Automation bias (AB)--the tendency to over-rely on automation--has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human-automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.

We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings.

Recent advances in information and communication technology allow the design and testing of new models of diabetes management, which are able to provide assistance to patients regardless of their distance from the health care providers. The M2DM project, funded by the European Commission, has the specific aim to investigate the potential of novel telemedicine services in diabetes management. A multi-access system based on the integration of Web access, telephone access through interactive voice response systems, and the use of palmtops and smart modems for data downloading has been implemented. The system is based on a technological platform that allows a tight integration between the access modalities through a middle layer called the multi-access organizer. Particular attention has been devoted to the design of the evaluation scheme for the system: A randomized controlled study has been defined, with clinical, organizational, economic, usability, and users' satisfaction outcomes. The evaluation of the system started in January 2002. The system is currently used by 67 patients and seven health care providers in five medical centers across Europe. After 6 months of usage of the system no major technical problems have been encountered, and the majority of patients are using the Web and data downloading modalities with a satisfactory frequency. From a clinical viewpoint, the hemoglobin A1c (HbA1c) of both active patients and controls decreased, and the variance of HbA1c in active patients is significantly lower than the control ones. The M2DM system allows for the implementation of an easy-to-use, user-tailored telemedicine system for diabetes management. The first clinical results are encouraging and seem to substantiate the hypothesis of its clinical effectiveness.