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PubMed2026

Robust replication of associations across patient-mediated and provider-sourced EHR data in the All of Us research program.

The All of Us Research Program is assembling a nationwide cohort with electronic health record (EHR) resources through two complementary pathways: healthcare provider organization (HPO)-sourced EHRs and patient-mediated EHR (PME) contributed through patient portal linkages. The comparative research utility of these two data sources has not been systematically evaluated. Here, we compared PME and HPO EHRs with respect to disease prevalence, phenotype-phenotype associations, and replication of established genotype-phenotype associations using data from 19,703 PME and 373,887 HPO participants. We benchmarked disease prevalence against national estimates, conducted phenome-wide association studies for 10 commonly studied diseases, and tested replication of more than 5000 established genotype-phenotype associations across multiple ancestral groups. Disease prevalence was consistently lower in PME than in HPO, although prevalence of most diseases in both cohorts exceeded national estimates. Both data sources reproduced known phenotype-phenotype associations and showed moderate-to-strong concordance in effect sizes across the phenome. The overall genotype-phenotype replication rate was 49.1% (5399/10,999) in HPO and 5.9% (381/6482) in PME across ancestral groups, with effect sizes strongly correlated among well-powered associations (R = 0.84, P < 0.001). To disentangle the impact of sample size from data quality, we performed 1:1 propensity score matching. After matching, the replication gap in genotype-phenotype associations narrowed from 8.3-fold to 1.3-fold, with equivalent replication rates among adequately powered associations and strongly concordant effect sizes; comorbidity patterns were also consistent across all 10 diseases tested. These findings demonstrate that both data sources are valuable for clinical and genomic research and can inform other cohorts integrating provider-derived and patient-mediated EHRs.

Computational biology and bioinformatics↗

PubMed2025

Characterizing trends in clinical genetic testing: A single-center analysis of EHR data from 1.8 million patients over two decades.

A lack of structural data in electronic health records (EHRs) makes assessing the impact of genetic testing on clinical practice challenging. We extracted clinical genetic tests from the EHRs of more than 1.8 million patients seen at Vanderbilt University Medical Center from 2002 to 2022. With these data, we quantified the use of clinical genetic testing in healthcare and described how testing patterns and results changed over time. We assessed trends in types of genetic tests, tracked usage across medical specialties, and introduced a new measure, the genetically attributable fraction (GAF), to quantify the proportion of observed phenotypes attributable to a genetic diagnosis over time. We identified 104,392 tests and 19,032 molecularly confirmed diagnoses. The proportion of patients with genetic testing in their EHRs increased from 1.0% in 2002 to 6.1% in 2022, and testing became more comprehensive with the growing use of multi-gene panels. The number of unique diseases diagnosed with genetic testing increased from 51 in 2002 to 509 in 2022, and there was a rise in the number of variants of uncertain significance. The phenome-wide GAF for 6,505,620 diagnoses made in 2022 was 0.46%, and the GAF was greater than 5% for 74 phenotypes, including pancreatic insufficiency (67%), chorea (64%), atrial septal defect (24%), microcephaly (17%), paraganglioma (17%), and ovarian cancer (6.8%). Our study provides a comprehensive quantification of the increasing role of genetic testing at a major academic medical institution and demonstrates its growing utility in explaining the observed medical phenome.

Humans↗

PubMed2026

An EHR-based framework for modeling growth curves and constructing growth centile charts for genetic disorders.

Growth modeling is central to human genetics, as deviations from typical growth can signal an underlying disorder. In this cohort study, we developed a generalizable framework for generating growth charts across genetic conditions using electronic health records (EHR). Leveraging 22 years of longitudinal EHR data from 452,470 patients across 15 genetic conditions and unaffected individuals, we generated sex- and condition-specific growth charts using Generalized Additive Models for Location, Scale, and Shape, and quantified differences in size, timing, and intensity using SuperImposition by Translation and Rotation (SITAR). SITAR-derived growth parameters showed strong concordance with established annotations in OMIM and Orphanet, and identified previously unreported growth patterns. We stratified cystic fibrosis by CFTR functional class and observed greater growth impairment in individuals with homozygous minimal-function variants compared to those with residual function. This framework provides a generalizable approach for leveraging EHR data to refine genotype-phenotype relationships and enable continuous updating of growth charts across genetic conditions.

