Prescribing errors in hospital practice

Br J Clin Pharmacol. 2012 Oct;74(4):668-75. doi: 10.1111/j.1365-2125.2012.04313.x.

Abstract

Prescribing errors that occur in hospitals have been a source of concern for decades. This narrative review describes some of the recent work in this field. There is considerable heterogeneity in definitions and methods used in research on prescribing errors. There are three definitions that are used most frequently (one for prescribing errors specifically and two for the broader arena of medication errors), although many others have also been used. Research methods used focus primarily on investigating either the prescribing process (such as errors in the dose prescribed) or the outcomes for the patient (such as preventable adverse drug events). This complicates attempts to calculate the overall prevalence or incidence of errors. Errors have been reported in handwritten descriptions of almost 15% and with electronic prescribing of up to 8% of orders. Errors are more likely to be identified on admission to hospital than at any other time (usually failure to continue ongoing medication) and errors of dose occur most commonly throughout the patients' stay. Although there is evidence that electronic prescribing reduces the number of errors, new types of errors also occur. The literature on causes of error shows some commonality with both handwritten and electronic prescribing but there are also causes that are unique to each. A greater understanding of the prevalence of the complex causal pathways found and the differences between the pathways of minor and severe errors is necessary. Such an understanding would underpin theoretically-based interventions to reduce the occurrence of prescribing errors.

Publication types

  • Review

MeSH terms

  • Drug Prescriptions / statistics & numerical data*
  • Electronic Prescribing / statistics & numerical data
  • Handwriting
  • Hospitals / statistics & numerical data*
  • Humans
  • Incidence
  • Medication Errors / statistics & numerical data*
  • Prevalence
  • Terminology as Topic