Factors impacting sleep center no-show rates post hospital discharge utilizing geospatial coding in Appalachia.

Study Objectives: Screening for early detection of sleep-disordered breathing (SDB) in hospitalized patients has been shown to reduce readmission rates. However, post-discharge polysomnography for confirmation of diagnosis is required. We analyzed factors for "no-shows" using geospatial techniques.

Leveraging a global, federated, real-world data network to optimize investigator-initiated pediatric clinical trials: the TriNetX Pediatric Collaboratory Network.

Objective: Clinical research networks facilitate collaborative research, but data sharing remains a common barrier.

Materials And Methods: The TriNetX platform provides real-time access to electronic health record (EHR)-derived, anonymized data from 173 healthcare organizations (HCOs) and tools for queries and analysis. In 2022, 4 pediatric HCOs worked with TriNetX leadership to found the Pediatric Collaboratory Network (PCN), facilitated via a multi-institutional data-use agreement (DUA).

Enhanced SARS-CoV-2 case prediction using public health data and machine learning models.

Objectives: The goal of this study is to propose and test a scalable framework for machine learning (ML) algorithms to predict near-term severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases by incorporating and evaluating the impact of real-time dynamic public health data.

Materials And Methods: Data used in this study include patient-level results, procurement, and location information of all SARS-CoV-2 tests reported in West Virginia as part of their mandatory reporting system from January 2021 to March 2022. We propose a method for incorporating and comparing widely available public health metrics inside of a ML framework, specifically a long-short-term memory network, to forecast SARS-CoV-2 cases across various feature sets.

Poverty and population health - The need for A Paradigm shift to capture the working poor and better inform public health planning.

Background: Community-level socioeconomic disparities have a significant impact on an individual's health and overall well-being. However, current estimates for poverty threshold, which are often used to assess community-level socioeconomic status, do not account for cost-of-living differences or geography variability. The goals of this study were to compare geographic county-level overlap and gaps in access to care for households within poverty and working poor designations.

Assessing the effects of therapeutic combinations on SARS-CoV-2 infected patient outcomes: A big data approach.

Background: The COVID-19 pandemic has demonstrated the need for efficient and comprehensive, simultaneous assessment of multiple combined novel therapies for viral infection across the range of illness severity. Randomized Controlled Trials (RCT) are the gold standard by which efficacy of therapeutic agents is demonstrated. However, they rarely are designed to assess treatment combinations across all relevant subgroups.