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Background As diagnostic tests for COVID-19 were broadly deployed under Emergency Use Authorization, there emerged a need to understand the real-world utilization and performance of serological testing across the United States. Methods Six health systems contributed electronic health records and/or claims data, jointly developed a master protocol, and used it to execute the analysis in parallel. We used descriptive statistics to examine demographic, clinical, and geographic characteristics of serology testing among patients with RNA positive for SARS-CoV-2. Results Across datasets, we observed 930,669 individuals with positive RNA for SARS-CoV-2. Of these, 35,806 (4%) were serotested within 90 days; 15% of which occurred 30%) in some datasets—limiting our ability to examine differences in serological testing by race. In datasets where race/ethnicity information was available, we observed a greater distribution of White individuals among those serotested; however, the time between RNA and serology tests appeared shorter in Black compared to White individuals. Test manufacturer data was available in half of the datasets contributing to the analysis. Conclusion Our results inform the underlying context of serotesting during the first year of the COVID-19 pandemic and differences observed between claims and EHR data sources–a critical first step to understanding the real-world accuracy of serological tests. Incomplete reporting of race/ethnicity data and a limited ability to link test manufacturer data, lab results, and clinical data challenge the ability to assess the real-world performance of SARS-CoV-2 tests in different contexts and the overall U.S. response to current and future disease pandemics.

Background Real-world performance of COVID-19 diagnostic tests under Emergency Use Authorization (EUA) must be assessed. We describe overall trends in the performance of serology tests in the context of real-world implementation. Methods Six health systems estimated the odds of seropositivity and positive percent agreement (PPA) of serology test among people with confirmed SARS-CoV-2 infection by molecular test. In each dataset, we present the odds ratio and PPA, overall and by key clinical, demographic, and practice parameters. Results A total of 15,615 people were observed to have at least one serology test 14–90 days after a positive molecular test for SARS-CoV-2. We observed higher PPA in Hispanic (PPA range: 79–96%) compared to non-Hispanic (60–89%) patients; in those presenting with at least one COVID-19 related symptom (69–93%) as compared to no such symptoms (63–91%); and in inpatient (70–97%) and emergency department (93–99%) compared to outpatient (63–92%) settings across datasets. PPA was highest in those with diabetes (75–94%) and kidney disease (83–95%); and lowest in those with auto-immune conditions or who are immunocompromised (56–93%). The odds ratios (OR) for seropositivity were higher in Hispanics compared to non-Hispanics (OR range: 2.59–3.86), patients with diabetes (1.49–1.56), and obesity (1.63–2.23); and lower in those with immunocompromised or autoimmune conditions (0.25–0.70), as compared to those without those comorbidities. In a subset of three datasets with robust information on serology test name, seven tests were used, two of which were used in multiple settings and met the EUA requirement of PPA ≥87%. Tests performed similarly across datasets. Conclusion Although the EUA requirement was not consistently met, more investigation is needed to understand how serology and molecular tests are used, including indication and protocol fidelity. Improved data interoperability of test and clinical/demographic data are needed to enable rapid assessment of the real-world performance of in vitro diagnostic tests.

Pregnant people face increased risk of severe COVID-19. Current guidelines recommend updated COVID-19 vaccination (2023–2024) for those aged ≥6 months, irrespective of pregnancy status. To refine recommendations for pregnant people, further data are needed. Using a test-negative design, we evaluated COVID-19 vaccine effectiveness against medically attended COVID-19 with COVID-19–like illness among pregnant people aged 18 to 45 years during June 2022 to August 2023. When doses were received during pregnancy, vaccine effectiveness was 52% (95% CI, 29%–67%); when received <6 months prior to pregnancy, 28% (95% CI, 11%–42%); and when received ≥6 months prior to pregnancy, 6% (95% CI, −11% to 21%). Pregnant people should stay up-to-date with recommended COVID-19 vaccination. [ABSTRACT FROM AUTHOR]

Introduction Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. Recommendations Here, we propose five independent fundamental dimensions of phenotyping algorithms—complexity, performance, efficiency, implementability, and maintenance—through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities. [ABSTRACT FROM AUTHOR]

