Success Factors and Barriers for Implementation of Advanced Clinical Decision Support Systems

Abstract

Ten years ago, the US Institute of Medicine (IOM) called for a massive redesign of the healthcare delivery system.(Committee on Quality of Health Care in America, 2001) Today it is clear that one of the goals, the nationwide use of an electronic medical record (EMR) by 2010, has failed to be reached as the process of adoption has been slow. Some may consider an EMR as a final destination, although in fact it is only the start point of a revolution in healthcare: the implementation of clinical decision support systems (CDSS) that ‘make it easy to do it right’.(James, 2001) These systems are able to address the large, potential additional value of the implementation of an EMR. When an EMR is available, this is already a step in the right direction, to have an easy and structured access to all patient data available for all healthcare professionals that need them. However, this is still a huge amount of data, but one should also have the ability to integrate all these data and use these data in making the right choices in therapy. Practice has shown that despite the availability of an EMR, still many medication errors are made. Therefore, CDSS are designed to aid clinical decision-making by matching patient characteristics to a computerized knowledge or rule base to generate patient-specific recommendations.(Kawamoto et al., 2005) In the trendsetting IOM reports ‘To Err is Human’ (2000) and ‘Crossing the quality chasm’ (2001), a CDSS was endorsed as one of the most powerful tools available for improving patient safety and healthcare quality.(Kohn et al., 2000;Committee on Quality of Health Care in America, 2001) It is difficult to accept that despite multiple opportunities and promising results, these systems instead of being common practice, still remain ‘next-generation’.(James, 2001) During the last five years, research gave more insight in the success factors that could accelerate the idle process of CDSS adoption. (Kawamoto et al., 2005; Garg et al., 2005;Nies et al., 2006) The conclusions however are not univocal because the reviews included a wide variety of systems ranging from computerised to non-computerised CDSS as well as from basic to advanced systems. Basic decision support includes checking on drug-drug interactions, duplicate therapy, drug-allergies and generalized drug dosing. Advanced CDSS, used in addition to basic CDSS, includes for example checking on contra-indications (disease and drugs), individualized dosing support during renal impairment or guidance