Journal Article↗

PubMed2026

A doubly robust framework for addressing outcome-dependent selection bias in multi-cohort EHR studies.

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple clinics/centers with varying selection mechanisms that may depend on the disease/outcome of interest. Traditional inverse-probability-weighted (IPW) methods, based on constructed parametric selection models, often struggle with misspecifications when selection mechanisms vary across cohorts. This paper introduces a new Joint Augmented Inverse Probability Weighted (JAIPW) method, which integrates individual-level data from multiple cohorts collected under potentially outcome-dependent selection mechanisms, with data from an external probability sample. JAIPW offers double robustness by incorporating a flexible auxiliary score model to address potential misspecifications in the selection models. We outline the asymptotic properties of the JAIPW estimator, and our simulations reveal that JAIPW achieves up to 6 times lower relative bias and 5 times lower root mean square error (RMSE) compared to the best performing joint IPW methods under scenarios with misspecified selection models. Applying JAIPW to the Michigan Genomics Initiative (MGI), a multi-clinic EHR-linked biobank, combined with external national probability samples, resulted in cancer-sex association estimates closely aligned with national benchmark estimates. We also analyzed the association between cancer and polygenic risk scores (PRS) in MGI to illustrate a situation where the exposure variable is not measured in the external probability sample.

Selection Bias↗

PubMed2025

Privacy-Enhancing Sequential Learning under Heterogeneous Selection Bias in Multi-Site EHR Data.

OBJECTIVE: To develop privacy-enhancing statistical methods for estimation of binary disease risk model association parameters across multiple electronic health record (EHR) sites with heterogeneous selection mechanisms, without sharing raw individual-level data. We illustrate their utility through a cross-biobank analysis of smoking and 97 cancer subtypes using data from the NIH All of Us (AOU) and the Michigan Genomics Initiative (MGI). MATERIALS AND METHODS: Large-scale biobanks often follow heterogeneous recruitment strategies and store data in separate cloud-based platforms, making centralized algorithms infeasible. To address this, we propose two decentralized sequential estimators namely, Sequential Pseudo-likelihood (SPL) and Sequential Augmented Inverse Probability Weighting (SAIPW) that leverage external population-level information to adjust for selection bias, with valid variance estimation. SAIPW additionally protects against misspecification of the selection model using flexible machine learning based auxiliary outcome models. We compare SPL and SAIPW with the existing Sequential Unweighted (SUW) estimator and with centralized and meta learning extensions of IPW and AIPW in simulations under both correctly specified and misspecified selection mechanisms. We apply the methods to harmonized data from MGI ( n = 50,935) and AOU ( n = 241,563) to estimate smoking-cancer associations. RESULTS: In simulations, SUW exhibited substantial bias and poor coverage. SPL and SAIPW yielded unbiased estimates with valid coverage probabilities under correct model specification, with SAIPW remaining robust under selection model misspecification. Both approaches showed no notable efficiency loss relative to centralized methods. Meta-learning methods were efficient for large sites but failed in settings with small cohort sizes and rare outcome prevalence. In real-data analysis, strong associations were consistently identified between smoking and cancers of the lung, bladder, and larynx, aligning with established epidemiological evidence. CONCLUSION: Our framework enables valid, privacy-enhancing inference across EHR cohorts with heterogeneous selection, supporting scalable, decentralized research using real-world data.

Journal Article↗

PubMed2026

RESCUE: An end-to-end multi-agent LLM system for proactive rare-disease patient screening in the EHR.

