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

Br J Clin Pharmacol. 2013 Nov;76(5):797-809. doi: 10.1111/bcp.12087.


Aims: 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.

Methods: A two-stage consensus technique (electronic Delphi) was carried out with 20 experts across England. Participants were asked to score prescribing errors using a five-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 of 80).

Conclusions: Eighty high-risk prescribing errors in the hospital setting have been identified by an expert panel. These indicators can serve as a standardized, validated tool for the collection of prescribing data in both paper-based and electronic prescribing processes. This can assess the impact of safety improvement initiatives, such as the implementation of electronic clinical decision support.

Keywords: clinical decision support; consensus; drug prescriptions; hospitals; medication errors; quality indicators.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Consensus
  • Decision Support Systems, Clinical
  • Delphi Technique
  • Electronic Prescribing / standards*
  • England
  • Humans
  • Likelihood Functions
  • Medication Errors / prevention & control*
  • Practice Patterns, Physicians' / standards*
  • Quality Indicators, Health Care*
  • Risk