An e‐Delphi study to obtain expert consensus on the level of risk associated with preventable e‐prescribing events. Issue 7 (8th March 2022)

• Record Type: Journal Article
• Authors: Heed, Jude; Klein, Stephanie; Slee, Ann; Watson, Neil; Husband, Andy; Slight, Sarah Patricia
• Is Part Of: British journal of clinical pharmacology. Volume 88:Issue 7(2022)
• Page Start: 3351
• Page End: 3359
• Publication Date: 2022-03-08
• Subjects: medication errors -- patient safety -- prescribing -- quality use of medicines; Pharmacology -- Periodicals; Drugs -- Periodicals; 615.1
• DOI: 10.1111/bcp.15284
• Languages: English
• ISSNs: 0306-5251

Abstract

Abstract : Aims: We aim to seek expert opinion and gain consensus on the risks associated with a range of prescribing scenarios, preventable using e‐prescribing systems, to inform the development of a simulation tool to evaluate the risk and safety of e‐prescribing systems (ePRaSE). Methods: We conducted a two‐round e‐Delphi survey where expert participants were asked to score pre‐designed prescribing scenarios using a five‐point Likert scale to ascertain the likelihood of occurrence of the prescribing event, likelihood of occurrence of harm and the severity of the harm. Results: Twenty‐four experts consented to participate with 15 pand 13 participants completing rounds 1 and 2, respectively. Experts agreed on the level of risk associated with 136 out of 178 clinical scenarios with 131 scenarios categorised as high or extreme risk. Conclusion: We identified 131 extreme or high‐risk prescribing scenarios that may be prevented using e‐prescribing clinical decision support. The prescribing scenarios represent a variety of categories, with drug–disease contraindications being the most frequent, representing 37 (27%) scenarios, and antimicrobial agents being the most common drug class, representing 28 (21%) of the scenarios. Our e‐Delphi study has achieved expert consensus on the risk associated with a range of clinical scenarios with most of the scenarios categorised as extreme or high risk. These prescribing scenarios represent the breadth of preventable prescribing error categories involving both basic and advanced clinical decision support. We will use the findings of this study to inform the development of the e‐prescribing risk and safety evaluation tool.