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Importance: The Sentinel System is a key component of the US Food and Drug Administration (FDA) postmarketing safety surveillance commitment and uses clinical health care data to conduct analyses to inform drug labeling and safety communications, FDA advisory committee meetings, and other regulatory decisions. However, observational data are frequently deemed insufficient for reliable evaluation of safety concerns owing to limitations in underlying data or methodology. Advances in large language models (LLMs) provide new opportunities to address some of these limitations. However, careful consideration is necessary for how and where LLMs can be effectively deployed for these purposes., Observations: LLMs may provide new avenues to support signal-identification activities to identify novel adverse event signals from narrative text of electronic health records. These algorithms may be used to support epidemiologic investigations examining the causal relationship between exposure to a medical product and an adverse event through development of probabilistic phenotyping of health outcomes of interest and extraction of information related to important confounding factors. LLMs may perform like traditional natural language processing tools by annotating text with controlled vocabularies with additional tailored training activities. LLMs offer opportunities for enhancing information extraction from adverse event reports, medical literature, and other biomedical knowledge sources. There are several challenges that must be considered when leveraging LLMs for postmarket surveillance. Prompt engineering is needed to ensure that LLM-extracted associations are accurate and specific. LLMs require extensive infrastructure to use, which many health care systems lack, and this can impact diversity, equity, and inclusion, and result in obscuring significant adverse event patterns in some populations. LLMs are known to generate nonfactual statements, which could lead to false positive signals and downstream evaluation activities by the FDA and other entities, incurring substantial cost., Conclusions and Relevance: LLMs represent a novel paradigm that may facilitate generation of information to support medical product postmarket surveillance activities that have not been possible. However, additional work is required to ensure LLMs can be used in a fair and equitable manner, minimize false positive findings, and support the necessary rigor of signal detection needed for regulatory activities.

Introduction: PCORnet, the National Patient-Centered Clinical Research Network, is a large research network of health systems that map clinical data to a standardized data model. In 2018, we expanded existing infrastructure to facilitate use for public health surveillance. We describe benefits and challenges of using PCORnet for surveillance and describe case studies., Methods: In 2018, infrastructure enhancements included addition of a table to store patients' residential zip codes and expansion of a modular program to generate population health statistics across conditions. Chronic disease surveillance case studies conducted in 2019 assessed atrial fibrillation (AF) and cirrhosis. In April 2020, PCORnet established an infrastructure to support COVID-19 surveillance with institutions frequently updating their electronic health record data., Results: By August 2023, 53 PCORnet sites (84%) had a 5-digit zip code available on at least 95% of their patient populations. Among 148,223 newly diagnosed AF patients eligible for oral anticoagulant (OAC) therapy, 43.3% were on any OAC (17.8% warfarin, 28.5% any novel oral anticoagulant) within a year of the AF diagnosis. Among 60,268 patients with cirrhosis (2015-2019), common documented etiologies included unknown (48%), hepatitis C infection (23%), and alcohol use (22%). During October 2022 through December 2023, across 34 institutions, the proportion of COVID-19 patients who were cared for in the inpatient setting was 9.1% among 887,051 adults aged 20 years or older and 6.0% among 139,148 children younger than 20 years., Conclusions: PCORnet provides important data that may augment traditional public health surveillance programs across diverse conditions. PCORnet affords longitudinal population health assessments among large catchments of the population with clinical, treatment, and geographic information, with capabilities to deliver rapid information needed during public health emergencies.

