Treating and precepting with RESPECT: a relational model addressing race, ethnicity, and culture in medical training.
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ABSTRACT:
SUBMITTER: Mostow C
PROVIDER: S-EPMC2847117 | biostudies-other | 2010 May
REPOSITORIES: biostudies-other
Similar Datasets
Project description:ObjectiveTo examine within-hospital racial and ethnic disparities in severe maternal morbidity rates and determine whether they are associated with differences in types of medical insurance.MethodsWe conducted a population-based, cross-sectional study using linked 2010-2014 New York City discharge and birth certificate data sets (N=591,455 deliveries) to examine within-hospital black-white, Latina-white, and Medicaid-commercially insured differences in severe maternal morbidity. We used logistic regression to produce risk-adjusted rates of severe maternal morbidity for patients with commercial and Medicaid insurance and for black, Latina, and white patients within each hospital. We compared these within-hospital adjusted rates using paired t-tests and conditional logit models.ResultsSevere maternal morbidity was higher among black and Latina women than white women (4.2% and 2.9% vs 1.5%, respectively, P<.001) and among women insured by Medicaid than those commercially insured (2.8% vs 2.0%, P<.001). Women insured by Medicaid compared with those with commercial insurance had similar risk for severe maternal morbidity within the same hospital (P=.54). In contrast, black women compared with white women had significantly higher risk for severe maternal morbidity within the same hospital (P<.001), as did Latina women (P<.001). Conditional logit analyses confirmed these findings, with black and Latina women compared with white women having higher risk for severe maternal morbidity (adjusted odds ratio [aOR] 1.52; 95% CI 1.46-1.62 and aOR 1.44; 95% CI 1.36-1.53, respectively) and women insured by Medicaid compared with those commercially insured having similar risk.ConclusionWithin hospitals in New York City, black and Latina women are at higher risk of severe maternal morbidity than white women; this is not associated with differences in types of insurance.
| S-EPMC7117864 | biostudies-literature
Project description:BackgroundEncouraging professional integrity is vital for providing a standard of excellence in quality medical care and education and in promoting a culture of respect and responsibility. The primary objective of this work consisted of studying the relationship of medical students to the right to patient privacy in Spain, specifically by analysing the conditions for accessing patient clinical histories (CHs).MethodsA cross-sectional study was conducted based on a questionnaire sent by e-mail to final-year students at 41 Spanish universities. It had 14 multiple choice and closed questions framed in 3 large blocks. The first question addressed basic general knowledge issues on the right to privacy and the obligation for confidentiality. The two remaining blocks were made up of questions directed towards evaluating the frequency with which certain requirements and action steps related to students attending patients were performed and regarding the guarantees associated with accessing and handling patient CHs both on paper and in the Electronic Medical Record.ResultsA total of 245 valid replies were considered. A total of 67.8 % of participants were women, with an average age of 24.05 ± 3.49 years. Up to 90.6 % were aware that confidentiality affected the data in CHs, although 43.3 % possessed non-anonymized photocopies of patient clinical reports outside the healthcare context, and only 49.8 % of the students were always adequately identified. A total of 59.2 % accessed patient CHs on some occasions by using passwords belonging to healthcare professionals, 77.2 % of them did not have the patients' express consent, and 71.9 % accessed a CH that was not anonymised.ConclusionsThe role of healthcare institutions and universities is considered to be fundamental in implementing educational measures regarding the risks and ethical and legal problems arising from the use of CHs among professionals and students. A thorough study of medical ethics is needed through the analysis of clinical cases and direct exposure to situations in which the patient's confidentiality is questioned.
