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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 systematically review studies reporting problems with information technology (IT) in health care and their effects on care delivery and patient outcomes.

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

To evaluate the safety of computerized physician order entry (CPOE) and associated clinical decision support (CDS) systems in electronic health record (EHR) systems at pediatric inpatient facilities in the US using the Leapfrog Group's pediatric CPOE evaluation tool.

Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings.

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.

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.

To understand the different types and causes of prescribing errors associated with computerized provider order entry (CPOE) systems, and recommend improvements in these systems.

The United States Office of the National Coordinator for Health Information Technology sponsored the development of a "high-priority" list of drug-drug interactions (DDIs) to be used for clinical decision support. We assessed current adoption of this list and current alerting practice for these DDIs with regard to alert implementation (presence or absence of an alert) and display (alert appearance as interruptive or passive).