Draft the following sections of Chapter 3 – Methods, using the topic unconscious biases in healthcare
Data Collection Procedures
Unconscious Biases in Healthcare
Healthcare in the United States has improved greatly over the last 30 years. Much of the studies and research surrounding healthcare disparities, understands the issue but fails to address how to overcome them. Automatic cognitive processors reduce practitioners†social norms to a dose able amount of information that categorizes individuals and situations. (Shavers, Fagan, Jones, Klein, Boyington, Moten, Rorie, 2012). Thinking without understanding is how we stereotype and concoct preconceived notions about individuals and situations, these can be negative or positive. These biases cause health disparities which lead to differential treatment, ordering of medical additional scans for diagnosis, and failure prescribe adequate medications. (Shavers, Fagan, Jones, Klein, Boyington, Moten, Rorie, 2012) Healthcare providers unconscious biases towards racial ethnic minorities is more often reported in minority groups and its prevalence negatively affects clinical relationships, patient satisfaction, and quality of care, directly contributing to poor health outcomes. (Pérez-Stable, El-Toukhy, 2018).
Purpose Statement
The purpose of this participatory action research project is to determine if unconscious biases in healthcare settings influence patient diagnosis, treatment, and interaction. This study seeks to examine healthcare providers ability to acquire new behaviors to enhance patient experiences.
Problem Statement
In this article we will investigate healthcare unconscious bias disparities. Current studies indicate a problem but research limitations fail to provide empirical evidence of the effects on patient interactions.
Study Question
The question addressed in this study is, “What is the relationship between cognitive function and unconscious bias?” I plan to formulate additional questions as I gather more information and gain a deeper understanding of the intended outcome. The intention is the information gathered in the DSP will be widely used to promote changes in the healthcare industry.
Conceptual Framework
The conceptual framework driving this study is the Commonwealth Fund International Health Policy Survey, developed in 2011 by Robin Osborn. Based on inputs from up to seven countries form general population and eleven countries physicians, this survey categorizes questions, based on access to care, doctor-patient relationship, patient safety, coordination of specialty care, ER visits, and communications, and demographics. Implicit Association Test (IAT) is used to assess providers unconscious biases using reflective tools (Teal, Gill, Green, Crandall, 2012). This conceptual framework provides the most essential framework and addresses the problems of this study. There is likely a framework that is further developed; however, this one is useful in discerning the categories associated with this study. 
Significance of the Study
The United States is leading in the world for lab test errors in medical homes. This is due to the biases faced by the geriatric community. This is only one example of provider-patient biases. Communities across the US face significantly higher poor physical health, mental health, and chronic illnesses. In general, the disparities including social exclusion, poverty, and other social determinants of health. (Pérez-Stable, El-Toukhy, 2018). By changing behaviors this study will provide thought enhancing reminders of the cognitive traps that lead to biases and other social determinants. Cognitive motivation will reduce the instances of provider-patient interactions that lead to conceptualized systemic discriminations. By articulating the disparities in patient interactions, this study will promote change and self-reflection in hopes to create intentional change within the medical society (Teal, Gill, Green, Crandall, 2012).
Shavers, V. L., Fagan, P., Jones, D., Klein, W. M., Boyington, J., Moten, C., & Rorie, E. (2012). The state of research on racial/ethnic discrimination in the receipt of health care. American journal of public health, 102(5), 953–966.
Teal, C. R., Gill, A. C., Green, A. R., & Crandall, S. (2012). Helping medical learners recognise and manage unconscious bias toward certain patient groups. Medical education, 46(1), 80–88.
Pérez-Stable, E., El-Toukhy, S. (2018). Communicating with diverse patients: How patient and clinician factors affect disparities. Patient Education and Counseling. 101(12), 2186-2194
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