Algorithmic Bias in Healthcare: A Billion-Dollar Blind Spot

We keep hearing that AI will fix healthcare by optimizing resources, finding at-risk patients, and making workflows simpler. But as more AI tools get adopted, we’re running into a huge problem: technical design choices are accidentally baking in and even worsening systemic biases against people who are already underserved. This isn’t just a theory from a research paper. It’s happening now, affecting real patients and forcing everyone to rethink how we build, launch, and audit these algorithms.

The Peril of Proxy Variables: When Commercial Logic Undermines Clinical Equity

The textbook case is the 2019 Science study by Ziad Obermeyer’s team on a widely used Optum algorithm. The model was supposed to find sick patients who needed extra care management, but it systematically ignored the needs of Black patients. Why? It used a simple, and deeply flawed, proxy for sickness: past healthcare costs. The logic, from a business perspective, seemed sound: sicker people spend more on healthcare. But in the real world, systemic inequities mean Black patients often have less access to care and therefore lower healthcare costs than white patients, even when they’re just as sick or sicker. The result was a disaster: the algorithm, by chasing high-cost patients, mostly found white patients, cutting the number of Black patients identified for these programs by more than 50% compared to if true health needs were the target variable original Science study on Optum algorithm bias. For any VC, compliance officer, or product manager in this space, this is your wake-up call. The target variable you choose for an algorithm is never neutral, it’s a choice that determines who gets care and who gets left behind. When you use a proxy like ‘cost’ that’s so tangled up in racial and economic disparities, you’re building bias directly into the code, creating a system that almost certainly runs afoul of HHS Section 1557 non-discrimination rules.

Deconstructing the Optum Cost-Proxy Mechanism: A Technical and Ethical Deep Dive

The Optum algorithm failed because it was built on a category error, confusing a patient’s actual health with their total medical bills. Cost is a terrible measure of sickness because it’s affected by everything from insurance type and zip code to a person’s historic ability to even see a doctor. The training data reflected this broken reality, showing that Black patients with the same chronic disease burden had lower healthcare utilization. So when the algorithm was told to find “high need” by looking for “high cost,” it taught itself a dangerous and racist shortcut: it learned to equate “whiteness” with higher need and “blackness” with lower need, even for patients with the exact same diagnoses. This is a perfect example of algorithmic drift, where a model’s predictions get worse because it’s learning from a real-world data distribution that is itself deeply inequitable. This is why groups like the National Academy of Medicine keep stressing equity in AI design. It’s not about engineers having bad intentions. It’s about the raw mechanics of machine learning, where an algorithm fed a biased history will simply automate that bias for the future.

Auditing Target Variables: A Mandate for Investors and Product Managers

For investors, PMs, and compliance folks, the takeaway is that your due diligence checklist needs an update. It’s not enough to see a 510(k) clearance and a good clinical efficacy study. Your technical diligence has to include a painful, deep audit of the algorithm’s choice of target variables. When you’re looking at a new AI tool or company, you have to be asking the hard questions.

• What, exactly, is this algorithm trying to predict?
• What data points is it using as a shortcut, a proxy, for that goal?
• Are those proxies (like cost, or zip code) already known to reflect demographic or racial gaps?
• How did you prove this thing performs fairly across different patient groups, especially the ones who always get the short end of the stick? Show me the stratified performance data.
• What’s your plan for monitoring this for equity drift after it’s deployed in the real world?

If a company brags about its “data moat,” you have to push back and ask how they’ve cleaned that proprietary data of the exact biases that lead to these inequitable outcomes. An “AI-native” company must have equity baked into its design from day one. It’s not an optional add-on. The FDA’s own GMLP (Good Machine Learning Practice) principles require attention to fairness, and as an investor, you should be demanding proof of it. FDA guidance on Good Machine Learning Practice

Towards Responsible AI: Implementing Equitable Algorithms

So what’s the playbook for building these tools responsibly? It comes down to a few core practices.

Direct Measurement of Need

Stop using lazy proxies. Whenever you can, train your algorithms on direct measurements of health and sickness, not on garbage proxies like historical spending. This means doing the hard work of pulling in better data, whether it’s more detailed clinical records, social determinants of health information, or even patient-reported outcomes.

Bias Detection and Mitigation Techniques

You have to actively hunt for bias while you’re building and testing your model. That means splitting out your performance metrics by race, ethnicity, income, and any other group that matters. If (or when) you find bias, you have to fix it with specific techniques, like re-weighting your training data to boost underrepresented groups, changing the model’s decision thresholds, or deploying fairness-aware algorithms.

Transparency and Explainability

Health systems and doctors using these tools can’t just trust the vendor’s black box. You have to demand transparency about how the algorithm works, what it was trained on, and what tests were run to check for fairness. So-called Explainable AI (XAI) tools can sometimes help show which features are driving a prediction, which can be useful for spotting potential bias, but they aren’t a magic wand.

Continuous Monitoring and Auditing

Algorithmic bias isn’t a set-it-and-forget-it problem. Models drift as patient populations and care patterns change. You need constant monitoring and periodic, independent audits to make sure the algorithm stays fair long after it’s been deployed. This goes far beyond whatever was required for the initial 510(k) or CE Mark. National Academy of Medicine report on AI in health equity The Optum debacle is a warning shot for the entire industry. Putting AI into clinical resource allocation has huge potential, but it requires constant vigilance against this exact kind of algorithmic bias. For VCs, compliance teams, and product managers, building equitable AI isn’t some fuzzy ethical goal. It’s a core requirement for building a sustainable product that patients and providers can actually trust. A biased algorithm is a defective one. The choice of a target variable is a decision with life-and-death consequences for health equity, and it deserves the same level of scrutiny you’d give to your clinical trial data and your FDA submission.