From Walled Gardens to Living Evidence: Why Evidence-Based Medicine Is Being Rewritten by Compute

The history of medicine is, at its core, a history of how we manage information. For decades, Evidence-Based Medicine (EBM) has been our North Star—a rigorous framework designed to move us from “eminence-based” anecdotes to “evidence-based” certainty. It was a monumental achievement.

But we are currently witnessing a quiet yet profound shift in the very epistemology of our field. The tension is no longer just about what the evidence says, but how that evidence is constructed, accessed, and scaled. We are moving away from the era of curated, subscription-based “walled gardens” and into an era of open, computationally scalable intelligence.

Medicine is not abandoning evidence; we are finally building the infrastructure to make it live.

The Era of the Walled Garden: Why Subscription Models Won

To understand where we are going, we must acknowledge why platforms like UpToDate became the gold standard. They solved the two greatest problems of the late 20th-century clinician: information scarcity and cognitive overload.

We needed editorial trust. We needed a centralized authority to synthesize the chaos of primary literature into a digestible consensus. The subscription model optimized for:

Stability: High-confidence recommendations.
Authority: Peer-reviewed, expert-led synthesis.
Consensus: A singular “standard of care.”

However, this model was never optimized for velocity or personalization. It was designed for a world where clinical knowledge doubled every few years, not every few months.

The Scaling Problem: When Literature Outpaces Curation

We have reached a breaking point. The exponential growth of biomedical literature has rendered the manual, human-curated update cycle insufficient. There is a growing “knowledge latency”—the gap between a landmark discovery and its appearance in a clinical guideline or a curated summary.

When we rely solely on static summaries, we miss the nuances of dynamic, evolving evidence. We are practicing medicine based on a snapshot of the past, rather than the reality of the present.

The Compute Inflection Point

What makes this moment different? It isn’t just “better software.” It is the arrival of Compute as a primary clinical tool.

For the physician-coder, this is where the abstract becomes tangible. The convergence of massive GPU power and transformer architectures (specifically multi-headed attention mechanisms) has changed the game.

We are moving from searching for information to synthesizing it. Scale allows for:

Cross-trial reasoning: Analyzing relationships between disparate studies that no single human meta-analysis has yet connected.
Pattern detection: Identifying subtle signals across vast populations that remain invisible in small-cohort trials.
Contextual inference: Adapting general evidence to the highly specific, often comorbid reality of the patient sitting in front of you.

From Product to Infrastructure: Knowledge Liquidity

We must stop viewing evidence as a “product” you buy from a vendor and start viewing it as “infrastructure” that flows through your practice.

Feature	Subscription Knowledge (The Garden)	Compute-Driven Knowledge (The Stream)
Source	Gated, proprietary databases	Abundant, open, multi-source
Nature	Static, episodic updates	Adaptive, real-time synthesis
Context	Population-level averages	Patient-specific precision
Access	Pay-walled and siloed	Fluid and integrated (Knowledge Liquidity)

Evidence-Based Evidence: A New Layer

I want to be clear—and perhaps address the skepticism that naturally arises in our profession: Compute does not replace the randomized controlled trial. AI does not abolish the clinical guideline.

Instead, compute provides a new layer of “Evidence-Based Evidence.” It changes how we integrate data and, more importantly, how we surface uncertainty. It allows us to move from “What does the guideline say?” to “What does the totality of evidence suggest for this specific patient, right now?”

In specialties like Maternal-Fetal Medicine or Oncology, where data evolves weekly, this transition is not just a luxury; it is a clinical necessity.

The Physician’s Role in the Age of Scale

As we move toward computational intelligence, the role of the physician becomes more critical, not less. We are the stewards of the system. Our new competencies must include:

Probabilistic Reasoning: Understanding that AI outputs are expressions of probability, not divine edicts.
Ethical Framing: Ensuring that the datasets fueling these models aren’t perpetuating historical biases.
Clinical Judgment: Providing the “ground truth” that models cannot see—the patient’s values, the bedside nuances, and the social determinants of health.

Why Doctors Who Code Must Lead

This is why I advocate so strongly for physicians to learn to build. This is not a tech trend; it is a shift in our professional boundaries.

If we disengage, we risk a future of “black-box medicine,” where standards of care are defined by vendors rather than clinicians. If we lead, we can build open clinical tools and transparent evidence pipelines that serve our patients better than any subscription service ever could.

Conclusion: A Generational Opportunity

We stand at a crossroads. The walls of the old gardens are not being torn down because they were “wrong,” but because the world outside them has changed. We have a generational opportunity to shape a system where medical knowledge is abundant, adaptive, and accessible.

It is time to move from being passive consumers of curated tools to being the architects of computational intelligence. The evidence is clear: the future of medicine is written in code.

Chuck