| S-EPMC8117324 | biostudies-literature
Project description:ImportanceEvidence from national studies indicates systematic differences in hospitals in which racial/ethnic minorities receive care, with most care obtained in a small proportion of hospitals. Little is known about the source of these differences.ObjectivesTo examine the patterns of emergency department (ED) destination of emergency medical services (EMS) transport according to patient race/ethnicity, and to compare the patterns between those transported by EMS and those who did not use EMS.Design, setting, and participantsThis cohort study of US EMS and EDs used Medicare claims data from January 1, 2006, to December 31, 2012. Enrollees aged 66 years or older with continuous fee-for-service Medicare coverage (N = 864 750) were selected for the sample. Zip codes with a sizable count (>10) of Hispanic, non-Hispanic black, and non-Hispanic white enrollees were used for comparison of EMS use across racial/ethnic subgroups. Data on all ED visits, with and without EMS use, were obtained. Data analysis was performed from December 18, 2018, to July 7, 2019.Main outcomes and measuresThe main outcome measure was whether an EMS transport destination was the most frequent ED destination among white patients (reference ED). The secondary outcomes were (1) whether the ED destination was a safety-net hospital and (2) the distance of EMS transport from the ED destination.ResultsThe study cohort comprised 864 750 Medicare enrollees from 4175 selected zip codes who had 458 701 ED visits using EMS transport. Of these EMS-transported enrollees, 26.1% (127 555) were younger than 75 years, and most were women (302 430 [66.8%]). Overall, the proportion of white patients transported to the reference ED was 61.3% (95% CI, 61.0% to 61.7%); this rate was lower among black enrollees (difference of -5.3%; 95% CI, -6.0% to -4.6%) and Hispanic enrollees (difference of -2.5%; 95% CI, -3.2% to -1.7%). A similar pattern was found among patients with high-risk acute conditions; the proportion transported to the reference ED was 61.5% (95% CI, 60.7% to 62.2%) among white enrollees, whereas this proportion was lower among black enrollees (difference of -6.7%; 95% CI, -8.3% to -5.0%) and Hispanic enrollees (difference of -2.6%; 95% CI, -4.5% to -0.7%). In major US cities, a larger black-white discordance in ED destination was observed (-9.3%; 95% CI, -10.9% to -7.7%). Black and Hispanic patients were more likely to be transported to a safety-net ED compared with their white counterparts; the proportion transported to a safety-net ED among white enrollees (18.5%; 95% CI, 18.1% to 18.7%) was lower compared with that among black enrollees (difference of 2.7%; 95% CI, 2.2% to 3.2%) and Hispanic enrollees (difference of 1.9%; 95% CI, 1.3% to 2.4%). Concordance rates of non-EMS-transported ED visits were statistically significantly lower than for EMS-transported ED visits; the concordance rate among white enrollees of 52.9% (95% CI, 52.1% to 53.6%) was higher compared with that among black enrollees (difference of -4.8%; 95% CI, -6.4% to -3.3%) and Hispanic enrollees (difference of -3.0%; 95% CI, -4.7% to -1.3%).Conclusions and relevanceThis study found race/ethnicity variation in ED destination for patients using EMS transport, with black and Hispanic patients more likely to be transported to a safety-net hospital ED compared with white patients living in the same zip code.
| S-EPMC6735492 | biostudies-literature
Project description:ObjectiveTo improve an existing method, Medicare Bayesian Improved Surname Geocoding (MBISG) 1.0 that augments the Centers for Medicare & Medicaid Services' (CMS) administrative measure of race/ethnicity with surname and geographic data to estimate race/ethnicity.Data sources/study settingData from 284 627 respondents to the 2014 Medicare CAHPS survey.Study designWe compared performance (cross-validated Pearson correlation of estimates and self-reported race/ethnicity) for several alternative models predicting self-reported race/ethnicity in cross-sectional observational data to assess accuracy of estimates, resulting in MBISG 2.0. MBISG 2.0 adds to MBISG 1.0 first name, demographic, and coverage predictors of race/ethnicity and uses a more flexible data aggregation framework.Data collection/extraction methodsWe linked survey-reported race/ethnicity to CMS administrative and US census data.Principal findingsMBISG 2.0 removed 25-39 percent of the remaining MBISG 1.0 error for Hispanics, Whites, and Asian/Pacific Islanders (API), and 9 percent for Blacks, resulting in correlations of 0.88 to 0.95 with self-reported race/ethnicity for these groups.ConclusionsMBISG 2.0 represents a substantial improvement over MBISG 1.0 and the use of CMS administrative data on race/ethnicity alone. MBISG 2.0 is used in CMS' public reporting of Medicare Advantage contract HEDIS measures stratified by race/ethnicity for Hispanics, Whites, API, and Blacks.
