Medication related Computerized Decision Support System (CDSS) are known to have a positive impact on Adverse Drug Events (ADE) prevention but they face acceptance problems due to over alerting and usability issues. We present here a Human factors approach to the design of these Clinical Decision Support (CDS) functions and to their integration into different Electronic Health Record (EHR) / Computerized Physicians Order Entry (CPOE) systems, so that the resulting CDSS corresponds to the users needs and fits clinical workflows and cognitive processes. We used ethnographic observations completed with semi-structured interviews to analyse existing work situations and work processes. These were then described in detail using the SHEL (Software, Hardware, Environment & Liveware) formalism, which enables a structured description of the work system and provides an appropriate classification of human errors potentially leading to ADEs. We then propose a Unified Modelling Language (UML) model supporting the characterization by the CDSS of the drug monitoring and clinical context of patients at risk of ADE. This model combines the status of the lab test orders on the one hand with the validity and normality of the lab results on the other hand. This makes the system able to catch the context of the monitoring of the drugs through their corresponding lab tests and lab results (e.g. kalemia for potassium) and also part of the context of the clinical status of the patient (actual lab values, but also diseases and other pathologies that are identified as potential causes of the ADE e.g. renal insufficiency and potassium). We show that making the system able to catch the monitoring and clinical contexts opens interesting opportunities for the design of the CDS information content and display mode. Implementing this model would allow the CDSS to take into account the actions already engaged by the healthcare team and to adapt the information delivered to the monitoring and clinical context, thus making the CDSS a partner to the clinicians, nurses and pharmacists.