Written by Amy Liao
Misdiagnosis and Miscounted Analytics
Misdiagnosis
Have you ever felt different - maybe a headache that is pounding harder than before; a cough that feels so deep that you might vomit? Did you then look up your symptoms on Google and read “COULD BE CANCER!” ?... The effects of self-diagnosis can be dangerous and can harm your well-being. Misdiagnosis ties in with self-diagnosis in a way: when you self-diagnose yourself, you can possibly risk misdiagnosing yourself.
When we say “misdiagnose”, we usually refer to situations when doctors diagnose you incorrectly like a flu that ended up actually being Lyme Disease. The correct definition can be described along the words as the lack of diagnosis, whether deliberately or accidentally; missing puzzle pieces of a puzzle. In fact, misdiagnosis is common. These errors can occur when either the symptoms are almost identical that you can not tell the two apart or when the lab results are interpreted incorrectly.
What Happens Now?
So you have been misdiagnosed, what happens now? Well, usually it does not make much of a difference as the problem would probably heal in a couple of days or weeks. Other times, the treatment ends up to be completely different and the illness or condition is unaffected by it.This can potentially lead to more development over time, or even growth due to treatment. In some cases, a misdiagnosis could even lead to death.
Why?
Now, let us ask the question “Why does misdiagnosis occur?”. As we take a look at three different scenarios, we can use this to uncover the reasoning behind the most common cases of misdiagnosis.
Time
According to an article published by Wolc Leydon, LLC called “Some Reasons Patients Are Misdiagnosed”, time is a crucial factor leading to misdiagnosis. On average, the timeframe a doctor spends diagnosing a patient is about fifteen minutes. Within this timeframe, only so much may be asked regarding any underlying conditions, past problems, or current condition of a patient. Due to this short timeframe, “doctors usually make a diagnosis that fits the most common diseases”. This results in missing and confusing common diseases with rarer ones.
Poverty
Another reason for misdiagnosis ties closely to poverty. Research shows that there is a higher chance of a clinic or hospital to turn you down if you “look poor”. This is because some people assume that those in poverty are seeking help in hospitals to get their hands on drugs and pills. This is incredibly dangerous because people living in poverty are more susceptible to illnesses because they lack money to pay for treatment, medicine, and food to keep themselves healthy, nourished, and sanitized. What once began as an assumption of a drug-hungry individual could potentially lead to death if left untreated.
Systemic Racism
In December of 2019, news on misdiagnosis and systemic racism arose. Matthew John Derrick-Huie, a 24-year-old Canadian Rapper in Toronto, was a victim of systemic racism for two years. In 2017, he sought out the help of a medical professional after experiencing chest pain and shortness of breath. Only after going to the ER five separate times was Derrick-Huie taken seriously and given a spinal tap to test for meningitis.
After this visit, he started feeling a “crushing feeling” in the back of his head that affected his ability to concentrate. For sixty days, he sought physicians, specialized clinics, and emergency rooms to hope for an answer. Unfortunately,the only treatment he was prescribed were antidepressants. He said ,“one physician told me that I was in a depressive state and I just didn’t realize.”
After almost losing hope, his family took this situation to social media in hopes for someone to take his case and see what is really going on. Finally, the family found a doctor; he discovered that the 24-year-old rapper’s “brain was sagging in his skull due to low cerebrospinal fluid levels”. The doctor believed that due to the spinal tap, a leak complicated the aftermath of the procedure. Before undergoing the surgery, the rapper understood that leaks could happen due to the procedure, but he did not understand why the doctors did not ask for a follow-up, which could have let them spot the complication earlier and give immediate treatment to fix it.
Research shows that Black members of the community are constantly being accused of seeking drugs instead of medical care. In Derrick-Huie’s case, he also felt some instances in which he was racially profiled by health-care providers. He was accused by nurses for “visiting hospitals solely for drugs”. By making these assumptions, the care provided for patients is affected due to the mind-set of the health care provider. This affects the way they diagnose and treat patients, leading to misdiagnosis.
The Connection Between Misdiagnosis and Miscounted Analytics
Miscounted analytics can be defined as incorrectly interpreting information. Data is collected in order to study and summarize the year. We use the collected data to connect trends; trends that tell us which group of people are more vulnerable to a certain disease, or the survival rate of a certain condition; however, we do not get the exact numbers due to misdiagnosis. If we go back to systemic racism, we see how people of black communities are specifically targeted for being “drug dealers” as soon as they step into a hospital “craving for drugs”. Due to these racist assumptions (even with other races), the chance of someone going to a hospital after being racially treated is low; consequently, significant data ends up being lost from that year from a significant community. It is much harder to connect trends of a disease or condition when that data is unavailable. Data could be unavailable because of two reasons: people are either uncomfortable visiting a hospital due to the unfair treatment they experienced before due to the color of their skin, or because health-care workers treat patients according to assumptions and skin color - with bias. So yes, misdiagnosis plays a huge part in miscounted analytics - especially in the communities of those who are not white.
Sources
Comments