Artificial intelligence is changing how healthcare operations run, from revenue cycle management (RCM) to medical billing and insurance processing. As these tools become more common, medical billing companies have a responsibility to address a serious risk that often hides in plain sight: latent bias. These are biases that get built into algorithms without anyone intending to discriminate, but they still affect patient care, billing accuracy, and compliance.
A widely cited study from 2019, published in the journal Science by Ziad Obermeyer, Brian Powers, Christine Vogeli, and Sendhil Mullainathan, shows exactly how this happens. The researchers examined an algorithm used by insurers and hospitals to manage the care of around 200 million people in the United States each year. They found that the algorithm assigned lower risk scores to Black patients than to White patients who had the same health conditions.
How the Bias Worked
The algorithm calculated risk scores partly by looking at annual healthcare spending. The assumption was simple: sicker patients spend more on care. But the data told a different story. Black patients received, on average, $1,800 less in care per year than White patients with the same number of chronic conditions. The algorithm read this lower spending as a sign of better health rather than what it actually reflected unequal access to care.
The result was fewer referrals for specialized treatment. In a fair system, 46.5% of the Black patients studied would have qualified for advanced care. With the biased algorithm, only 17.7% were referred.
This is latent bias in action. Historical inequities in access to care, socioeconomic conditions, and spending patterns get encoded in training data, and AI systems then reinforce the same disparities they should help correct.
What This Means for Revenue Cycle Management and Medical Billing
At Coastline RCM, we recognize that AI tools are now used across claims processing, risk adjustment, prior authorization, predictive analytics, and patient segmentation. These technologies bring real improvements to medical billing services, but when bias goes unchecked, the consequences are serious:
- Unequal risk scoring and resource allocation across patient groups
- Disparities in claims approval and reimbursement
- Lower quality of care that indirectly affects billing outcomes and compliance
- Regulatory and reputational risk for healthcare providers and payers
Latent bias in billing algorithms reaches further than patient outcomes. It also distorts financial forecasting, care management programs, and value-based care initiatives that depend on accurate data.
The Regulatory Picture
In 2024, the U.S. Department of Health and Human Services finalized updates to Section 1557 of the Affordable Care Act, which now explicitly addresses algorithmic discrimination in patient care decisions. The National Association of Insurance Commissioners (NAIC) has also published a model bulletin on the use of AI by insurers, and the American Medical Association has issued guidance on AI bias in clinical and administrative settings. Medical billing companies that use AI tools are increasingly expected to show their work to document how their systems are audited, what data they use, and how they handle disparities when found.
Moving Toward Fair AI in Healthcare
Addressing these challenges takes deliberate action:
- Regular algorithm audits. Evaluate AI models for performance gaps across racial, ethnic, and socioeconomic groups.
- Representative training data. Make sure datasets reflect the full range of patient populations being served.
- Transparent model governance. Use explainable AI practices and ongoing monitoring.
- Cross-functional collaboration. Bring clinical, RCM, compliance, and data science teams together to find and reduce bias.
- Ethical AI standards. Adopt industry frameworks that put fairness alongside accuracy and efficiency.
Our Approach at Coastline RCM
At Coastline RCM, we work to prevent bias from entering our processes in the first place and to correct it wherever we find it. This means reviewing our AI-supported workflows regularly, asking hard questions about the data behind them, and staying current with regulatory guidance from HHS, CMS, and industry bodies.
As AI continues to develop, healthcare organizations carry both an ethical and an operational responsibility to work with billing partners who put fairness at the center of how they operate. Coastline RCM is committed to supporting accurate, ethical revenue cycle solutions that serve all patients fairly. By staying alert to bias in billing and insurance technologies, we help build a healthcare system that delivers better clinical and financial outcomes for everyone.
If you are looking for a medical billing partner that combines current technology with strong ethical standards, Coastline RCM is here to help. Contact us today to learn how our services can support your revenue cycle while holding to the highest standards of fairness and integrity.
Reference
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.