Telemedicine and In-Person Visit Modality Mix and Electronic Health Record Use in Primary Care.

This cross-sectional study investigates the association between day-to-day changes in telemedicine share and clinician time spent on electronic health record (EHR) use.

Development and validation of a pulmonary function test data extraction tool for the US department of veterans affairs electronic health record.

OBJECTIVE: Pulmonary function test (PFT) results are recorded variably across hospitals in the Department of Veterans Affairs (VA) electronic health record (EHR), using both unstructured and semi-structured notes. We developed and validated a hospital-specific code to extract pre-bronchodilator measures of obstruction (ratio of forced expiratory volume in one second [FEV1] to forced vital capacity [FVC]) and severity of obstruction (percent predicted of FEV1). RESULTS: Among 36 VA facilities with the most PFTs completed between 2018 and 2022 from a parent cohort of veterans receiving long-acting controller inhalers, 12 had a consistent syntactical convention or template for reporting PFT data in the EHR. Of the 42,718 PFTs identified from these 12 facilities, the hospital-specific text processing pipeline yielded 24,860 values for the FEV1:FVC ratio and 23,729 values for FEV1. A ratio of FEV1:FVC less than 0.7 was identified in 17,615 of 24,922 studies (70.7%); 8864 of 24,922 (35.6%) had a severe or very severe reduction in FEV1 (< 50% of the predicted value). Among 100 randomly selected PFT reports reviewed by two pulmonary physicians, the coding solution correctly identified the presence of obstruction in 99 out of 100 studies and the degree of obstruction in 96 out of 100 studies.

A matching-based machine learning approach to estimating optimal dynamic treatment regimes with time-to-event outcomes.

Observational data (e.g. electronic health records) has become increasingly important in evidence-based research on dynamic treatment regimes, which tailor treatments over time to patients based on their characteristics and evolving clinical history. It is of great interest for clinicians and statisticians to identify an optimal dynamic treatment regime that can produce the best expected clinical outcome for each individual and thus maximize the treatment benefit over the population. Observational data impose various challenges for using statistical tools to estimate optimal dynamic treatment regimes. Notably, the task becomes more sophisticated when the clinical outcome of primary interest is time-to-event. Here, we propose a matching-based machine learning method to identify the optimal dynamic treatment regime with time-to-event outcomes subject to right-censoring using electronic health record data. In contrast to the established inverse probability weighting-based dynamic treatment regime methods, our proposed approach provides better protection against model misspecification and extreme weights in the context of treatment sequences, effectively addressing a prevalent challenge in the longitudinal analysis of electronic health record data. In simulations, the proposed method demonstrates robust performance across a range of scenarios. In addition, we illustrate the method with an application to estimate optimal dynamic treatment regimes for patients with advanced non-small cell lung cancer using a real-world, nationwide electronic health record database from Flatiron Health.

Electronic sanitary database: a new potential tool to identify occult chronic liver disease in general population.

Chronic liver disease (CLD) is a leading global cause of mortality, morbidity, and healthcare resource utilization. However, the burden of CLD is underestimated because the course of the disease is often asymptomatic until clinical decompensation and the development of life-threatening complications. In this study, we assessed the use of available blood tests from electronic medical records for identifying individuals with undiagnosed CLD in the general population. We analyzed a total of 202,529 blood tests obtained from 99,848 adults recorded in the Electronic Health Records of the Padova Teaching Hospital. Transaminases levels > 1.5 times the normal value indicated occult CLD, while platelet counts < 120,000/µL identified occult cirrhosis. We characterized patients using Italian Medical Exemptions (IME), excluding oncologic cases. Overt and occult cirrhosis prevalence was 1% and 4.18%, respectively, while overt and occult CLD affected 2.85% and 4.61% of the population. The epidemiology of patients with overt and occult cirrhosis was similar but significantly different from that of the controls. Among subjects aged 60-70 years, working disability was twofold higher in those with occult cirrhosis compared to those with overt cirrhosis. Occult CLD and cirrhosis had higher prevalence rates than diagnosed cases in the general population. Electronic medical record data may serve as a valuable tool for CLD identification, potentially reducing cirrhosis development and clinical decompensation. This, in turn, may lead to a decrease in the economic impact on the healthcare system.

South Asian, Black and White ethnicity and the effect of potentially modifiable risk factors for dementia: A study in English electronic health records.

