The recognition that all over the world healthcare services are falling significantly short of the highest quality standards is now complemented by a body of evidence that Clinical Decision Support (CDS) can make a significant contribution by helping to improve consistency and quality of routine clinical practice at the point of care. There are many kinds of decision support systems ranging from simple prompts and reminders for doctors and nurses, to systems that give advice on a patient's diagnosis or treatment options, to emerging technologies that can help in the construction and maintenance of personalised care plans. What all these techniques have in common is that they help to bring together all the relevant information and current knowledge that is relevant to a patient's particular circumstances and help to make the right decision for that individual.
Three recent systematic reviews indicate that Clinical Decision Support (CDS) systems have considerable promise for helping clinicians use and comply with clinical guidelines to improve quality and safety of patient care (Garg et al 2005; Kawamoto et al 2005; Chaudrhy et al, 2006).
Garg et al (JAMA 2005) reviewed 100 published trials of simple CDSs such as alerts and reminders. The review showed that 64 % of applications produced significant improvements in decision making. Kawamoto et al (BMJ 2005) carried out a review of 70 systems with similar results (68% were successful). In addition Kawamoto analysed the primary success factors and found that when 4 particular design features were all present 94% of the applications produced significant improvements in decision-making.
More complex decision support techniques also appear to have considerable promise. For example an assessment of the pros and cons of alternative treatments can be given together with various styles of evidence-based justification. Our own trials have consistently shown the potential of such services, in primary, secondary and tertiary settings. Applications which have been trialed to date include family history and genetic risk assessment tools; interpretation of medical images; prescribing, in general practice and in specialist treatment test and treatment selection and personalized treatment planning. All indicated substantial improvements using various measures of decision quality.
After more than 40 years of research, clinical decision support technologies which provide patient-specific advice are becoming mainstream. Decision support and related knowledge based services can help to improve performance in most areas of medicine. It seems evident that it is time for a substantial effort to show how such technologies can be implemented in a practical, usable and scalable way, providing an integrated set of services for supporting care from initial presentation and risk assessment to diagnosis and the planning of treatment. A goal of Safe and Sound was to develop the case for this in detail (see project report LINK), comprehensively setting out requirements and constraints, and proving the feasibility of the concept with a practical demonstration.
Grand challenges in clinical decision support technology
Sittig et al (2008) set out ten “grand challenges” for the successful development and deployment of Clinical Decision Support services in order to “inspire stakeholders in a position to advance the state of CDS technology and practice”. The 10 challenges were identified and prioritized on the basis of empirical experience and with the expectation that overcoming these challenges will depend heavily on practical problem solving and finding out “what works” in clinical use. As active participants in this field we take the position that a pragmatic approach to design must be accompanied by sound theoretical principles and safe engineering methods. We illustrate this with an approach to CDS design that starts with a formal model of knowledge-based decision-making, clinical processes and distributed care services, identifying four key pillars of theory, and relationships between them. Sittig et al’s challenges are reviewed to consider how such a framework can facilitate application design and implementation, clinical use, service interoperability etc. We do not claim a formal approach to design is an alternative to empirical evolution of clinical services but is a foundation on which practical experience can be understood, shared and built upon.
