A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System

Racial Fairness of Individual- and Community-Level Proxies of Socioeconomic Status Among Birthing Parent-Child Dyads

BACKGROUND

While precision medicine algorithms can be used to improve health outcomes, concerns have been raised about racial equity and unintentional harm from encoded biases. In this study, we evaluated the fairness of using common individual- and community-level proxies of pediatric socioeconomic status (SES) such as insurance status and community deprivation index often utilized in precision medicine algorithms.

METHODS

Using 2012-2021 vital records obtained from the Ohio Department of Health, we geocoded and matched each residential birth address to a census tract to obtain community deprivation index. We then conducted sensitivity and specificity analyses to determine the degree of match between deprivation index, insurance status, and birthing parent education level for all, Black, and White children to assess if there were differences based on race.

RESULTS

We found that community deprivation index and insurance status fail to accurately represent individual SES, either alone or in combination. We found that deprivation index had a sensitivity of 61.2% and specificity of 74.1%, while insurance status had a higher sensitivity of 91.6% but lower specificity of 60.1%. Furthermore, these inconsistencies were race-based across all proxies evaluated, with greater sensitivities for Black children but greater specificities for White children.

CONCLUSION

This may explain some of the racial disparities present in precision medicine algorithms that utilize SES proxies. Future studies should examine how to mitigate the biases introduced by using SES proxies, potentially by incorporating additional data on housing conditions.

Understanding enterprise data warehouses to support clinical and translational research: impact, sustainability, demand management, and accessibility

OBJECTIVES

Healthcare organizations, including Clinical and Translational Science Awards (CTSA) hubs funded by the National Institutes of Health, seek to enable secondary use of electronic health record (EHR) data through an enterprise data warehouse for research (EDW4R), but optimal approaches are unknown. In this qualitative study, our goal was to understand EDW4R impact, sustainability, demand management, and accessibility.

MATERIALS AND METHODS

We engaged a convenience sample of informatics leaders from CTSA hubs (n = 21) for semi-structured interviews and completed a directed content analysis of interview transcripts.

RESULTS

EDW4R have created institutional capacity for single- and multi-center studies, democratized access to EHR data for investigators from multiple disciplines, and enabled the learning health system. Bibliometrics have been challenging due to investigator non-compliance, but one hub's requirement to link all study protocols with funding records enabled quantifying an EDW4R's multi-million dollar impact. Sustainability of EDW4R has relied on multiple funding sources with a general shift away from the CTSA grant toward institutional and industry support. To address EDW4R demand, institutions have expanded staff, used different governance approaches, and provided investigator self-service tools. EDW4R accessibility can benefit from improved tools incorporating user-centered design, increased data literacy among scientists, expansion of informaticians in the workforce, and growth of team science.

DISCUSSION

As investigator demand for EDW4R has increased, approaches to tracking impact, ensuring sustainability, and improving accessibility of EDW4R resources have varied.

CONCLUSION

This study adds to understanding of how informatics leaders seek to support investigators using EDW4R across the CTSA consortium and potentially elsewhere.

Epidemiology and prevalence of dementia and Alzheimer's disease in American Indians: Data from the Strong Heart Study

INTRODUCTION

Accurate epidemiologic estimates for dementia are lacking for American Indians, despite substantive social and health disparities.

METHODS

The Strong Heart Study, a population-based cohort of 11 American Indian tribes, conducted detailed cognitive testing and examinations over two visits approximately 7 years apart. An expert panel reviewed case materials for consensus adjudication of cognitive status (intact; mild cognitive impairment [MCI]; dementia; other impaired/not MCI) and probable etiology (Alzheimer's disease [AD], vascular bain injury [VBI], traumatic brain injury [TBI], other).

RESULTS

American Indians aged 70-95 years had 54% cognitive impairment including 10% dementia. VBI and AD were primary etiology approximately equal proportions (>40%). Apolipoprotein (APO) Eε4 carriers were more common among those with dementia (p = 0.040). Plasma pTau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) were higher among those with cognitive impairment, but not amyloid beta (Aβ). Cognitive intact had mean 3MSE 92.2 (SD 6.4) and mean Montreal Cognitive Assessment (MoCA) score of 21.3 (SD 3.2).