Background During the 2022–2023 influenza season, the United States experienced the highest influenza-associated pediatric hospitalization rate since 2010–2011. Influenza A/H3N2 infections were predominant. Methods We analyzed acute respiratory illness (ARI)–associated emergency department or urgent care (ED/UC) encounters or hospitalizations at 3 health systems among children and adolescents aged 6 months–17 years who had influenza molecular testing during October 2022–March 2023. We estimated influenza A vaccine effectiveness (VE) using a test-negative approach. The odds of vaccination among influenza-A–positive cases and influenza-negative controls were compared after adjusting for confounders and applying inverse-propensity-to-be-vaccinated weights. We developed overall and age-stratified VE models. Results Overall, 13 547 of 44 787 (30.2%) eligible ED/UC encounters and 263 of 1862 (14.1%) hospitalizations were influenza-A–positive cases. Among ED/UC patients, 15.2% of influenza-positive versus 27.1% of influenza-negative patients were vaccinated; VE was 48% (95% confidence interval [CI], 44–52%) overall, 53% (95% CI, 47–58%) among children aged 6 months–4 years, and 38% (95% CI, 30–45%) among those aged 9–17 years. Among hospitalizations, 17.5% of influenza-positive versus 33.4% of influenza-negative patients were vaccinated; VE was 40% (95% CI, 6–61%) overall, 56% (95% CI, 23–75%) among children ages 6 months–4 years, and 46% (95% CI, 2–70%) among those 5–17 years. Conclusions During the 2022–2023 influenza season, vaccination reduced the risk of influenza-associated ED/UC encounters and hospitalizations by almost half (overall VE, 40–48%). Influenza vaccination is a critical tool to prevent moderate-to-severe influenza illness in children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Although psychiatric disorders have been associated with reduced immune responses to other vaccines, it remains unknown whether they influence COVID‐19 vaccine effectiveness (VE). This study evaluated risk of COVID‐19 hospitalization and estimated mRNA VE stratified by psychiatric disorder status. Methods: In a retrospective cohort analysis of the VISION Network in four US states, the rate of laboratory‐confirmed COVID‐19‐associated hospitalization between December 2021 and August 2022 was compared across psychiatric diagnoses and by monovalent mRNA COVID‐19 vaccination status using Cox proportional hazards regression. Results: Among 2,436,999 adults, 22.1% had ≥1 psychiatric disorder. The incidence of COVID‐19‐associated hospitalization was higher among patients with any versus no psychiatric disorder (394 vs. 156 per 100,000 person‐years, p < 0.001). Any psychiatric disorder (adjusted hazard ratio [aHR], 1.27; 95% CI, 1.18–1.37) and mood (aHR, 1.25; 95% CI, 1.15–1.36), anxiety (aHR, 1.33, 95% CI, 1.22–1.45), and psychotic (aHR, 1.41; 95% CI, 1.14–1.74) disorders were each significant independent predictors of hospitalization. Among patients with any psychiatric disorder, aHRs for the association between vaccination and hospitalization were 0.35 (95% CI, 0.25–0.49) after a recent second dose, 0.08 (95% CI, 0.06–0.11) after a recent third dose, and 0.33 (95% CI, 0.17–0.66) after a recent fourth dose, compared to unvaccinated patients. Corresponding VE estimates were 65%, 92%, and 67%, respectively, and were similar among patients with no psychiatric disorder (68%, 92%, and 79%). Conclusion: Psychiatric disorders were associated with increased risk of COVID‐19‐associated hospitalization. However, mRNA vaccination provided similar protection regardless of psychiatric disorder status, highlighting its benefit for individuals with psychiatric disorders. [ABSTRACT FROM AUTHOR]

