Privacy-preserving record linkage across disparate institutions and datasets to enable a learning health system: The national COVID cohort collaborative (N3C) experience.

Introduction: Research driven by real-world clinical data is increasingly vital to enabling learning health systems, but integrating such data from across disparate health systems is challenging. As part of the NCATS National COVID Cohort Collaborative (N3C), the N3C Data Enclave was established as a centralized repository of deidentified and harmonized COVID-19 patient data from institutions across the US. However, making this data most useful for research requires linking it with information such as mortality data, images, and viral variants.

Sex differences in COVID-19 symptom severity and trajectories among ambulatory adults.

Background: The ongoing COVID-19 pandemic has led to hundreds of millions of infections worldwide. Although differences in COVID-19 hospitalization rates between males and females have been described, many infections in the general population have been mild, and the severity of symptoms during the course of COVID-19 in non-hospitalized males and females is not well understood.

Methods: We conducted a case-ascertained study to examine household transmission of SARS-CoV-2 infections in Nashville, Tennessee, between April 2020 and April 2021.

Better together: Integrating biomedical informatics and healthcare IT operations to create a learning health system during the COVID-19 pandemic.

The growing availability of multi-scale biomedical data sources that can be used to enable research and improve healthcare delivery has brought about what can be described as a healthcare "data age." This new era is defined by the explosive growth in bio-molecular, clinical, and population-level data that can be readily accessed by researchers, clinicians, and decision-makers, and utilized for systems-level approaches to hypothesis generation and testing as well as operational decision-making. However, taking full advantage of these unprecedented opportunities presents an opportunity to revisit the alignment between traditionally academic biomedical informatics (BMI) and operational healthcare information technology (HIT) personnel and activities in academic health systems.

Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.

Development of evidence-based practice requires practice-based evidence, which can be acquired through analysis of real-world data from electronic health records (EHRs). The EHR contains volumes of information about patients-physical measurements, diagnoses, exposures, and markers of health behavior-that can be used to create algorithms for risk stratification or to gain insight into associations between exposures, interventions, and outcomes. But to transform real-world data into reliable real-world evidence, one must not only choose the correct analytical methods but also have an understanding of the quality, detail, provenance, and organization of the underlying source data and address the differences in these characteristics across sites when conducting analyses that span institutions.