INTRODUCTION: We aimed to investigate ethnic differences in the associations of potentially modifiable risk factors with dementia. METHODS: We used anonymised data from English electronic primary care records for adults aged 65 and older between 1997 and 2018. We used Cox regression to investigate main effects for each risk factor and interaction effects between each risk factor and ethnicity. RESULTS: We included 865,674 people with 8,479,973 person years of follow up. Hypertension, dyslipidaemia, obesity and diabetes were more common in people from minority ethnic groups than White people. The impact of hypertension, obesity, diabetes, low HDL and sleep disorders on dementia risk was increased in South Asian people compared to White people. The impact of hypertension was greater in Black compared to White people. DISCUSSION: Dementia prevention efforts should be targeted towards people from minority ethnic groups and tailored to risk factors of particular importance.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity.

Importance: US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement. Objective: To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care. Design, Setting, and Participants: This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023. Exposures: The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities. Main Outcomes and Measures: The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles. Results: A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020. Conclusions and Relevance: In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Anemia In Pregnancy: Examining Missed Diagnoses and Racial Disparities in the Upper Midwest.

Anemia in pregnancy (AIP) is associated with poor maternal/fetal outcomes. The prevalence of AIP globally ranges from 44-53% and varies drastically depending on maternal race/ethnicity and other factors. Screening and treatment of AIP is disputed. This study is a retrospective review of electronic medical records (EMR) of pregnant adults over three years (2018-2020, inclusive) of Sanford Health, a large healthcare system in the upper Midwest. AIP was determined by either diagnosis or lab values (hemoglobin, hematocrit, and ferritin) overlapping with pregnancy. A missed diagnosis was characterized by confirmed anemia through lab values but lacking a diagnosis of anemia within EMR. A total of 35,498 patients were included in this study, 42.9% were determined to have AIP. Of AI/AN (American Indian/Alaska Native) patients, 58.3% were anemic and 55.1% of Black/African American patients were anemic compared to 40.0% of anemic white patients. Of anemic patients, 81.1% did not have an anemia diagnosis listed in EMR. This study identifies racial and ethnic disparities of AIP among patients in the upper Midwest. In addition, this study highlights the need for improved data integrity within EMR.

Association Between Billing Patient Portal Messages as e-Visits and Patient Messaging Volume.

This study evaluates the adoption of clinician billing for patient portal messages as e-visits, prompted by significant increases in patient messaging after the onset of the COVID-19 pandemic.

Factors Associated with Reducing Disparities in Electronic Personal Heath Records Use Among Non-Hispanic White and Hispanic Adults.

OBJECTIVES: Personal health records (PHR) use has improved individuals' health outcomes. The adoption of PHR remains low with documented racial disparities. We aim to determine factors associated with reducing racial and ethnic disparities among Hispanic adults in PHR use. METHODS: Participants included non-Hispanic White (NHW) and Hispanic adults (age ≥ 18 years) enrolled in Health Information National Trends Survey in 2018 and 2019. We identified PHR use as online medical record access in the last 12 months. We considered three factors (1. accessing mHealth Apps on the phone, 2. having a usual source of care, and 3. electronically communicating (e-communication) with healthcare providers) as facilitating PHR use. Multivariable logistic regressions with replicate weights were analyzed to determine factors associated with racial/ethnic disparities in PHR use after controlling for general characteristics (i.e., sex, age, education, insurance status, and income). RESULTS: A lower percentage of Hispanics than NHWs used PHR (42.0% vs. 53.5%, P < .001). When adjusted for individual general characteristics, the adjusted odds ratio (AOR) of e-communication with healthcare providers associated with PHR use was 1.49 (1.19-1.86, P < .001), AOR was 2.06 (1.62-2.6, P < .001) on accessing to mHealth App, and 2.60 (1.86-3.63, P < .001) on having a usual source of care. However, the racial difference was not statistically significant after adjusting three factors promoting PHR use (AOR = 0.90, 95% CI = 0.66, 1.22, P = .48). CONCLUSIONS: Ethnic disparities were reduced when PHR use was facilitated by having a usual source of care, active e-communication, and having access to mHealth apps. Interventions focusing on these three factors may potentially reduce racial/ethnic disparities.

