Framework for workflow-driven Clinical Decision Support in oncology

Successful implementation of meaningful Clinical Decision Support (CDS) solutions in healthcare has the potential to reduce the knowledge gap between clinical research and practice, especially in a complex genetic disease such as cancer. While significant effort has been invested in the implementation of tools for CDS in the last few decades, their uptake in the clinic has been limited. The barriers to adoption have been extensively discussed in the literature. In oncology, current CDS solutions are not able to support the complex decisions required for stratification and personalized treatment of patients and to keep up with the high rate of change in therapeutic options and knowledge. We propose a CDS framework that facilitates the implementation of decision support that flexibly integrates a large variety of clinical models and can bring to the clinic comprehensive solutions leveraging the latest available knowledge. We include both literature-based models and models built within the p-medicine research project using the available comprehensive datasets from clinical trials and care. The solution is open to the biomedical community, enabling the reuse of existing models for third-party CDS implementations and for the development of new models, and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and support the complexity of patient management along the care continuum, we also propose to support and leverage the clinical processes defined and adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow modeling and execution functionality to leverage the existing clinical processes. The knowledge models are embedded in the workflow models and executed at the right time, when and where the recommendation is needed in the clinical process. Next to supporting the decisions, this solution supports by default the decision processes as well and exploits the knowledge embedded in those processes.