Efforts in Organized Medicine to Eliminate Harmful Race-Based Clinical Algorithms.
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ABSTRACT:
SUBMITTER: Cleveland Manchanda EC
PROVIDER: S-EPMC10915685 | biostudies-literature | 2024 Mar
REPOSITORIES: biostudies-literature
Publications
Efforts in Organized Medicine to Eliminate Harmful Race-Based Clinical Algorithms.
Cleveland Manchanda Emily C EC Aikens Blair B De Maio Fernando F Jordan William W Brown Jennifer T JT Sivashanker Karthik K Maybank Aletha A
JAMA network open 20240304 3
Similar Datasets
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Project description:BackgroundClinical algorithms that incorporate race as a modifying factor to guide clinical decision-making have recently been criticized for propagating racial bias in medicine. Equations used to calculate lung or kidney function are examples of clinical algorithms that have different diagnostic parameters depending on an individual's race. While these clinical measures have multiple implications for clinical care, patients' awareness of and their perspectives on the application of such algorithms are unknown.ObjectiveTo examine patients' perspectives on race and the use of race-based algorithms in clinical decision-making.DesignQualitative study using semi-structured interviews.ParticipantsTwenty-three adult patients recruited at a safety-net hospital in Boston, MA.ApproachInterviews were analyzed using thematic content analysis and modified grounded theory.Key resultsAmong the 23 study participants, 11 were women and 15 self-identified as Black or African American. Three categories of themes emerged: The first theme described definitions and the individual meanings participants ascribed to the term race. The second theme described perspectives on the role and consideration of race in clinical decision-making. Most study participants were unaware that race has been used as a modifying factor in clinical equations and rejected the incorporation of race in these equations. The third theme related to exposure to and experience of racism in healthcare settings. Experiences described by non-White participants ranged from microaggressions to overt acts of racism, including perceived racist encounters with healthcare providers. In addition, patients alluded to a deep mistrust in the healthcare system as a major barrier to equitable care.ConclusionsOur findings suggest that most patients are unaware of how race has been used to make risk assessments and guide clinical care. Further research on patients' perspectives is needed to inform the development of anti-racist policies and regulatory agendas as we move forward to combat systemic racism in medicine.
| S-EPMC9945816 | biostudies-literature
Project description:Large language models (LLMs) are being integrated into healthcare systems; but these models may recapitulate harmful, race-based medicine. The objective of this study is to assess whether four commercially available large language models (LLMs) propagate harmful, inaccurate, race-based content when responding to eight different scenarios that check for race-based medicine or widespread misconceptions around race. Questions were derived from discussions among four physician experts and prior work on race-based medical misconceptions believed by medical trainees. We assessed four large language models with nine different questions that were interrogated five times each with a total of 45 responses per model. All models had examples of perpetuating race-based medicine in their responses. Models were not always consistent in their responses when asked the same question repeatedly. LLMs are being proposed for use in the healthcare setting, with some models already connecting to electronic health record systems. However, this study shows that based on our findings, these LLMs could potentially cause harm by perpetuating debunked, racist ideas.
| S-EPMC10589311 | biostudies-literature
Project description:IntroductionMedical curricula implicitly teach that race has a biological basis. Clinical rotations reinforce this misconception as race-based algorithms are used to guide clinical decision-making. This module aims to expose the fallacy of race in clinical algorithms, using the estimated glomerular filtration rate (eGFR) equation as an example.MethodsWe created a 60-minute module in consultation with nephrologists. The format was an interactive, case-based presentation with a didactic section. A third-year medical student facilitated the workshops to medical students. Evaluation included pre/post surveys using 5-point Likert scales to assess awareness regarding use of race as a biological construct. Higher scores indicated increased awareness.ResultsFifty-five students participated in the module. Pre/post results indicated that students significantly improved in self-perceived knowledge of the history of racism in medicine (2.6 vs. 3.2, p < .001), awareness of race in clinical algorithms (2.7 vs. 3.7, p < .001), impact of race-based eGFR on quality of life/treatment outcomes (4.5 vs. 4.8, p = .01), differences between race and ancestry (3.7 vs. 4.3, p < .001), and implications of not removing race from the eGFR equation (2.7 vs. 4.2, p < .001). Students rated the workshops highly for quality and clarity.DiscussionOur module expands on others' work to expose the fallacy of race-based algorithms and define its impact on health equity. Limitations include a lack of objective assessment of knowledge acquisition. We recommend integrating this module into preclinical and clinical curricula to discuss the use of race in medical literature and clinical practice.
