Distributed, Collaborative Intelligent Agents for Proactive Post-Marketing Drug Safety Surveillance

Abstract

Healthcare systems and insurers nationwide regularly make decisions regarding which drugs to include or exclude from their formularies based on evidence concerning benefits, risks, and costs of the medications. A major barrier to effective drug selection is the lack of sufficient published information on the safety of drugs, particularly new drugs. In this paper, we propose an innovative multi-agent system, named ADRMonitor, for actively monitoring and detecting signal pairs implicating anticipated or potential adverse drug reactions (ADRs) of interest at a healthcare facility. Each intelligent agent is empowered by a fuzzy logic-based computational recognition-primed decision (RPD) model where fuzzy logic is utilized to represent, interpret, and compute vague and/or subjective information. We conducted a simulation study based on thousands of hypothetical patient cases that were created on the basis of real patients who were prescribed the drug Cisapride in a local hospital. At the current stage, our focus is to establish that the system can outperform the spontaneous reporting approach in identifying signal pairs. Under certain conditions (e.g., without agent collaboration), our simulation results show that (1) ADRMonitor detected 21 out of 27 (78%) ADRs when the optimized RPD model was used as a gold standard; (2) the number of ADRs detected by the agents is (many) more than those detected by the spontaneous reporting strategy (assuming 10% reporting rate - high end of rates reported in the literature) at any particular time. The second result implies that useful information could be collected more timely by the proposed agent system for formulary decisions.