DISCUSSION

This is the first population-based study to estimate the prevalence of vascular and Alzheimer's dementias in a population-based study of American Indians.

HIGHLIGHTS

The Strong Heart Study is a population-based cohort of American Indian tribes, conducted over 30+ years and three US geographic regions (Northern Plains, Southern Plains, Southwest). Our teams conducted detailed cognitive testing, neurological examination, and brain imaging over two visits approximately 7 years apart. An expert panel reviewed collected materials for consensus-based adjudication of cognitive status (intact; MCI; dementia; other impaired/not MCI) and probable underlying etiology (AD; VBI; TBI; other). In this cohort of American Indians aged 70-95, 54% were adjudicated with cognitive impairment, including approximately 35% MCI and 10% dementia. These data expand on prior reports from studies using electronic health records, which had suggested prevalence, and incidence of dementia in American Indians to be more comparable to the majority population or non-Hispanic White individuals, perhaps due to latent case undercounts in clinical settings. Vascular and neurodegenerative injuries were approximately equally responsible for cognitive impairment, suggesting that reduction of cardiovascular disease is needed for primary prevention. Traumatic injury was more prevalent than in other populations, and common among those in the "other/not MCI" cognitive impairment category. Mean scores for common dementia screening instruments-even among those adjudicated as unimpaired-were relatively low compared to other populations (mean unimpaired 3MSE 92.2, SD 6.4; mean unimpaired MoCA 21.3, SD 3.2), suggesting the need for cultural and environmental adaptation of common screening and evaluation instruments.

Identifying and improving the "ground truth" of race in disparities research through improved EMR data reporting. A systematic review

BACKGROUND

Studies about racial disparities in healthcare are increasing in quantity; however, they are subject to vast differences in definition, classification, and utilization of race/ethnicity data. Improved standardization of this information can strengthen conclusions drawn from studies using such data. The objective of this study is to examine how data related to race/ethnicity are recorded in research through examining articles on race/ethnicity health disparities and examine problems and solutions in data reporting that may impact overall data quality.

METHODS

In this systematic review, Business Source Complete, Embase.com, IEEE Xplore, PubMed, Scopus and Web of Science Core Collection were searched for relevant articles published from 2000 to 2020. Search terms related to the concepts of electronic medical records, race/ethnicity, and data entry related to race/ethnicity were used. Exclusion criteria included articles not in the English language and those describing pediatric populations. Data were extracted from published articles. This review was organized and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement for systematic reviews.

FINDINGS

In this systematic review, 109 full text articles were reviewed. Weaknesses and possible solutions have been discussed in current literature, with the predominant problem and solution as follows: the electronic medical record (EMR) is vulnerable to inaccuracies and incompleteness in the methods that research staff collect this data; however, improved standardization of the collection and use of race data in patient care may help alleviate these inaccuracies.

INTERPRETATION

Conclusions drawn from large datasets concerning peoples of certain race/ethnic groups should be made cautiously, and a careful review of the methodology of each publication should be considered prior to implementation in patient care.

The accuracy of race & ethnicity data in US based healthcare databases: A systematic review

BACKGROUND

The availability and accuracy of data on a patient's race/ethnicity varies across databases. Discrepancies in data quality can negatively impact attempts to study health disparities.

METHODS

We conducted a systematic review to organize information on the accuracy of race/ethnicity data stratified by database type and by specific race/ethnicity categories.

RESULTS

The review included 43 studies. Disease registries showed consistently high levels of data completeness and accuracy. EHRs frequently showed incomplete and/or inaccurate data on the race/ethnicity of patients. Databases had high levels of accurate data for White and Black patients but relatively high levels of misclassification and incomplete data for Hispanic/Latinx patients. Asians, Pacific Islanders, and AI/ANs are the most misclassified. Systems-based interventions to increase self-reported data showed improvement in data quality.