BACKGROUND: Rare diseases affect a significant portion of the global population, yet patients often endure a lengthy diagnostic odyssey, frequently missing the opportunity for timely diagnoses with exome or genome sequencing (ES/GS). Existing informatics tools often rely on pre-identified patients or rigid, institution-specific rule sets, failing to address the broader operational question of clinical utility and feasibility. METHODS: We introduce RESCUE (Rare Disease Detection and Escalation Support via a Learning Health System), an end-to-end, multi-agent LLM-powered workflow designed for proactive rare-disease diagnosis across the entire electronic health record (EHR). RESCUE utilizes a team of specialized agents including Ontology, Modeling, Screening, and Review, to automate the screening process to identify candidates for diagnostic testing based on their clinical features. The Ontology Agent classifies clinical data into a four-tier genetic-evidence taxonomy; the Modeling Agent builds a positive-unlabeled (PU) XGBoost classifier to identify potential cases; the Screening Agent applies these models across the EHR population; and the Review Agent evaluates candidates by sampling clinical notes to ensure medical necessity and operational feasibility for genomic testing. RESULTS: Using electronic medical record data from a pediatric hospital, our retrospective evaluation on a holdout set (n=12,591) demonstrates strong discrimination between patients who received diagnostic genomic testing and those who did not (AUC 0.808). Of nearly 500,000 patients in the institutional base, 175,842 met inclusion criteria for screening; among these, RESCUE-flagged candidates were 7.4-fold more likely to receive subsequent genomic assessments compared to controls. Blinded manual chart reviews confirmed that RESCUE identifies previously missed, medically appropriate patients for ES/GS with 80% precision, while simultaneously accounting for prior testing history. CONCLUSIONS: By decoupling expert roles into modular agents, RESCUE offers a flexible, scalable, and adaptable framework for screening patients for rare-disease diagnostic genomic testing. This approach overcomes the limitations of traditional rule-based methods and provides a reproducible, agentic pathway to reduce diagnostic delays and improve patient care at an institutional scale.

Journal Article↗

PubMed2026

Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.

Electronic health records (EHRs) contain extensive multidimensional patient data, presenting challenges for the discovery of novel and meaningful clinical patterns. Unsupervised clustering of high-dimensional clinical data holds great potential for identifying novel clinical patterns. Here, we performed unsupervised clustering and characterized 100,272 patients in the Electronic Medical Records and GEnomics (eMERGE) Network. We identified 70 clusters defined by distinct comorbidity patterns. Meanwhile, age and sex are also strongly associated with patient stratification, influencing phenotype prevalence and onset time. Notably, phenotype onset time accurately predicted chronological age and was significantly associated with overall mortality risk. Besides age and sex, we assessed the contribution of genetic variation to phenotype development and observed evidence of cross-phenotype associations influencing cluster membership and comorbidity patterns. However, the role of genetics recedes during aging. We also identified several high-risk clusters with elevated Charlson Comorbidity Index (CCI) scores and validated these findings in an independent cohort. Further analysis of these clusters revealed phenotypes linked to premature aging and highlighted a survival selection among older participants in observational studies. Overall, this study enables phenome-wide unsupervised patient stratification for multimorbidity discovery in largely unannotated clinical data, offering valuable insights into patient stratification, comorbidity analysis, aging, and health outcomes.

Journal Article↗

PubMed2025

Integrating explainable AI with multiomics systems biology and EHR data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health record (EHR) data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; nine tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations (SHAP) identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct "subtissues" (clusters of samples); and gene-gene co-expression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six FDA-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large U.S. de-identified insurance-claims database (n = 364733), exposure to promethazine, one of the candidate drugs, was associated with a 57-62 % lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both p < 0.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multi-omics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Computational Biology↗

PubMed2026

Feasibility of implementation, diagnostic accuracy, and end-user impact of an electronic health record (EHR)-based ureteral stent tracking tool in a pediatric population.

