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      The DEMATEL method explores the interdependent relationship structure and weights for diagnosis-related groups system

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          Abstract

          Purpose

          This study constructs a structure of interaction between dimensions and criteria within the diagnosis-related groups (DRGs) system from a quantitative system and identifies key factors affecting the overall performance of medical services.

          Method

          From September to December 2020, the influence relation structure diagram (IRSD) of the dimensions and corresponding criteria was developed from the practical experience of a group of domain experts, based on the DEMATEL method. Subsequently, all dimensions and criteria construct influential weights from a systems perspective. Finally, the main influential factors were identified based on the analysis results.

          Results

          The IRSD results showed that, in the overall performance of medical services, “Medical service capacity ( C 1)” was the main influential dimension, influencing both “Medical service efficiency ( C 2)” and “Medical service safety ( C 3).” At the criteria level, “Case-mix index (CMI) ( C 12),” “Time efficiency index (C 21),” and “Inpatient mortality of medium-to-low group (C 32)” were the main influential criteria in the corresponding dimensions. The influential weight results showed that “Medical service capacity ( C 1)” was also a key dimension. “Case-mix index (CMI) ( C 12),” “Cost efficiency index ( C 22),” and “Inpatient mortality of medium-to-low group ( C 32)” were the key criteria in their respective dimensions.

          Conclusion

          Patients and managers should first focus on the capacity of medical service providers when making a choice or deciding using the results of the DRGs system. Furthermore, they should pay more attention to medical safety even if it is not as weighted as medical efficiency.

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          Most cited references29

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          10 years of health-care reform in China: progress and gaps in Universal Health Coverage

          In 2009, China launched a major health-care reform and pledged to provide all citizens with equal access to basic health care with reasonable quality and financial risk protection. The government has since quadrupled its funding for health. The reform's first phase (2009-11) emphasised expanding social health insurance coverage for all and strengthening infrastructure. The second phase (2012 onwards) prioritised reforming its health-care delivery system through: (1) systemic reform of public hospitals by removing mark-up for drug sales, adjusting fee schedules, and reforming provider payment and governance structures; and (2) overhaul of its hospital-centric and treatment-based delivery system. In the past 10 years, China has made substantial progress in improving equal access to care and enhancing financial protection, especially for people of a lower socioeconomic status. However, gaps remain in quality of care, control of non-communicable diseases (NCDs), efficiency in delivery, control of health expenditures, and public satisfaction. To meet the needs of China's ageing population that is facing an increased NCD burden, we recommend leveraging strategic purchasing, information technology, and local pilots to build a primary health-care (PHC)-based integrated delivery system by aligning the incentives and governance of hospitals and PHC systems, improving the quality of PHC providers, and educating the public on the value of prevention and health maintenance.
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            Data feedback efforts in quality improvement: lessons learned from US hospitals.

            Data feedback is a fundamental component of quality improvement efforts, but previous studies provide mixed results on its effectiveness. This study illustrates the diversity of hospital based efforts at data feedback and highlights successful strategies and common pitfalls in designing and implementing data feedback to support performance improvement. Open ended interviews with 45 clinical and administrative staff in eight US hospitals in 2000 concerning their perceptions about the effectiveness of data feedback in supporting performance improvement efforts were analysed. The hospitals were chosen to represent a range of sizes, geographical regions, and beta blocker improvement rates over a 3 year period. Data were organized and analyzed in NUD-IST 4 using the constant comparative method of qualitative data analysis. Although the data feedback efforts at the hospitals were diverse, the interviews suggested that seven key themes may be important: (1) data must be perceived by physicians as valid to motivate change; (2) it takes time to develop the credibility of data within a hospital; (3) the source and timeliness of data are critical to perceived validity; (4) benchmarking improves the meaningfulness of data feedback; (5) physician leaders can enhance the effectiveness of data feedback; (6) data feedback that profiles an individual physician's practices can be effective but may be perceived as punitive; (7) data feedback must persist to sustain improved performance. Embedded in several themes was the view that the effectiveness of data feedback depends not only on the quality and timeliness of the data, but also on the organizational context in which such efforts are implemented. Data feedback is a complex and textured concept. Data feedback strategies that might be most effective are suggested, as well as potential pitfalls in using data to promote performance improvement.
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              Case mix definition by diagnosis-related groups.

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                Author and article information

                Contributors
                Journal
                Front Public Health
                Front Public Health
                Front. Public Health
                Frontiers in Public Health
                Frontiers Media S.A.
                2296-2565
                04 August 2022
                2022
                : 10
                : 872434
                Affiliations
                [1] 1Institute for Hospital Management, Tsing Hua University , Shenzhen, China
                [2] 2Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University , Taizhou, China
                [3] 3Business College, Taizhou University , Taizhou, China
                [4] 4Institute of Public Health and Emergency Management, Taizhou University , Taizhou, China
                [5] 5Evidence-Based Medicine Center, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University , Linhai, China
                Author notes

                Edited by: Sanaa Al Ahdab, AlBaath University, Syria

                Reviewed by: Simon Grima, University of Malta, Malta; WeiLun Huang, Wenzhou Business College, China; Minchih Hsieh, University of Shanghai for Science and Technology, China; Sun-Weng Huang, China University of Technology, Taiwan; James Liou, National Taipei University of Technology, Taiwan

                *Correspondence: Tao-Hsin Tung ch2876@ 123456gmail.com

                This article was submitted to Health Economics, a section of the journal Frontiers in Public Health

                †These authors have contributed equally to this work

                Article
                10.3389/fpubh.2022.872434
                9386257
                35991048
                1d10dca9-febc-4297-ab62-fe930b6b20f0
                Copyright © 2022 Zou, Jin, Chuang, Chien and Tung.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 09 February 2022
                : 30 June 2022
                Page count
                Figures: 1, Tables: 6, Equations: 12, References: 30, Pages: 0, Words: 5707
                Categories
                Public Health
                Original Research

                diagnosis-related groups (drgs),the interdependent relationship structure and weights,decision-making trial and evaluation laboratory (dematel),key factors,multiple criteria decision-making (mcdm)

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