CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization.

Objective: This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.

Materials And Methods: The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date.

Characteristics and Outcomes of Over a Million Patients with Inflammatory Bowel Disease in Seven Countries: Multinational Cohort Study and Open Data Resource.

Background And Aims: Observational healthcare data are an important tool for delineating patients' inflammatory bowel disease (IBD) journey in real-world settings. However, studies that characterize IBD cohorts typically rely on a single resource, apply diverse eligibility criteria, and extract variable sets of attributes, making comparison between cohorts challenging. We aim to longitudinally describe and compare IBD patient cohorts across multiple geographic regions, employing unified data and analysis framework.

Health-Analytics Data to Evidence Suite (HADES): Open-Source Software for Observational Research.

The Health-Analytics Data to Evidence Suite (HADES) is an open-source software collection developed by Observational Health Data Sciences and Informatics (OHDSI). It executes directly against healthcare data such as electronic health records and administrative claims, that have been converted to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Using advanced analytics, HADES performs characterization, population-level causal effect estimation, and patient-level prediction, potentially across a federated data network, allowing patient-level data to remain locally while only aggregated statistics are shared.

Comparing broad and narrow phenotype algorithms: differences in performance characteristics and immortal time incurred.

When developing phenotype algorithms for observational research, there is usually a trade-off between definitions that are sensitive or specific. The objective of this study was to estimate the performance characteristics of phenotype algorithms designed for increasing specificity and to estimate the immortal time associated with each algorithm. We examined algorithms for 11 chronic health conditions.