Andy Fox1, Sarah Pontefract2, David Brown3, Jane Portlock4, Jamie Coleman5. 1. Southampton Pharmacy Research Centre, University Hospitals Southampton, Southampton, Hampshire,, SO16 6YD. 2. NIHR Doctoral Research Fellow, School of Pharmacy, College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT. 3. School of Pharmacy, University of Portsmouth, Portsmouth, PO1 2DT. 4. Head of Pharmacy Practice Division, School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, PO1 2DT. 5. Department of Clinical Pharmacology, Medical School, University of Birmingham, Birmingham, B15 2TT, United Kingdom.
Abstract
AIMS: The aim of the study was to develop a list of hospital based paediatric prescribing indicators that can be used to assess the impact of electronic prescribing or clinical decision support tools on paediatric prescribing errors. METHODS: Two rounds of an electronic consensus method (eDelphi) were carried out with 21 expert panellists from the UK. Panellists were asked to score each prescribing indicator for its likelihood of occurrence and severity of outcome should the error occur. The scores were combined to produce a risk score and a median score for each indicator calculated. The degree of consensus between panellists 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 higher was achieved and were in the high risk categories. RESULTS: Each of the 21 panellists completed an exploratory round and two rounds of scoring. This identified 41 paediatric prescribing indicators with a high risk rating and greater than 80% consensus. The most common error type within the indicators was wrong dose (n = 19) and the most common drug classes were antimicrobials (n = 10) and cardiovascular (n = 7). CONCLUSIONS: A set of 41 paediatric prescribing indicators describing potential harm for the hospital setting has been identified by an expert panel. The indicators provide a standardized method of evaluation of prescribing data on both paper and electronic systems. They can also be used to assess implementation of clinical decision support systems or other quality improvement initiatives.
AIMS: The aim of the study was to develop a list of hospital based paediatric prescribing indicators that can be used to assess the impact of electronic prescribing or clinical decision support tools on paediatric prescribing errors. METHODS: Two rounds of an electronic consensus method (eDelphi) were carried out with 21 expert panellists from the UK. Panellists were asked to score each prescribing indicator for its likelihood of occurrence and severity of outcome should the error occur. The scores were combined to produce a risk score and a median score for each indicator calculated. The degree of consensus between panellists 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 higher was achieved and were in the high risk categories. RESULTS: Each of the 21 panellists completed an exploratory round and two rounds of scoring. This identified 41 paediatric prescribing indicators with a high risk rating and greater than 80% consensus. The most common error type within the indicators was wrong dose (n = 19) and the most common drug classes were antimicrobials (n = 10) and cardiovascular (n = 7). CONCLUSIONS: A set of 41 paediatric prescribing indicators describing potential harm for the hospital setting has been identified by an expert panel. The indicators provide a standardized method of evaluation of prescribing data on both paper and electronic systems. They can also be used to assess implementation of clinical decision support systems or other quality improvement initiatives.
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