Deanna C. Fang, MD, FASCP, is the Associate Director of Immunogenetics and Transplantation Laboratory and of Transfusion Medicine at UC San Diego Health. In this Q&A, she takes a holistic and laboratory-math view of racism in healthcare and shares her insight below.
In your experience, how do Asian Americans and/or Pacific Islanders interact with health care? What obstacles do they face?
This is very difficult to say because there are so many generational differences. For example, a first-generation vs a fourth-generation Asian American/Pacific Islander (AAPI) can expect to have different experiences with their health care. Even then, nothing is guaranteed because of—or in spite of—one’s race. I think the best I can say is about the role of cultural and racial awareness is that we should be aware of how a person's ethnicity or cultural background can be a factor and, at the same time, be aware that they may not be necessarily a factor. I suppose it is like saying that a stereotype can be true sometimes and other times may turn out not to match. It is so individual. I would also point out that what I say really applies beyond just the AAPI experience (e.g., for African Americans, women, LGBTQ, etc.).
How can healthcare providers in the U.S. get a better understanding of cultural needs or differences for Asian Americans/Pacific Islanders in order to better provide earlier screening and prevention, and better health care overall? How can the laboratory play a role in this?
Saving money and being careful about one's dollar can be a deeply ingrained mindset in Asian culture. The ability or virtue of tolerating suffering for certain values—in other words, to not complain—can also be part of Asian culture (but it feels strange to say this because I could say this for any culture; it is just different suffering for different values). So this can (though not always) pose a barrier to people who are AAPI seeking medical health. I give two examples from my healthcare profession relatives:
One relative worked in a clinic for the underserved local Chinese community. She frequently has patients who will decline routine laboratory testing for preventative monitoring or who will refuse diagnostic imaging, even when it is physician recommended. Because she is aware of their cultural background and that their decisions may not be medically based, she makes sure to let them know that these services are provided free of charge by the clinic's program and then they immediately are more than happy to proceed.
Another relative works in the emergency department. If she sees an Asian patient waiting for the emergency room, she is immediately on alert that this patient may need to be triaged sooner because of her awareness that there is a cultural reluctance to seek urgent (and expensive) medical care unless a condition is symptomatically serious. She is also aware that pain or the severity of a complaint may be mildly reported compared to what the patient is actually experiencing.
Tell us about your own personal experiences with racism in health care, and how it has affected your career and/or education.
I find it challenging to speak briefly on my own experiences. So, instead, I am going to use a lab-math analogy to racism. Consider an "experience" to be a single data point. By itself, you really cannot say much. You can suspect you have encountered or witnessed racism. You may be right, you may be wrong. Something might be due to (or affected by) racism, might be due to something else, or might have turned out that way "just by chance.” A single experience would have to be very overt for one to call out racism on that one data point. (If you want to have a pathology-oriented metaphor, you can think of a Levey-Jennings chart and apply the Westgard Rules.)
So, in the absence of an overt and obvious "data point experience," the only way to truly see the presence of racism is through multiple data points and by comparing it to other data points (other people's experiences). The thing is, that takes time. It takes having to go through a lot of experiences before one can confidently call a trend or a statistically significant disparity in how one is being treated. Think of the sample size that would be required to definitively make a call. To rule out other influences. Also, it is one thing to look at a data point on a graph or in a table in a lab. But if we consider that a data point is an individual's experience, then, on a human level, it can also be mentally and emotionally exhausting to monitor every data point—every experience—for possible racism.
I also want to point out that that an experience (or a data point) can happen in spite of racism. Just because a data point falls on other side of the expected mean, it does not mean that the influence of racism was absent.
If we want to consider racism in a society, then, likewise, the experience of one person (one data point) is not enough to make the call. It requires looking at the experiences of many to truly see an impact. Is a system in place to view and assess all those data points—those experiences of multiple individuals? It is like Lab QC/QA:
You are not going to see anything if you do not have a system in place to monitor it.
If something wrong is identified, we need to implement a corrective action.
If a corrective action is implemented, we need to do follow up monitoring to see if the corrective action was effective.
What metrics do we use? How do we avoid the trap of following the easier metrics that us look and feel good – versus the ones that are truly meaningful?
To read more Q&As with Asian American and Pacific Islander members of the laboratory, click
here.
