Is your healthcare AI platform exhibiting biases? We can help fix it

In 2019, researchers uncovered a startling truth: a widely used healthcare algorithm was systematically discriminating against Black patients.

The artificial intelligence (AI) system, which influenced the care of millions, was less likely to refer Black patients for advanced care than equally sick white patients. This bias, rooted in the algorithm’s reliance on historical cost data reflecting long-standing disparities, laid bare the substantial threat of biased data in healthcare.

As AI permeates every corner of healthcare, from bedside monitors to population health management systems, the stakes have never been higher. Biased algorithms risk misdiagnosing diseases, recommending inappropriate treatments, and exacerbating health inequities that have plagued communities of color for generations. Data bias, left unchecked, threatens to erode trust in healthcare systems.

Understanding data bias in healthcare AI

One of the most significant sources of bias is the historical datasets used to train these systems.

Many of these datasets reflect the deeply entrenched inequities that have shaped healthcare access and outcomes for generations. For instance, if a dataset primarily consists of medical records from affluent, predominantly white communities, the resulting AI system may fail to accurately predict disease risk or treatment responses for patients from marginalized backgrounds.

The lack of diversity in clinical trials is another major contributor. Despite efforts to increase representation, minority groups remain underrepresented in these critical research endeavors. This gap means that the safety and efficacy data used to inform AI algorithms may not adequately capture diverse patient populations’ unique needs and characteristics.

But data bias isn’t just a matter of incomplete or distorted datasets. The humans who design and develop AI systems bring their own biases to the table. Unconscious assumptions about race, gender, and other patient characteristics can inadvertently shape how algorithms are structured and trained. Without diverse perspectives in the development process, these biases can go unchecked and become embedded in the very systems meant to promote equitable care.

Confronting data bias in healthcare AI requires an unflinching look at the data we use, how we design algorithms, and the diversity of the teams driving these innovations. Only by understanding the sources and manifestations of bias can we begin to develop strategies to mitigate its impact and ensure that AI is a tool for equity, not a barrier to it.

Strategies for mitigating bias

Healthcare organizations must take a hard look at the data they collect, how it’s collected, and from whom. This means investing in efforts to diversify datasets, ensuring that they adequately represent the full spectrum of patient populations. It also involves implementing rigorous data governance practices to ensure data quality, integrity, and transparency.

Algorithmic auditing is another critical piece of the puzzle. Before any AI system is deployed, it must undergo thorough testing for bias that goes beyond simply evaluating overall performance metrics. It requires a deep dive into the model’s decision-making processes, examining how it handles edge cases and assessing its impact on different patient subgroups. Ongoing monitoring and refinement based on real-world performance are also essential to identify and correct any biases that may emerge over time.

Diversity in AI development is paramount. Teams that are homogeneous in race, gender, and background are at higher risk of creating algorithms that reflect their narrow perspectives. Building AI systems that genuinely serve all patients requires bringing a wide range of voices to the table. We must actively recruit developers, data scientists, and healthcare experts from underrepresented communities while fostering a culture of inclusivity where diverse perspectives are actively sought out.

Yet, even with these measures, human oversight and discretion remain imperative. Clinicians must be empowered to interpret and contextualize AI recommendations, bringing their expertise and understanding of individual patients’ needs to bear. AI should be a tool to augment, not replace, human judgment. Clear guidelines and training are needed to ensure healthcare professionals can effectively leverage AI insights while knowing when to question or override them.

Finally, regulators must develop clear, enforceable standards for AI fairness, transparency, and accountability in healthcare. This could include mandates for algorithmic “nutrition labels” that disclose critical details about training data and model performance across different patient subgroups. It may also involve guidelines for the rigorous testing and approval of AI tools before deployment in clinical settings.

Successfully mitigating bias in healthcare AI will require sustained effort and collaboration across the ecosystem.

Advancing equity with SHI

Confronting bias in healthcare AI is a complicated challenge that no organization can handle alone. Fortunately, you don’t have to.

SHI’s Healthcare practice is committed to helping you leverage data to drive equitable, patient-centric care. Our “health to home” philosophy underpins a comprehensive approach to data strategy that enables you to extend the benefits of unbiased AI across the care continuum.

SHI’s Data Strategy workshops are the foundation of this approach. These collaborative engagements help you evaluate your current data landscape, identify potential bias, and chart a path to a more robust, equitable data future. From assessing data sources and validation processes to aligning stakeholders around governance best practices, SHI provides the expertise and tools to build a data foundation that supports fair, effective AI.

As you move to implement AI solutions, SHI’s Data Management and Analytics services help you translate strategy into action. Our data experts work with you to select, deploy, and optimize solutions that align with your equity goals. With a focus on rigorous testing, ongoing monitoring, and continuous refinement, SHI helps ensure your AI initiatives deliver value for all patients.

The urgency of unbiased healthcare AI

Skewed datasets that fail to represent diverse patient populations, flawed clinical trial designs that exclude marginalized groups, and the unconscious biases of those coding the algorithms all contribute to AI systems that can fail the very patients they’re meant to serve. For an industry built on the promise of “do no harm,” the consequences of inaction are grave.

This problem is not an easy fix and won’t be solved overnight. But from providers to policymakers to technology partners across the healthcare ecosystem, there is a growing recognition of the urgent need to address bias in AI. With commitment, collaboration, and a steadfast focus on the patients we serve, we can build a future where the power of AI is harnessed for the good of all.

Ready to take the first step toward unbiased AI in your healthcare organization? Contact SHI to learn how our data strategy and management solutions can help you build a foundation for equitable, patient-centric care.