CONCLUSION

Data on race/ethnicity that is collected with the purpose of research and quality improvement appears most reliable. Data accuracy can vary by race/ethnicity status and better collection standards are needed.

Electronic health record data quality assessment and tools: a systematic review

OBJECTIVE

We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies.

MATERIALS AND METHODS

We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process.

RESULTS

We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology.

DISCUSSION

There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality.

CONCLUSION

Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.

Psychiatric Boarding Patterns Among Publicly Insured Youths Evaluated by Mobile Crisis Teams Before and During the COVID-19 Pandemic

Importance

Psychiatric boarding occurs when patients needing intensive psychiatric services who are already under clinical supervision experience delays in their admission to psychiatric facilities. Initial reports have suggested that the US had a psychiatric boarding crisis during the COVID-19 pandemic; however, little is known about the consequences of this crisis for publicly insured youths.

Objective

To estimate pandemic-associated changes in psychiatric boarding rates and discharge modalities for people aged 4 to 20 years who accessed psychiatric emergency services (PES) through a mobile crisis team (MCT) evaluation and were covered by Medicaid or health safety net programs.

Design, Setting, and Participants

This retrospective cross-sectional study used data from the MCT encounters of a multichannel PES program in Massachusetts. A total of 7625 MCT-initiated PES encounters with publicly insured youths who lived in Massachusetts between January 1, 2018, and August 31, 2021, were assessed.

Main Outcomes and Measures

Encounter-level outcomes (psychiatric boarding status, repeat visits, and discharge disposition) during a prepandemic period (January 1, 2018, to March 9, 2020) were compared with outcomes during a pandemic period (March 10, 2020, to August 31, 2021). Descriptive statistics and multivariate regression analysis were used.

Results

Among 7625 MCT-initiated PES encounters, the mean (SD) age of publicly insured youths was 13.6 (3.7) years; most youths identified as male (3656 [47.9%]), were of Black race (2725 [35.7%]) or Hispanic ethnicity (2708 [35.5%]), and spoke English (6941 [91.0%]). During the pandemic period, the mean monthly boarding encounter rate was 25.3 percentage points higher than the prepandemic period. After adjustment for covariates, the odds of an encounter resulting in boarding doubled during the pandemic (adjusted odds ratio [AOR], 2.03; 95% CI, 1.82-2.26; P < .001), and boarding youths were 64% less likely to be discharged to inpatient psychiatric care (AOR, 0.36; 95% CI, 0.31-0.43; P < .001). Publicly insured youths who boarded during the pandemic had significantly higher rates of 30-day readmissions (incidence rate ratio, 2.17; 95% CI, 1.88-2.50; P < .001). Boarding encounters during the pandemic were significantly less likely to end in discharge to inpatient psychiatric units (AOR, 0.36; 95% CI, 0.31-0.43; P < .001) or community-based acute treatment facilities (AOR, 0.70; 95% CI, 0.55-0.90; P = .005).

Conclusions and Relevance

In this cross-sectional study, publicly insured youths were more likely to experience psychiatric boarding during the COVID-19 pandemic and, if boarding, were less likely to transfer to a 24-hour level of care. These findings suggest that psychiatric service programs for youths were not prepared to support the levels of acuity and demand that emerged from the pandemic.

Racial Disparities in the 30-Day Outcomes of Gender-affirming Chest Surgeries

OBJECTIVE

To determine if and how race impacts the 30-day outcomes of gender-affirming chest surgeries.

BACKGROUND

Little is currently known about how race may affect the outcomes of gender-affirming surgeries.

METHODS

We analyzed data from the National Surgical Quality Improvement Program (NSQIP) database of 30-day complications of gender-affirming chest surgeries from 2005 to 2019. All participants had a postoperative diagnosis code for gender dysphoria and at least one procedure code for bilateral mastectomy, bilateral breast reduction, or bilateral augmentation mammoplasty. Differences by racial group were analyzed through Pearson χ 2 and multivariate logistic regression.

