Literature DB >> 28193619

Improving specialist drug prescribing in primary care using task and error analysis: an observational study.

Narinder Chana1, Talya Porat2, Cate Whittlesea3, Brendan Delaney4.   

Abstract

BACKGROUND: Electronic prescribing has benefited from computerised clinical decision support systems (CDSSs); however, no published studies have evaluated the potential for a CDSS to support GPs in prescribing specialist drugs. AIM: To identify potential weaknesses and errors in the existing process of prescribing specialist drugs that could be addressed in the development of a CDSS. DESIGN AND
SETTING: Semi-structured interviews with key informants followed by an observational study involving GPs in the UK.
METHOD: Twelve key informants were interviewed to investigate the use of CDSSs in the UK. Nine GPs were observed while performing case scenarios depicting requests from hospitals or patients to prescribe a specialist drug. Activity diagrams, hierarchical task analysis, and systematic human error reduction and prediction approach analyses were performed.
RESULTS: The current process of prescribing specialist drugs by GPs is prone to error. Errors of omission due to lack of information were the most common errors, which could potentially result in a GP prescribing a specialist drug that should only be prescribed in hospitals, or prescribing a specialist drug without reference to a shared care protocol. Half of all possible errors in the prescribing process had a high probability of occurrence.
CONCLUSION: A CDSS supporting GPs during the process of prescribing specialist drugs is needed. This could, first, support the decision making of whether or not to undertake prescribing, and, second, provide drug-specific parameters linked to shared care protocols, which could reduce the errors identified and increase patient safety. © British Journal of General Practice 2017.

Entities:  

Keywords:  clinical decision support system; drug prescribing; general practice; primary health care; specialist drugs

Mesh:

Substances:

Year:  2017        PMID: 28193619      PMCID: PMC5325657          DOI: 10.3399/bjgp17X689389

Source DB:  PubMed          Journal:  Br J Gen Pract        ISSN: 0960-1643            Impact factor:   5.386


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