What does it mean to provide decision support to a responsible and competent expert?

Abstract

Decision support consists in helping a decision-maker to improve his/her decisions. However, clients requesting decision support are often themselves experts and are often taken by third parties and/or the general public to be responsible for the decisions they make. This predicament raises complex challenges for decision analysts, who have to avoid infringing upon the expertise and responsibility of the decision-maker. The case of diagnosis decision support in healthcare contexts is particularly illustrative. To support clinicians in their work and minimize the risk of medical error, various decision support systems have been developed, as part of information systems that are now ubiquitous in healthcare contexts. To develop, in collaboration with the hospitals of Lyon, a diagnostic decision support system for day-today customary consultations, we propose in this paper a critical analysis of current approaches to diagnostic decision support, which mainly consist in providing them with guidelines or even full-fledged diagnosis recommendations. We highlight that the use of such decision support systems by physicians raises responsibility issues, but also that it is at odds with the needs and constraints of customary consultations. We argue that the historical choice to favor guidelines or recommendations to physicians implies a very specific vision of what it means to support physicians, and we argue that the flaws of this vision partially ex-A.

A virtual assistant dedicated to supporting day-to-day medical consultations

“…However, the introduction of a new tool in physicians' work processes unavoidably entails an entry cost [22], [30]. Therefore, we expect to observe, in many cases, a rise in the number of pieces of information searched, the number of mouse clicks, and the duration of consultations, due to the introduction of CoBoy in the work processes of physicians.…”

“…These recommendations are generally "gold-standard" guidelines or suggestions of diagnoses. In previous works [22], we have argued that this lack of acceptability is due to the fact that the support provided by current DDSSs is at odds with the constraints implied by supporting decisions during day-to-day practices and non-complex situations, like customary medical consultations. The main constraint that a DDSS have to deal with, in such situations, is the strong will of the physicians to stay in charge of their decision processes and to stay responsible of the safety of their patient.…”

“…The main constraint that a DDSS have to deal with, in such situations, is the strong will of the physicians to stay in charge of their decision processes and to stay responsible of the safety of their patient. We concluded that an adapted approach for decision support during customary medical consultations must respect the know-how of physicians and leave them the responsibility of the decisions taken during these consultations, by limiting itself to providing what physicians need most during consultations: pieces of information concerning their patients [22], [23].…”

Evaluation Framework for Successful Artificial Intelligence–Enabled Clinical Decision Support Systems: Mixed Methods Study

“…They are designed to aid clinicians by converting raw medical-related data, documents, and expert practice into a set of sophisticated algorithms, applying techniques such as machine learning, knowledge graphs, natural language processing, and computer vision so that users find suitable solutions to their medical problems and make clinical decisions [ 7 ]. AI-enabled clinical decision support systems have the potential to improve clinicians’ performance, quality of health care, and patient safety [ 8 ].…”

“…First, the added value of AI-enabled clinical decision support system implementations in a clinical setting is not firmly established, though evidence exists that such implementations offer potential benefit to patients, clinicians, and health care in general [ 19 ]. Introducing this type of system in clinical settings is not without risk [ 8 ]. Similar to any other newly introduced technology, AI-enabled clinical decision support systems may disrupt clinical service, threaten patient safety [ 20 ], and cause more negative than positive impacts [ 19 ].…”