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To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.

The overuse of antibiotics for acute otitis media (AOM) in children is a healthcare quality issue in part arising from conflicting parent and physician understanding of the risks and benefits of antibiotics for AOM. Our objective was to develop a conversation aid that supports shared decision making (SDM) with parents of children who are diagnosed with non-severe AOM in the acute care setting.

To determine utilization and impacts of a mobile electronic clinical decision support (mECDS) on pediatric asthma care quality in emergency department and inpatient settings.

The objective of this study is to describe the algorithm and technical implementation of a mobile app that uses adaptive testing to assess an efficient mobile app for the diagnosis of delirium.

We examined correlates of registration and utilization of the Veteran Health Administration's (VHA) personal health record (PHR), My HealtheVet (MHV), among a national cohort of veterans living with HIV.

Delivering clinical decision support (CDS) at the point of care has long been considered a major advantage of computerized physician order entry (CPOE). Despite the widespread implementation of CPOE, medication ordering errors and associated adverse events still occur at an unacceptable level. Previous attempts at indication- and kidney function-based dosing have mostly employed intrusive CDS, including interruptive alerts with poor usability. This descriptive work describes the design, development, and deployment [...]

To outline the development of a software solution to improve medication management after hospital discharge, including its design, data sources, intrinsic features, and to evaluate the usability and the perception of use by end-users.

Case-control study designs are commonly used in retrospective analyses of real-world evidence (RWE). Due to the increasingly wide availability of RWE, it can be difficult to determine whether findings are robust or the result of testing multiple hypotheses.

Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks.

Guided by the concept of digital phenotypes, the objective of this study was to identify engagement phenotypes among individuals with atrial fibrillation (AF) using mobile health (mHealth) technology for 6 months.