Clinical algorithms, racism, and "fairness" in healthcare
A case of bounded justice
Datos Bibliográficos
| ID | 5260480 |
|---|---|
| Autores | Sarah El-Azab (0000-0003-4435-4700, University of Michigan, autor de correspondencia), Paige Nong (0000-0002-2849-9005, University of Minnesota) |
| Año | 2023 |
| Volumen | 10 |
| Número | 2 |
| Fecha de publicación | 2023-07-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Big Data & Society (JOURNAL) |
| Identificadores de la revista | ISSN: 2053-9517 • E-ISSN: 2053-9517 |
| Editorial | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/20539517231213820 |
| OpenAlex | W4388738041 |
| Idioma | EN |
| Citas recibidas | 3 |
| Referencias citadas | 97 |
To date, attempts to address racially discriminatory clinical algorithms have largely focused on fairness and the development of models that "do no harm." While the push for fairness is rooted in a desire to avoid or ameliorate health disparities, it generally neglects the role of racism in shaping health outcomes and does little to repair harm to patients. These limitations necessitate reconceptualizing how clinical algorithms should be designed and employed in pursuit of racial justice and health equity. A useful lens for this work is bounded justice, a concept and research analytic proposed by Melissa Creary to guide multidisciplinary health equity interventions. We describe how bounded justice offers a lens for (1) articulating the deep injustices embedded in the datasets, methodologies, and sociotechnical infrastructure underlying design and implementation of clinical algorithms and (2) envisioning how these algorithms can be redesigned to contribute to larger efforts that not only address current inequities, but to redress the historical mistreatment of communities of color by biomedical institutions. Thus, the aim of this article is two-fold. First, we apply the bounded justice analytic to fairness and clinical algorithms by describing structural constraints on health equity efforts such as medical device regulatory frameworks, race-based medicine, and racism in data. We then reimagine how clinical algorithms could function as a reparative technology to support justice and empower patients in the healthcare system
Algorithm · Economic Justice · Harm · Health care · Health equity · Political science · Psychological intervention · Public relations · Racism · Redress · Sociology · Computer Science · Ethics in Clinical Research · Law · Medicine · Nursing · Race, Genetics, and Society · Sex and Gender in Healthcare
Backdoor to Eugenics
Structural Racism In Historical And Modern US Health Care Policy
Algorithmic Fairness
Race and Reification in Science
Assessing risk, automating racism
High-performance medicine
Racism and Health
The Social Life of DNA
Where fairness fails
Escaping the Impossibility of Fairness
Exploiting Race in Drug Development
Genes, Race, and Population
Health Disparities and Health Equity
The Structuring Work of Algorithms
Health Equity Beyond Data
Race Based Medicine, Colorblind Disease
Examining racism in health services research
Systemic racism and U.S. health care
Why The Nation Needs A Policy Push On Patient-Centered Health Care
White Feminist Gaslighting
Situating questions of data, power, and racial formation
Algorithmic reparation
The limitations of patient-centered care
The public health critical race methodology
The social life of biomedical data
From Here to Equality
Social Conditions as Fundamental Causes of Health Inequalities
Is Racism a Fundamental Cause of Inequalities in Health
The multiple dimensions of race
The Impact of Racism on Clinician Cognition, Behavior, and Clinical Decision Making
| Obras citantes distintas | 3 |
|---|---|
| Citas por año | 3 |
| Intervalo de citas | 2025 - 2026 (2) |
| Velocidad de citación | current |
| Altamente citado | No |
| Tipos de cita | Neutras: 3 |