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Common data models (CDMs) enable data to be standardized, and facilitate data exchange, sharing, and storage, particularly when the data have been collected via distinct, heterogeneous systems. Moreover, CDMs provide tools for data quality assessment, integration into models, visualization, and analysis. The observational medical outcome partnership (OMOP) provides a CDM for organizing and standardizing databases. Common data models not only facilitate data integration but also (and especially for the OMOP [...]

Electronic health record (EHR) alert fatigue, while widely recognized as a concern nationally, lacks a corresponding comprehensive mitigation plan.

Implementation of disease-specific order sets has improved compliance with standards of care for a variety of diseases. Evidence of the impact admission order sets can have on care is limited.

To evaluate informatics-enabled quality improvement (QI) strategies for promoting time spent on face-to-face communication between ophthalmologists and patients.

To understand the ways in which providers use a mobile photography application integrated with the electronic health record (EHR) to facilitate clinical care, and the process outcomes that result from the application's use.

The aim of this study is to define data model requirements supporting the development of a digital cognitive aid (CA) for intraoperative crisis management in anesthesia, including medical emergency text modules (text elements) and branches or loops within emergency instructions (control structures) as well as their properties, data types, and value ranges.

The OpenNotes initiative launched an international movement aimed at making health care more transparent by improving communication with, and access to, information for patients through provider note sharing. Little has been written either on provider note sharing in pediatric and adolescent populations or on the impact of system default settings versus voluntary provider note sharing.

Early electronic identification of patients at the highest risk for heart failure (HF) readmission presents a challenge. Data needed to identify HF patients are in a variety of areas in the electronic medical record (EMR) and in different formats.

Healthcare systems devote substantial resources to the development of clinical decision support (CDS) largely independently. The process of translating evidence-based practice into useful and effective CDS may be more efficient and less duplicative if healthcare systems shared knowledge about the translation, including workflow considerations, key assumptions made during the translation process, and technical details.