In ‘Crossing the quality chasm,’ the Institute of Medicine pointed out the wide variations in healthcare practice, and the inefficiencies, dangers, and inequalities that have resulted from nonoptimal patient care.1
Evidence-based medicine (EBM) aims to reduce practice variation and improve quality of care. It does so by combining the clinical skills and experience of the healthcare professional and preferences of the patient with the best external clinical evidence available in order to make balanced decisions about medical care.2
EBM has, since its introduction in the 1980s, become widespread and has been adopted by international healthcare organizations such as the WHO and the Institute of Medicine. EBM seems a fairly common-sense solution, but it has proved to be far from simple to implement. A report from Grol et al
provides a brief overview of strategies for the effective implementation of change in patient care.3
One of the interventions discussed is the use of reminders and computers for the implementation of evidence in daily practice. It is concluded that, among other interventions on the organizational and team level, professional development needs to be built into daily patient care as much as possible. This preferably should take place at the point of care with clinical decision-support tools and real-time patient-specific reminders to help doctors make the best decisions. Clinical decision support is defined as: ‘providing clinicians or patients with computer-generated clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care.’4
Clinical knowledge incorporated in clinical decision-support systems (CDSS), for instance, can be based on available best evidence which is represented in guideline recommendations.
There are many different types of clinical tasks that can be supported by CDSS. A well-known and frequently applied CDSS is the patient-monitoring device (eg, an ECG or pulse oximeter) that warns of changes in a patient's condition. CDSS integrated in Electronic Medical Record systems (EMRs) and computerized physician order entry systems (CPOEs) can send reminders or warnings for deviating laboratory test results, check for drug–drug interactions, dosage errors, and other prescribing contraindications such as a patient's allergies, and generate lists of patients eligible for a particular intervention (eg, immunizations or follow-up visits). When a patient's case is complex or rare, or the healthcare practitioner making the diagnosis is inexperienced, a CDSS can help in formulating likely diagnoses based on (a) patient data and (b) the system's knowledge base of diseases. Subsequently, the CDSS can formulate treatment suggestions based upon treatment guidelines.
Research into the impact of CDSS on healthcare practitioner performance and patient outcomes in hospital settings has increased, and evidence of the effectiveness of CDSS has been synthesized into several systematic reviews (SRs). However, an overview of this evidence based on a critical appraisal of SRs focusing on CDSS impact is not available. Therefore, we set out to provide a synthesis of high-quality SRs examining CDSS interventions in hospital settings. The objective is (a) to summarize their effects on practitioner performance and patient outcome, and (b) to highlight areas where more research is needed.