Accuracy of race, ethnicity, and language preference in an electronic health record.
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ABSTRACT:
SUBMITTER: Klinger EV
PROVIDER: S-EPMC4441665 | biostudies-literature | 2015 Jun
REPOSITORIES: biostudies-literature
Publications
Accuracy of race, ethnicity, and language preference in an electronic health record.
Klinger Elissa V EV Carlini Sara V SV Gonzalez Irina I Hubert Stella St SS Linder Jeffrey A JA Rigotti Nancy A NA Kontos Emily Z EZ Park Elyse R ER Marinacci Lucas X LX Haas Jennifer S JS
Journal of general internal medicine 20141220 6
<h4>Background</h4>Collection of data on race, ethnicity, and language preference is required as part of the "meaningful use" of electronic health records (EHRs). These data serve as a foundation for interventions to reduce health disparities.<h4>Objective</h4>Our aim was to compare the accuracy of EHR-recorded data on race, ethnicity, and language preference to that reported directly by patients.<h4>Design/subjects/main measures</h4>Data collected as part of a tobacco cessation intervention for ...[more]
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Project description:ImportanceClinical decision support (CDS) algorithms are increasingly being implemented in health care systems to identify patients for specialty care. However, systematic differences in missingness of electronic health record (EHR) data may lead to disparities in identification by CDS algorithms.ObjectiveTo examine the availability and comprehensiveness of cancer family history information (FHI) in patients' EHRs by sex, race, Hispanic or Latino ethnicity, and language preference in 2 large health care systems in 2021.Design, setting, and participantsThis retrospective EHR quality improvement study used EHR data from 2 health care systems: University of Utah Health (UHealth) and NYU Langone Health (NYULH). Participants included patients aged 25 to 60 years who had a primary care appointment in the previous 3 years. Data were collected or abstracted from the EHR from December 10, 2020, to October 31, 2021, and analyzed from June 15 to October 31, 2021.ExposuresPrior collection of cancer FHI in primary care settings.Main outcomes and measuresAvailability was defined as having any FHI and any cancer FHI in the EHR and was examined at the patient level. Comprehensiveness was defined as whether a cancer family history observation in the EHR specified the type of cancer diagnosed in a family member, the relationship of the family member to the patient, and the age at onset for the family member and was examined at the observation level.ResultsAmong 144 484 patients in the UHealth system, 53.6% were women; 74.4% were non-Hispanic or non-Latino and 67.6% were White; and 83.0% had an English language preference. Among 377 621 patients in the NYULH system, 55.3% were women; 63.2% were non-Hispanic or non-Latino, and 55.3% were White; and 89.9% had an English language preference. Patients from historically medically undeserved groups-specifically, Black vs White patients (UHealth: 17.3% [95% CI, 16.1%-18.6%] vs 42.8% [95% CI, 42.5%-43.1%]; NYULH: 24.4% [95% CI, 24.0%-24.8%] vs 33.8% [95% CI, 33.6%-34.0%]), Hispanic or Latino vs non-Hispanic or non-Latino patients (UHealth: 27.2% [95% CI, 26.5%-27.8%] vs 40.2% [95% CI, 39.9%-40.5%]; NYULH: 24.4% [95% CI, 24.1%-24.7%] vs 31.6% [95% CI, 31.4%-31.8%]), Spanish-speaking vs English-speaking patients (UHealth: 18.4% [95% CI, 17.2%-19.1%] vs 40.0% [95% CI, 39.7%-40.3%]; NYULH: 15.1% [95% CI, 14.6%-15.6%] vs 31.1% [95% CI, 30.9%-31.2%), and men vs women (UHealth: 30.8% [95% CI, 30.4%-31.2%] vs 43.0% [95% CI, 42.6%-43.3%]; NYULH: 23.1% [95% CI, 22.9%-23.3%] vs 34.9% [95% CI, 34.7%-35.1%])-had significantly lower availability and comprehensiveness of cancer FHI (P < .001).Conclusions and relevanceThese findings suggest that systematic differences in the availability and comprehensiveness of FHI in the EHR may introduce informative presence bias as inputs to CDS algorithms. The observed differences may also exacerbate disparities for medically underserved groups. System-, clinician-, and patient-level efforts are needed to improve the collection of FHI.