INTRODUCTION & OBJECTIVES: Ureteral stent tracking systems have reduced stent retention in adults, but their accuracy and impact in pediatrics have been minimally explored. With low event rates in children, such tools may yield high false positives, raising questions on balancing event prevention with provider burden. We aimed to evaluate the feasibility, diagnostic accuracy, and end-user impact of an Electronic Surveillance Tool for Evaluating Nephroureteral stent Tracking (eSTENT) at our institution. STUDY DESIGN: eSTENT, implemented in 1/2024, flags ureteral stents at risk for retention based on implant documentation, expected explant date, and explant documentation. Monthly reports are generated for stents missing explant documentation. We retrospectively evaluated the diagnostic performance of eSTENT from 1/2024-8/2025 at our pediatric hospital. A usability survey including a validated 1-7 implementation score (higher = easier implementation) was distributed to pediatric urologists and operating room nurses. RESULTS: Of 172 cases with ureteral stent placement, eSTENT flagged 28 events (16%) in 24 patients. Of these, 26 represented documentation gaps where explant had been appropriate. Two flags had no documentation of explant, representing near miss events that were identified. No retained stents occurred, consistent with high sensitivity and modest specificity. There were no flags in the last 6 months of the study period. Survey response rate was 100% for surgeons and 55% for nurses. Before eSTENT, stents were not routinely tracked. All surgeons and 93% of nurses reported no added burden, despite occasional misidentification of retained stents. Three surgeons found eSTENT beneficial, four were neutral, and free-text responses generally cited eSTENT's "fail safe" nature as positive. Nurses suggested improvements, including user support and integrated documentation reminders. The average implementation score among both groups was 6/7, indicating easy adoption. DISCUSSION: While the impact of stent tracking tools in adult literature has been positive, our study emphasizes the feasibility of broader adoption at a pediatric hospital. Integration of eSTENT may avoid the potentially devastating consequences of a retained stent. Prioritizing sensitivity over specificity appears acceptable for a "never event" in patient safety. Our study is limited by the retrospective nature of data collection and survey bias. CONCLUSIONS: Though no stents were retained in the study period, eSTENT appropriately flagged two cases without added burden to most end-users. Further optimization is warranted, but adoption in pediatric centers may enhance care reliability.

Humans↗

PubMed2024

Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores.

The All of Us Research Program (AoU) is an initiative designed to gather a comprehensive and diverse dataset from at least one million individuals across the USA. This longitudinal cohort study aims to advance research by providing a rich resource of genetic and phenotypic information, enabling powerful studies on the epidemiology and genetics of human diseases. One critical challenge to maximizing its use is the development of accurate algorithms that can efficiently and accurately identify well-defined disease and disease-free participants for case-control studies. This study aimed to develop and validate type 1 (T1D) and type 2 diabetes (T2D) algorithms in the AoU cohort, using electronic health record (EHR) and survey data. Building on existing algorithms and using diagnosis codes, medications, laboratory results, and survey data, we developed and implemented algorithms for identifying prevalent cases of type 1 and type 2 diabetes. The first set of algorithms used only EHR data (EHR-only), and the second set used a combination of EHR and survey data (EHR+). A universal algorithm was also developed to identify individuals without diabetes. The performance of each algorithm was evaluated by testing its association with polygenic scores (PSs) for type 1 and type 2 diabetes. We demonstrated the feasibility and utility of using AoU EHR and survey data to employ diabetes algorithms. For T1D, the EHR-only algorithm showed a stronger association with T1D-PS compared to the EHR + algorithm (DeLong p-value = 3 × 10-5). For T2D, the EHR + algorithm outperformed both the EHR-only and the existing T2D definition provided in the AoU Phenotyping Library (DeLong p-values = 0.03 and 1 × 10-4, respectively), identifying 25.79% and 22.57% more cases, respectively, and providing an improved association with T2D PS. We provide a new validated type 1 diabetes definition and an improved type 2 diabetes definition in AoU, which are freely available for diabetes research in the AoU. These algorithms ensure consistency of diabetes definitions in the cohort, facilitating high-quality diabetes research.

Humans↗

PubMed2026

Menopause in the All of Us Research Program: a descriptive summary of electronic health record and survey response across sociodemographic characteristics.

OBJECTIVES: Menopause is a significant physiological transition with implications for health outcomes (eg, cardiometabolic disease), yet gaps remain in understanding this transition, including how menopause timing and type influence health outcomes. Large-scale cohort studies in midlife (age=40-60) females, including the All of Us Research Program (AoURP), provide opportunities to study menopause across diverse populations and data modalities. We characterized menopause-related data in AoURP, focusing on age distributions and concordance between electronic health record (EHR) diagnosis codes and survey responses. METHODS: We analyzed menopause-related surveys, EHR diagnostic codes, and genomic data among ~396,000 AoURP female participants. We summarized menopause-related variables across data sources, evaluated overlap between survey, EHR, and genomic data sets, and described age distributions overall and across sociodemographic characteristics. RESULTS: Among ~396,000 females, survey responses captured ~193,000 menopause observations, nearly seven times more than EHR diagnoses (~28,000), suggesting under-ascertainment in EHR data. Nearly all females (~99%) with an EHR menopause diagnosis reported menopause in the survey. Approximately 22,000 participants had overlapping menopause-related EHR, survey, and genomic data. Survey age patterns matched expectations, with participants predominantly <40 years reporting premenopausal status and those >60 years reporting postmenopausal status. A small subset with age >70 years (N≈1,700; 4%) reported no menopause, suggesting response or recall bias. EHR menopause codes were concentrated after age 45 years, with a notable spike at age 65. Modest differences in survey-based menopause age distributions were observed across sociodemographic characteristics (eg, race and ancestry). CONCLUSIONS: These findings inform sampling strategies, power calculations, phenotype definition, and study design for menopause research using AoURP data.

