Statistically Prioritized and Contextualized Clinical Decision Support Systems, the Future of Adverse Drug Events Prevention?

ABSTRACT

Clinical decision support systems (CDSS) fail to prevent adverse drug events (ADE), notably due to over-alerting and alert-fatigue. Many methods have been proposed in the literature to reduce over-alerting of CDSS enhancing post-alert medical management, taking into account user-related context, patient-related context and temporal aspects, improving medical relevance of alerts, filtering or tiering alerts on the basis of their strength of evidence, their severity, their override rate, or the probability of outcome. This paper analyzes the different options, and proposes the setup of SPC-CDSS (statistically prioritized and contextualized CDSS). The principle is that, when a SPC-CDSS is implemented in a medical unit, it first reuses actual clinical data, and searches for traceable outcomes. Then, for each rule trying to prevent this outcome, the SPC-CDSS automatically estimates the conditional probability of outcome knowing that the conditions of the rule are met, by retrospective secondary use of data. The alert can be turned off below a chosen probability threshold. This probability computation can be performed in each medical unit, in order to take into account its sensitivity to context.