Método de captura y recaptura: una experiencia de su aplicación para determinar la incidencia de diabetes gestacional / Capture and recapture method: anexperience of itsapplication to estimatetheincidence of gestational diabetes

Los métodos de captura y recaptura (MCR) se emplean en la estimación de poblaciones mediante la utilización de diferentes fuentes de datos, disponibles e incompletas, que registran por separado un mismo evento. En esta metodología, las fuentes son utilizadas para extrapolar el número de individuos no registrados, usando la información recopilada sobre los individuos sí registrados. Este artículo describe todos los pasos de su aplicación práctica, a partir de un ejemplo de estimación de la incidencia de diabetes gestacional en una institución, a partir de cinco fuentes documentales. (AU)

Capture-recapture (CRM) methods are widely used to estimate populations by using different data sources, available and incomplete, that record the same event separately. In these methods, the available sources are used to extrapolate the number of unregistered individuals, using the information collected on the individuals that are registered. This article describes all the steps of its practical application, based on an example of estimating the incidence of gestational diabetes in an institution based on five documentary sources. (AU)

Association Between Gout Flare and Subsequent Cardiovascular Events Among Patients With Gout.

Importance: Gout is associated with cardiovascular diseases. The temporal association between gout flares and cardiovascular events has not been investigated. Objective: To investigate whether there is a transient increase in risk of cardiovascular events after a recent gout flare. Design, Setting, and Participants: A retrospective observational study was conducted using electronic health records from the Clinical Practice Research Datalink in England between January 1, 1997, and December 31, 2020. A multivariable nested case-control study was performed among 62 574 patients with gout, and a self-controlled case series, adjusted for season and age, was performed among 1421 patients with gout flare and cardiovascular event. Exposures: Gout flares were ascertained using hospitalization, primary care outpatient, and prescription records. Main Outcomes and Measures: The primary outcome was a cardiovascular event, defined as an acute myocardial infarction or stroke. Association with recent prior gout flares was measured using adjusted odds ratios (ORs) with 95% CIs in a nested case-control study and adjusted incidence rate ratios (IRRs) with 95% CIs in a self-controlled case series. Results: Among patients with a new diagnosis of gout (mean age, 76.5 years; 69.3% men, 30.7% women), 10 475 patients with subsequent cardiovascular events were matched with 52 099 patients without cardiovascular events. Patients with cardiovascular events, compared with those who did not have cardiovascular events, had significantly higher odds of gout flare within the prior 0 to 60 days (204/10 475 [2.0%] vs 743/52 099 [1.4%]; adjusted OR, 1.93 [95% CI, 1.57-2.38]) and within the prior 61 to 120 days (170/10 475 [1.6%] vs 628/52 099 [1.2%]; adjusted OR, 1.57 [95% CI, 1.26-1.96]). There was no significant difference in the odds of gout flare within the prior 121 to 180 days (148/10 475 [1.4%] vs 662/52 099 [1.3%]; adjusted OR, 1.06 [95% CI, 0.84-1.34]). In the self-controlled case series (N = 1421), cardiovascular event rates per 1000 person-days were 2.49 (95% CI, 2.16-2.82) within days 0 to 60; 2.16 (95% CI, 1.85-2.47) within days 61 to 120; and 1.70 (95% CI, 1.42-1.98) within days 121 to 180 after a gout flare, compared with cardiovascular event rates of 1.32 (95% CI, 1.23-1.41) per 1000 person-days within the 150 days before or the 181 to 540 days after the gout flare. Compared with 150 days before or the 181 to 540 days after a gout flare, incidence rate differences for cardiovascular events were 1.17 (95% CI, 0.83-1.52) per 1000 person-days, and adjusted IRRs were 1.89 (95% CI, 1.54-2.30) within days 0 to 60; 0.84 (95% CI, 0.52-1.17) per 1000 person-days and 1.64 (95% CI, 1.45-1.86) within days 61 to 120; and 0.38 (95% CI, 0.09-0.67) per 1000 person-days and 1.29 (95% CI, 1.02-1.64) within days 121 to 180 after a gout flare. Conclusions and Relevance: Among individuals with gout, those who experienced a cardiovascular event, compared with those who did not experience such an event, had significantly higher odds of a recent gout flare in the preceding days. These findings suggest gout flares are associated with a transient increase in cardiovascular events following the flare.

Effective of Smart Mathematical Model by Machine Learning Classifier on Big Data in Healthcare Fast Response.

In the past few years, big data related to healthcare has become more important, due to the abundance of data, the increasing cost of healthcare, and the privacy of healthcare. Create, analyze, and process large and complex data that cannot be processed by traditional methods. The proposed method is based on classifying data into several classes using the data weight derived from the features extracted from the big data. Three important criteria were used to evaluate the study as well as to benchmark the current study with previous studies using a standard dataset.

