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.

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

BACKGROUND: 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. AIMS: To assess the impact of digital pathology adoption on logistic laboratory tasks (i.e. not including pathologists' time for diagnosis-making) in the Laboratorium Pathologie Oost Nederland, a large regional pathology laboratory in The Netherlands. METHODS AND RESULTS: To quantify the benefits of digitisation, we analysed the differences between the traditional analogue and new digital workflows, carried out detailed measurements of all relevant steps in key analogue and digital processes, and compared the time spent. We modelled and assessed the logistic savings in five workflows: (i) routine diagnosis; (ii) multidisciplinary meeting; (iii) external revision requests; (iv) extra stainings; and (v) external consultation. On average, >19 working hours were saved on a typical day by working digitally, with the highest savings in routine diagnosis and multidisciplinary meeting workflows. CONCLUSIONS: By working digitally, a significant amount of time could be saved in a large regional pathology laboratory with a typical case mix. We also present the data in each workflow per task and concrete logistic steps to allow extrapolation to the context and case mix of other laboratories.

Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL.

This paper describes a new Cohort Selection application implemented to support streamlining the definition phase of multi-centric clinical research in oncology. Our approach aims at both ease of use and precision in defining the selection filters expressing the characteristics of the desired population. The application leverages our standards-based Semantic Interoperability Solution and a Groovy DSL to provide high expressiveness in the definition of filters and flexibility in their composition into complex selection graphs including splits and merges. Widely-adopted ontologies such as SNOMED-CT are used to represent the semantics of the data and to express concepts in the application filters, facilitating data sharing and collaboration on joint research questions in large communities of clinical users. The application supports patient data exploration and efficient collaboration in multi-site, heterogeneous and distributed data environments.

Workflow-driven clinical decision support for personalized oncology.

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.

The INTEGRATE project: Delivering solutions for efficient multi-centric clinical research and trials.

The objective of the INTEGRATE project (http://www.fp7-integrate.eu/) that has recently concluded successfully was the development of innovative biomedical applications focused on streamlining the execution of clinical research, on enabling multidisciplinary collaboration, on management and large-scale sharing of multi-level heterogeneous datasets, and on the development of new methodologies and of predictive multi-scale models in cancer. In this paper, we present the way the INTEGRATE consortium has approached important challenges such as the integration of multi-scale biomedical data in the context of post-genomic clinical trials, the development of predictive models and the implementation of tools to facilitate the efficient execution of postgenomic multi-centric clinical trials in breast cancer. Furthermore, we provide a number of key "lessons learned" during the process and give directions for further future research and development.

Supporting patient screening to identify suitable clinical trials.

To support the efficient execution of post-genomic multi-centric clinical trials in breast cancer we propose a solution that streamlines the assessment of the eligibility of patients for available trials. The assessment of the eligibility of a patient for a trial requires evaluating whether each eligibility criterion is satisfied and is often a time consuming and manual task. The main focus in the literature has been on proposing different methods for modelling and formalizing the eligibility criteria. However the current adoption of these approaches in clinical care is limited. Less effort has been dedicated to the automatic matching of criteria to the patient data managed in clinical care. We address both aspects and propose a scalable, efficient and pragmatic patient screening solution enabling automatic evaluation of eligibility of patients for a relevant set of trials. This covers the flexible formalization of criteria and of other relevant trial metadata and the efficient management of these representations.

Alignment method for spectrograms of DNA sequences.

DNA spectrograms express the periodicities of each of the four nucleotides A, T, C, and G in one or several genomic sequences to be analyzed. DNA spectral analysis can be applied to systematically investigate DNA patterns, which may correspond to relevant biological features. As opposed to looking at nucleotide sequences, spectrogram analysis may detect structural characteristics in very long sequences that are not identifiable by sequence alignment. Alignment of DNA spectrograms can be used to facilitate analysis of very long sequences or entire genomes at different resolutions. Standard clustering algorithms have been used in spectral analysis to find strong patterns in spectra. However, as they use a global distance metric, these algorithms can only detect strong patterns coexisting in several frequencies. In this paper, we propose a new method and several algorithms for aligning spectra suitable for efficient spectral analysis and allowing for the easy detection of strong patterns in both single frequencies and multiple frequencies.

Service-oriented architecture for grid-enabling medical applications.

Grid technologies have the potential to enable healthcare organizations to efficiently use powerful tools, applications and resources, many of which were so far inaccessible to them. This paper introduces a service-oriented architecture meant to Grid-enable several classes of computationally intensive medical applications for improved performance and cost-effective access to resources. We apply this architecture to fiber tracking [1,2], a computationally intensive medical application suited for parallelization through decomposition, and carry out experiments with various sets of parameters, in realistic environments and with standard network solutions. Furthermore, we deploy and assess our solution in a hospital environment, at the Amsterdam Medical Center, as part of our cooperation in the Dutch VL-e project. Our results show that parallelization and Grid execution may bring significant performance improvements and that the overhead introduced by making use of remote, distributed resources is relatively small.