| S-EPMC9533178 | biostudies-literature
Project description:ImportanceStigmatizing language in the electronic health record (EHR) may alter treatment plans, transmit biases between clinicians, and alienate patients. However, neither the frequency of stigmatizing language in hospital notes, nor whether clinicians disproportionately use it in describing patients in particular demographic subgroups are known.ObjectiveTo examine the prevalence of stigmatizing language in hospital admission notes and the patient and clinician characteristics associated with the use of such language.Design, setting, and participantsThis cross-sectional study of admission notes used natural language processing on 48 651 admission notes written about 29 783 unique patients by 1932 clinicians at a large, urban academic medical center between January to December 2018. The admission notes included 8738 notes about 4309 patients with diabetes written by 1204 clinicians; 6197 notes about 3058 patients with substance use disorder by 1132 clinicians; and 5176 notes about 2331 patients with chronic pain by 1056 clinicians. Statistical analyses were performed between May and September 2021.ExposuresPatients' demographic characteristics (age, race and ethnicity, gender, and preferred language); clinicians' characteristics (gender, postgraduate year [PGY], and credential [physician vs advanced practice clinician]).Main outcome and measuresBinary indicator for any vs no stigmatizing language; frequencies of specific stigmatizing words. Linear probability models were the main measure, and logistic regression and odds ratios were used for sensitivity analyses and further exploration.ResultsThe sample included notes on 29 783 patients with a mean (SD) age of 46.9 (27.6) years. Of these patients, 1033 (3.5%) were non-Hispanic Asian, 2498 (8.4%) were non-Hispanic Black, 18 956 (63.6%) were non-Hispanic White, 17 334 (58.2%) were female, and 2939 (9.9%) preferred a language other than English. Of all admission notes, 1197 (2.5%) contained stigmatizing language. The diagnosis-specific stigmatizing language was present in 599 notes (6.9%) for patients with diabetes, 209 (3.4%) for patients with substance use disorders, and 37 (0.7%) for patients with chronic pain. In the whole sample, notes about non-Hispanic Black patients vs non-Hispanic White patients had a 0.67 (95% CI, 0.15 to 1.18) percentage points greater probability of containing stigmatizing language, with similar disparities in all 3 diagnosis-specific subgroups. Greater diabetes severity and the physician-author being less advanced in their training was associated with more stigmatizing language. A 1 point increase in the diabetes severity index was associated with a 1.23 (95% CI, .23 to 2.23) percentage point greater probability of a note containing stigmatizing language. In the sample restricted to physicians, a higher PGY was associated with less use of stigmatizing language overall (-0.05 percentage points/PGY [95% CI, -0.09 to -0.01]).Conclusions and relevanceIn this cross-sectional study, stigmatizing language in hospital notes varied by medical condition and was more often used to describe non-Hispanic Black patients. Training clinicians to minimize stigmatizing language in the EHR might improve patient-clinician relationships and reduce the transmission of bias between clinicians.
| S-EPMC8796019 | biostudies-literature
Project description:ObjectiveTo describe data collection methods and to audit staff data entry of patient self-reported race/ethnicity/ancestry and preferred spoken language (R/E/A/L) information.Data source/study settingLarge mixed payer outpatient health care organization in Northern California, June 2009.Study designSecondary analysis of an audit planned and executed by the Department of Clinical Services.Data collection/extraction methodsWe analyzed concordance between patient written responses and staff data entry.Principal findingsThe data entry accuracy rate across questions was high, ranging from 92 to 97 percent. Inaccuracies were due to human error (62 percent), flaws in system design (2 percent), or some combination of both (35 percent).ConclusionsThis study highlights the high accuracy of patient self-reported R/E/A/L data entry and identifies some areas for improvement in staff training and technical system design to facilitate further progress.
| S-EPMC3447251 | biostudies-literature
Project description:Introduction:Electronic health record (EHR)-driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time-consuming, error-prone, and platform-specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high-throughput, cross-platform phenotyping. Methods:We selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results. Results:CQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross-platform execution resulted in identical patient cohorts generated by both data platforms. Conclusions:CQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross-platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR-driven phenotyping and scale in learning health systems.
