Can IT Help to Remove Racism from Healthcare?

There are 2 types of racism that impact healthcare: institutional racism and personally-mediated racism. The first refers to the “differential
access to goods, services, and opportunities by race” – including access to health insurance and therefore preventive and acute care. The second type refers to individual prejudice or discrimination based on race. Personally-mediated racism can impact the types of treatment options offered to patients and the level of shared decision-making.

Evidence-based practice seeks to guide care decisions through best evidence, physician experience, and patient preferences. To best embody evidence-based practice, physicians should look at racial background in terms of understanding how each patient may be impacted by systemic racism and discrimination. For example, physicians should be aware of historical context, including medical experimentation on slaves and the Tuskegee experiment in the 1900s, in which a group of Black men were unknowingly infected with syphilis by physicians, who observed their symptoms over 40 years without providing any treatment.

These events have led to deep mistrust of physicians within the Black community. In light of this, non-Black physicians should understand these historical impacts and seek to develop good relationships with their patients to build trust. Furthermore, physicians should know about trauma-induced risk factors resulting from stress responses to racial discrimination. These physiological changes can build over time and lead to increased risk of conditions such as cardiovascular disease. Lastly, care providers should work to overcome limitations to access due to lack of insurance coverage. Free options should be presented for testing and other services where patients are not required to have insurance.

When it comes to practice-based evidence, most research focusing on racism in healthcare has used data on patient perceptions of discrimination. These perceptions can have impacts on health, and there has not been much progress in developing accurate methods for assessing physician bias. As long as data on race is captured accurately for patients, quality of care and the services utilized by patients can be analyzed across racial categories. Implicit bias, a major way in which racism impacts quality of care, can be assessed through data collection on the conversations physicians have with patients and what treatments they offer to patients.

So, where does informatics come into the picture?

Informatics plays a huge role in identifying inequities in healthcare practice, as well as collecting data that can be used to understand the causes of these inequities. For example, in order to analyze the quality of care and the services provided to patients across different racial backgrounds, researchers need access to EMR data that has ICD-10 diagnosis codes, CPT codes, and racial data.

Natural language processing (NLP) is a developing area of informatics using machine learning to analyze voice or written conversation data. As voice-driven EMR documentation increases to improve physician user experience, the data collected can be analyzed through NLP to determine attitudes, relationship closeness, and balance of conversation between patients and providers. Coded language terms that indicate bias or discrimination can also be tagged and identified through data mining of the conversation data. This would help to improve the measurement of implicit bias within physicians.

Another approach is to update the standards for social determinants of health to include racism. Based on the potential health impacts of facing discrimination, updating EMRs and other informatics systems to collect patient data on experienced discrimination would help to ensure proper evidence-based practice.

This update to SDOH data could help to improve race-based clinical decision support algorithms that have been criticized for perpetuating system racism through harmful “race-correcting” of outputs. See the chart below from The New England Journal of Medicine for examples of how these algorithms are potentially contributing to disparities in quality of care, rather than mending them.

