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Electronic health records (EHRs) in physician offices can both enhance and detract from the patient experience. Best practices have emerged focusing on screen sharing. We sought to determine if adding a second monitor, mirroring the EHR for patients, would be welcome and useful for patients and clinicians.

The Common Formats, published by the Agency for Healthcare Research and Quality, represent a standard for safety event reporting used by Patient Safety Organizations (PSOs). We evaluated its ability to capture patient-reported safety events.

Observational studies analyzing multiple exposures simultaneously have been limited by difficulty distinguishing relevant results from chance associations due to poor specificity. Set-based methods have been successfully used in genomics to improve signal-to-noise ratio. We present and demonstrate medication class enrichment analysis (MCEA), a signal-to-noise enhancement algorithm for observational data inspired by set-based methods.

Defining clinical conditions from electronic health record (EHR) data underpins population health activities, clinical decision support, and analytics. In an EHR, defining a condition commonly employs a diagnosis value set or "grouper." For constructing value sets, Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) offers high clinical fidelity, a hierarchical ontology, and wide implementation in EHRs as the standard interoperability vocabulary for problems.

There are few published studies of the use of portable or handheld computers in health care, but these devices have the potential to transform multiple aspects of clinical teaching and practice.

Pediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we describe the implementation and evaluation of criteria to identify high-risk patients from a paper-based checklist into a clinical decision support (CDS) tool in the electronic health record (EHR).

U.S. poison control centers pose a special case for patient identity matching because they collect only minimal patient identifying information.

Laboratory-based utilization management programs typically rely primarily on data derived from the laboratory information system to analyze testing volumes for trends and utilization concerns. We wished to examine the ability of an electronic health record (EHR) laboratory orders database to improve a laboratory utilization program.