As with other forms of structural racism in the U.S., healthcare for Asian Americans has suffered from poor quality data collection and management, along with racialized stereotypes that affect diagnosis, treatment, and ongoing healthcare management.
Recent studies have noted that clinical decision-making is negatively influenced by providers’ racial biases, disease stereotyping by race, and increasingly by the use of racially biased clinical algorithms for diagnosis and treatment. While some conditions may be more closely linked to patients with certain genetic ancestries, genetic ancestry is often poorly correlated to disproven notions of race, and contributes to ongoing racial disparities in healthcare.
Disparity in healthcare for Asian Americans is affected by three pervasive racial stereotypes that are part of ongoing systemic racism in the U.S., and perpetuate the myth that Asian Americans do not experience health disparities: the model minority, perpetual foreigner and healthy immigrant. The model minority myth in relation to healthcare perpetuates the myth that Asian Americans are closer to ‘white’ in terms of their healthcare parity (as opposed to Black Americans, serving once again to foster animosity between the two groups). The healthy immigrant myth believes that recent Asian immigrants to the U.S. are healthier than Asian Americans born in the U.S., again not taking into effect the ethnic and cultural differences of the various sub-groups. The perpetual foreigner myth is one that perpetuates a stereotype that Asians, despite being a model minority, will never fully assimilate, and has led to anti-Asian hate as seen with COVID-19.
As was highly evident in the data reporting of COVID-19, inaccuracies in data systems and missing data reports in the numbers of cases and deaths were noted, with a large percentage of these from Asian American and other minority groups. Ongoing systemic mismanagement of data collection includes
- lack of data disaggregation with indiscriminate grouping of various individuals with differing health profiles into one overall racial category of ‘Asian’,
- not addressing the unique ethnic and cultural profiles of the various sub-populations within the East Asian, Southeast Asian, and South Asian populations.
Flawed data and reporting perpetuate the Asian American stereotypes in both healthcare management and resource funding for Asian American communities. Poor data management and systemic racism has led to inadequate diagnosis and treatment of Asian Americans, for example in using white body mass thresholds for Asian Americans in diagnosis of diabetes; reporting of cancer as the leading cause of death in all Asian Americans (when heart disease is the leading cause of death in South Asians); in mental health management, and more.
The use of Artificial Intelligence (AI) and algorithms as tools for diagnosis and healthcare management has great potential for improving quality health care. However, as currently used, they have been shown to have racial biases within the tools as well as in the training of health care professionals who use them. This often leads to misdiagnosis and mismanagement of patient care across a range of diseases.
Some states (California, Oregon, Minnesota, and New York) have made progress on disaggregation of data, but many, including at the Federal level, still only capture broad racial and ethnic categories. The New York City Department of Health has also launched a Coalition to End Racism in Clinical Algorithms (CERCA) following on from their landmark report last year declaring that racism is a public health crisis.
On August 31, 2022, California’s Attorney General Rob Bonta launched an Inquiry into Racial and Ethnic Bias in Healthcare Algorithms at all hospitals, healthcare facilities, and other providers. The inquiry requests how they are identifying and addressing racial and ethnic disparities in the AI tools that they use. Attorney General Bonta is committed to addressing disparities in healthcare.
The AAPI Global Caucus supports this first step by California and looks forward to improvements in addressing racial biases in the healthcare of our communities.