| S-EPMC6338295 | biostudies-literature
Project description:To efficiently estimate race/ethnicity using administrative records to facilitate health care organizations' efforts to address disparities when self-reported race/ethnicity data are unavailable.Surname, geocoded residential address, and self-reported race/ethnicity from 1,973,362 enrollees of a national health plan.We compare the accuracy of a Bayesian approach to combining surname and geocoded information to estimate race/ethnicity to two other indirect methods: a non-Bayesian method that combines surname and geocoded information and geocoded information alone. We assess accuracy with respect to estimating (1) individual race/ethnicity and (2) overall racial/ethnic prevalence in a population.The Bayesian approach was 74 percent more efficient than geocoding alone in estimating individual race/ethnicity and 56 percent more efficient in estimating the prevalence of racial/ethnic groups, outperforming the non-Bayesian hybrid on both measures. The non-Bayesian hybrid was more efficient than geocoding alone in estimating individual race/ethnicity but less efficient with respect to prevalence (p<.05 for all differences).The Bayesian Surname and Geocoding (BSG) method presented here efficiently integrates administrative data, substantially improving upon what is possible with a single source or from other hybrid methods; it offers a powerful tool that can help health care organizations address disparities until self-reported race/ethnicity data are available.
| S-EPMC2653886 | biostudies-other
Project description:ImportancePrevious studies have shown that medical student mistreatment is common. However, few data exist to date describing how the prevalence of medical student mistreatment varies by student sex, race/ethnicity, and sexual orientation.ObjectiveTo examine the association between mistreatment and medical student sex, race/ethnicity, and sexual orientation.Design, setting, and participantsThis cohort study analyzed data from the 2016 and 2017 Association of American Medical Colleges Graduation Questionnaire. The questionnaire annually surveys graduating students at all 140 accredited allopathic US medical schools. Participants were graduates from allopathic US medical schools in 2016 and 2017. Data were analyzed between April 1 and December 31, 2019.Main outcomes and measuresPrevalence of self-reported medical student mistreatment by sex, race/ethnicity, and sexual orientation.ResultsA total of 27 504 unique student surveys were analyzed, representing 72.1% of graduating US medical students in 2016 and 2017. The sample included the following: 13 351 female respondents (48.5%), 16 521 white (60.1%), 5641 Asian (20.5%), 2433 underrepresented minority (URM) (8.8%), and 2376 multiracial respondents (8.6%); and 25 763 heterosexual (93.7%) and 1463 lesbian, gay, or bisexual (LGB) respondents (5.3%). At least 1 episode of mistreatment was reported by a greater proportion of female students compared with male students (40.9% vs 25.2%, P < .001); Asian, URM, and multiracial students compared with white students (31.9%, 38.0%, 32.9%, and 24.0%, respectively; P < .001); and LGB students compared with heterosexual students (43.5% vs 23.6%, P < .001). A higher percentage of female students compared with male students reported discrimination based on gender (28.2% vs 9.4%, P < .001); a greater proportion of Asian, URM, and multiracial students compared with white students reported discrimination based on race/ethnicity (15.7%, 23.3%, 11.8%, and 3.8%, respectively; P < .001), and LGB students reported a higher prevalence of discrimination based on sexual orientation than heterosexual students (23.1% vs 1.0%, P < .001). Moreover, higher proportions of female (17.8% vs 7.0%), URM, Asian, and multiracial (4.9% white, 10.7% Asian, 16.3% URM, and 11.3% multiracial), and LGB (16.4% vs 3.6%) students reported 2 or more types of mistreatment compared with their male, white, and heterosexual counterparts (P < .001).Conclusions and relevanceFemale, URM, Asian, multiracial, and LGB students seem to bear a disproportionate burden of the mistreatment reported in medical schools. It appears that addressing the disparate mistreatment reported will be an important step to promote diversity, equity, and inclusion in medical education.