Table 1. Examples Of Race Correction in Clinical Medicine* Tool and Clinical Utility Cardiology The American Heart Association's Get with the Guidelines—Heart Failure9 (https://wv.æ.' .mdcalc.com/gwtg-heart-failure-risk-score) Predicts in-hospital mortality in patients with acute heart failure. Clinicians are advised to use this risk stratification to guide decisions regarding initiating medical therapy. Cardiac surgery The Society of Thoracic Surgeons Short Term Risk Calculator10 (http://riskcalc.sts.org/ stswebriskcalc/calculate) Calculates a patient's risks of complications and death with the most Common cardiac surgeries. Considers variables, some of which are listed here. Nephrology Estimated glomerular filtration rate (eGFR) MDRD and CKD-EPI equationsn (https:// ukidney.com/nephrology-resources/egfr -calculator) Estimates glomerularfiltration rate on the basis of a measurement of serum creatinine. Organ Procurement and Transplantation Network: Kidney Donor Risk Index (KDRl)L2 (https:// optn.transplant.hrsa.gov/resources/allocation -calculators/kdpi-calculator/) Estimates predicted risk of donor kidney graft fail- ure, which is used to predict viability ofpotential kidney donor. T Obstetrics Vaginal Birth after Cesarean (VBAC) Risk Calculator13'1' (https://mfmunetwork.bsc.gwu .edu/PublicBSC/MFMU/VGBirthCalc/vagbirth .html) Estimates the probability of successful paginal birth after prior cesarean section. Clinicians can use this estimate to counsel people who have to decide whether to attempt a trial Of labor rather than undergo a repeat cesarean section. Urology STONE ScorelS,16 Predicts the risk of a ureteral stone in patients who present with flank pain Urinary tract infection (UTI) calculatorl' (https:// uticalc.pitt.edu/) Estimates the risk of UTI in children 2—23 mo Of age to guide decisions about when to pursue urine testing for definitive diagnosis Oncology Rectal Cancer Survival Calculator18 (http:// wævw3.mdanderson.0rg/app/medcalc/index Estimates conditional survival 1—5 yr after diag- nosis with rectal cancer National Cancer Institute Breast Cancer Risk Assessment Tool (https://bcrisktool.cancer .gov/calculator.html) Estimates 5-yr and lifetime risk of developing breast cancer, for women without prior history of breast cancer, DCIS, or LCIS. Breast Cancer Surveillance Consortium Risk Calculator19 (https://tools.bcsc-scc.org/ BC5yearRisk/calculator.htm) Estimates 5- and 10-yr risk of developing breast cancer in women with no previous diagnosis Of breast cancer, DCIS, prior breast augmentation, or prior mastectomy Endocrinology Osteoporosis Risk SCORE (Simple Calculated Osteoporosis Risk Estimation)20 (https://www .mdapp.co/osteoporosis-risk-score-calculator -3161) Determines whether a woman is at low, moder- ate, or high risk for low bone density in order to guide decisions about screening with DXA scan Fracture Risk Assessment Tool (FRAX)21 (https:// www.sheffield.ac.uk/FRAX/tool.aspx) Estimates 1 Oyr risk Of a hip fracture or Other ma- jor osteoporoticfracture on the basis of patient demographics and risk-factor profile. Calculators are country-specific. Pu Imonology Pulmonary-function tests22 Uses spirometry to measure lung volume and the rate Of flow through airways in order to diagnose and monitor pulmonary disease Input Variables Systolic blood pressure Blood urea nitrogen Sodium Age Heart rate History Of COPD Race: black or nonblack Operation type Age and sex Race: black/African American, Asian, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander, or "Hispanic, Latino or Spanish ethnic- ity"; white race is the default setting. BMI Serum creatinine Age and sex Race: black vs. white or other Age Hypertension, diabetes Serum creatinine level Cause of death (e.g., cerebrovascular accident) Donation after cardiac death Hepatitis C Height and weight HLA matching Cold ischemia En bloc transplantation Double kidney transplantation Race: African American Age BMI Prior vaginal delivery Prior VBAC Recurring indication for cesarean sec- tion African-American race Hispanic ethnicity Sex Acute onset of pain Race: black or nonblack Nausea or vomiting Hematuria Age months Maximum temperature >390C Race: Describes self as black (fully or partially) Female or uncircumcised male Other fever source Age and sex Race: white, black, Other Grade Stage Surgical history Current age, age at menarche, and age at first live birth First-degree relatives with breast cancer Prior benign biopsies, atypical biopsies Race/ethnicity: white, African American, Hispanic/Latina, Asian American, American Indian/Alaska Native, unknown Age Race/ethnicity: white, black, Asian, Native American, other/multiple races, unknown BIRADS breast density score First-degree relative with breast cancer Pathology results from prior biopsies Rheumatoid arthritis History of fracture Age Estrogen use Weight Race: black or not black Age and sex Weight and height Previous fracture Parent who had a hip fracture Current smoking Glucocorticoid use Rheumatoid arthritis Secondary osteoporosis Alcohol use, 23 drinks per day Femoral neck bone mineral density Age and sex Height Race/ethnicity Use Of Race Adds 3 points to the risk score ifthe patient is identified as nonblack. This addition increases the estimated probability Of death (higher scores predict higher mortality). The risk score for operative mortality and major complications increases (in some cases, by 20%) ifa patient is identified as black. Identification as another non- white race or ethnicity does not increase the risk score for death, but it does change the risk score for major compli- cations such as renal failure, stroke, and prolonged ventilation. The MDRD equation reports a higher eGFR (by a factor of 1.210) ifthe patient is identified as black. This adjustment is similar in magnitude to the correction for sex (0.742 if female). The CKD-EPI equation (which included a larger number ofblack patients in the study population), proposes a more modest race correction (by a factor of 1.159) if the patient is identified as black. This correction is larger than the correction for sex (1.018 iffemale). Increases the predicted risk of kidney graft failure ifthe potential donor is identified as African American (coefficient, 0.179), a risk adjustment intermediate between those for hypertension (0.126) and diabetes (0.130) and that for elevated creatinine (0.209-0.220). The African-American and Hispanic