RESULTS

There were no racial differences in the all-complication rates for both transmasculine and transfeminine individuals undergoing gender-affirming chest surgeries. Black patients undergoing masculinizing procedures were significantly more likely to experience mild systemic [adjusted odds ratio (aOR): 2.17, 95% confidence interval (CI): 1.02-4.65] and severe complications (aOR: 5.63, 95% CI: 1.99-15.98) when compared with White patients. Patients of unknown race had increased odds of experiencing severe complications for masculinizing procedures compared with White patients (aOR: 3.77, 95% CI: 1.39-10.24). Transmasculine individuals whose race was unknown were 1.98 times more likely (95% CI: 1.03-3.81) to experience an unplanned reoperation compared with White individuals. Black transfeminine individuals were 10.50 times more likely to experience an unplanned reoperation (95% CI: 1.15-95.51) than their White peers.

CONCLUSIONS

Although overall complications are uncommon, there is evidence to suggest that there are racial disparities in certain 30-day outcomes of gender-affirming chest surgeries.

Long-Term Trends in Psychiatric Emergency Services Delivered by the Boston Emergency Services Team

Rising psychiatric emergency department (ED) presentations pose significant financial and administrative burdens to hospitals. Alternative psychiatric emergency services programs have the potential to alleviate this strain by diverting non-emergent mental health issues from EDs. This study explores one such program, the Boston Emergency Services Team (BEST), a multi-channel psychiatric emergency services provider intended for the publicly insured and uninsured population. BEST provides evaluation and treatment for psychiatric crises through specialized psychiatric EDs, a 24/7 hotline, psychiatric urgent care centers, and mobile crisis units. This retrospective review examines the sociodemographic and clinical characteristics of 225,198 BEST encounters (2005-2016). Of note, the proportion of encounters taking place in ED settings decreased significantly from 70 to 58% across the study period. Findings suggest that multi-focal, psychiatric emergency programs like BEST have the potential to reduce the burden of emergency mental health presentations and improve patient diversion to appropriate psychiatric care.

Implications of racial/ethnic perinatal health inequities on long-term neurodevelopmental outcomes and health services utilization

Infants born preterm and with low birth weight have increased risk for neurodevelopmental challenges later in life compared to term-born peers. These include functional motor impairment, cognitive and speech delays, neurobehavioral disorders, and atypical social development. There are well-documented inequities in the population distributions of preterm birth and associated short-term morbidities by race, ethnicity, language, and nativity. Far less is known about how these inequities affect long-term outcomes, though the impact of unequal access to post-discharge support services for preterm infants raises concerns about widening gaps in health, development, and functioning. In this review, we describe what is currently known about the impact of race, ethnicity, nativity, and language on long-term outcomes. We provide a framework for understanding inequities in social, political, and historical context. And we offer guidance for next steps to delineate mechanistic pathways and to identify interventions to eliminate inequities in long-term neurodevelopmental outcomes through research, intervention, and advocacy.

Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications

BACKGROUND

With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data.

OBJECTIVE

In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness.

METHODS

We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis-specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes).

RESULTS

A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset.

CONCLUSION

We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

Development, implementation, and use of an "equity lens" integrated into an institutional quality scorecard

Few healthcare provider organizations systematically track their healthcare equity, and fewer enable direct interaction with such data by their employees. From May to August 2019, we enhanced the data architecture and reporting functionality of our existing institutional quality scorecard to allow direct comparisons of quality measure performance by gender, age, race, ethnicity, language, zip code, and payor. The Equity Lens was made available to over 4000 staff in September 2019 for 82 institutional quality measures. During the first 11 months, 235 unique individuals used the tool; users were most commonly from the quality and equity departments. Two early use cases evaluated hypertension control and readmissions by race, identifying potential inequities. This is the first description of an interactive equity lens integrated into an institutional quality scorecard made available to healthcare system employees. Early evidence suggests the tool is used and can inform quality improvement initiatives.