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      Designing a novel hybrid healthcare teleconsultation network: a benchtop study of telepathology in Iran and a systematic review

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          Abstract

          Background

          Growing demand for medical services has increased patient waiting time due to the limited number or unbalanced distribution of healthcare centers. Healthcare teleconsultation networks are one of the potentially powerful systems to overcome this problem. Medical pathology can hugely benefit from teleconsultation networks because having second opinions is precious for many cases; however, resource planning (i.e., assignment and distribution of pathology consultation requests) is challenging due to bulky medical images of patients. This results in high setup and operational costs. The aim of this study is to design an optimal teleconsultation network for pathology labs under the supervision of medical sciences universities in Tehran, Iran.

          Methods

          To avoid the setup cost, we first propose a modified hybrid peer-to-peer (P2P) overlay architecture for our telepathology network, using Iran’s National Healthcare Information Network (SHAMS) as the underlying infrastructure. Then we apply optimization techniques to solve the request assignment and distribution problems in the network. Finally, we present a novel mathematical model with the objective of minimizing the variable operational costs of the system.

          Results

          The efficiency of the proposed method was evaluated by a set of practical-sized network instances simulated based on the characteristics of SHAMS. The results show that the presented model and architecture can obtain optimal solutions for network instances up to 350 nodes, which covers our target network.

          Conclusions

          We believe that the proposed method can be beneficial for designing large-scale medical teleconsultation networks by adjusting the constraints according to the rules and conditions of each country. Our findings showed that teleconsultation networks in countries with strong information technology (IT) infrastructures are under the influence of consultation fees, while in countries with weak IT infrastructure, the transmission costs are more critical. To the best of our knowledge, no research has so far addressed resource planning in medical teleconsultation networks using optimization techniques. Besides, the target network, i.e., pathology labs under the supervision of medical sciences universities in Tehran and the SHAMS network, are discussed for the first time in this work.

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          Most cited references33

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          Pathology Databanking and Biobanking in The Netherlands, a Central Role for PALGA, the Nationwide Histopathology and Cytopathology Data Network and Archive

          Since 1991, a nationwide histopathology and cytopathology network and archive is in operation in The Netherlands under the name PALGA, encompassing all sixty-four pathology laboratories in The Netherlands. The overall system comprises decentralized systems at the participating laboratories, a central databank, and a dedicated communication and information exchange tool. Excerpts of all histopathology and cytopathology reports are generated automatically at the participating laboratories and transferred to the central databank. Both the decentralized systems and the central system perform checks on the quality and completeness of excerpts. Currently, about 42 million records on almost 10 million patients are stored in the central databank. Each excerpt contains patient identifiers, including demographic data and the so-called PALGA diagnosis. The latter is structured along five classification axes: topography, morphology, function, procedure, and diseases. All data transfer and communication occurs electronically with encryption of patient and laboratory identifiers. All excerpts are continuously available to all participating pathology laboratories, thus contributing to the quality of daily patient care. In addition, external parties may obtain permission to use data from the PALGA system, either on an ongoing basis or on the basis of a specific permission. Annually, 40 to 60 applications for permission to use PALGA data are submitted. Among external users are the Dutch cancer registry, population-based screening programs for cancer of the uterine cervix and breast cancer in The Netherlands, and individual investigators addressing a range of research questions. Many scientific papers and theses incorporating PALGA data have been published already. In conclusion, the PALGA system is a unique system that requires a minimal effort on the part of the participating laboratories, while providing them a powerful tool in their daily practices.
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            Measuring and comparing health care waiting times in OECD countries.

            Waiting times for elective treatments are a key health-policy concern in several OECD countries. This study describes common measures of waiting times from administrative data across OECD countries. It focuses on common elective procedures, such as hip and knee replacement, and cataract surgery, where waiting times are notoriously long. It provides comparative data on waiting times across 12 OECD countries and presents trends in waiting times over the last decade. Waiting times appear to be low in the Netherlands and Denmark. In the last decade the United Kingdom (in particular England), Finland and the Netherlands have witnessed large reductions in waiting times which can be attributed to a range of policy initiatives, including higher spending, waiting-times target schemes and incentive mechanisms, which reward higher levels of activity. The negative trend in these countries has, however, halted or reversed in recent years. The analysis also emphasizes systematic differences across different waiting-time measures, in particular between the distribution of waiting times of patients treated versus that of patients on the list. Mean waiting times are systematically higher than median waiting times and the difference can be quantitatively large.
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              Constrained Optimization Methods in Health Services Research-An Introduction: Report 1 of the ISPOR Optimization Methods Emerging Good Practices Task Force.

              Providing health services with the greatest possible value to patients and society given the constraints imposed by patient characteristics, health care system characteristics, budgets, and so forth relies heavily on the design of structures and processes. Such problems are complex and require a rigorous and systematic approach to identify the best solution. Constrained optimization is a set of methods designed to identify efficiently and systematically the best solution (the optimal solution) to a problem characterized by a number of potential solutions in the presence of identified constraints. This report identifies 1) key concepts and the main steps in building an optimization model; 2) the types of problems for which optimal solutions can be determined in real-world health applications; and 3) the appropriate optimization methods for these problems. We first present a simple graphical model based on the treatment of "regular" and "severe" patients, which maximizes the overall health benefit subject to time and budget constraints. We then relate it back to how optimization is relevant in health services research for addressing present day challenges. We also explain how these mathematical optimization methods relate to simulation methods, to standard health economic analysis techniques, and to the emergent fields of analytics and machine learning.
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                Author and article information

                Contributors
                mtaghipour@modares.ac.ir
                mehdi.sepehri@modares.ac.ir
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                12 August 2020
                12 August 2020
                2020
                : 20
                : 186
                Affiliations
                GRID grid.412266.5, ISNI 0000 0001 1781 3962, The Laboratory for Healthcare Systems Optimization, Engineering, and Informatics, Faculty of Industrial and Systems Engineering, , Tarbiat Modares University, ; Tehran, 1411713116 Iran
                Author information
                http://orcid.org/0000-0002-9920-7452
                Article
                1170
                10.1186/s12911-020-01170-6
                7477836
                32787833
                e388699b-ecb5-496f-befe-752ac6f37a01
                © The Author(s). 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 6 January 2019
                : 26 June 2020
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2020

                Bioinformatics & Computational biology
                medical teleconsultation,telepathology,healthcare networks,mathematical modeling,resource optimization,cost management

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