Improving the implementation of clinical decision support systems

Rüping, Stefan; Anguita, Alberto; Bucur, Anca; Cirstea, Traian Cristian; Jacobs, Bjorn; Torge, Antje

doi:10.1109/embc.2013.6610225

Cited by 6 publications

(4 citation statements)

References 9 publications

(6 reference statements)

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“…The p-medicine data warehouse aggregates the large oncology datasets available in the project. The data are used to develop models that range from predictive models derived by data mining techniques [ 11 ] to complex multiscale VPH models [ 13 ]. To support the complex decisions required for treating cancer patients no single model is sufficient.…”

Section: Methodsmentioning

confidence: 99%

“…In this implementation we consider two important sources of models: (i) Validated knowledge described in the literature and (ii) models derived on the comprehensive datasets from clinical trials and care available through the p-medicine infrastructure (which can be used in care after prospective validation). In [ 11 ] we reported on the development and integration into the CDS framework of predictive models developed by mining a clinical trial dataset available in p-medicine. We demonstrated that the framework enables efficient collaboration among clinical researchers, knowledge modelers, data miners and CDS implementers.…”

Section: Introductionmentioning

confidence: 99%

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Workflow-driven clinical decision support for personalized oncology

Bucur

Leeuwen

Christodoulou

et al. 2016

BMC Med Inform Decis Mak

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Workflow-driven clinical decision support for personalized oncology

Bucur

Leeuwen

Christodoulou

et al. 2016

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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“…Access to EBD research should also be improved (93,101,102,(104)(105)(106)(107)(108)(109)(110)(111) and better awareness of platforms is needed (78,84). New tools for rapid access of evidence-based information are also needed in order to minimise the amount of time needed to establish an EBP.…”

Section: Future Research and Recommendationsmentioning

confidence: 99%

Evidence‐based dentistry for planning restorative treatments: barriers and potential solutions

Afrashtehfar

Eimar

Yassine

et al. 2016

Eur J Dental Education

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“…One is based on the clinical knowledge provided in the literature (evidence generated by the wide community), while another source is based on data mining the large repository of clinical research and care data built within the p_Medicine context. We have currently implemented both types of models: In [6] we have reported on the development and integration into the CDS framework of predictive models developed by mining a large clinical trial dataset available in the p-Medicine data environment. We have demonstrated that the solution enables efficient collaboration among clinical researchers, knowledge modellers, data miners and CDS implementers.…”

Section: Introductionmentioning

confidence: 99%

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

Bucur

Leeuwen

Cirstea

et al. 2013

13th IEEE International Conference on BioInformatics and BioEngineering

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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.

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Improving the implementation of clinical decision support systems

Cited by 6 publications

References 9 publications

Workflow-driven clinical decision support for personalized oncology

Workflow-driven clinical decision support for personalized oncology

Evidence‐based dentistry for planning restorative treatments: barriers and potential solutions

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

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