Aims 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.

Background Medication errors are a major cause for concern in the NHS. Prescribing is part of the medication use process and is a complex task requiring an understanding of medicines, disease processes, and patient parameters. Systematic reviews have reported that medication errors occur in as many as 50% of hospital admissions and prescribing error rates in the UK hospitals vary between 9% and 15%.

Prescribing for children is further complicated by the need to take into account weight, altered physiology and pharmacokinetics. Prescribing error rates of 13.1% have been reported in children with a potentially greater impact due to the nature of the patients.

Electronic prescribing (EP) while relatively uncommon in UK hospitals is an important tool in reducing prescribing errors. EP systems have been shown to have a positive impact on prescribing errors, however methodologies vary and the reduction in harm is rarely investigated. A standard tool to allow an evaluation of the harm reduction is desirable and currently does not exist for the paediatric setting.

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 An expert panel consisting of 8 pharmacists and 13 paediatricians with a total of 437 years of clinical experience 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 have been identified by an expert panel. The indicators provide a standardised 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.