correc- tion factors subtract from the estimated success rate for any person identified as black or Hispanic. The decrement for black (0.671) or Hispanic (0.680) is almost as large as the benefit from prior vaginal delivery (0.888) or prior VBAC (1.003). Produces a score on a 13-point scale, with a higher score indicating a higher risk of a ureteral stone; 3 points are added for nonblack race. This adjustment is the same magnitude as for hematuria. Assigns a lower likelihood of UTI ifthe child is black (i.e., reports a roughly 2.5-times increased risk in patients who do not describe themselves as black). White patients are assigned a regression coeffcient Of l, with higher coefficients (depending on stage) assigned to black patients (1.18—1.72). The calculator returns lower risk estimates for women who are African American, Hispanic/Latina, or Asian American (e.g., Chinese). The coefficients rank the race/ethnicity categories in the following descending order Of risk: white, American Indian, black, Hispanic, Asian. Assigns 5 additional points (maximum score of50, indicating highest risk) ifthe patient is identified as nonblack The U.S. calculator returns a lower fracture risk ifa female patient is identified as black (by a factor Of 0.43) , Asian (0.50), or Hispanic (0.53). Estimates are not provided for Native American patients Or for multiracial patients. In the U.S., spirometers use correction factors for persons labeled as black (10-15%) or Asian (4-6%). Equity Concern The original study envisioned using this score to "increase the use of recommended medical therapy in high-risk patients and reduce resource utilization in those at low risk. "9 The race correction regards black patients as lower risk and may raise the threshold for using clinical resources for black patients. When used preoperatively to assess a patient's risk, these calculations could steer minority patients, deemed higher risk, away from these procedures. Both equations report higher eGFR values (given the same creatinine measurement) for patients identified as black, suggesting better kidney function. These higher eGFR values may delay referral to specialist care or listing for kidney transplantation. Use ofthis tool may reduce the pool of African- American kidney donors in the United States. Since African-American patients are more likely to receive kidneys from African- American donors, by reducing the pool of available kidneys, the KDRI could exacer- bate this racial inequity in access to kidneys for transplantation. The VBAC score predicts a lower chance Of suc- cess ifthe person is identified as black or Hispanic. These lower estimates may dis- suade clinicians from offering trials of labor to people of color. By systematically reporting lower risk for black patients than for all nonblack patients, this calculator may steer clinicians away from aggressive evaluations Of black patients. By systematically reporting lower risk for black children than for all nonblack children, this calculator may deter clinicians from pursu- ing definitive diagnostic testing for black children presenting with symptoms of UT l. The calculator predicts that black patients will have shorter cancer-specific survival from rectal cancer than white patients. Clinicians might be more or less likely to offer inter- ventions to patients with lower predicted survival rates. Though the model is intended to help concep- tualize risk and guide screening decisions, it may inappropriately discourage more ag- gressive screening among some groups of nonwhite women. Returns lower risk estimates for all nonwhite race/ethnicity categories, potentially reduc- ing the likelihood Of close surveillance in these patients. By systematically lowering the estimated risk ofosteoporosis in black patients, SCORE may discourage clinicians from pursuing further evaluation (e.g., DXA scan) in black patients, potentially delaying diagnosis and intervention. The calculator reports 10-yr risk Of major os- teoporotic fracture for black women as less than halfthat for white women with identi- cal risk factors. For Asian and Hispanic women, risk is estimated at about halfthat for white women. This lower risk reported for nonwhite women may delay intervention with osteoporosis therapy. Inaccurate estimates of lung function may result in the misclassification of disease severity and impairment for racial/ethnic minorities (e.g., in asthma and COPD).23 * BIRADS denotes Breast Imaging Reporting and Data System, BMI body-mass index (the weight in kilograms divided by the square of the height in meters), CKD-EPI Chronic Kidney Disease Epidemiology Collaboration, COPD chronic obstructive pulmonary disease, DCIS ductal carcinoma in situ, DXA dual-energy x-ray absorptiometry, LCIS lobular carcinoma in situ, and MDRD Modification of Diet in Renal Disease study. -i- The current calculator uses Ethnicity/Race, with the following options: American Indian or Alaska Native, Asian, Black or African American, Hispanic/Latino, Native Hawaiian or Other Pacific Islander, White, and Multiracial. Three countries' calculators are further subcategorized by race, ethnicity, or location: China (Mainland China, Hong Kong), Singapore (Chinese, Malay, Indian), and the United States (Caucasian, black, Hispanic, Asian).

Inaccurately identifying patients as high or low-risk based on race can lead to changes in the services or treatments offered. These algorithms need to be trained to incorporate the nuances of the combined effects of systemic racism and discrimination. As with all informatics research and innovation, sample data used to train any healthcare AI models needs to include equal representation for individuals of all races and demographics to ensure that the technology benefits all demographic groups equally.

References:

Vyas, D. A., M.D., Eisenstein, L. G., M.D., & Jones, D. S., M.D. Ph.D. (2020). Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms. The New England Journal ofMedicine, (383). https://doi.org/10.1056/NEJMms2004740

Published by Sara Hou

Sara Hou is a Chicago-based writer, healthcare IT consultant, and Product Manager. She is currently pursuing her M.S. in Health Informatics at DePaul University. Sara is passionate about making healthcare more holistic, personalized, and accessible through innovation in technology and the overall U.S. healthcare system.

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