“…External factors like ED location influence triage decisions (Gorick 2022;Suamchaiyaphum, Jones, and Markaki 2023). Sociodemographic factors, including ethnicity, sex/gender, age, and insurance coverage, also impact mistriage (Zhang et al 2020a;Peitzman et al 2023;Essa et al 2023;Martin et al 2023;Fekonja et al 2023), disproportionately affecting certain groups (Banco et al 2022). While age and ethnicity influence prioritization, findings on sex/gender (Arslanian-Engoren 2000; Onal et al 2022) are less conclusive, with other factors interacting.…”
Section: Sociodemographic Disparities In Edmentioning
There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.
“…External factors like ED location influence triage decisions (Gorick 2022;Suamchaiyaphum, Jones, and Markaki 2023). Sociodemographic factors, including ethnicity, sex/gender, age, and insurance coverage, also impact mistriage (Zhang et al 2020a;Peitzman et al 2023;Essa et al 2023;Martin et al 2023;Fekonja et al 2023), disproportionately affecting certain groups (Banco et al 2022). While age and ethnicity influence prioritization, findings on sex/gender (Arslanian-Engoren 2000; Onal et al 2022) are less conclusive, with other factors interacting.…”
Section: Sociodemographic Disparities In Edmentioning
There is a burgeoning interest in harnessing artificial intelligence (AI) to enhance patient flow within emergency departments (EDs). However, this advancement is accompanied by a significant risk: by relying on historical healthcare data, these AI tools may perpetuate existing systemic biases associated with gender, age, ethnicity, and socioeconomic status. This paper surveys studies identifying biases in ED data, offering context for concern about these biases. These insights are valuable for researchers developing AI to optimize ED workflows while accounting for ethical considerations.
“…Children and caregivers who experience social injustices through marginalization (people placed through intention and societal structure in a subjugated position through often intersectional identities such as race, ethnicity, gender, class, ability, sexual orientation, age, religion [ 13 ]) are more likely to experience healthcare disparities in their pain care [ 14 , 15 ].…”
Introduction
Pain affects all children, and in hospitals across North America, this pain is often undertreated. Children who visit the emergency department (ED) experience similar undertreatment, and they will often experience a painful procedure as part of their diagnostic journey. Further, children and their caregivers who experience social injustices through marginalization are more likely to experience healthcare disparities in their pain management. Still, most of our knowledge about children’s pain management comes from research focused on well-educated, white children and caregivers from a middle- or upper-class background. The aim of this scoping review is to identify, map, and describe existing research on (a) how aspects of marginalization are documented in randomized controlled trials related to children’s pain and (b) to understand the pain treatment and experiences of marginalized children and their caregivers in the ED setting.
Methods and analysis
The review will follow Joanna Briggs Institute methodology for scoping reviews using the Participant, Concept, Context (PCC) framework and key terms related to children, youth, pain, ED, and aspects of marginalization. We will search Medline, Embase, PsychInfo, CINAHL, Web of Science, Cochrane Library Trials, iPortal, and Native Health Database for articles published in the last 10 years to identify records that meet our inclusion criteria. We will screen articles in a two-step process using two reviewers during the abstract and full-text screening stages. Data will be extracted using Covidence for data management and we will use a narrative approach to synthesize the data.
Ethics and dissemination
Ethical approval is not required for this review. Findings will be disseminated in academic manuscripts, at academic conferences, and with partners and knowledge users including funders of pain research and healthcare professionals. Results of this scoping review will inform subsequent quantitative and qualitative studies regarding pain experiences and treatment of marginalized children in the ED.
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