Deep phenotyping of Alzheimer's disease leveraging electronic medical records identifies sex-specific clinical associations.

Alzheimer's Disease (AD) is a neurodegenerative disorder that is still not fully understood. Sex modifies AD vulnerability, but the reasons for this are largely unknown. We utilize two independent electronic medical record (EMR) systems across 44,288 patients to perform deep clinical phenotyping and network analysis to gain insight into clinical characteristics and sex-specific clinical associations in AD. Embeddings and network representation of patient diagnoses demonstrate greater comorbidity interactions in AD in comparison to matched controls. Enrichment analysis identifies multiple known and new diagnostic, medication, and lab result associations across the whole cohort and in a sex-stratified analysis. With this data-driven method of phenotyping, we can represent AD complexity and generate hypotheses of clinical factors that can be followed-up for further diagnostic and predictive analyses, mechanistic understanding, or drug repurposing and therapeutic approaches.

Evaluation of a Natural Language Processing Approach to Identify Social Determinants of Health in Electronic Health Records in a Diverse Community Cohort.

BACKGROUND: Health care systems in the United States are increasingly interested in measuring and addressing social determinants of health (SDoH). Advances in electronic health record systems and Natural Language Processing (NLP) create a unique opportunity to systematically document patient SDoH from digitized free-text provider notes. METHODS: Patient SDoH status [recorded by Your Current Life Situation (YCLS) Survey] and associated provider notes recorded between March 2017 and June 2020 were extracted (32,261 beneficiaries; 50,722 YCLS surveys; 485,425 provider notes).NLP patterns were generated using a machine learning test statistic (Term Frequency-Inverse Document Frequency). Patterns were developed and assessed in a training, training validation, and final validation dataset (64%, 16%, and 20% of total data, respectively).NLP models analyzed SDoH-specific categories (housing, medical care, and transportation needs) and a combined SDoH metric. Model performance was assessed using sensitivity, specificity, and Cohen κ statistic, assuming the YCLS Survey to be the gold standard. RESULTS: Within the training validation dataset, NLP models showed strong sensitivity and specificity, with moderate agreement with the YCLS Survey (Housing: sensitivity=0.67, specificity=0.89, κ=0.51; Medical care: sensitivity=0.55, specificity=0.73, κ=0.20; Transportation: sensitivity=0.79, specificity=0.87, κ=0.58). Model performance in the training and training validation datasets were comparable.In the final validation dataset, a combined SDoH prediction metric showed sensitivity=0.77, specificity=0.69, κ=0.45. CONCLUSION: This NLP algorithm demonstrated moderate performance in identification of unmet patient social needs. This novel approach may enable improved targeting of interventions, allocation of limited resources and monitoring a health care system's addressing its patients' SDoH needs.

Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

BACKGROUND: Accurate, pragmatic risk stratification for postoperative delirium (POD) is necessary to target preventative resources toward high-risk patients. Machine learning (ML) offers a novel approach to leveraging electronic health record (EHR) data for POD prediction. We sought to develop and internally validate a ML-derived POD risk prediction model using preoperative risk features, and to compare its performance to models developed with traditional logistic regression. METHODS: This was a retrospective analysis of preoperative EHR data from 24,885 adults undergoing a procedure requiring anesthesia care, recovering in the main post-anesthesia care unit, and staying in the hospital at least overnight between December 2016 and December 2019 at either of two hospitals in a tertiary care health system. One hundred fifteen preoperative risk features including demographics, comorbidities, nursing assessments, surgery type, and other preoperative EHR data were used to predict postoperative delirium (POD), defined as any instance of Nursing Delirium Screening Scale ≥2 or positive Confusion Assessment Method for the Intensive Care Unit within the first 7 postoperative days. Two ML models (Neural Network and XGBoost), two traditional logistic regression models ("clinician-guided" and "ML hybrid"), and a previously described delirium risk stratification tool (AWOL-S) were evaluated using the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive likelihood ratio, and positive predictive value. Model calibration was assessed with a calibration curve. Patients with no POD assessments charted or at least 20% of input variables missing were excluded. RESULTS: POD incidence was 5.3%. The AUC-ROC for Neural Net was 0.841 [95% CI 0. 816-0.863] and for XGBoost was 0.851 [95% CI 0.827-0.874], which was significantly better than the clinician-guided (AUC-ROC 0.763 [0.734-0.793], p < 0.001) and ML hybrid (AUC-ROC 0.824 [0.800-0.849], p < 0.001) regression models and AWOL-S (AUC-ROC 0.762 [95% CI 0.713-0.812], p < 0.001). Neural Net, XGBoost, and ML hybrid models demonstrated excellent calibration, while calibration of the clinician-guided and AWOL-S models was moderate; they tended to overestimate delirium risk in those already at highest risk. CONCLUSION: Using pragmatically collected EHR data, two ML models predicted POD in a broad perioperative population with high discrimination. Optimal application of the models would provide automated, real-time delirium risk stratification to improve perioperative management of surgical patients at risk for POD.

Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems.

Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud's security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers' interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people's knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.

Characteristics of pediatric emergency department frequent visitors and their risk of a return visit: A large observational study using electronic health record data.

BACKGROUND: Among pediatric emergency department (ED) visits, a subgroup of children repeatedly visits the ED, making them frequent visitors (FVs). The aim of this study is to get insight into the group of pediatric ED FVs and to determine risk factors associated with a revisit. METHODS AND FINDINGS: Data of all children aged 0-18 years visiting the ED of a university hospital in the Netherlands between 2017 and 2020 were included in this observational study based on routine data extraction. Children with 4 or more ED visits within 365 days were classified as FVs. Descriptive analysis of the study cohort at patient- and visit-level were performed. Risk factors for a recurrent ED visit were determined using a Prentice Williams and Peterson gap time cox-based model. Our study population of 10,209 children with 16,397 ED visits contained 500 FVs (4.9%) accounting for 3,481 visits (21.2%). At patient-level, FVs were younger and more often suffered from chronic diseases (CDs). At visit-level, frequent visits were more often initiated by self-referral and were more often related to medical problems (compared to trauma's). Overall, FVs presented at the ED more often because of an infection (41.3%) compared to non-FVs (27.4%), either associated or not with the body system affected by the CD. We identified the presence of a comorbidity (non-complex CD HR 1.66; 1.52-1.81 and complex CD HR 2.00; 1.84-2.16) as determinants with the highest hazard for a return visit. CONCLUSION: Pediatric ED FVs are a small group of children but account for a large amount of the total ED visits. FVs are younger patients, suffering from (complex) comorbidities and present more often with infectious conditions compared to non-FVs. Healthcare pathways, including safety-netting strategies for acute manifestations from their comorbidity, or for infectious conditions in general may contribute to support parents and redirect some patients from the ED.

The COVID-19 Pandemic and Access to Selected Ambulatory Care Services Among Populations With Severely Uncontrolled Diabetes and Hypertension in Massachusetts.

OBJECTIVES: The outbreak of COVID-19 in Massachusetts may have reduced ambulatory care access. Our study aimed to quantify this impact among populations with severely uncontrolled diabetes and hypertension; these populations are at greatest risk for adverse outcomes caused by disruptions in care. METHODS: We analyzed multidisciplinary ambulatory electronic health record data from MDPHnet. We established 3 cohorts of patients with severely uncontrolled diabetes and 3 cohorts of patients with severely uncontrolled hypertension using 2017, 2018, and 2019 data, then followed each cohort through the subsequent 15 months. For the diabetes cohorts, we generated quarterly counts of glycated hemoglobin A1c (HbA1c) tests. For the hypertension cohorts, we generated monthly counts of blood pressure measurements. Finally, we assessed telehealth use among the 2019 diabetes and hypertension cohorts from January 2020 through March 2021. RESULTS: HbA1c testing and blood pressure monitoring dropped considerably during the pandemic compared with previous years. In the 2019 diabetes cohort, HbA1c measurements declined from 44.0% in January-March 2020 (baseline) to 15.9% in April-June 2020 and was 11.8 percentage points below baseline in January-March 2021. In the 2019 hypertension cohort, blood pressure measurements declined from 40.0% in January 2020 to 4.5% in April 2020 and was 23.5 percentage points below baseline in March 2021. Telehealth use increased precipitously during the pandemic but was not uniform across subpopulations. CONCLUSIONS: Access to selected diabetes and hypertension services declined sharply during the pandemic among populations with severely uncontrolled disease. Although telehealth is an important strategy, ensuring equity in access is essential. Telehealth hybrid models can also minimize disruptions in care.

Examination of Stigmatizing Language in the Electronic Health Record.

Importance: Stigmatizing 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. Objective: To 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 Participants: This 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. Exposures: Patients' 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 Measures: Binary 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. Results: The 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 Relevance: In 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.