Age↗

PubMed2025

Negative descriptors in electronic health records of patients with diabetes.

BACKGROUND: Negative descriptors in electronic health records (EHR) contribute to worse health outcomes; studies show they are also more prevalent in EHRs of women and racial minorities and affect downstream research biases. Similar and unique patterns of negative descriptors may also exist in the records of blind patients, including those with diabetic retinopathy. Diabetic retinopathy is a preventable but leading cause of blindness in the US that is disproportionally high among women and racial and ethnic minorities. METHODS: Using EHR from a large medical center, we created "matched" cohorts of patients with a type 2 diabetes-only diagnosis and patients with a diagnosis of diabetic retinopathy. We identified previously used and new, disability and patient-related negative descriptors and assessed patterns of biased language in the EHR, comparing patients by retinopathy diagnosis (yes/no), and changes in patterns of language usage pre- and post- the retinopathy diagnosis. We also assessed differences between patients with type 2 diabetes at the intersection of blindness (ie, retinopathy diagnosis) and self-reported gender and race and ethnicity marginalization. RESULTS: The EHRs of patients with diabetic retinopathy were significantly more likely than those of patients with diabetes-only diagnoses to contain biased language, across queried negative descriptors. The biasing language was consistently more prevalent in EHRs of patients with diabetic retinopathy identifying as women, Black/African Americans and Hispanic compared to White men and more likely to occur following patients' retinopathy diagnosis. CONCLUSIONS: Our study indicates the presence of both disability- and intersectional biases in EHRs. We discuss findings' implications and suggest steps to address them.

Humans↗

PubMed2026

Real-World Treatment Patterns and Clinical Outcomes After First-Line Therapy in Patients with KRAS G12C-Mutant Advanced Non-Small-Cell Lung Cancer in the United States.

BACKGROUND: Approximately 13% of NSCLC cases have KRAS G12C mutations. As therapeutic strategies targeting KRAS G12C-mutant NSCLC evolve, it is important to understand clinical presentation and current outcomes for these patients. METHODS: This retrospective study used data from two US nationwide databases, an electronic health records (EHR) database and a clinico-genomic database (CGDB) of EHR data linked to data from comprehensive genomic profiling tests. Eligible patients had advanced NSCLC, initiated first-line therapy from August 2018 to December 2022, and had KRAS test results. Clinicopathologic characteristics, treatments, real-world progression-free survival (rwPFS), and overall survival (OS) were analyzed. RESULTS: There were 1227 patients with KRAS G12C-mutant NSCLC in the EHR database and 447 in the CGDB. First-line regimen was platinum-based chemotherapy plus pembrolizumab for 46% and pembrolizumab monotherapy for 20%. Less than 40% of patients received second-line therapy. Median (95% CI) OS for KRAS G12C-mutant NSCLC patients in the EHR was 17.0 (15.2-18.9) months. Variables significantly associated with shorter OS included PD-L1 <1%, brain metastases, STK11 co-mutation, and poor performance status. Patients treated with platinum-based chemotherapy plus pembrolizumab had median rwPFS of 5.3 (4.5-7.3) months and OS of 12.8 (11.1-17.3) months in the CGDB; median OS was 15.6 (12.5-18.6) months in the EHR. Patients with PD-L1 ≥ 50% treated with pembrolizumab monotherapy had median rwPFS of 4.6 (3.0-15.6) months and OS of 20.4 (10.3-38.5) months in the CGDB; median OS was 22.1 (18.7-30.7) in the EHR. CONCLUSIONS: These data provide a real-world benchmark of outcomes for patients with KRAS G12C-mutant NSCLC receiving the current standard of care and indicate an unmet need for more effective first-line therapies.

