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Qu Z, Krauth C, Amelung VE, Kaltenborn A, Gwiasda J, Harries L, Beneke J, Schrem H, Liersch S. Decision modelling for economic evaluation of liver transplantation. World J Hepatol 2018; 10:837-848. [PMID: 30533184 PMCID: PMC6280166 DOI: 10.4254/wjh.v10.i11.837] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Revised: 08/22/2018] [Accepted: 10/09/2018] [Indexed: 02/06/2023] Open
Abstract
As the gap between a shortage of organs and the immense demand for liver grafts persists, every available donor liver needs to be optimized for utility, urgency and equity. To overcome this challenge, decision modelling might allow us to gather evidence from previous studies as well as compare the costs and consequences of alternative options. For public health policy and clinical intervention assessment, it is a potentially powerful tool. The most commonly used types of decision analytical models include decision trees, the Markov model, microsimulation, discrete event simulation and the system dynamic model. Analytic models could support decision makers in the field of liver transplantation when facing specific problems by synthesizing evidence, comprising all relevant options, generalizing results to other contexts, extending the time horizon and exploring the uncertainty. For modeling studies of economic evaluation for transplantation, understanding the current nature of the disease is crucial, as well as the selection of appropriate modelling techniques. The quality and availability of data is another key element for the selection and development of decision analytical models. In addition, good practice guidelines should be complied, which is important for standardization and comparability between economic outputs.
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Affiliation(s)
- Zhi Qu
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
| | - Christian Krauth
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
| | - Volker Eric Amelung
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
| | - Alexander Kaltenborn
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
| | - Jill Gwiasda
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
| | - Lena Harries
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
| | - Jan Beneke
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
| | - Harald Schrem
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- General, Visceral and Transplant Surgery, Hannover Medical School, Hannover 30625, Germany
| | - Sebastian Liersch
- Core Facility Quality Management and Health Technology Assessment in Transplantation, Integrated Research and Treatment Facility Transplantation (IFB-Tx), Hannover Medical School, Hannover 30625, Germany
- Institute for Epidemiology, Social Medicine and Health Systems Research, Hannover Medical School, Hannover 30625, Germany
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Zhang C, Grandits T, Härenstam KP, Hauge JB, Meijer S. A systematic literature review of simulation models for non-technical skill training in healthcare logistics. Adv Simul (Lond) 2018; 3:15. [PMID: 30065851 PMCID: PMC6062859 DOI: 10.1186/s41077-018-0072-7] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 06/25/2018] [Indexed: 12/31/2022] Open
Abstract
Background Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care. Methods A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality. Results Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low. Conclusions An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.
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Affiliation(s)
- Chen Zhang
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, 2010, Röntgenvägen 1, 14152 Huddinge, Sweden
| | - Thomas Grandits
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, Hälsovägen 11, 14152 Huddinge, Sweden
| | - Karin Pukk Härenstam
- Pediatric Emergency Department, Karolinska University Hospital, Tomtebodavägen 18a, 17177 Stockholm, Sweden
- Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Tomtebodavägen 18a, 17177 Stockholm, Sweden
| | - Jannicke Baalsrud Hauge
- School of Industrial Engineering and Management, Royal Institute of Technology, Mariekällgatan 3, 15144 Södertälje, Sweden
| | - Sebastiaan Meijer
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Royal Institute of Technology, Hälsovägen 11, 14152 Huddinge, Sweden
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