Search AMIA.org

To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions.

Clinical information models are formal specifications for representing the structure and semantics of the clinical content within electronic health record systems. This research aims to define, test, and validate evaluation metrics for software tools designed to support the processes associated with the definition, management, and implementation of these models.

First, to evaluate the effect of standard vs pictograph-enhanced discharge instructions on patients' immediate and delayed recall of and satisfaction with their discharge instructions. Second, to evaluate the effect of automated pictograph enhancement on patient satisfaction with their discharge instructions.

Traditionally, patient groups with a phenotype are selected through rule-based definitions whose creation and validation are time-consuming. Machine learning approaches to electronic phenotyping are limited by the paucity of labeled training datasets. We demonstrate the feasibility of utilizing semi-automatically labeled training sets to create phenotype models via machine learning, using a comprehensive representation of the patient medical record.

We describe use cases and an institutional reference architecture for maintaining high-capacity, data-intensive network flows (e.g., 10, 40, 100 Gbps+) in a scientific, medical context while still adhering to security and privacy laws and regulations.

To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness.

To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data.

The management of HIV infection requires extensive, longitudinal information record-keeping and coordination to ensure optimal monitoring and outcomes of care and treatment.

As health information technologies become more prevalent in physician workflow, it is increasingly important to understand how physicians are using and interacting with these systems. This includes understanding how physicians search for information presented through health information technology systems. Eye tracking technologies provide a useful technique to understand how physicians visually search for information. However, analyzing eye tracking data can be challenging and is often done by measuring summative metrics [...]