Objective: To describe PCORnet, a clinical research network developed for patient-centered outcomes research on a national scale., Study Design and Setting: Descriptive study of the current state and future directions for PCORnet. We conducted cross-sectional analyses of the health systems and patient populations of the 9 Clinical Research Networks and 2 Health Plan Research Networks that are part of PCORnet., Results: Within the Clinical Research Networks, electronic health data are currently collected from 337 hospitals, 169,695 physicians, 3,564 primary care practices, 338 emergency departments, and 1,024 community clinics. Patients can be recruited for prospective studies from any of these clinical sites. The Clinical Research Networks have accumulated data from 80 million patients with at least one visit from 2009 to 2018. The PCORnet Health Plan Research Network population of individuals with a valid enrollment segment from 2009 to 2019 exceeds 60 million individuals, who on average have 2.63 years of follow-up., Conclusion: PCORnet's infrastructure comprises clinical data from a diverse cohort of patients and has the capacity to rapidly access these patient populations for pragmatic clinical trials, epidemiological research, and patient-centered research on rare diseases., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Pragmatic clinical trials often entail the use of electronic health record (EHR) and claims data, but bias and quality issues associated with these data can limit their fitness for research purposes particularly for study end points. Patient-reported health (PRH) data can be used to confirm or supplement EHR and claims data in pragmatic trials, but these data can bring their own biases. Moreover, PRH data can complicate analyses if they are discordant with other sources. Using experience in the design and conduct of multi-site pragmatic trials, we itemize the strengths and limitations of PRH data and identify situational criteria for determining when PRH data are appropriate or ideal to fill gaps in the evidence collected from EHRs. To provide guidance for the scientific rationale and appropriate use of patient-reported data in pragmatic clinical trials, we describe approaches for ascertaining and classifying study end points and addressing issues of incomplete data, data alignment, and concordance. We conclude by identifying areas that require more research., (© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Stakeholders in the clinical research enterprise are aligned around the need to make clinical research in general, and randomized controlled trials in particular, more meaningful and efficient. To that end, we have built distributed research networks (DRNs) for the Sentinel System, the National Institutes of Health (NIH) Collaboratory, and the National Patient-Centered Clinical Research Network (PCORnet). DRNs reuse electronic health record (EHR) and claims data for research. The design and use of health data DRNs is complicated by lack of uniformity in data collection, a fragmented healthcare system, and the imperative to protect research participants. We describe the key elements of successful DRNs, as well as methods, challenges, and solutions we have encountered in using DRNs to support different phases of randomized, multi-site, clinical research. This work supports "real-world" efforts to build a learning health system and will enable others to conduct randomized clinical trial research using a DRN., Competing Interests: This work is supported within the National Institutes of Health (NIH) Health Care Systems Research Collaboratory by the NIH Common Fund through cooperative agreement U24AT009676 from the Office of Strategic Coordination within the Office of the NIH Director. The views presented here are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. JB, BH and RP have no conflicts of interest to disclose; BS reports grants from NIH during the conduct of the study; KM reports grants from PCORI, during the conduct of the study; personal fees from Novartis, outside the submitted work; AH reports grants and personal fees from AstraZeneca, grants and personal fees from Bayer, personal fees from Boehringer Ingelheim, grants from American Regent, grants and personal fees from Novartis, grants and personal fees from Merck, grants from Verily, outside the submitted work; LC reports grants from GlaxoSmithKline, grants from Novartis, grants from Boston Scientific, from St. Jude, outside the submitted work; SR reports grants from GSK, outside the submitted work. There are no other financial relationships with any organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work., (© 2019 Published by Elsevier Inc.)

Objective: Electronic medication lists may be useful in clinical decision support and research, but their accuracy is not well described. Our aim was to assess the completeness of the medication list compared to the clinical narrative in the electronic health record., Methods: We reviewed charts of 30 patients with inflammatory bowel disease (IBD) from each of 6 gastroenterology centers. Centers compared IBD medications from the medication list to the clinical narrative., Results: We reviewed 379 IBD medications among 180 patients. There was variation by center, from 90% patients with complete agreement between the medication list and clinical narrative to 50% agreement., Conclusions: There was a range in the accuracy of the medication list compared to the clinical narrative. This information may be helpful for sites seeking to improve data quality and those seeking to use medication list data for research or clinical decision support.

Past studies have suggested that conditions experienced by women during pregnancy (e.g. obesity and gestational diabetes mellitus (GDM)) may be associated with having a child with autism spectrum disorder (ASD). Our objective was to compare mothers who had a child diagnosed with ASD to mothers of children with a non-ASD developmental disorder (DD) or without any reported DD (controls). To accomplish the objective we collected medical record data from patients who resided in the Cincinnati Children's Hospital Medical Center's (CCHMC) primary catchment area and linked those data to data from birth certificates (to identify risk factors). Two comparison groups were analyzed; one with DD; and the other, controls without a reported ASD or DD. Descriptive statistics and regression analyses evaluated differences. Differences were greater comparing mothers of ASD to controls than comparing ASD to DD. Maternal obesity and GDM were associated with a statistically significant approximately 1.5-fold increased odds of having a child with an ASD. For mothers with both GDM and obesity, the association was twofold for having a child with ASD compared with controls. Maternal obesity and GDM might be associated with an increased risk of ASD in the offspring; however, no difference in risk of ASD according to BMI and GDM was seen when comparing to DD. Autism Res 2016, 9: 829-837,. © 2016 International Society for Autism Research, Wiley Periodicals, Inc., (© 2016 International Society for Autism Research, Wiley Periodicals, Inc.)

