To review the use of clinical decision support systems (CDSS) available in the pediatric intensive care unit (PICU).
Relevant English language publications indexed in Medline, as well as CDSS-related white papers and texts.
Studies related to CDSS were considered.
CDSS are operationally defined as computer software programs that aid healthcare providers in their clinical decision making. Once used solely for diagnostic support, many CDSS now have the ability to transform clinical practice through interactive assistance with therapeutic best practices. The recent emphasis on improving quality and patient safety through the incorporation of electronic health records as supported by Leapfrog and other agencies has encouraged advancements in the use of CDSS tools that leverage the capabilities of stand-alone electronic health records. CDSS are of particular interest in the PICU where rapid decision-making benefits from tools that can improve patient safety. CDSS have been described in the PICU with varying effects on healthcare outcomes. A growing consensus indicates that the success of such interventions depends as much or more on how they are implemented and used in such complex environments as on their programming. In the current review, the types and features of various CDSS tools and the supporting evidence are discussed. Factors such as liability, human factors engineering, alert fatigue, and audit trails are also covered.
CDSS have the potential to improve clinical practice in PICU settings. Care should be taken when selecting and implementing such systems to achieve the goal of improved clinical practice while avoiding potential adverse impacts sometimes associated with the implementation of new technologies in complex healthcare settings.
From the Division of Critical Care Medicine, Cincinnati Children’s Hospital Medical Center; Department of Pediatrics, University of Cincinnati College of Medicine; The Kindervelt Laboratory for Critical Care Medicine Research, Cincinnati Children’s Hospital Medical Center (EHM, DSW), Cincinnati, OH; and Department of Medicine and Center for Health Informatics, University of Cincinnati College of Medicine (PJE), Cincinnati, OH.
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The authors have not disclosed any potential conflicts of interest.