Objectives: To explore expert perspectives on risks associated with the pediatric electronic health record (EHR) for intimate partner violence (IPV) survivors and their children and to identify strategies that may mitigate these risks.
Methods: We conducted semistructured interviews with multidisciplinary pediatric IPV experts (nursing, physicians, social workers, hospital security, IPV advocates) recruited via snowball sampling. We coded interview transcripts using thematic analysis, then consolidated codes into themes.
Results: Twenty-eight participants completed interviews. Participants identified the primary source of risk as an abuser's potential access to a child's EHR by legal and illegal means. They noted that abuser's access to multiple pediatric EHR components (eg, online health portals, clinical notes, contact information) may result in escalated violence, stalking, and manipulation of IPV survivors. Suggested risk mitigation strategies included limited and coded documentation, limiting EHR access, and discussing documentation with the IPV survivor. Challenges to using these strategies included healthcare providers' usual practice of detailed documentation and that information documented may confer both risk and benefit concurrently. Reported potential benefits of the pediatric EHR for IPV survivors included ensuring continuity of care, decreasing need to repeatedly talk about trauma histories, and communication of safety plans.
Conclusions: Our findings suggest the pediatric EHR may confer both risks and benefits for IPV survivors and their children. Further work is needed to develop best practices to address IPV risks related to the pediatric EHR, to ensure consistent use of these practices, and to include these practices as standard functionalities of the pediatric EHR.
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http://dx.doi.org/10.1016/j.acap.2021.08.013 | DOI Listing |
CJC Open
January 2025
Genetics and Genome Biology, Research Institute, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, Ontario, Canada.
Sudden cardiac death is a leading cause of mortality in children with hypertrophic cardiomyopathy (HCM). The PRecIsion Medicine in CardiomYopathy consortium developed a validated tool (PRIMaCY) for sudden cardiac death risk prediction to help with implantable cardioverter defibrillator shared decision-making, as recommended by clinical practice guidelines. The mplemeting a udden Cardiac Dath isk Assessment ool in hildhood (INSERT-HCM) study aims to implement PRIMaCY into electronic health records (EHRs) and assess implementation determinants and outcomes.
View Article and Find Full Text PDFPatient Educ Couns
January 2025
Department of Education Studies, University of Bologna, Via Filippo Re 6, Bologna, 40126, Italy. Electronic address:
Objective: Electronic health records (EHRs) have increasingly become integral to contemporary medical consultations, including pediatric care. This study aims at exploring the interactional use of the EHR during naturally occurring pediatric well-child visits, focusing specifically on how pediatricians and parents manage knowledge concerning infants' growth inscribed in the EHR.
Methods: Conversation analysis is used to analyze 23 video-recorded Italian well-child visits involving two pediatricians and twenty-two families with children aged 0-18 months.
Stat Med
February 2025
Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, Pennsylvania.
An important aspect of precision medicine focuses on characterizing diverse responses to treatment due to unique patient characteristics, also known as heterogeneous treatment effects (HTE) or individualized treatment effects (ITE), and identifying beneficial subgroups with enhanced treatment effects. Estimating HTE with right-censored data in observational studies remains challenging. In this paper, we propose a pseudo-ITE-based framework for analyzing HTE in survival data, which includes a group of meta-learners for estimating HTE, a variable importance metric for identifying predictive variables to HTE, and a data-adaptive procedure to select subgroups with enhanced treatment effects.
View Article and Find Full Text PDFEClinicalMedicine
February 2025
Division of Respiratory Medicine, Department of Pediatrics, University of California San Diego, Rady Children's Hospital of San Diego, San Diego, CA, USA.
Background: Children from racial and ethnic minority groups are at greater risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but it is unclear whether they have increased risk for post-acute sequelae of SARS-CoV-2 (PASC). Our objectives were to assess whether the risk of respiratory and neurologic PASC differs by race/ethnicity and social drivers of health.
Methods: We conducted a retrospective cohort study of individuals <21 years seeking care at 24 health systems across the U.
JAMIA Open
February 2025
Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States.
Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).
Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.
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