Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review

Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.Author summary: New technologies and tools for Artificial intelligence (AI) and machine learning (ML) in healthcare are continually advancing, leading to new digital tools that can improve the delivery of care. However, as these computer-based tools improve, they become more complicated and less transparent. These tools use gathered data from medical practice or clinical outcomes to build mathematical models to make recommendations that assist clinicians to treat patients. Unfortunately, when the data going in is biased, then the digital tools themselves are corrupted to perpetuate or even amplify the health disparities, leading to worsened inequity against already vulnerable populations. Data poverty describes when certain people groups are underrepresented in generated health data, so that they may actually be harmed by these new tools. Our review looks at the established state of research into health data poverty. We attempt to characterize the scope and findings of these papers, assess the challenges within the field, and draw some recommendations on how to begin to approach the difficult problem of health data poverty.