Jasper van Leeuwen

Clinical decision support framework for validation of multiscale models and personalization of treatment in oncology

The implementation of Clinical Decision Support (CDS) solutions is an important prerequisite for reducing the knowledge gap between clinical research and practice, especially in a complex genetic disease such as cancer. However, current CDS solutions are unable to support all the complex decisions required for personalized treatment of cancer patients and become quickly obsolete due to the high rate of change in therapeutic options and knowledge. Our CDS framework enables the development of decision support tools that flexibly integrate a large variety of multiscale models and can leverage the efforts of a large community of modellers. In our implementation, we combine community-developed models described in the literature (e.g. the St. Gallen stratification for early breast cancer) and models derived by mining the comprehensive datasets from clinical trials and care brought together in the p_Medicine collaborative research project. This framework and its underlying solution for models storage, management and execution will also constitute a platform for continuous validation of existing models on new data. Our goal is to enable the reuse of existing models for CDS and for the development of new models, and to support collaboration among modellers, CDS implementers, biomedical researchers and clinicians. We initially develop and deploy our solution in the context of the p-Medicine project in the oncology domain, but we aim to expand our scope and to reach out to a wide community of users in the biomedical area.

Identifying the semantics of eligibility criteria of clinical trials based on relevant medical ontologies

An important objective of the INTEGRATE project1 is to build tools that support the efficient execution of post-genomic multi-centric clinical trials in breast cancer, which includes the automatic assessment of the eligibility of patients for available trials. The population suited to be enrolled in a trial is described by a set of free-text eligibility criteria that are both syntactically and semantically complex. At the same time, the assessment of the eligibility of a patient for a trial requires the (machine-processable) understanding of the semantics of the eligibility criteria in order to further evaluate if the patient data available for example in the hospital EHR satisfies these criteria. This paper presents an analysis of the semantics of the clinical trial eligibility criteria based on relevant medical ontologies in the clinical research domain: SNOMED-CT, LOINC, MedDRA. We detect subsets of these widely-adopted ontologies that characterize the semantics of the eligibility criteria of trials in various clinical domains and compare these sets. Next, we evaluate the occurrence frequency of the concepts in the concrete case of breast cancer (which is our first application domain) in order to provide meaningful priorities for the task of binding/mapping these ontology concepts to the actual patient data. We further assess the effort required to extend our approach to new domains in terms of additional semantic mappings that need to be developed.

Workflow-driven clinical decision support for personalized oncology

BackgroundThe 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.ResultsTo 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.ConclusionsIn 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.

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.

Evaluating the benefits of digital pathology implementation: time savings in laboratory logistics

The benefits of digital pathology for workflow improvement and thereby cost savings in pathology, at least partly outweighing investment costs, are being increasingly recognised. Successful implementations in a variety of scenarios have started to demonstrate the cost benefits of digital pathology for both research and routine diagnosis, contributing to a sound business case encouraging further adoption. To further support new adopters, there is still a need for detailed assessment of the impact that this technology has on the relevant pathology workflows, with an emphasis on time‐saving.

iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops.

Recruitment and feasibility tool

Tool description The recruitment and feasibility tool (earlier named Yakobo) was developed in the EURECA project to assess protocol feasibility and to find eligible patients for clinical trials. Protocol feasibility functions analyze the feasibility of a trial protocol by assessing the expected patient enrollment rate of a selection of sites given the protocol’s eligibility criteria, based on “historical” data (existing patient data). It allows for answering questions such as whether inclusion and exclusion criteria are useful for defining the proper study population, whether it is likely that the necessary volume of patients can be recruited in time to collect data with sufficient statistical power and/or the expected duration of a trial. Criteria are expressed in a domain specific language [1]. A Trial metadata repository contains protocol definitions (Figure 1) and the SNAQL engine executes the DSL of the criteria to find patients belonging to the cohort. The SNAQL engine accesses the semantic integration services which provide a query interface [2] with reasoning abilities. Once a clinical protocol has been finalized, the tool is used to find patients eligible for enrollment.