Adoption of Electronic Medical Record-Based Decision Support for Otitis Media in Children

Substantial investment in electronic health records (EHRs) has provided an unprecedented opportunity to use clinical decision support (CDS) to increase guideline adherence. To inform efforts to maximize adoption, we characterized the adoption of an otitis media (OM) CDS system, the impact of performance feedback on adoption, and the effects of adoption on guideline adherence

A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record

Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites

CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record

Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS)

Teaching and Learning of Calculus

This survey focuses on the main trends in the field of calculus education. Despite their variety, the findings reveal a cornerstone issue that is strongly linked to the formalism of calculus concepts and to the difficulties it generates in the learning and teaching process. As a complement to the main text, an extended bibliography with some of the most important references on this topic is included. Since the diversity of the research in the field makes it difficult to produce an exhaustive state-of-the-art summary, the authors discuss recent developments that go beyond this survey and put forward new research questions

Trends in Vaccine Refusal and Acceptance Using Electronic Health Records from a Large Pediatric Hospital Network, 2013–2020: Strategies for Change

Understanding trends in vaccine refusal is critical to monitor as small declines in vaccination coverage can lead to outbreaks of vaccine-preventable diseases. Using electronic heath record (EHR) data from the Children’s Hospital of Philadelphia’s 31 outpatient primary care sites, we created a cohort of 403,448 children less than age 20 years who received at least one visit from 1 January 2013 through 31 December 2020. The sample represented 1,449,061 annualized patient and 181,131 annualized preventive vaccination visits per year. We characterized trends in vaccine refusal and acceptance using a repeated cross-sectional observational analysis of electronic health records (EHR) data using a single annual merged observation measure for patients seen multiple times for preventive healthcare within a calendar year. Refusals were identified for 212,900 annualized patient-visit year observations, which represented 14.6% of annualized patient-visit year observations and 25.1% of annualized vaccine patient-year observations. The odds of having a refusal marker were significantly increased in patients seen in suburban practices (aOR [CI]: 2.35 [2.30–2.40, p < 0.001]), in patients with increased age 11–17 years (aOR [CI]: 3.85 [3.79–3.91], p < 0.001), and those eligible for the VFC program (aOR [CI]: 1.10 [1.08–1.11]. Parental refusal (61.0%) and provider decisions (32.0%) were the most common documented in progress notes for not administering vaccines, whereas contraindications (2.5%) and supply issues (1.8%) were the least common. When offered, vaccine acceptance increased for human papillomavirus, hepatitis B, measles-mumps-rubella-containing and varicella-containing vaccines and decreased for hepatitis A and meningococcal vaccines. Repeated offering of vaccines was central to increasing acceptance, in part due to increased opportunities to address specific concerns

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

BackgroundRapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data capable of recognizing infant sepsis at least 4 hours prior to clinical recognition.Methods and findingsWe performed a retrospective case control study of infants hospitalized ≥48 hours in the Neonatal Intensive Care Unit (NICU) at the Children's Hospital of Philadelphia between September 2014 and November 2017 who received at least one sepsis evaluation before 12 months of age. We considered two evaluation outcomes as cases: culture positive-positive blood culture for a known pathogen (110 evaluations); and clinically positive-negative cultures but antibiotics administered for ≥120 hours (265 evaluations). Case data was taken from the 44-hour window ending 4 hours prior to evaluation. We randomly sampled 1,100 44-hour windows of control data from all times ≥10 days removed from any evaluation. Model inputs consisted of up to 36 features derived from routine EHR data. Using 10-fold nested cross-validation, 8 machine learning models were trained to classify inputs as sepsis positive or negative. When tasked with discriminating culture positive cases from controls, 6 models achieved a mean area under the receiver operating characteristic (AUC) between 0.80-0.82 with no significant differences between them. Including both culture and clinically positive cases, the same 6 models achieved an AUC between 0.85-0.87, again with no significant differences.ConclusionsMachine learning models can identify infants with sepsis in the NICU hours prior to clinical recognition. Learning curves indicate model improvement may be achieved with additional training examples. Additional input features may also improve performance. Further research is warranted to assess potential performance improvements and clinical efficacy in a prospective trial