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Absolute risk models estimate an individual's future disease risk over a specified time interval. Applications utilizing server-side risk tooling, the R-based iCARE (R-iCARE), to build, validate, and apply absolute risk models, face limitations in portability and privacy due to their need for circulating user data in remote servers for operation. We overcome this by porting iCARE to the web platform.

[This corrects the article DOI: 10.1093/jamiaopen/ooae039.].

Anaphylaxis is a severe life-threatening allergic reaction, and its accurate identification in healthcare databases can harness the potential of "Big Data" for healthcare or public health purposes.

Decision support can improve shared decision-making for breast cancer treatment, but workflow barriers have hindered widespread use of these tools. The goal of this study was to understand the workflow among breast cancer teams of clinicians, patients, and their family caregivers when making treatment decisions and identify design guidelines for informatics tools to better support treatment decision-making.

To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms.

To describe development and application of a checklist of criteria for selecting an automated machine learning (Auto ML) platform for use in creating clinical ML models.

To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data.

To address database interoperability challenges to improve collaboration among disparate organizations.

This paper reports on a mixed methods formative evaluation to support the design and implementation of information technology (IT) tools for a primary care weight management intervention delivered through the patient portal using primary care staff as coaches.

To evaluate Phenotype Execution and Modelling Architecture (PhEMA), to express sharable phenotypes using Clinical Quality Language (CQL) and intensional Systematised Nomenclature of Medicine (SNOMED) Clinical Terms (CT) Fast Healthcare Interoperability Resources (FHIR) valuesets, for exemplar chronic disease, sociodemographic risk factor, and surveillance phenotypes.