Canada is celebrated for providing free healthcare for all Canadian citizens, and Canada takes pride in the Charter of Rights and Freedoms guaranteeing equality, but systemic racism persists in the health system. One of the most underrated problems with the Canadian healthcare system is the algorithmic racism programmed into healthcare machinery. One of the most underrated problems with the healthcare system is the algorithmic racism programmed into healthcare machinery. Artificial intelligence is subject to the errors made by humans because it is supposed to mirror human intelligence and thought processes. Thus, though artificial intelligence itself cannot be racist, the subconscious ignorance of individuals who create machinery for healthcare can be integrated into their system.

According to Fixing Medical Devices That Are Biased against Race or Gender by Claudia Wallis, there are three prongs to the racial bias integrated in the machinery used to evaluate the illnesses and injuries of non-Caucasian patients: physical bias, computational bias and interpretation bias (1). Most significant is the issue of the computational bias which lies in the data sets and software employed to create the algorithms that test physiological variables in an individual (1). Computational bias in machinery can be seen through the color blindness of devices testing physiological variables in minority ethnicities.

Taking the recent example of the SARS-CoV2 pandemic, we can focus on the use of machinery such as the spirometer which is known for its function in diagnosing as well as treating respiratory diseases (2). It is a standardized machine used on individuals of every ethnicity to test their lung function and screen for diseases (3).

However, it has been proven that lung function does vary from race to race, with certain racial groups maintaining a higher or lower oxygen level than the average Caucasian individual (4). However, if the standard spirometer is not a race adjusted tool, it won’t highlight deviations from the equilibrium of an individual who is South Asian, African American or European, to name a few. If the device that is supposed to be measuring a certain physiological variable isn’t even a valid measure of that variable, it can greatly alter the diagnosis and treatment plan for an individual experiencing issues in breathing function, for example. This can lead to unnecessary deaths that could have been likely prevented if racial subgrouping was a component of the medical device.

Nonetheless, machinery employed in healthcare is generally manufactured by large conglomerates which produce technology in mass numbers. Alas, hospitals, which are regulated at the provincial level, albeit with some federal input, are majorly standardized in the machinery they employ- such as MRIs, Ultrasound machines, heart monitors, ventilators and more (5).

Now if this machinery is guilty of computational bias, to correct this error, it must be recalled from nearly every hospital in a given province, let alone provinces that share their medical machinery companies. This is a time-consuming and extremely costly endeavor for not only the government, but the tax-paying individuals in that province or territory. Therefore, instead of sending out machinery that compromises the healthcare received by non-Caucasisn individuals, the machinery’s software should be edited to account for disparities in the resting physiological variables of individuals due to race.

Unfortunately, Canada has not begun to see this initiative be performed and thus thousands, if not millions of individuals are not given the healthcare they need, falling victim to illnesses and healthcare measures that could easily have been avoided.

Moreover, aside from computational bias, physical bias and interpretational bias are also significant factors in the racial inequality practiced in the healthcare system. In terms of physical bias, which stems from the hardware and set-up of the device, engineers should be trained on designing the mechanics of medical machinery to consider these various physiological discrepancies (1).

Furthermore, interpretational bias, the final piece of the puzzle, stems from the healthcare professionals, such as technicians, employing and interpreting healthcare machinery (1). This component of systemic racism in the medical community can be accounted for by training medical professionals and technicians on the differences in physiological variables of ethnic groups. Thus, they would learn to assign different weights to different physiological variables, such as oxygen levels, and significant healthcare factors such as increased risk for ectopic pregnancies, differently based on the ethnic makeup of each individual. This would allow for the proper diagnoses, making healthcare professionals aware of risks to screen for and levels of medication to prescribe.

Unfortunately, healthcare will not truly be equal or fair until it is recognized that race does affect one’s medical history and overall health. Programming medical machinery by racially adjusting them to recognize differences in physiological variables and aggregating those variables in a method that validly diagnoses non-Caucasian individuals is crucial. Taking this initiative can intercept thousands of unnecessary deaths of several non-Caucasian individuals around the world and allow healthcare professionals to truly care for their patients.

References

1. Wallis, C. (2021, June 1). Fixing medical devices that are biased against race or gender. Scientific American. Retrieved February 7, 2022, from https://www.scientificamerican.com/article/fixing-medical-devices-that-are-biased-against-race-or-gender/
2. Braun, L. (2015). Race, ethnicity and Lung Function: A brief history. Canadian journal of respiratory therapy : CJRT = Revue canadienne de la therapie respiratoire : RCTR. Retrieved February 9, 2022, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631137/
3. How to use your incentive spirometer. Memorial Sloan Kettering Cancer Center. (n.d.). Retrieved February 9, 2022, from https://www.mskcc.org/cancer-care/patient-education/how-use-your-incentive-spirometer
4. Anderson , M. A., Malhotra , A., & Non, A. L. (2020, December 10). Could routine race-adjustment of spirometers exacerbate racial disparities in COVID-19 recovery? The Lancet Respiratory Medicine. Retrieved February 10, 2022, from https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30571-3/fulltext#:~:text=Currently%2C%20there%20is%20no%20known,occupational%20hazards%20clearly%20influence%20capacity.&text=Understanding%20socioeconomic%20and%20racial%20differences%20in%20adult%20lung%20function.
5. Canada, H. (2019, September 17). Government of Canada. Canada.ca. Retrieved February 11, 2022, from https://www.canada.ca/en/health-canada/services/health-care-system/reports-publications/health-care-system/canada.html