| S-EPMC7556419 | biostudies-literature
Project description:ObjectivesPhenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance the efficiency of EHR phenotyping by generating high-quality algorithm drafts.Materials and methodsWe prompted four LLMs-GPT-4 and GPT-3.5 of ChatGPT, Claude 2, and Bard-in October 2023, asking them to generate executable phenotyping algorithms in the form of SQL queries adhering to a common data model (CDM) for three phenotypes (i.e., type 2 diabetes mellitus, dementia, and hypothyroidism). Three phenotyping experts evaluated the returned algorithms across several critical metrics. We further implemented the top-rated algorithms and compared them against clinician-validated phenotyping algorithms from the Electronic Medical Records and Genomics (eMERGE) network.ResultsGPT-4 and GPT-3.5 exhibited significantly higher overall expert evaluation scores in instruction following, algorithmic logic, and SQL executability, when compared to Claude 2 and Bard. Although GPT-4 and GPT-3.5 effectively identified relevant clinical concepts, they exhibited immature capability in organizing phenotyping criteria with the proper logic, leading to phenotyping algorithms that were either excessively restrictive (with low recall) or overly broad (with low positive predictive values).ConclusionGPT versions 3.5 and 4 are capable of drafting phenotyping algorithms by identifying relevant clinical criteria aligned with a CDM. However, expertise in informatics and clinical experience is still required to assess and further refine generated algorithms.
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Project description:BackgroundWhile mass media communications can be an important source of health information, there are substantial social disparities in health knowledge that may be related to media use. The purpose of this study is to investigate how the use of cancer-related health communications is patterned by race, ethnicity, language, and social class.Methodology/principal findingsIn a nationally-representative cross-sectional telephone survey, 5,187 U.S. adults provided information about demographic characteristics, cancer information seeking, and attention to and trust in health information from television, radio, newspaper, magazines, and the Internet. Cancer information seeking was lowest among Spanish-speaking Hispanics (odds ratio: 0.42; 95% confidence interval: 0.28-0.63) compared to non-Hispanic whites. Spanish-speaking Hispanics were more likely than non-Hispanic whites to pay attention to (odds ratio: 3.10; 95% confidence interval: 2.07-4.66) and trust (odds ratio: 2.61; 95% confidence interval: 1.53-4.47) health messages from the radio. Non-Hispanic blacks were more likely than non-Hispanic whites to pay attention to (odds ratio: 2.39; 95% confidence interval: 1.88-3.04) and trust (odds ratio: 2.16; 95% confidence interval: 1.61-2.90) health messages on television. Those who were college graduates tended to pay more attention to health information from newspapers (odds ratio: 1.98; 95% confidence interval: 1.42-2.75), magazines (odds ratio: 1.86; 95% confidence interval: 1.32-2.60), and the Internet (odds ratio: 4.74; 95% confidence interval: 2.70-8.31) and had less trust in cancer-related health information from television (odds ratio: 0.44; 95% confidence interval: 0.32-0.62) and radio (odds ratio: 0.54; 95% confidence interval: 0.34-0.86) compared to those who were not high school graduates.Conclusions/significanceHealth media use is patterned by race, ethnicity, language and social class. Providing greater access to and enhancing the quality of health media by taking into account factors associated with social determinants may contribute to addressing social disparities in health.
| S-EPMC3022647 | biostudies-literature
Project description:Patients and families at risk for health disparities may also be at higher risk for diagnostic errors but less likely to report them. This study aimed to explore differences in race, ethnicity, and language preference associated with patient and family contributions and concerns using an electronic previsit tool designed to engage patients and families in the diagnostic process (DxP). Cross-sectional study of 5,731 patients and families presenting to three subspecialty clinics at an urban pediatric hospital May to December 2021 who completed a previsit tool, codeveloped and tested with patients and families. Prior to each visit, patients/families were invited to share visit priorities, recent histories, and potential diagnostic concerns. We used logistic regression to determine factors associated with patient-reported diagnostic concerns. We conducted chart review on a random subset of visits to review concerns and determine whether patient/family contributions were included in the visit note. Participants provided a similar mean number of contributions regardless of patient race, ethnicity, or language preference. Compared with patients self-identifying as White, those self-identifying as Black (odds ratio [OR]: 1.70; 95% confidence interval [CI]: [1.18, 2.43]) or "other" race (OR: 1.48; 95% CI: [1.08, 2.03]) were more likely to report a diagnostic concern. Participants who preferred a language other than English were more likely to report a diagnostic concern than English-preferring patients (OR: 2.53; 95% CI: [1.78, 3.59]. There were no significant differences in physician-verified diagnostic concerns or in integration of patient contributions into the note based on race, ethnicity, or language preference. Participants self-identifying as Black or "other" race, or those who prefer a language other than English were 1.5 to 2.5 times more likely than their counterparts to report potential diagnostic concerns when proactively asked to provide this information prior to a visit. Actively engaging patients and families in the DxP may uncover opportunities to reduce the risk of diagnostic errors and potential safety disparities.
| S-EPMC10651368 | biostudies-literature
Project description:Background and study aims
The Federal Ministry of Education and Research (BMBF) has funded the development of a patient controlled “Personal Electronic Health Record” (PEPA) as part of their current INFOPAT project. PEPA allows for the exchange of medical data between all interested groups, including the patients thenselves. A PEPA contains medical reports and findings including, for example, x-rays, CT scans and MRIs. The patients can safeguard their information and access all content. The aim of this study is to test the PEPA-prototype for cancer patients being treated at the National Center for Tumor Diseases (NCT) in Heidelberg.
