The Experts below are selected from a list of 6690 Experts worldwide ranked by ideXlab platform
Vojtech Huser - One of the best experts on this subject based on the ideXlab platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a Workflow Engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a Workflow editor for modeling clinical scenarios and a Workflow Engine for execution of those scenarios. We demonstrate, with an open-source and publicly available Workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free Workflow technology software suite (available at http://code.google.com/p/healthflow ) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that Workflow Engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of Workflow Engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Background Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.
Justin Starren - One of the best experts on this subject based on the ideXlab platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a Workflow Engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a Workflow editor for modeling clinical scenarios and a Workflow Engine for execution of those scenarios. We demonstrate, with an open-source and publicly available Workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free Workflow technology software suite (available at http://code.google.com/p/healthflow ) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that Workflow Engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of Workflow Engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Background Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.
Mark Neeser - One of the best experts on this subject based on the ideXlab platform.
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automated data reduction Workflows for astronomy
arXiv: Instrumentation and Methods for Astrophysics, 2013Co-Authors: Wolfram Freudling, M Romaniello, D M Bramich, Pascal Ballester, Vincenzo Forchi, C E Garciadablo, S Moehler, Mark NeeserAbstract:Data from complex modern astronomical instruments often consist of a large number of different science and calibration files, and their reduction requires a variety of software tools. The execution chain of the tools represents a complex Workflow that needs to be tuned and supervised, often by individual researchers that are not necessarily experts for any specific instrument. The efficiency of data reduction can be improved by using automatic Workflows to organise data and execute the sequence of data reduction steps. To realize such efficiency gains, we designed a system that allows intuitive representation, execution and modification of the data reduction Workflow, and has facilities for inspection and interaction with the data. The European Southern Observatory (ESO) has developed Reflex, an environment to automate data reduction Workflows. Reflex is implemented as a package of customized components for the Kepler Workflow Engine. Kepler provides the graphical user interface to create an executable flowchart-like representation of the data reduction process. Key features of Reflex are a rule-based data organiser, infrastructure to re-use results, thorough book-keeping, data progeny tracking, interactive user interfaces, and a novel concept to exploit information created during data organisation for the Workflow execution. Reflex includes novel concepts to increase the efficiency of astronomical data processing. While Reflex is a specific implementation of astronomical scientific Workflows within the Kepler Workflow Engine, the overall design choices and methods can also be applied to other environments for running automated science Workflows.
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automated data reduction Workflows for astronomy the eso reflex environment
Astronomy and Astrophysics, 2013Co-Authors: Wolfram Freudling, M Romaniello, D M Bramich, Pascal Ballester, Vincenzo Forchi, C E Garciadablo, S Moehler, Mark NeeserAbstract:Context. Data from complex modern astronomical instruments often consist of a large number of di erent science and calibration files, and their reduction requires a variety of software tools. The execution chain of the tools represents a complex Workflow that needs to be tuned and supervised, often by individual researchers that are not necessarily experts for any specific instrument. Aims. The e ciency of data reduction can be improved by using automatic Workflows to organise data and execute a sequence of data reduction steps. To realize such e ciency gains, we designed a system that allows intuitive representation, execution and modification of the data reduction Workflow, and has facilities for inspection and interaction with the data. Methods. The European Southern Observatory (ESO) has developed Reflex, an environment to automate data reduction Workflows. Reflex is implemented as a package of customized components for the Kepler Workflow Engine. Kepler provides the graphical user interface to create an executable flowchart-like representation of the data reduction process. Key features of Reflex are a rule-based data organiser, infrastructure to re-use results, thorough book-keeping, data progeny tracking, interactive user interfaces, and a novel concept to exploit information created during data organisation for the Workflow execution. Results. Automated Workflows can greatly increase the e ciency of astronomical data reduction. In Reflex, Workflows can be run noninteractively as a first step. Subsequent optimization can then be carried out while transparently re-using all unchanged intermediate products. We found that such Workflows enable the reduction of complex data by non-expert users and minimizes mistakes due to book-keeping errors. Conclusions. Reflex includes novel concepts to increase the e ciency of astronomical data processing. While Reflex is a specific implementation of astronomical scientific Workflows within the Kepler Workflow Engine, the overall design choices and methods can also be applied to other environments for running automated science Workflows.
Ryan Oberg - One of the best experts on this subject based on the ideXlab platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a Workflow Engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a Workflow editor for modeling clinical scenarios and a Workflow Engine for execution of those scenarios. We demonstrate, with an open-source and publicly available Workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free Workflow technology software suite (available at http://code.google.com/p/healthflow ) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that Workflow Engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of Workflow Engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Background Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.
Luke V Rasmussen - One of the best experts on this subject based on the ideXlab platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic. We present our implementation of a Workflow Engine technology that addresses the two above-described challenges in delivering clinical decision support. Our system is based on a cross-industry standard of XML (extensible markup language) process definition language (XPDL). The core components of the system are a Workflow editor for modeling clinical scenarios and a Workflow Engine for execution of those scenarios. We demonstrate, with an open-source and publicly available Workflow suite, that clinical decision support logic can be executed on retrospective data. The same flowchart-based representation can also function in a prospective mode where the system can be integrated with an EHR system and respond to real-time clinical events. We limit the scope of our implementation to decision support content generation (which can be EHR system vendor independent). We do not focus on supporting complex decision support content delivery mechanisms due to lack of standardization of EHR systems in this area. We present results of our evaluation of the flowchart-based graphical notation as well as architectural evaluation of our implementation using an established evaluation framework for clinical decision support architecture. We describe an implementation of a free Workflow technology software suite (available at http://code.google.com/p/healthflow ) and its application in the domain of clinical decision support. Our implementation seamlessly supports clinical logic testing on retrospective data and offers a user-friendly knowledge representation paradigm. With the presented software implementation, we demonstrate that Workflow Engine technology can provide a decision support platform which evaluates well against an established clinical decision support architecture evaluation framework. Due to cross-industry usage of Workflow Engine technology, we can expect significant future functionality enhancements that will further improve the technology's capacity to serve as a clinical decision support platform.
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implementation of Workflow Engine technology to deliver basic clinical decision support functionality
BMC Medical Research Methodology, 2011Co-Authors: Vojtech Huser, Luke V Rasmussen, Ryan Oberg, Justin StarrenAbstract:Background Workflow Engine technology represents a new class of software with the ability to graphically model step-based knowledge. We present application of this novel technology to the domain of clinical decision support. Successful implementation of decision support within an electronic health record (EHR) remains an unsolved research challenge. Previous research efforts were mostly based on healthcare-specific representation standards and execution Engines and did not reach wide adoption. We focus on two challenges in decision support systems: the ability to test decision logic on retrospective data prior prospective deployment and the challenge of user-friendly representation of clinical logic.
