Solving the Dichotomy Between Personalized Medicine and Population-Level Care

At The Healthcare Syndicate, one of the things we like to challenge the companies we evaluate for investment on is their Total Adressable Market by asking for a bottoms-up calculation for their total potential revenue.

This exercise often reveals a fascinating dichotomy between a company’s total addressable market (TAM) and its ability to monetize this market. After all, even a billion-dollar market holds no value if the business model cannot convert potential into action and revenue.

This same dichotomy exists in healthcare, especially around treatment guidelines. Historically, healthcare has focused on population-level analyses to guide treatment—a "top-down" approach that aggregates data across thousands to find averages. But what happens when the future of healthcare demands bottom-up thinking? Personalized medicine challenges us to start with the individual and build outward, rather than fitting everyone into a one-size-fits-all solution.

How does the interplay between top-down analysis and bottom-up calculation shape personalized medicine? Why are N-of-1 experiments uniquely positioned to bridge this gap? And what do these advances mean for the future of healthcare innovation and investment?

The Healthcare Shift: Why Personalized Medicine Matters Now

The future of healthcare is shifting from managing acute and communicative diseases to addressing chronic and non-communicative conditions like diabetes and heart failure. This shift changes everything—from how patients engage with healthcare to where and when treatment occurs.

In the past, healthcare dealt primarily with episodic care for acute diseases like influenza or tuberculosis, with treatment often occurring reactively in inpatient settings. The disease states for these acute diseases also did not vary much from patient to patient. Today, as chronic conditions like cancer and heart attacks dominate, care has become a mix of episodic and proactive approaches, blending inpatient and outpatient settings. Looking ahead, the focus will shift even further toward longitudinal care for conditions like diabetes and heart failure, emphasizing proactive and at-home treatments that vary significantly from patient to patient.

This progression underscores the need for personalized medicine. In order to achieve that though, we need to shift away from traditional, population-level healthcare protocols that flatten out individual variability, and make a shift towards bottom up approaches to developing care plans for patients. We believe that techniques like N of 1 experiments can help bridge that gap.

N of 1: What It Is and Why It’s Different

N of 1 experiment is exactly what it sounds like: a clinical trial of one. Forget aggregating data from thousands of people. This approach focuses exclusively on identifying what works for a single individual. The idea is simple: instead of treating a population average as the gold standard, an N of 1 trial starts and ends with the individual’s unique physiology.

Here’s how it works:

• Treatments or interventions are tested in alternating periods (e.g., treatment vs. no treatment).

• The individual’s responses are meticulously measured, compared, and analyzed.

The beauty of this approach lies in its precision. By eliminating inter-patient variability, we cut through the noise and discover what genuinely delivers results for that specific person, and design care plans around the individual patient.

For a more indepth read on N of 1 experiments, check out this website.

Why Now? The Technology is Ready

N of 1 experiments aren’t new, but two advancements make them scalable and practical today:

1. Connected Devices and Wearables: From continuous glucose monitors to smartwatches, these tools capture real-time, context-rich data.

2. AI and Machine Learning: These systems can analyze complex, dynamic datasets to design experiments, interpret results, and generate actionable insights in real-time.

Together, they enable a level of precision and scalability that was impossible just a decade ago.

Individual Experiments, Universal Insights

N of 1 experiments are powerful because they focus on the individual. But how do we connect their insights back to the population level? The process hinges on understanding the interplay between personalized data and broader evidence-based protocols.

While the immediate aim of an N of 1 trial is to tailor care to one person, its results often highlight physiological principles that apply to others. Generalizing these findings involves identifying patterns—shared response profiles—across similar individuals. For instance, if a patient’s trial demonstrates an unusual response to a standard therapy, researchers can use this insight to explore whether other patients with comparable traits might react the same way.

Aggregating data from thousands of N of 1 experiments creates a robust dataset for refining population-level guidelines. This enables clinicians to:

• Pinpoint subgroups with specific response patterns.

• Adjust generalized protocols to better suit individual variability.

• Establish a continuous feedback loop where population-level practices are iteratively informed by individual-level data.

In this way, N of 1 trials don’t just personalize care; they actively evolve the gold standard, ensuring protocols are as adaptable and precise as the patients they serve.

What Does This Mean For Investors?

Connected medical devices are poised to play a central role in the AI-driven transformation of healthcare to manage chronic diseases. But here’s the catch: data capture alone isn’t enough. The real winners will be companies that don’t just collect information but use methodologies like N-of-1 experiments to turn data into actionable, personalized insights.

As an investor, when evaluating companies involved in chronic disease care, look for the following:

• Leverage wearables and connected devices to collect unique, high-quality data.

• Use AI to unlock insights from individual-level experiments.

• Have a clear strategy for scaling personalized care into broader, population-level applications.

The future of healthcare belongs to those who can bridge the gap between individual needs and universal best practices.

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