Search AMIA.org

To describe current practices and stakeholder perspectives of patient portals in the acute care setting. We aimed to: (1) identify key features, (2) recognize challenges, (3) understand current practices for design, configuration, and use, and (4) propose new directions for investigation and innovation.

Routine health information systems (RHISs) provide data that are vital for planning and monitoring individual health. Data from RHISs could also be used for purposes for which they were not originally intended, provided that the data are of sufficient quality. For example, morbidity data could be used to inform burden of disease estimations, which serve as important evidence to prioritize interventions and promote health. The objective of this study was [...]

Monitoring the supervision of residents can be a challenging task. We describe our experience with the implementation of a templated note system for documenting procedures with the aim of enabling automated, discrete, and standardized capture of documentation of supervision of residents performing floor-based procedures, with minimal extra effort from the residents.

The study objective was to evaluate the accuracy, validity, and clinical usefulness of medication error alerts generated by an alerting system using outlier detection screening.

A major focus of health care today is a strong emphasis on improving the health and quality of care for entire patient populations. One common approach utilizes electronic clinical alerts to prompt clinicians when certain interventions are due for individual patients being seen. However, these alerts have not been consistently effective, particularly for less visible (though important) conditions such as hearing loss (HL) screening.

To conduct a systematic review and meta-analysis of the impact of commercial computerized provider order entry (CPOE) and clinical decision support systems (CDSSs) on medication errors, length of stay (LOS), and mortality in intensive care units (ICUs).

To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them.

While potentially reducing decision errors, decision support systems can introduce new types of errors. Automation bias (AB) happens when users become overreliant on decision support, which reduces vigilance in information seeking and processing. Most research originates from the human factors literature, where the prevailing view is that AB occurs only in multitasking environments.