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Clinical Judgement Analysis: An innovative approach to explore the individual decision-making processes of pharmacists

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)2097-2107
Number of pages11
JournalResearch in Social and Administrative Pharmacy
Volume17
Issue number12
DOIs
Accepted/In press2021
PublishedDec 2021

Bibliographical note

Funding Information: Thank you to Dr Jig Patel, Dr John Bartoli-Abdou, Professor John Weinman and Dr Kia-Chong Chua for their valued support and guidance. Publisher Copyright: © 2021 Elsevier Inc.

King's Authors

Abstract

Background: Pharmacy stands increasingly on the frontline of patient care, yet current studies of clinical decision-making by pharmacists only capture deliberative processes that can be stated explicitly. Decision-making incorporates both deliberative and intuitive processes. Clinical Judgement Analysis (CJA) is a method novel to pharmacy that uncovers intuitive decision-making and may provide a more comprehensive understanding of the decision-making processes of pharmacists. Objectives: This paper describes how CJA potentially uncovers the intuitive clinical decision-making processes of pharmacists. Using an illustrative decision-making example, the application of CJA will be described, including: • Scenario and associated task development around a defined judgement • Capture of pharmacists' decision-making processes and analysis using appropriate statistical methods Method: An illustrative study was used, applying an established method for CJA. The decision to initiate anticoagulation, alongside appropriate risk judgements, was chosen as the context. Expert anticoagulation pharmacists were interviewed to define and then refine variables (cues) involved in this decision. Decision tasks with sixty scenarios were developed to explore the effect of these cues on pharmacists’ decision-making processes and distributed to participants for completion. Descriptive statistical and regression analyses were conducted for each participant. Results: The method produced individual judgement models for each participant, for example, demonstrating that when judging stroke risk each participant's judgements could be accurately predicted using only 3 or 4 out of the possible 11 cues given. The method also demonstrated that participants appeared to consider multiple cues when making risk judgements but used an algorithmic approach based on one or two cues when making the clinical decision. Conclusion: CJA generates insights into the clinical decision-making processes of pharmacists not uncovered by the current literature. This provides a springboard for more in-depth explorations; explorations that are vital to the understanding and ongoing development of the role of pharmacists.

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