Kenneth Eugene Paik, Rachel Hicklen, Fred Kaggwa, Corinna Victoria Puyat, Luis Filipe Nakayama, Bradley Ashley Ong, Jeremey N. I. Shropshire, Cleva Villanueva
Abstract
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.
Introduction
While many laud the potential for technology improving the quality and delivery of healthcare, we must be vigilant to avoid exacerbating existing health disparities [1]. One area of focus toward addressing these inequalities is to resolve the expanding problem of health data poverty, defined as "the inability for individuals, groups, or populations to benefit from a discovery or innovation due to insufficient data that is adequately representative" [1].
Materials and method
A comprehensive search of the literature was constructed and performed by a qualified medical librarian (RSH). Medline (Ovid), Embase (Ovid), Scopus, and Google Scholar were queried using both natural language and controlled vocabulary terms for data poverty, digital health, artificial intelligence, vulnerable/underrepresented populations, bias, inequities, and health outcomes (S1 Appendix).
Results
While the topic of data poverty in healthcare is uncommon, our initial search produced a fair number (n = 186) of published papers. Our first screening filtered these papers based on adherence to data poverty: whether they directly acknowledged data poverty or indirectly addressed a cause or effect of AI-exacerbated biases.
Discussion
Improving health care itself is an immensely multifaceted problem. When compounded by highly technical digital technologies, the variables and outcomes are exponentially more difficult to monitor. In this review, we discuss how health data poverty is a complicated problem without a straightforward solution. Disparities can infiltrate anywhere along the application development process. Disparities are also inherently a systemic problem, where existing biases will propagate even if the tools and processes are unbiased.
Conclusions
Because health data inherently influences the output of AI/ML, transformative efforts must be focused on the refinement of the pre-selection process for datasets while continuing to monitor the technology throughout its development.
Citation: Paik KE, Hicklen R, Kaggwa F, Puyat CV, Nakayama LF, Ong BA, et al. (2023) Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review. PLOS Digit Health 2(10): e0000313. https://doi.org/10.1371/journal.pdig.0000313
Editor: Alvin Marcelo, St Luke’s Medical Center, PHILIPPINES
Received: January 15, 2023; Accepted: July 2, 2023; Published: October 12, 2023
Copyright: © 2023 Paik et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The article’s scores and classification included in the review can be found at: https://github.com/criticaldata/datapovertypaper.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000313#sec020
