Design of a Clinical Decision Support System for Assisting in Empiric Antibiotic Treatments

Calvillo-Arbizu, Jorge; Roa-Romero, Laura M.

doi:10.1007/978-3-319-03005-0_77

Cited by 1 publication

(1 citation statement)

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“…CDSS are already used for a variety of purposes to improve health care: screening and prevention [22], medication decision-making [7], therapeutic planning and diagnostics [14], etc. However, the use of CDSS is not without problems.…”

Section: Introductionmentioning

confidence: 99%

The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems

Bussone

Stumpf

O’Sullivan

2015

2015 International Conference on Healthcare Informatics

253

209

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Abstract-Clinical decision support systems (CDSS) are increasingly used by healthcare professionals for evidence-based diagnosis and treatment support. However, research has suggested that users often over-rely on system suggestions -even if the suggestions are wrong. Providing explanations could potentially mitigate misplaced trust in the system and overreliance. In this paper, we explore how explanations are related to user trust and reliance, as well as what information users would find helpful to better understand the reliability of a system's decision-making. We investigated these questions through an exploratory user study in which healthcare professionals were observed using a CDSS prototype to diagnose hypothetic cases using fictional patients suffering from a balancerelated disorder. Our results show that the amount of system confidence had only a slight effect on trust and reliance. More importantly, giving a fuller explanation of the facts used in making a diagnosis had a positive effect on trust but also led to over-reliance issues, whereas less detailed explanations made participants question the system's reliability and led to selfreliance problems. To help them in their assessment of the reliability of the system's decisions, study participants wanted better explanations to help them interpret the system's confidence, to verify that the disorder fit the suggestion, to better understand the reasoning chain of the decision model, and to make differential diagnoses. Our work is a first step toward improved CDSS design that better supports clinicians in making correct diagnoses.

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Section: Introductionmentioning

confidence: 99%