Building a collaborative cloud platform to accelerate heart, lung, blood, and sleep research.

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Racial and ethnic disparities in the observed COVID-19 case fatality rate among the U.S. population.

PURPOSE: During the initial 12 months of the pandemic, racial and ethnic disparities in COVID-19 death rates received considerable attention but it has been unclear whether disparities in death rates were due to disparities in case fatality rates (CFRs), incidence rates or both. We examined differences in observed COVID-19 CFRs between U.S. White, Black/African American, and Latinx individuals during this period. METHODS: Using data from the COVID Tracking Project and the Centers for Disease Control and Prevention COVID-19 Case Surveillance Public Use dataset, we calculated CFR ratios comparing Black and Latinx to White individuals, both overall and separately by age group. We also used a model of monthly COVID-19 deaths to estimate CFR ratios, adjusting for age, gender, and differences across states and time. RESULTS: Overall Black and Latinx individuals had lower CFRs than their White counterparts. However, when adjusting for age, Black and Latinx had higher CFRs than White individuals among those younger than 65. CFRs varied substantially across states and time. CONCLUSIONS: Disparities in COVID-19 case fatality among U.S. Black and Latinx individuals under age 65 were evident during the first year of the pandemic. Understanding racial and ethnic differences in COVID-19 CFRs is challenging due to limitations in available data.

Economical Utilization of Health Information with Learning Healthcare System Data Commons By Thomas

Finding, accessing, sharing, and analyzing patient data from a clinical setting for collaborative research has continually proven to be a challenge in healthcare organizations.The human and technological architecture required to perform these services exist at the largest academic institutions but are usually under-funded.At smaller, less academically focused healthcare organizations across the United States, where the majority of care is delivered, they are generally absent.Here we propose a solution called the Learning Healthcare System Data Commons where cost is usage-based and the most basic elements are designed to be extensible, allowing it to evolve with the changing landscape of healthcare.Herein we also discuss our reference implementation of this platform tailored specifically for operational sustainability and governance using the data generated in a hospital setting for research, quality, and educational purposes. Introduction Information management professionals within healthcare organizations navigate a high degree of complexity for each project and for each data source used for research and quality improvement services.?ata and data policy must be governed tightly, consistently, and transparently to meet the expectations of patients and to comply with the high ethical and legal standards in the healthcare industry.2Even prior to the pandemic, access and sharing of patient data has been of paramount importance to assess current status of medical knowledge, as well as to accelerate clinical research related to diagnosis, prognosis, and therapeutic intervention in the context of cancer care;complex, or rare disease;and in the face of rapidly changing technologies for telehealth, surveillance, engagement, and intervention.3,4 The COVID-19 pandemic has highlighted the need for unified and harmonized data sets. The diversity of patients' current health and medical history relative to various viral strains presents issues for all medical research institutions both in the capacity to access data in real time and the costs to maintain such flexible, agile analytics environments. Implementation Data Assets, and Assets Loaded (Counts of Files by Type) Rush University, operating as a major medical hospital in a diverse major city, is home to diverse troves of multimodal (i.e., wholly different information categories: medical images, genomic sequences, and clinical records) diagnostic and medical treatment outcomes data assets.