Background The epidemiology of coronavirus disease 2019 (COVID-19) continues to develop with emerging variants, expanding population-level immunity, and advances in clinical care. We describe changes in the clinical epidemiology of COVID-19 hospitalizations and risk factors for critical outcomes over time. Methods We included adults aged ≥18 years from 10 states hospitalized with COVID-19 June 2021–March 2023. We evaluated changes in demographics, clinical characteristics, and critical outcomes (intensive care unit admission and/or death) and evaluated critical outcomes risk factors (risk ratios [RRs]), stratified by COVID-19 vaccination status. Results A total of 60 488 COVID-19–associated hospitalizations were included in the analysis. Among those hospitalized, median age increased from 60 to 75 years, proportion vaccinated increased from 18.2% to 70.1%, and critical outcomes declined from 24.8% to 19.4% (all P <.001) between the Delta (June–December, 2021) and post-BA.4/BA.5 (September 2022–March 2023) periods. Hospitalization events with critical outcomes had a higher proportion of ≥4 categories of medical condition categories assessed (32.8%) compared to all hospitalizations (23.0%). Critical outcome risk factors were similar for unvaccinated and vaccinated populations; presence of ≥4 medical condition categories was most strongly associated with risk of critical outcomes regardless of vaccine status (unvaccinated: adjusted RR, 2.27 [95% confidence interval {CI}, 2.14–2.41]; vaccinated: adjusted RR, 1.73 [95% CI, 1.56–1.92]) across periods. Conclusions The proportion of adults hospitalized with COVID-19 who experienced critical outcomes decreased with time, and median patient age increased with time. Multimorbidity was most strongly associated with critical outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Research driven by real‐world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID‐19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants. The objective of this project was to establish privacy‐preserving record linkage (PPRL) methods to ensure that patient‐level EHR data remains secure and private when governance‐approved linkages with other datasets occur. Methods: Separate agreements and approval processes govern N3C data contribution and data access. The Linkage Honest Broker (LHB), an independent neutral party (the Regenstrief Institute), ensures data linkages are robust and secure by adding an extra layer of separation between protected health information and clinical data. The LHB's PPRL methods (including algorithms, processes, and governance) match patient records using "deidentified tokens," which are hashed combinations of identifier fields that define a match across data repositories without using patients' clear‐text identifiers. Results: These methods enable three linkage functions: Deduplication, Linking Multiple Datasets, and Cohort Discovery. To date, two external repositories have been cross‐linked. As of March 1, 2023, 43 sites have signed the LHB Agreement; 35 sites have sent tokens generated for 9 528 998 patients. In this initial cohort, the LHB identified 135 037 matches and 68 596 duplicates. Conclusion: This large‐scale linkage study using deidentified datasets of varying characteristics established secure methods for protecting the privacy of N3C patient data when linked for research purposes. This technology has potential for use with registries for other diseases and conditions. [ABSTRACT FROM AUTHOR]

Adults with disabilities are at increased risk for severe coronavirus disease 2019 (COVID-19). Using data across 9 states during Delta- and Omicron-predominant periods (June 2021–September 2022), we evaluated the effectiveness of the original monovalent COVID-19 messenger RNA vaccines among 521 206 emergency department/urgent care encounters (11 471 [2%] in patients with a documented disability) and 139 548 hospitalizations (16 569 [12%] in patients with a disability) for laboratory-confirmed COVID-19 illness in adults (aged ≥18 years). Across variant periods and for the primary series or booster doses, vaccine effectiveness was similar in those with and those without a disability. These findings highlight the importance of adults with disabilities staying up to date with COVID-19 vaccinations. [ABSTRACT FROM AUTHOR]

We aimed to assess ChatGPT's performance on the Clinical Informatics Board Examination and to discuss the implications of large language models (LLMs) for board certification and maintenance. We tested ChatGPT using 260 multiple-choice questions from Mankowitz's Clinical Informatics Board Review book, omitting 6 image-dependent questions. ChatGPT answered 190 (74%) of 254 eligible questions correctly. While performance varied across the Clinical Informatics Core Content Areas, differences were not statistically significant. ChatGPT's performance raises concerns about the potential misuse in medical certification and the validity of knowledge assessment exams. Since ChatGPT is able to answer multiple-choice questions accurately, permitting candidates to use artificial intelligence (AI) systems for exams will compromise the credibility and validity of at-home assessments and undermine public trust. The advent of AI and LLMs threatens to upend existing processes of board certification and maintenance and necessitates new approaches to the evaluation of proficiency in medical education. [ABSTRACT FROM AUTHOR]

Development of evidence‐based practice requires practice‐based evidence, which can be acquired through analysis of real‐world data from electronic health records (EHRs). The EHR contains volumes of information about patients—physical measurements, diagnoses, exposures, and markers of health behavior—that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real‐world data into reliable real‐world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions. This manuscript explores the idiosyncrasies inherent in the capture, formatting, and standardization of EHR data and discusses the clinical domain and informatics competencies required to transform the raw clinical, real‐world data into high‐quality, fit‐for‐purpose analytical data sets used to generate real‐world evidence. [ABSTRACT FROM AUTHOR]

The primary challenge stems from the fact that data collected through routine healthcare practice often has limited utility for secondary uses such as research and quality improvement activities. Learning from data: A recurring feature on the science and practice of data-driven learning health systems As we look ahead toward building learning health systems, we must seek to overcome the myriad challenges described in order to realize the promise of leveraging real-world data through advanced informatics and data science approaches. [Extracted from the article]

