16 Mar What have the researchers learned about the effects of anxiety on academic performance of medical school students? With the information you gathered for your Research
Research Paper Presentation Instructions
Research question
What have the researchers learned about the effects of anxiety on academic performance of medical school students?
With the information you gathered for your Research Paper, you will now create an intriguing visual presentation and present it to the class.
Presentation should include following slides:
- Introduction (1 slide)
- Research question (1slide)
- literature review (2 slide)
- Analysis of the literature (1 slide)
- Discussion paragraphs 1, 2, 3 (2 slide)
- Conclusion (1slide)
- Title page and reference list (2 slides)
Racial bias in healthcare algorithms
JK, MS
Department of Data Science, Monroe College, King Graduate School
KG604: Graduate Research & Critical Analysis
Dr. Puri
12/04/2022
Introduction: racial Disparities in healthcare in the U.s
10.4% African-Americans uninsured VS. 5.4% non-Hispanic Whites (U.S. Census Bureau, 2021)
Increased use of algorithms in the healthcare industry might perpetuate racial disparities
200 million Americans could be affected every year by racial bias in healthcare algorithms (Gawronski, 2019)
There are important racial disparities in healthcare in the U.S. in terms of coverage, access to care, and chronic health
it is important to understand if those models perpetuate the racial bias and what their impact might be on health care disparities among minorities in the United States.
As many as 200 million Americans could be affected every year by racial bias embedded in healthcare algorithms
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Literature review
Obermeyer et al. (2019)
26% more chronic illness in Black patient at same predicted risk score
47% (VS. 18%) Black patients identified for care management if algorithm didn’t have any bias
Relying on wrong metric: past healthcare cost
Park et al. (2021)
White mothers 2X as likely to be diagnosed with PPD compared to Black patients with same risk factors
White mothers 1.37 times more likely to visit a mental health provider compared to Black patients with same risk factors
Reweighting decreased bias
Obermeyer
they found that it heavily relied on past healthcare costs to predict future health need.
Park
inherent disparity present in the data (Black patients’ feelings around healthcare and the increased difficulties in access )
reweighting was found to increase the disparate impact from 0.31 to 0.79 (with 1 signifying fairness) and bring the equal opportunity difference closer to zero (meaning no bias) by going from -0.19 to 0.02
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Literature review (cont.)
Abubakar et al. (2020)
87% accuracy of model built using Caucasian dataset and tested on Black skin
83% accuracy of model built using Black dataset and tested on White skin
99% accuracy of algorithm when build using diverse data of both Black and White patients
–
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Methods
Quantitative methods
Building algorithms based on large datasets
Ways to reduce racial bias
Obermeyer et al. (2019): use different metrics
Park et al. (2021): reweighting technique
Abubakar et al. (2020): data representative of diverse racial population
Recommendations
Literature Review analysis
Findings
Evidence of racial bias
News Article
Racial bias in algorithms that decide a patient place on a transplant list, eligibility for care management programs, brain injury payouts (Christensen et al., 2021)
Detriment to minorities of color in the U.S.
U.S. Department of Health and Human Services (HHS) proposed a new rule amending Section 1557 of the Affordable Care Act (ACA)
Targets bias that comes from the use of clinical algorithms to make decisions (Health and Human Services [HHS], 2022).
EFFECTS:
Regulation of healthcare algorithms at national level
Forces covered entities to be accountable for the models they use
Legal and/or financial repercussions (Keith, 2022)
policy
Discussion
Discussion (cont.)
Solution only acts against entities/programs that:
receive federal funding from Health and Human Services (HHS)
are led by HHS or created under Title I of the Affordable Care Act (Keith, 2022)
Extend the solution to make sure that organizations and programs that are not under the scope of HHS are also held accountable.
Replicate Section 1557 of ACA to implement additional regulations at a company, state, and national level for every entity using healthcare algorithms
RECOMMENDATION
Conclusion
Evidence of racial bias found in healthcare algorithms which perpetuates health inequalities in the U.S. (Abubakar et al., 2020; Christensen et al., 2021; Obermeyer et al., 2019; Park et al., 2021).
New rule amending Section 1557 of the Affordable Care Act was proposed by the U.S Department of Health and Human Services.
Consequences if covered entities make clinical decisions using algorithms that exhibit racial bias (Health and Human Services [HHS], 2022; Keith, 2022).
Recommendation: Extend regulations to make sure all scientists building healthcare algorithms are held accountable
– news institutions and academic journals have reported evidence of racial bias found in healthcare algorithms in the U.S. (Abubakar et al., 2020; Christensen et al., 2021; Obermeyer et al., 2019; Park et al., 2021).
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References
Abubakar, A., Ugail, H., & Bukar, A.M. (2020). Assessment of human skin burns: A deep transfer learning approach. Journal of Medical and Biological Engineering, 40, 321–333. https://doi.org/10.1007/s40846-020-00520-z.
Christensen, D. M., Manley, J., & Resendez, J. (2021, September 9). Medical algorithms are failing communities of color. Health Affairs Forefront. https://www.healthaffairs.org/do/10.1377/forefront.20210903.976632/full/
Gawronski, Q (2019, November 6). Racial bias found in widely used health care algorithm. NBC News. https://www.nbcnews.com/news/nbcblk/racial-bias-found-widely-used-health-care-algorithm-n1076436
Keith, K. (2022, July 27). HHS proposes revised ACA anti-discrimination rule. Health Affairs Forefront. https://www.healthaffairs.org/content/forefront/hhs-proposes-revised-aca-anti-discrimination-rule
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342
Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa-Mullan, I., Koski, E., & Das, A. K. (2021). Comparison of methods to reduce bias from clinical prediction models of postpartum depression. JAMA Network Open, 4(4). https://doi.org/10.1001/jamanetworkopen.2021.3909
U.S. Census Bureau. (2021, September 14). Health insurance coverage in the United States: 2020. United States Census Bureau. https://www.census.gov/library/publications/2021/demo/p60-274.html
U.S. Department of Health and Human Services. (2022, July 25). HHS announces proposed rule to strengthen nondiscrimination in health care. HHS. https://www.hhs.gov/about/news/2022/07/25/hhs-announces-proposed-rule-to-strengthen-nondiscrimination-in-health-care.html
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