Lessons learned: using adverse incident reports to investigate the characteristics and causes of prescribing errors.

Introduction: Prescribing errors are a principal cause of preventable harm in healthcare. This study aims to establish a systematic approach to analysing prescribing-related adverse incident reports, in order to elucidate the characteristics and contributing factors of common prescribing errors and target multifaceted quality improvement initiatives.

Methods: All prescribing-related adverse incident reports submitted across one NHS board over 12 months were selected. Incidents involving commonly implicated drugs (involved in ≥10 incidents) underwent analysis to establish likely underlying causes using Reason's Model of Accident Causation.

Results: 330 prescribing-related adverse incident reports were identified. Commonly implicated drugs were insulin (10% of incidents), gentamicin (7%), co-amoxiclav (5%) and amoxicillin (5%). The most prevalent error types were prescribing amoxicillin when contraindicated due to allergy (5%); prescribing co-amoxiclav when contraindicated due to allergy (5%); prescribing the incorrect type of insulin (3%); and omitting to prescribe insulin (3%). Error-producing factors were identified in 86% of incidents involving commonly implicated drugs. 53% of incidents involved error-producing factors related to the working environment; 38% involved factors related to the healthcare team; and 37% involved factors related to the prescriber.

Discussion: This study establishes that systematic analysis of adverse incident reports can efficiently identify the characteristics and contributing factors of common prescribing errors, in a manner useful for targeting quality improvement. Furthermore, this study produced a number of salient findings. First, a narrow range of drugs were implicated in the majority of incidents. Second, a small number of error types were highly recurrent. Lastly, a range of contributing factors were evident, with those related to the working environment contributing to the majority of prescribing errors analysed.