Publications by authors named "S Brett Heavner"
A growing body of critical care research draws on real-world data from electronic health records (EHRs). The bedside clinician has myriad data sources to aid in clinical decision-making, but the lack of data sharing and harmonization standards leaves much of this data out of reach for multi-institution critical care research. The Society of Critical Care Medicine (SCCM) Discovery Data Science Campaign convened a panel of critical care and data science experts to explore and document unique advantages and opportunities for leveraging EHR data in critical care research.
View Article and Find Full Text PDFArticle Synopsis
- Manual data extraction from electronic health records is resource-intensive, and automating this process can alleviate burdens and enhance research capabilities.
- The study assesses the effectiveness of automated data extraction in a large cohort of adult COVID-19 patients, comparing it with traditional manual methods.
- Results show that automated extraction achieves nearly perfect agreement for most categorical variables and very high correlations for continuous variables, indicating its reliability and feasibility.
During the COVID-19 pandemic, healthcare workers (HCW) were categorized as "essential" and "non-essential", creating a division where some were "locked-in" a system with little ability to prepare for or control the oncoming crisis. Others were "locked-out" regardless of whether their skills might be useful. The purpose of this study was to systematically gather data over the course of the COVID-19 pandemic from HCW through an interprofessional lens to examine experiences of locked-out HCW.
View Article and Find Full Text PDFArticle Synopsis
- COVID-19 underscored the importance of real-world data (RWD) for clinical and policy decision-making in critical care, emphasizing the need for effective data analysis.
- Extracting quality RWD from electronic health records (EHRs) requires significant infrastructure and resources, which prompted the development of customizable public tools for data harmonization.
- The CURE ID platform facilitates access to challenging clinical case reports and repurposed treatments, enhancing collaboration between the National Institutes of Health and the Food and Drug Administration for improved critical care outcomes.
Objectives: We sought to identify the most common diagnostic categories linked to dispensed opioid prescriptions among children 1-36 months old and changes in patterns over the years 2000 to 2017.
Methods: This study used South Carolina's Medicaid claims data of pediatric dispensed outpatient opioid prescriptions between 2000 and 2017. The major opioid-related diagnostic category (indication) for each prescription was identified using visit primary diagnoses and the Clinical Classification System (AHRQ-CCS) software.