Journal of general internal medicine
-
Equity in Using Artificial Intelligence Mortality Predictions to Target Goals of Care Documentation.
Artificial intelligence (AI) algorithms are increasingly used to target patients with elevated mortality risk scores for goals-of-care (GOC) conversations. ⋯ Using AI predictions of mortality to target GOC documentation may create differences in documentation prevalence between patients with and without AI mortality prediction scores with similar severity of illness. These finding suggest using AI to target GOC documentation may have the unintended consequence of disadvantaging severely ill patients lacking AI-generated scores from receiving targeted GOC documentation, including patients who are more likely to be non-White and have Medicaid insurance.
-
Medical mistrust among Black patients has been used to explain the existence of well-documented racial inequities at the end of life that negatively impact this group. However, there are few studies that describe patient perspectives around the impact of racism and discriminatory experiences on mistrust within the context of serious illness. ⋯ This study found high levels of mistrust among Black patients with serious illness. Suspicion of HCWs, disparities in healthcare by race, and a lack of support from HCWs were overarching themes that influenced medical mistrust. Critical, race-conscious approaches are needed to create strategies and frameworks to improve the trustworthiness of healthcare institutions and workers.
-
Champions of AI-facilitated clinical documentation have suggested that the emergent technology may decrease the administrative loads of physicians, thereby reducing cognitive burden and forestalling burnout. Explorations of physicians' experiences with automated documentation are critical in evaluating these claims. ⋯ According to physician interviewees, automated AI-driven clinical documentation has the potential to significantly reduce the administrative burden associated with particular types of provider-patient encounters. Addressing the growing pains of the incipient technology, identified here, may allow for a broader applicability for clinical practice.
-
Despite greater care needs, patients with limited English proficiency (LEP) are less likely to use telemedicine. Given the expansion of telemedicine since the COVID-19 pandemic, identifying ways to narrow the telemedicine care gaps experienced by people with LEP is essential. ⋯ Mandarin-speaking adults with LEP see telemedicine as a convenient and necessary service. Issues with healthcare providers' and interpreters' communication skills and impatience were common. The lack of wrap-around language-concordant care beyond the visit itself was cited as an ongoing and unaddressed care barrier. Healthcare provider and interpreter training is important, as is availability of personalized and comprehensive language services in promoting patient autonomy, alleviating the burden on patients' families, and thus ensuring equitable healthcare access.
-
The opioid overdose epidemic disproportionately impacts people experiencing homelessness. Outpatient-based opioid treatment (OBOT) programs have been established in homeless health care settings across the USA, but little is known about the success of these programs in engaging and retaining this highly marginalized patient population in addiction care. ⋯ In this study, over half initially engaged with the OBOT program, with initial engagement emerging as a strong predictor of subsequent OBOT program attendance. Interventions aimed at enhancing initial OBOT program engagement, including those focused on housing and buprenorphine initiation, may improve longer-term outcomes in this marginalized population.