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Developing consensus on hospital prescribing indicators of potential harms amenable to decision support

Thomas, S. K., McDowell, S. E., Hodson, J., Nwulu, U., Howard, R. L., Avery, A. J., Slee, A. and Coleman, J. J. (2013) Developing consensus on hospital prescribing indicators of potential harms amenable to decision support. British Journal of Clinical Pharmacology, 76 (5). pp. 797-809. ISSN 0306-5251

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To link to this item DOI: 10.1111/bcp.12087


Aim: To develop a list of prescribing indicators specific for the hospital setting that would facilitate the prospective collection of high severity and/or high frequency prescribing errors, which are also amenable to electronic clinical decision support (CDS). Method: A three-stage consensus technique (electronic Delphi) was carried out with 20 expert pharmacists and physicians across England. Participants were asked to score prescribing errors using a 5-point Likert scale for their likelihood of occurrence and the severity of the most likely outcome. These were combined to produce risk scores, from which median scores were calculated for each indicator across the participants in the study. The degree of consensus between the participants was defined as the proportion that gave a risk score in the same category as the median. Indicators were included if a consensus of 80% or more was achieved. Results: A total of 80 prescribing errors were identified by consensus as being high or extreme risk. The most common drug classes named within the indicators were antibiotics (n=13), antidepressants (n=8), nonsteroidal anti-inflammatory drugs (n=6), and opioid analgesics (n=6).The most frequent error type identified as high or extreme risk were those classified as clinical contraindications (n=29/80). Conclusion: 80 high risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as the basis for a standardised, validated tool for the collection of data in both paperbased and electronic prescribing processes, as well as to assess the impact of electronic decision support implementation or development.

Item Type:Article
Divisions:Life Sciences > School of Chemistry, Food and Pharmacy > School of Pharmacy > Pharmacy Practice Research Group
ID Code:30594

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