AI in Healthcare: An Exploration of Health Disparities in Technology Implementation

Osborne, Kailey, Thareja, Garvita, Bjorklun, Natalie, and Eljazouli, Youssef

Aging populations, chronic disease, government-imposed restrictions, rising costs of care, patient billing complications, and shortages in staffing continuously overburden the U.S. healthcare system. Data consistently shows that these systematic burdens disproportionately affect underrepresented and vulnerable populations such as communities with minoritized identities, lower socioeconomic status, and rural communities (7) As a result, these individuals and families suffer from higher health disparities than the general population For the purpose of this article, health disparities are defined as “preventable differences in the burden of disease, injury, violence, or opportunities to achieve optimal health that are experienced by socially disadvantaged populations'' (2). It is important to note that, “while the term disparities is often used or interpreted to reflect differences between racial or ethnic groups, disparities can exist across many other dimensions as well, such as gender, sexual orientation, age, disability status, socioeconomic status, and geographic location” (5).

The recent COVID-19 pandemic exposed the severity of the challenges faced by the healthcare industry and left many organizations and leaders searching for new models of care and supportive technologies, such as Artificial Intelligence (AI), to provide relief However, do these technology advancements address social determinants of health sufficiently, enabling a reduction in the health disparities and inequities that currently plague the healthcare system rendering it ineffective for many Americans? Or does the integration of AI only further the divide?

Intelligent and complex computer-based models function through algorithms and existed with limited medical functionality as as early as the mid-20th century (4) Although not a novel idea, AI has gained substantial traction over the last decade and has developed many subfields Early prototypes operated “as a simple series of “if, then rules” and have advanced over several decades to include more complex algorithms that perform similarly to the human brain” (4). Whereas the previous technology could attach a diagnosis only if specific symptoms were displayed via a fixed, rigid algorithm, it can now go beyond this effort by identifying patterns and making decisions more individualized to the patient. “Predictive models can be used for diagnosis of diseases, prediction of therapeutic response, and potentially preventative medicine in the future” (4) This refinement allows for more personalized medicine and streamlines modern healthcare delivery

As the US healthcare system becomes increasingly technology-driven, developing a holistic understanding of how technological tools impact healthcare delivery across various socioeconomic groups is imperative. While AI can potentially improve healthcare operations and patient care by simplifying overly convoluted systems, many challenges and ethical concerns must be considered. Over the next decade, health organizations need a clear, targeted pathway for integrating AI technology to impact healthcare disparities and inequities positively Despite the importance of creating a sustainable framework that centers AI integration for health equity, there needs to be more focus on the topic

In addition to AI implementation, a significant concern is reducing the risk associated with the potential bias these AI systems can produce. As sophisticated as these systems and algorithms are, it is essential to remember that they are only as equitable as the humans that program them Thus, any biases the creators of these systems have will then be (inadvertently) passed on to these systems The biases can be due to the reliability of the instrument managed by AI, such as racial and gender bias. A study reported that pulse oximetry measured different oxygen levels in black patients at risk for hypoxia compared to white patients, thereby creating a racial bias (6). Because there are multiple pathways of how bias can exist in these models throughout their entire lifecycle, the rate of occurrence is more frequent and challenging to eradicate One research gap identified is how much of an effect these biases may have on patients when this technology is scaled and applied more widely Given these uncertainties, it is vital to implement a robust, standardized review process with federal oversight to ensure that AI implementation is thoroughly scrutinized before being approved. The fact that AI technologies have been operational for over 70 years and this does not exist is concerning.

To explore best practices around AI implementation for reducing health disparities through present healthcare systems, we conducted a qualitative review Search engines such as PUBMED, CINAHL, and Web of Science were utilized Various descriptive elements such as healthcare setting, role of AI in healthcare, regulations, and necessary considerations in implementing healthcare AI, were used to narrow our research focus. In addition, key terms such as “artificial intelligence in healthcare”, “robotics”, “bias in AI”, and “regulatory measures in AI” were applied. The search criteria was limited to studies conducted between 2020-2022 and in peer reviewed publications.

The articles were first reviewed with the title and abstract; and finally full text in-depth reviews of the selected articles were conducted independently by three reviewers (KO, BN & EY) A comprehensive qualitative data review of these five studies was performed by using lineby-line coding by all three reviewers Discrepancies were resolved by mutual discussions, and five articles were selected for the final review The final stage of our complete analysis produced five articles for review.

Article one by Bajwa et al. in 2021 concluded that healthcare systems implementing AI must have in-depth training for employees accessing the technology. Next, an article by Thomasian et al. in 2021 indicated that using regulatory strategies, government oversight, and an established framework is critical in implementing AI in healthcare This will help reduce bias and inconsistency in the usage of AI for measuring parameters such as oxygen levels, blood pressure, etc Further, the article by Troncoso (2020) indicated that a shift to preventative healthcare with active patient participation could use AI technology to facilitate an easier transition from reactive medicine. Gurevich et al. (2022) revealed that AI could be used in health equity. The authors advocated that AI can have positive and negative sides, and both must be explored in depth before adopting it in the healthcare system. The final article in our review by Takshi (2021) indicated that AI is extensively utilized in the healthcare industry but is not regulated. This can cause bias and reliability issues with the patient’s data and healthcare delivery.

Many obstacles remain for healthcare organizations to use AI health technology to its fullest potential. The relationship between AI technology and health disparities is a new avenue for understanding the impact of technology on healthcare access and equity. The next decade will be critical for implementing and training AI technology in healthcare As AI grows in complexity and use, best practices state that health organizations need a targeted framework focused on antidiscrimination and human-centered usage to stay on track for AI's proper and equitable incorporation in healthcare

AI improves healthcare delivery and patient turnaround time, reducing day-to-day stress on healthcare providers and ultimately benefiting the workforce shortage. Coming from an overwhelmed world of pandemics and great resignation, AI is a unique resource to tap into and implement. However, the lack of regulatory measures, training, reliability, and data security, in addition to putting the patients' healthcare measurements at risk, still needs to be addressed Further research is recommended in this area so that AI is integrated appropriately into the future of the healthcare industry.