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Medical errors in blood product orders and administration are common, especially for pediatric patients. A failure modes and effects analysis in our health care system indicated high risk from the electronic blood ordering process.

The introduction of the electronic health record (EHR) has had a significant impact on provider-patient interactions, particularly revolving around patient-centeredness. More research is needed to understand the provider perspective of this interaction.

The testing of informatics tools designed for use during mass casualty incidents presents a unique problem as there is no readily available population of victims or identical exposure setting. The purpose of this article is to describe the process of designing, planning, and executing a functional exercise to accomplish the research objective of validating an informatics tool specifically designed to identify and triage victims of irritant gas syndrome agents.

Electronic health records (EHRs) were expected to yield numerous benefits. However, early studies found mixed evidence of this. We sought to determine whether widespread adoption of modern EHRs in the US has improved clinical care.

We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects.

Alert fatigue limits the effectiveness of medication safety alerts, a type of computerized clinical decision support (CDS). Researchers have suggested alternative interactive designs, as well as tailoring alerts to clinical roles. As examples, alerts may be tiered to convey risk, and certain alerts may be sent to pharmacists. We aimed to evaluate which variants elicit less alert fatigue.

The study sought to develop and evaluate an electronic health record-based child abuse clinical decision support system in 2 general emergency departments.

This study tested validity, accuracy, and efficiency of the Orthopaedic Minimal Data Set Episode of Care (OME) compared with traditional operative report in arthroscopic surgery for shoulder instability. As of November 2017, OME had successfully captured baseline data on 97% of 18 700 eligible cases.