KRAS G12C↗

PubMed2023

Phenotypic presentation of Mendelian disease across the diagnostic trajectory in electronic health records.

PURPOSE: To investigate the phenotypic presentation of Mendelian disease across the diagnostic trajectory in the electronic health record (EHR). METHODS: We applied a conceptual model to delineate the diagnostic trajectory of Mendelian disease to the EHRs of patients affected by 1 of 9 Mendelian diseases. We assessed data availability and phenotype ascertainment across the diagnostic trajectory using phenotype risk scores and validated our findings via chart review of patients with hereditary connective tissue disorders. RESULTS: We identified 896 individuals with genetically confirmed diagnoses, 216 (24%) of whom had fully ascertained diagnostic trajectories. Phenotype risk scores increased following clinical suspicion and diagnosis (P < 1 × 10-4, Wilcoxon rank sum test). We found that of all International Classification of Disease-based phenotypes in the EHR, 66% were recorded after clinical suspicion, and manual chart review yielded consistent results. CONCLUSION: Using a novel conceptual model to study the diagnostic trajectory of genetic disease in the EHR, we demonstrated that phenotype ascertainment is, in large part, driven by the clinical examinations and studies prompted by clinical suspicion of a genetic disease, a process we term diagnostic convergence. Algorithms designed to detect undiagnosed genetic disease should consider censoring EHR data at the first date of clinical suspicion to avoid data leakage.

Humans↗

PubMed2026

Associations between smart infusion pump-electronic health record interoperability and healthcare outcomes: A systematic review.

OBJECTIVE: This study synthesized available evidence on the associations between smart infusion pump-electronic health record (EHR) interoperability and healthcare outcomes. METHODS: A systematic review of PubMed, CINAHL, Embase, and Scopus databases identified 901 records, which were imported into Rayyan® for duplicate removal, independent screening by three reviewers, and resolution of discrepancies. Eligible studies were peer-reviewed, data-driven, and reported associations between smart infusion pump-EHR interoperability and healthcare outcomes. Studies focused solely on technical validation or interoperability prototypes were excluded. A backward citation search identified additional studies. Two reviewers independently extracted and cross-validated study characteristics using standardized templates. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Twenty records of 14 full-text studies and 6 conference proceedings were included. Most records reported positive associations between smart infusion pump-EHR interoperability and outcomes related to safety (e.g., medication administration errors, safety-reported events, pump alerts, and compliance with interoperability and drug library), operational efficiency (e.g., programming and documentation time and technical issues), financial performance (e.g., charges captured, and cost avoided), and user experience domains. Most studies used observational designs, reflecting real-world interoperability implementations, where controlling confounding factors is challenging. Limited reporting of baseline characteristics, pump type, and sample sizes limited comparability across studies. CONCLUSIONS: Smart infusion pump-EHR interoperability was associated with improvements in patient safety, efficiency, charge capture, and user experience, with variable findings across studies. Future research should use rigorous methodologies and standardized measures, examine relationships across outcome domains, assess limitations of pump-EHR interoperability, and evaluate underexplored outcomes, including team communication, cognitive workload, and AI-enabled pumps. IMPLICATIONS FOR CLINICAL PRACTICE: Interoperability should be viewed as a component of a broader sociotechnical system, in which technology, user, workflow, clinical content, and organizational practices collectively determine overall effectiveness.

Humans↗

PubMed1977

Influence of intrinsic sympathomimetic activity and cardioselectivity on beta adrenoceptor blockade.

