

Because better outcomes don’t just come from better data. They come from better models.