Clinical Decision Support Systems in Nursing

Clinical Decision Support Systems in Nursing

Dawn Dowding (University of York, UK), Rebecca Randell (City University, UK), Natasha Mitchell (University of York, UK), Rebecca Foster (University of Southampton, UK), Valerie Lattimer (University of Southampton, UK) and Carl Thompson (University of York, UK)
Copyright: © 2009 |Pages: 15
DOI: 10.4018/978-1-60566-234-3.ch003


Increasingly, new and extended roles and responsibilities for nurses are being supported through the introduction of clinical decision support systems (CDSS). This chapter provides an overview of research on nurses’ use of CDSS, considers the impact of CDSS on nurse decision making and patient outcomes, and explores the socio-technical factors that impact the use of CDSS. In addition to summarising previous research, both on nurses’ use of CDSS and on use of CDSS more generally, the chapter presents the results of a multi-site case study that explored how CDSS are used by nurses in practice in a range of contexts. The chapter takes a socio-technical approach, exploring the barriers and facilitators to effective CDSS use at a level of the technology itself, the ways people work, and the organisations in which they operate.
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Clinicians have a range of decision tools available to them to support their decision making (Liu, Wyatt, & Altman, 2006). Examples of decision tools include nomograms (charts that simplify complex information such as Body Mass Index (BMI)), templates incorporated into electronic patient records (EPRs), predictive scores (such as early warning scoring systems for clinical event risk), formularies to support prescribing, and patient information leaflets. CDSS are a computer-based form of decision tool, integrating information (ideally from high-quality research studies) with the characteristics of individual patients, to provide advice to clinicians (Dowding, 2007). As such, CDSS are seen as a potential way of improving the quality, safety and effectiveness of clinical decisions, leading to improvement in clinician performance and patient outcomes (Garg et al., 2005). CDSS vary in their functionality, from ‘passive’ systems that only provide information to a clinician when they request it, through to ‘active’ systems that provide patient specific recommendations to a clinician automatically (Hajioff, 1998). For example, computerised clinical reminders (CRs) are an example of an active system, typically being integrated with an electronic patient record (EPR) and presenting reminders to the clinician regarding potentially appropriate interventions, based on an evaluation of the available patient data (Patterson, Nguyen, Halloran, & Asch, 2003Saleem et al., 2005). Computerised provider order entry (CPOE), which enables computer-based ordering of medication, can also be a form of active CDSS, when decision support alerts the user to the risk of a dangerous drug interaction or advises the user of appropriate dosages (Aarts, Doorewaard, & Berg, 2004). Passive CDSS include electronic information tools that provide clinicians with access to online clinical practice guidelines and research evidence (Randell, Mitchell, Thompson, McCaughan, & Dowding, In press-a).

CDSS have a long history; a systematic review of the impact of CDSS contained 5 studies that were published in the 1970s (Garg et al., 2005). The review identified 100 studies, covering the activities of diagnosis, disease management and drug dosing and prescribing, and a range of clinical areas, including both primary and secondary care.

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