Dose-response curves for propranolol and oxprenolol were studied in healthy volunteers, with a standardized excercise test and percentage reduction in excercise heart rate (EHR) as the index of drug effect. The dose-response curves obtained were compared with similar curves previously reported for sotalol, practolol, and atenolol with identical experimental methods. Two distinct types of response were identified: in the first, shown by propranolol and sotalol, increasing doses of the beta adrenoceptor-blocking drug continued to produce increasing effects to the limits of the dose levels examined; with the second (oxprenolol and practolol), increasing the dose initially resulted in substantial increase in effect but subsequently larger doses produced almost no increase in effect. Consideration of the additional properties of these beta adrenoceptor-blocking drugs revealed that both practolol and oxprenolol have intrinsic sympathomimetric activity (ISA), whereas propranolol and sotalol do not. In addition, practolol is cardioselective. Further investigation of the possible influence of ISA or cardioselectivity on beta adrenoceptor-blocking activity was undertaken by studying the effects of combinations of drugs on EHR. Sotalol produced greater effect when given 2 hr after sotalol, oxprenolol, practolol, or atenolol. When oxprenolol was given after sotalol or oxprenolol, or practolol was given after sotalol or practolol, there was no further increase in percentage reduction in EHR. When atenolol was given, the combinations of sotalol and atenolol together with two doses either of sotalol or atenolol all induced increases and similar final percentage reductions in EHR. Thus atenolol induces effects like those of sotalol, which are quite different from those of oxprenolol or practolol. The presence or absence of ISA would appear to be the important difference between these two groups of drugs: ISA would, therefore, appear to be demonstrated in man by flattening of the dose-response curves with exercise.

Adrenergic beta-Antagonists↗

PubMed2026

Comparison of summative assessments between simulated electronic health records versus traditional paper-based patient cases: A non-inferiority randomized controlled trial.

INTRODUCTION: Electronic health records are fundamental to contemporary pharmacy practice, yet evidence supporting their use in pharmacy education is lacking. This single-center, non-inferiority randomized controlled trial with blinded outcome assessment evaluated whether delivering patient cases via a simulated academic EHR (aEHR) was non-inferior to a traditional paper-based format in student exam performance. METHODS: 53 third-year PharmD students at the University of British Columbia were randomized 1:1 to complete a mock summative examination using either the aEHR or paper-based case delivery, stratified by self-reported EHR comfort level. The primary outcome was mean written exam score (%). Non-inferiority was pre-specified at a margin of 14%. Adjusted linear regression was used for the primary analysis, with a multiple imputation sensitivity analysis. Student perceptions were explored through post-exam focus groups analyzed using inductive thematic analysis. RESULTS: 42 students (21 per group) completed the exam and were included in the primary analysis. Mean scores were 66% (SD 11) in the aEHR group and 68% (SD 10) in the paper group. The adjusted mean difference (paper minus aEHR) was -2.2% (95% CI -9.2% to +4.8%), satisfying non-inferiority but not superiority. Sensitivity analysis (n = 53) yielded consistent results (-2.3%; 95% CI -7.1% to +4.1%). Focus groups revealed initial student anxiety with the aEHR but recognized its alignment with clinical practice. DISCUSSION: These findings support the feasibility of integrating simulated EHRs into summative pharmacy assessments without compromising performance. CONCLUSION: Simulated EHRs are a non-inferior assessment medium compared with paper-based formats and represent a viable step toward technology-driven pharmacy practice environments.

Humans↗

PubMed2025

Imprecision medicine: Systematic gaps in reporting variants of uncertain significance (VUS) and their reclassifications.

PURPOSE: Variants of uncertain significance (VUS) are frequently encountered during clinical genetic testing. To explore the clinical burden of VUS, we developed the Brotman Baty Institute Clinical Variant Database, which is an electronic health record (EHR)-linked database of clinical germline genetic variant information from patients with rare genetic disorders seen at 2 tertiary academic medical centers. METHODS: We retrospectively reviewed EHRs and genetic testing reports from 5158 patients seen across diverse adult genetics practices at these institutions from 2015 to 2024. We also compared these EHR-based variant classifications with those in ClinVar. RESULTS: The number of reported VUS relative to pathogenic or likely pathogenic variants can vary by over 14-fold depending on the primary indication for genetic testing and 3-fold depending on self-reported race. Furthermore, at least 1.6% of variant classifications used in the EHR for clinical care are outdated based on ClinVar variant classifications, including 26 instances in which the testing lab updated ClinVar, but the reclassification was never communicated to the patient. CONCLUSION: Our findings reveal that the clinical burden of VUS in adult medical genetics is unequally distributed across patients. We also highlight a deficiency in existing systems for communicating variant reclassifications to ClinVar, patients, and providers.

Humans↗