Knowledge-based hybrid variance handling for patient care workflows based on clinical pathways

Yan, Yonghong; Jiang, Zhibin; Diao, Xiaodi; Du, Gang

doi:10.1109/soli.2009.5203896

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…Secondly, TFPN-PK explicitly defines the associations between concrete domain concepts and processrelated descriptions by place mappings and therefore combines domain knowledge with process knowledge, facilitating the integration of knowledge reasoning and workflow application. For the descriptions of TFPN-PK and its reasoning algorithm, please refer to [8]. Fig.…”

Section: Variance Management Systemmentioning

confidence: 99%

A Knowledge-Based Variance Management System for Supporting the Implementation of Clinical Pathways

Yan

Diao

Jiang

et al. 2009

2009 International Conference on Management and Service Science

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Variance management is important for computerized implementation of clinical pathways (CPs) to dynamically execute patient care processes and effectively provide high quality and efficient healthcare services. However, current related efforts are not adequate to support variance handling process involving two decision strategies, imprecise knowledge and the interaction with standardized CP workflow. This paper presents a knowledge-based variance management system that is developed using object-oriented analysis and design techniques, especially unified modeling language (UML), and implements effective analysis and handling of various variances through the construction and fuzzy reasoning of generalized fuzzy ECA (GFECA) rules and typed fuzzy Petri net extended by process knowledge (TFPN-PK) models related to the clinical pathway ontology and healthcare domain ontology.

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Section: Variance Management Systemmentioning

confidence: 99%

A Knowledge-Based Variance Management System for Supporting the Implementation of Clinical Pathways

Yan

Diao

Jiang

et al. 2009

2009 International Conference on Management and Service Science

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“…In [33] Kotb and Baumgart present an extended workflow PN, which synchronizes activity flow in the critical sections to control concurrency between process activities and apply this formalism to the modelling of a hospital operation theatre. Additionally, Ye et al [48] propose a fuzzy PN formalism for patient care workflow analysis and hospital department performance evaluation. A contribution is proposed by Darabi et al [11] to present a comprehensive formalism for modelling hospital operations based on the use of workflow PN.…”

Section: Introductionmentioning

confidence: 99%

Discrete event systems models and methods for different problems in healthcare management

Fanti

Ukovich

2014

Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA)

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The rapidly growing of the world's population makes efficient Healthcare System (HS) management more and more important to all stakeholders, from patients and healthcare providers, to insurance companies and governments. The use of advanced information and networking technologies to advance health care management has long been recognized as the most effective way. Hence, healthcare organizations are often compared with manufacturing systems, and techniques and models from production systems are adapted to solve healthcare management problems. This paper presents a review about the relevant approaches in the related literature to model and simulate HSs with particular focus on discrete event system approaches. In particular, some works are recalled with more detail to show the integrated use of different formalisms in order to model and manage conventional HSs (e.g., hospital departments) as well as novel healthcare paradigms, like health at home and electronic health. © 2014 IEEE

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“…The fuzzy models can describe vague statements as in natural language. Therefore, to cope with this difficulty, a typed fuzzy Petri net extended by process knowledge (TFPN-PK) model method is presented to provide support for the handling decision of different types of CP variances (shown in Table 1), and the different types of variances can be dealt with based on different strategies [13][14][15]. Moreover, an extended event-condition-action (ECA) rules method is proposed to model and handle the CP variances validly [16].…”

Section: Introductionmentioning

confidence: 99%

Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

Jiang

Diao

et al. 2010

J Med Syst

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Clinical pathways' variances present complex, fuzzy, uncertain and high-risk characteristics. They could cause complicating diseases or even endanger patients' life if not handled effectively. In order to improve the accuracy and efficiency of variances handling by Takagi-Sugeno (T-S) fuzzy neural networks (FNNs), a new variances handling method for clinical pathways (CPs) is proposed in this study, which is based on T-S FNNs with novel hybrid learning algorithm. And the optimal structure and parameters can be achieved simultaneously by integrating the random cooperative decomposing particle swarm optimization algorithm (RCDPSO) and discrete binary version of PSO (DPSO) algorithm. Finally, a case study on liver poisoning of osteosarcoma preoperative chemotherapy CP is used to validate the proposed method. The result demonstrates that T-S FNNs based on the proposed algorithm achieves superior performances in efficiency, precision, and generalization ability to standard T-S FNNs, Mamdani FNNs and T-S FNNs based on other algorithms (CPSO and PSO) for variances handling of CPs.

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Knowledge-based hybrid variance handling for patient care workflows based on clinical pathways

Cited by 7 publications

References 7 publications

A Knowledge-Based Variance Management System for Supporting the Implementation of Clinical Pathways

A Knowledge-Based Variance Management System for Supporting the Implementation of Clinical Pathways

Discrete event systems models and methods for different problems in healthcare management

Variances Handling Method of Clinical Pathways Based on T-S Fuzzy Neural Networks with Novel Hybrid Learning Algorithm

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