Introduction: The rapid advancement of artificial intelligence (AI) has led to significant transformations in health and healthcare. As AI technologies continue to evolve, there is an urgent need to establish a unified framework that guides the design, implementation, and evaluation of AI-driven interventions across individual and population health contexts., Approach: In response to this need, the National Academy of Medicine (NAM) has initiated the development of an AI code of conduct (AICC) through its Digital Health Action Collaborative. This code of conduct is grounded in shared principles and commitments, aiming to actualize ethical and effective AI practices within the broader health and healthcare ecosystem. Given its specialized expertise and insight, the biomedical informatics (BMI) community plays a pivotal role in shaping and applying these guidelines., Recommendations: We, as members of the AICC Steering Committee and the NAM Digital Health Action Collaborative, urge BMI educators, researchers, and practitioners to engage actively in refining and implementing the AICC. This involvement is critical to ensuring that the code is robust, applicable, and continuously improved to meet the evolving challenges facing health and healthcare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: The 2022-2023 United States influenza season had unusually early influenza activity with high hospitalization rates. Vaccine-matched A(H3N2) viruses predominated, with lower levels of A(H1N1)pdm09 activity also observed., Methods: Using the test-negative design, we evaluated influenza vaccine effectiveness (VE) during the 2022-2023 season against influenza A-associated emergency department/urgent care (ED/UC) visits and hospitalizations from October 2022 to March 2023 among adults (aged ≥18 years) with acute respiratory illness (ARI). VE was estimated by comparing odds of seasonal influenza vaccination among case-patients (influenza A test positive by molecular assay) and controls (influenza test negative), applying inverse-propensity-to-be-vaccinated weights., Results: The analysis included 85 389 ED/UC ARI encounters (17.0% influenza A positive; 37.8% vaccinated overall) and 19 751 hospitalizations (9.5% influenza A positive; 52.8% vaccinated overall). VE against influenza A-associated ED/UC encounters was 44% (95% confidence interval [CI], 40%-47%) overall and 45% and 41% among adults aged 18-64 and ≥65 years, respectively. VE against influenza A-associated hospitalizations was 35% (95% CI, 27%-43%) overall and 23% and 41% among adults aged 18-64 and ≥65 years, respectively., Conclusions: VE was moderate during the 2022-2023 influenza season, a season characterized with increased burden of influenza and co-circulation with other respiratory viruses. Vaccination is likely to substantially reduce morbidity, mortality, and strain on healthcare resources., Competing Interests: Potential conflicts of interest. During the conduct of the study, all Westat- and Kaiser Permanente Northern California Division of Research–affiliated authors reported receiving contractual support from the CDC via payments made to their respective institutions. Additionally, all authors affiliated with Baylor Scott & White Health, Children's Minnesota, Columbia University Irving Medical Center, HealthPartners Institute, Intermountain Healthcare, Kaiser Permanente Center for Health Research, Regenstrief Institute, University of Colorado Anschutz Medical Campus, and Vanderbilt University Medical Center reported receiving contractual support from the CDC during the conduct of the study, via subcontracts from Westat, Inc, with payments made to their respective institutions. Unrelated to the submitted work, the following disclosures were reported from the past 36 months: A. L. N. received grants from Pfizer and Vir Biotechnology; M. N. received grants directly from CDC and from CDC via subcontracts from Abt Associates and Vanderbilt University Medical Center to her institution; C. E. M. received grants AstraZeneca; S. R. received grants from GSK; and N. P. K. received research support from Pfizer, Merck, GlaxoSmithKline, Sanofi Pasteur, and Seqirus. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (Published by Oxford University Press on behalf of Infectious Diseases Society of America 2023.)

Background: The insulin ordering process is an opportunity to provide clinicians with hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin ordering process exist., Methods: We used data on adult patients, admitted in 2019 to non-ICU floors of a large teaching hospital, who had orders for subcutaneous insulin. Our outcome was hypoglycemia, defined as a blood glucose (BG) <70 mg/dL within 24 hours after ordering insulin. We trained and evaluated models to predict hypoglycemia at the time of placing an insulin order, using logistic regression, random forest, and extreme gradient boosting (XGBoost). We compared performance using area under the receiver operating characteristic curve (AUCs) and precision-recall curves. We determined recall at our goal precision of 0.30., Results: Of 21 052 included insulin orders, 1839 (9%) were followed by a hypoglycemic event within 24 hours. Logistic regression, random forest, and XGBoost models had AUCs of 0.81, 0.80, and 0.79, and recall of 0.44, 0.49, and 0.32, respectively. The most significant predictor was the lowest BG value in the 24 hours preceding the order. Predictors related to the insulin order being placed at the time of the prediction were useful to the model but less important than the patient's history of BG values over time., Conclusions: Hypoglycemia within the next 24 hours can be predicted at the time an insulin order is placed, providing an opportunity to integrate decision support into the medication ordering process to make insulin therapy safer., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.