The American healthcare system is at a crossroads, and analytics, as an organizational skill, figures to play a pivotal role in its future. As more healthcare systems capture information electronically and as they begin to collect more novel forms of data, such as human DNA, how will we leverage these resources and use them to improve human health at a manageable cost? In this article, we argue that analytics will play a fundamental role in the transformation of the American healthcare system. However, there are numerous challenges to the application and use of analytics, namely the lack of data standards, barriers to the collection of high-quality data, and a shortage of qualified personnel to conduct such analyses. There are also multiple managerial issues, such as how to get end users of electronic data to employ it consistently for improving healthcare delivery, and how to manage the public reporting and sharing of data. In this article, we explore applications of analytics in healthcare, barriers and facilitators to its widespread adoption, and how analytics can help us achieve the goals of the modern healthcare system: high-quality, responsive, affordable, and efficient care.

Background: Eosinophilic esophagitis (EoE) is an emerging chronic inflammatory disease mediated by immune hypersensitization to multiple foods and strongly associated with atopy and esophageal remodeling., Objective: We provide clinical and molecular evidence indicating a high prevalence of EoE in patients with inherited connective tissue disorders (CTDs)., Methods: We examined the rate of EoE among patients with CTDs and subsequently analyzed esophageal mRNA transcript profiles in patients with EoE with or without CTD features., Results: We report a cohort of 42 patients with EoE with a CTD-like syndrome, representing 0.8% of patients with CTDs and 1.3% of patients with EoE within our hospital-wide electronic medical record database and our EoE research registry, respectively. An 8-fold risk of EoE in patients with CTDs (relative risk, 8.1; 95% confidence limit, 5.1-12.9; χ(2)1 = 112.0; P < 10(-3)) was present compared with the general population. Esophageal transcript profiling identified a distinct subset of genes, including COL8A2, in patients with EoE and CTDs., Conclusion: There is a remarkable association of EoE with CTDs and evidence for a differential expression of genes involved in connective tissue repair in this cohort. Thus, we propose stratification of patients with EoE and CTDs into a subset referred to as EoE-CTD., (Copyright © 2013 American Academy of Allergy, Asthma & Immunology. Published by Mosby, Inc. All rights reserved.)

Objective: The objectives of this study were to determine (1) adherence to the immunization schedule for the human papillomavirus quadrivalent vaccine and (2) factors associated with completion of the 3-dose series., Methods: This was a retrospective review of health information records from an academic medical center. The sample included all 9- to 26-year-old female patients who initiated vaccination within 2 years after quadrivalent vaccine availability. Multivariable logistic regression models were estimated to determine associations with completion of the 3-dose series within 7 and 12 months., Results: Among the 3297 female patients who initiated vaccination with human papillomavirus quadrivalent vaccine, 67% self-identified as black and 29% self-identified as white. Fewer than 3% of vaccine doses were received earlier than recommended, but >50% of doses were received late. Completion rates were 14% by 7 months and 28% by 12 months. Independent predictors of completion by 7 months included white versus black race (odds ratio [OR]: 2.04 [95% confidence interval (CI): 1.64-2.56]; P < .001), use of contraception that required intramuscular injections every 3 months (OR: 1.53 [95% CI: 1.12-1.95]; P < .001), and private versus public insurance (OR: 1.31 [95% CI: 1.06-1.63]; P < .05). Age and clinic type were not independent predictors of completion., Conclusions: Adherence to recommended intervals and completion of the vaccine series were low. Lower rates of completion in black patients compared with white patients raises concern that disparities in vaccine completion could exacerbate existing disparities in cervical cancer.

Unlabelled: The purpose of this study was to prospectively assess the effects of two adaptive postprocessing techniques on the evaluation of myocardial function with displacement-encoded magnetic resonance (MR) imaging, including sensitivity for abnormal wall motion, with two-dimensional echocardiography as the reference standard. Sixteen patients (11 men, five women; age range, 26-74 years) and 12 volunteers (six men, six women; age range, 29-53 years) underwent breath-hold MR imaging. Institutional review board approval and informed consent were obtained. Adaptive phase-unwrapping and spatial filtering techniques were compared with conventional phase-unwrapping and spatial filtering techniques. Use of the adaptive techniques led to a reduced rate of failure with the phase-unwrapping technique from 18.9% to 0.6% (P < .001), resulted in lower variability of segmental strain measurements among healthy volunteers (P < .001 to P = .02), and increased the sensitivity of quantitative detection of abnormal segments in patients from 82.5% to 87.7% (P = .034). The adaptive techniques improved the semiautomated postprocessing of displacement-encoded cardiac images and increased the sensitivity of detection of abnormal wall motion in patients., Supplemental Material: http://radiology.rsnajnls.org/cgi/content/full/246/1/229/DC1., (RSNA, 2008)