DNP 840 Discuss the importance of the ethical delivery of care and regulatory reporting as it relates to the doctorally prepared advanced nurse’s practice

It is now clear that healthcare inequalities are encoded right into the health data that serve as the substrate for healthcare machine learning (ML) research and model development. ML researchers have exposed the presence of pernicious bias (ie, differences in health conditions related to social inequality) within health data and their impact on model performance. The persistence of healthcare inequalities poses an ethical threat to the core values of health institutions. As ML is adopted within medical practice, attention to how factors such as bias may impact the use of ML in vulnerable populations is imperative. According to McCradden et al. (2020), despite long-standing recognition of disparities, scientific knowledge about how social determinants of health drive disparate outcomes continues to evolve. The effects of differential access, distrust of medical institutions, housing or food security, racialization, health insurance, and others can be replicated in ML models. The implications of imprudent incorporation of biased model predictions in clinical decision-making can be troubling.

The diversity of religion within our world’s population brings challenges for health care providers and systems to deliver culturally competent medical care. Cultural competence is the ability of health providers and organizations to deliver health care services that meet the cultural, social, and religious needs of patients and their families. Culturally competent care can improve patient quality and care outcomes. Strategies to move health professionals and systems towards these goals include providing cultural competence training and developing policies and procedures that decrease barriers to providing culturally competent patient care. If providers and health care systems are not working together to provide culturally competent care, patients may have untoward health consequences, receive poor quality care, and be dissatisfied with the care they receive. The quality of patient-health professional interactions is decreased. Lower-quality patient-health professional interactions are associated with decreased satisfaction in the healthcare provider. In fact, African Americans, Asian Americans, Latinos, and Muslims report that the quality of their care was diminished because of their ethnicity or race. As a DNP prepared advocate I can help our health systems and healthcare providers wiht developing strategies and techniques to respond to the religious and spiritual needs of patients and families for a number of reasons. One reason is that, in addition to TJC, state and federal guidelines encourage institutional responsiveness to population diversity. These strategies are essential to meeting the federal government’s Healthy People goal of eliminating ethnic and racial health disparities.

McCradden, M. D., Joshi, S., Anderson, J. A., Mazwi, M., Goldenberg, A., & Zlotnik Shaul, R. (2020). Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning. Journal of the American Medical Informatics Association : JAMIA, 27(12), 2024–2027. https://doi.org/10.1093/jamia/ocaa085

Swihart DL, Yarrarapu SNS, Martin RL. (2023). Cultural Religious Competence In Clinical Practice. [Updated 2022 Nov 14]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing;Jan-. Available from: https://www.ncbi.nlm.nih.gov/books/NBK493216/