Risk scores are used everywhere in healthcare and public health.
They are designed to identify who is most at risk and where interventions should be targeted.

But what if those scores are quietly reflecting unequal systems of care rather than true need?

In this episode, we break down how bias enters risk models through utilization, access, and data structure—and why even high-performing models can fail the populations that need help most. You will learn why fairness cannot be solved by metrics alone and what better model design and evaluation actually look like.

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P-values are everywhere in research. They are treated as the standard for determining whether a result is real, meaningful, or worth acting on. But what if statistical significance is answering the wrong question? 

In this episode, we break down why p-values often fail when the real goal is causal inference. You will learn what a p-value actually measures, why it cannot establish causality, and how study design, confounding, and bias matter far more than a single threshold like 0.05. 

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Infectious disease models shape some of the biggest public health decisions in the world—from vaccine strategy to lockdown timing to hospital surge planning. 

But what do these models actually do? How do SIR models work? Why do forecasts change? And why do so many people misunderstand what a model is supposed to tell us?

In this episode, we break down infectious disease modeling in plain English: the core compartments, the meaning of R0, how calibration works, why assumptions matter, and what these models can—and cannot—predict.

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Dashboards are supposed to make decision-making easier.
They make data visible, trends accessible, and performance look measurable in real time.

But what if that visibility is giving leaders the wrong kind of confidence?

In this episode, we break down why dashboards often fail to improve real decision-making—especially in public health and data science. From missing context and misleading metrics to surveillance lag and what I call metric theater, this episode explains why seeing more data is not the same as understanding more.

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What if one of the most common methods in medical research is quietly giving you the wrong answer?

In studies where patients can experience more than one outcome, standard survival analysis methods like Kaplan-Meier can overestimate risk—sometimes by a lot.

In this episode, we break down competing risks analysis, why traditional approaches fail, and how methods like cause-specific hazards and the Fine-Gray model give you the correct answer.

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Every 7 seconds, a preventable maternal death occurs worldwide.
And in the United States, the numbers are getting worse—not better.

So what’s really going on?

In this episode, we break down maternal and perinatal epidemiology—the field that reveals the true state of a nation’s health system. From rising maternal mortality rates to racial disparities and healthcare access gaps, the data tells a story most people never see.

If you want to understand where healthcare systems succeed—or fail—look at pregnancy outcomes.

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AI can now write like an experienced epidemiologist.
Clear. Structured. Confident.
But what happens when it’s wrong?

In this episode, we break down how large language models (LLMs) are being used in public health — from surveillance summaries to clinical decision support — and why their biggest strength is also their biggest risk.

You’ll learn:
What LLMs actually are (and what they’re not)
Where they’re already used in public health
Why hallucinations happen — and why they’re dangerous
The guardrails (RAG, validation, governance) that make them usable
The future of public health will use AI.
The real question is whether it can be trusted.

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Healthcare is producing more data than ever before — electronic health records, wearables, genomic data, insurance claims, and real-time patient monitoring.

But data alone doesn’t solve problems.

In this episode, we break down health data science — the field where biostatistics, machine learning, and modern healthcare systems come together. You’ll learn what health data science actually is, how it differs from traditional biostatistics, and why it’s one of the fastest-growing careers in public health and data science.

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We’ve known the major causes of heart disease for decades.

Smoking. Cholesterol. Blood pressure. Diabetes.

So why is it still the leading cause of death worldwide?

In this episode, we break down cardiovascular epidemiology — from the Framingham Heart Study to modern advances in genetics, wearables, and AI.

You’ll learn:

• How risk factors were discovered
• Why “residual risk” still exists
• How genetics and polygenic risk scores are changing the field
• The role of policy, prevention, and health disparities

This isn’t just history — it’s where the future of public health is being built.

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Everyone talks about neural networks.
But the systems quietly running public health? They don’t learn — they follow rules.
In this episode, we break down symbolic AI — the rule-based systems behind clinical decision support, disease surveillance, and health regulations.

You’ll learn:
What symbolic AI actually is
Where it’s already used in healthcare and public health
Why neural networks fall short in high-stakes systems
Why the future is hybrid (neuro-symbolic AI)
This is the side of AI most people overlook — but it’s the one systems trust.

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