| S-EPMC11219082 | biostudies-literature
Project description:BackgroundScientifically, there is little genetic variation among humans and race has no biological basis. However, medical school preclinical curricula tend to misrepresent race and reify biologically essentialist explanations for disease. The social construct of race is, therefore, used to inform health care providers' treatment decisions. Use of race-based medicine has been identified as a contributor to racial health disparities, spurring a growing movement to challenge race essentialism and race-based medicine. The current research tested an intervention that educates college students about the historical construction of racial categories in the United States.MethodsParticipants who were randomly assigned to the intervention condition read an article highlighting the history of the sociopolitical construction of race. They were then prompted to discuss in dyads how racial categories were created and changed over history, and-in light of all this-the appropriateness of race-based medicine. Those assigned to the control condition advanced directly to the outcome measures.ResultsParticipants in the intervention condition reported less race essentialism, less support for race-based medicine, and greater belief that race-based medicine contributes to racial health disparities. Findings were not moderated by premed status.DiscussionOur data provide initial evidence that our interactive intervention could effectively reduce biological essentialism and support for race-based medicine in both premed and non-premed students.Health equity implicationsThis intervention has the potential to shape the way health care providers in-training understand race, their internalization of biologically essentialist explanations for disease, and willingness to adopt race-based treatment plans.
| S-EPMC10698761 | biostudies-literature
Project description:The objective consensus methodology has recently been applied in consensus finding in several studies on medical decision-making among clinical experts or guidelines. The main advantages of this method are an automated analysis and comparison of treatment algorithms of the participating centers which can be performed anonymously.Based on the experience from completed consensus analyses, the main steps for the successful implementation of the objective consensus methodology were identified and discussed among the main investigators.The following steps for the successful collection and conversion of decision trees were identified and defined in detail: problem definition, population selection, draft input collection, tree conversion, criteria adaptation, problem re-evaluation, results distribution and refinement, tree finalisation, and analysis.This manuscript provides information on the main steps for successful collection of decision trees and summarizes important aspects at each point of the analysis.
| S-EPMC5559810 | biostudies-other
Project description:Despite ethical and historical arguments for removing race from clinical algorithms, the consequences of removal remain unclear. Here, we highlight a largely undiscussed consideration in this debate: varying data quality of input features across race groups. For example, family history of cancer is an essential predictor in cancer risk prediction algorithms but is less reliably documented for Black participants and may therefore be less predictive of cancer outcomes. Using data from the Southern Community Cohort Study, we assessed whether race adjustments could allow risk prediction models to capture varying data quality by race, focusing on colorectal cancer risk prediction. We analyzed 77,836 adults with no history of colorectal cancer at baseline. The predictive value of self-reported family history was greater for White participants than for Black participants. We compared two cancer risk prediction algorithms-a race-blind algorithm which included standard colorectal cancer risk factors but not race, and a race-adjusted algorithm which additionally included race. Relative to the race-blind algorithm, the race-adjusted algorithm improved predictive performance, as measured by goodness of fit in a likelihood ratio test (P-value: <0.001) and area under the receiving operating characteristic curve among Black participants (P-value: 0.006). Because the race-blind algorithm underpredicted risk for Black participants, the race-adjusted algorithm increased the fraction of Black participants among the predicted high-risk group, potentially increasing access to screening. More broadly, this study shows that race adjustments may be beneficial when the data quality of key predictors in clinical algorithms differs by race group.
| S-EPMC11348319 | biostudies-literature
Project description:IntroductionLegislation requires the U.S. Food and Drug Administration (FDA) to release information to the public about harmful constituents in tobacco and tobacco smoke. To inform these efforts, we sought to better understand how smokers and nonsmokers think about tobacco constituents.MethodsIn October 2012, 300 U.S. adults aged 18-66 years completed a cross-sectional Internet survey. The questions focused on 20 harmful tobacco constituents that the FDA has prioritized for communicating with the public.ResultsMost participants had heard of 7 tobacco constituents (ammonia, arsenic, benzene, cadmium, carbon monoxide, formaldehyde, and nicotine), but few participants had heard of the others (e.g., acrolein). Few participants correctly understood that many constituents were naturally present in tobacco. Substances that companies add to cigarette tobacco discouraged people from wanting to smoke more than substances that naturally occur in cigarette smoke (p < .001). Ammonia, arsenic, carbon monoxide, and formaldehyde being in cigarettes elicited the most discouragement from smoking. Constituents elicited greater discouragement from wanting to smoke if respondents were nonsmokers (? = -.34, p < .05), had negative images of smokers (i.e., negative smoker prototypes; ? = .19, p < .05), believed constituents are added to tobacco (? = .14, p < .05), or were older (? = .16, p < .05).ConclusionsOur study found low awareness of most tobacco constituents, with greater concern elicited by additives. Efforts to communicate health risks of tobacco constituents should consider focusing on ones that elicited the most discouragement from smoking.
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Project description: Not available
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Project description:With the advent of high-throughput molecular technologies, several precision medicine (PM) studies are currently ongoing that include molecular screening programs and PM clinical trials. Molecular profiling programs establish the molecular profile of patients' tumors with the aim to guide therapy based on identified molecular alterations. The aim of prospective PM clinical trials is to assess the clinical utility of tumor molecular profiling and to determine whether treatment selection based on molecular alterations produces superior outcomes compared with unselected treatment. These trials use treatment algorithms to assign patients to specific targeted therapies based on tumor molecular alterations. These algorithms should be governed by fixed rules to ensure standardization and reproducibility. Here, we summarize key molecular, biological, and technical criteria that, in our view, should be addressed when establishing treatment algorithms based on tumor molecular profiling for PM trials.
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