Who can participate?
Adult colorectal cancer patients being treated at the National Center for Tumor Diseases (NCT) in Heidelberg.
What does the study involve?
There are two part to this study. The first part involves focus groups, in which a group of people are asked their opinion and attitudes towards, for example, an idea, product or service. Here, focus groups are set up that involve the patients, doctors and other health professionals to discuss current problems with the coordination of care offered to cancer patients, as well as discuss what people want to get from using PEPA (i.e. user requirements). The PEPA-prototype is then developed taking into account the conclusions from the focus groups. In the second part of the study, patients are encouraged to use the PEPA-prototype to prepare and follow up meetings with their physicians.
| 2424890 | ecrin-mdr-crc
Project description:Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical approach for conducting validation studies for usage log data in a controlled environment. By developing test runs based on clinical workflows and conducting them within a test EHR environment, it allows for both comparison of the recorded timings and retrospective investigation of any discrepancies. In this case report, we describe the utility of this approach for validating our physician EHR usage logs at a large academic teaching mental health hospital in Canada. A total of 10 test runs were conducted across 3 days to validate 8 EHR usage log metrics, finding differences between recorded measurements and the usage analytics platform ranging from 9 to 60%.
| S-EPMC9534596 | biostudies-literature
Project description:ImportanceEmergency department (ED) triage substantially affects how long patients wait for care but triage scoring relies on few objective criteria. Prior studies suggest that Black and Hispanic patients receive unequal triage scores, paralleled by disparities in the depth of physician evaluations.ObjectivesTo examine whether racial disparities in triage scores and physician evaluations are present across a multicenter network of academic and community hospitals and evaluate whether patients who do not speak English face similar disparities.Design, setting, and participantsThis was a cross-sectional, multicenter study examining adults presenting between February 28, 2019, and January 1, 2023, across the Mass General Brigham Integrated Health Care System, encompassing 7 EDs: 2 urban academic hospitals and 5 community hospitals. Analysis included all patients presenting with 1 of 5 common chief symptoms.ExposuresEmergency department nurse-led triage and physician evaluation.Main outcomes and measuresAverage Triage Emergency Severity Index [ESI] score and average visit work relative value units [wRVUs] were compared across symptoms and between individual minority racial and ethnic groups and White patients.ResultsThere were 249 829 visits (149 861 female [60%], American Indian or Alaska Native 0.2%, Asian 3.3%, Black 11.8%, Hispanic 18.8%, Native Hawaiian or Other Pacific Islander <0.1%, White 60.8%, and patients identifying as Other race or ethnicity 5.1%). Median age was 48 (IQR, 29-66) years. White patients had more acute ESI scores than Hispanic or Other patients across all symptoms (eg, chest pain: Hispanic, 2.68 [95% CI, 2.67-2.69]; White, 2.55 [95% CI, 2.55-2.56]; Other, 2.66 [95% CI, 2.64-2.68]; P < .001) and Black patients across most symptoms (nausea/vomiting: Black, 2.97 [95% CI, 2.96-2.99]; White: 2.90 [95% CI, 2.89-2.91]; P < .001). These differences were reversed for wRVUs (chest pain: Black, 4.32 [95% CI, 4.25-4.39]; Hispanic, 4.13 [95% CI, 4.08-4.18]; White 3.55 [95% CI, 3.52-3.58]; Other 3.96 [95% CI, 3.84-4.08]; P < .001). Similar patterns were seen for patients whose primary language was not English.Conclusions and relevanceIn this cross-sectional study, patients who identified as Black, Hispanic, and Other race and ethnicity were assigned less acute ESI scores than their White peers despite having received more involved physician workups, suggesting some degree of mistriage. Clinical decision support systems might reduce these disparities but would require careful calibration to avoid replicating bias.
| S-EPMC10570890 | biostudies-literature