Workflow-driven clinical decision support for personalized oncology

Abstract

Background: The adoption in oncology of Clinical Decision Support (CDS) may help clinical users to efficiently deal with the high complexity of the domain, lead to improved patient outcomes, and reduce the current knowledge gap between clinical research and practice. While significant effort has been invested in the implementation of CDS, the 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.

Results: To address these challenges, we propose a framework enabling efficient implementation of meaningful CDS that incorporates a large variety of clinical knowledge models to bring to the clinic comprehensive solutions leveraging the latest domain knowledge. We use both literature-based models and models built within the p-medicine project using the rich datasets from clinical trials and care provided by the clinical partners. The framework is open to the biomedical community, enabling reuse of deployed models by third-party CDS implementations and supporting collaboration among modelers, CDS implementers, biomedical researchers and clinicians. To increase adoption and cope with the complexity of patient management in oncology, we also support and leverage the clinical processes adhered to by healthcare organizations. We design an architecture that extends the CDS framework with workflow functionality. The clinical models are embedded in the workflow models and executed at the right time, when and where the recommendations are needed in the clinical process.

Conclusions: In this paper we present our CDS framework developed in p-medicine and the CDS implementation leveraging the framework. To support complex decisions, the framework relies on clinical models that encapsulate relevant clinical knowledge. Next to assisting the decisions, this solution supports by default (through modeling and implementation of workflows) the decision processes as well and exploits the knowledge embedded in those processes.

Keywords: CDS adoption; Clinical decision support; Clinical workflows; Knowledge models; Oncology.

Fig. 1

Architecture and context of use of the CDS framework

Fig. 2

Diagnosis screen

Fig. 3

Treatment selection screen

Fig. 4

CDS integration of breast and nephroblastoma branches

Fig. 5

Details of two simulated bevacizumab monotherapy schemes (dosage, frequency of administration and timepoints of administration). The treatment scheme details are extracted from European medicines agency (Avastin, INN - bevacizumab - WC500029271.pdf) and constitute the two suggested modes of bevacizumab administration for metastatic breast cancer

Fig. 6

Tumour volume time evolution for a tumour treated with bevacizumab according to a treatment scheme consisting of the administration of 15 mg/kg of bevacizumab as a single-agent, once every three weeks for a total of 4 doses (blue line) and a fractionated version of the aforementioned treatment scheme consisting of the administration of 10 mg/kg of bevacizumab as a single-agent, every other week for a total of 6 doses (green line)

Fig. 7

Simplified workflow for early Breast Cancer involving several specialists and departments. The workflow is represented using BPMN [20]

Fig. 8

The overall architecture of the workflow-driven CDS framework