| S-EPMC7042809 | biostudies-literature
Project description:BackgroundDeep learning algorithms derived in homogeneous populations may be poorly generalizable and have the potential to reflect, perpetuate, and even exacerbate racial/ethnic disparities in health and health care. In this study, we aimed to (1) assess whether the performance of a deep learning algorithm designed to detect low left ventricular ejection fraction using the 12-lead ECG varies by race/ethnicity and to (2) determine whether its performance is determined by the derivation population or by racial variation in the ECG.MethodsWe performed a retrospective cohort analysis that included 97 829 patients with paired ECGs and echocardiograms. We tested the model performance by race/ethnicity for convolutional neural network designed to identify patients with a left ventricular ejection fraction ≤35% from the 12-lead ECG.ResultsThe convolutional neural network that was previously derived in a homogeneous population (derivation cohort, n=44 959; 96.2% non-Hispanic white) demonstrated consistent performance to detect low left ventricular ejection fraction across a range of racial/ethnic subgroups in a separate testing cohort (n=52 870): non-Hispanic white (n=44 524; area under the curve [AUC], 0.931), Asian (n=557; AUC, 0.961), black/African American (n=651; AUC, 0.937), Hispanic/Latino (n=331; AUC, 0.937), and American Indian/Native Alaskan (n=223; AUC, 0.938). In secondary analyses, a separate neural network was able to discern racial subgroup category (black/African American [AUC, 0.84], and white, non-Hispanic [AUC, 0.76] in a 5-class classifier), and a network trained only in non-Hispanic whites from the original derivation cohort performed similarly well across a range of racial/ethnic subgroups in the testing cohort with an AUC of at least 0.930 in all racial/ethnic subgroups.ConclusionsOur study demonstrates that while ECG characteristics vary by race, this did not impact the ability of a convolutional neural network to predict low left ventricular ejection fraction from the ECG. We recommend reporting of performance among diverse ethnic, racial, age, and sex groups for all new artificial intelligence tools to ensure responsible use of artificial intelligence in medicine.
| S-EPMC7158877 | biostudies-literature
Project description:ObjectiveWe aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data.Materials and methodsUsing EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.ResultsFor the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity.DiscussionStructured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.ConclusionsBlack or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.
| S-EPMC6696506 | biostudies-literature
Project description:Over 80% of bias-motivated violent victimization is motivated by race or ethnicity and over 50% of bias victimization occurs in non-Hispanic Whites (NHW). Our aim was to determine the risk and health impacts of race/ethnicity-motivated violent victimization by victim race/ethnicity. We examined data from the National Crime Victimization Survey (2003-2015) to estimate violent victimization risk by victim race/ethnicity across race/ethnicity bias victimization, other types of bias victimizations, and non-bias violent victimizations. We examined incident and offender characteristics for race/ethnicity-motivated victimization by victim race/ethnicity. The risk of race/ethnicity-motivated violent victimization was greater for non-Hispanic Blacks (NHB) and Hispanics than for NHWs (incidence rate ratios [IRR] = 1.4; 95% confidence interval [CI] = [1.0, 2.0], and IRR = 1.6; 95% CI = [1.2, 2.1]). This translates into an additional 46.7 incidents per 100,000 person-years (95% CI = [1.4, 92.1]) for the NHB population and an additional 60.3 incidents per 100,000 person-years (95% CI = [20.3, 100.4]) for the Hispanic population. Violent incidents for NHB victims more frequently resulted in injury or medical care. Nearly 40% of NHB victims reported difficulties at school or work related to the incident where only 21.5% of NHWs and 11.7% of Hispanic victims reported similar problems. Roughly 37% of NHB victims identified a NHW offender and 45% of NHW victims identified a NHB offender. Hispanic victims identified NHB or NHW offenders in over 70% of incidents. Although literature suggests that NHWs account for the majority of bias victimizations, the risk of non-fatal violent victimization motivated by race/ethnicity is greater for NHBs and Hispanics. Crimes perpetrated against NHBs are likely more severe and victim/offender racial incongruity is common. Findings provide empiric evidence on race/ethnicity-related structural disadvantage with adverse health consequences.
| S-EPMC6579725 | biostudies-literature
Project description:We investigated the association between a U.S. National Institutes of Health (NIH) R01 applicant's self-identified race or ethnicity and the probability of receiving an award by using data from the NIH IMPAC II grant database, the Thomson Reuters Web of Science, and other sources. Although proposals with strong priority scores were equally likely to be funded regardless of race, we find that Asians are 4 percentage points and black or African-American applicants are 13 percentage points less likely to receive NIH investigator-initiated research funding compared with whites. After controlling for the applicant's educational background, country of origin, training, previous research awards, publication record, and employer characteristics, we find that black applicants remain 10 percentage points less likely than whites to be awarded NIH research funding. Our results suggest some leverage points for policy intervention.
| S-EPMC3412416 | biostudies-literature