Development and application of a real-world-data-based indicator system to evaluate the use of diagnostic large medical equipment_Chinese Journal of Evidence-Based Medicine

Authors：

XIONG Yihao ¹ , MA Yu ^2,3 , HE Tao ⁴ , YANG Fuzhou ⁵ , HE Xingming ⁶ , CUI Futao ¹ , WANG Hui ⁴ , LIU Yemei ⁵ , LU Wanjun ⁶ , TIAN Rong ¹ , LI Ling ^2,3 ,  SUN Xin ^2,3 ,  LI Qianrui ^1,2,3

1. Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Clinical Epidemiology and Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. NHC Key Laboratory of Clinical Epidemiology and Evidence-based Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
4. Department of Nuclear Medicine, Panzhihua Central Hospital, Panzhihua 617000, P. R. China;
5. Department of Nuclear Medicine, Ya’an People’s Hospital, Ya’an 625000, P. R. China;
6. Department of Nuclear Medicine, Guangyuan Central Hospital, Guangyuan 628000, P. R. China;

Corresponding?author：

SUN Xin, Email: sunxin@wchscu.cn; LI Qianrui, Email: liqianrui@wchscu.cn

Keywords：

Large medical equipment; Comprehensive evaluation; Indicator system; Analytic hierarchy process; Real-word data

DOI：

10.7507/1672-2531.202504186

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Objective To develop a real-world-data-based monitoring system for diagnostic large medical equipment, and to use PET/CT as a carrier for validation. Methods We used literature survey, site investigation, and two-rounds of modified Delphi methods to develop the indicator system, and used the analytic hierarchy process method to determine the weight of each indicator. We collected real-world PET/CT data from four tertiary hospitals from July to December 2022, and monitored the use of PET/CT in each hospital. Results Questionnaire recovery rates of 2 rounds were 100% and 88%, respectively, the expert authority coefficient was greater than 0.70, and the coordination coefficients of experts in the total index were 0.307 and 0.471 (P<0.001). A three-level indicator system was established to monitor the use of large medical equipment, with three first-level indicators (clinical use, implementation, and other efficiencies), eight second-level indicators, and 15 third-level indicators. Empirical experiment found different hospitals vary in efficiency (of clinical use), staff status, and economic and research efficiency, while remained similar in other indicators. Conclusion We developed a monitoring system for diagnostic large medical equipment based on real-world data, and used PET/CT as a carrier for validation. These findings provided theoretical and empirical foundations for the management of diagnostic large medical equipment in China.

Citation： XIONG Yihao, MA Yu, HE Tao, YANG Fuzhou, HE Xingming, CUI Futao, WANG Hui, LIU Yemei, LU Wanjun, TIAN Rong, LI Ling, SUN Xin, LI Qianrui. Development and application of a real-world-data-based indicator system to evaluate the use of diagnostic large medical equipment. Chinese Journal of Evidence-Based Medicine, 2025, 25(12): 1395-1401. doi: 10.7507/1672-2531.202504186 Copy

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28.	楊玉潔, 毛阿燕, 邱五七, 等. 醫療機構大型醫用設備使用管理評價指標體系構建. 中國醫院管理, 2022, 42(4): 26-30.
29.	衛生部. 衛生部關于印發《2011-2015年全國正電子發射型斷層掃描儀配置規劃》的通知.
30.	國家衛生健康委三級公立醫院績效考核工作領導小組. 國家三級公立醫院績效考核操作手冊(2020版).

1. 國家衛生健康委員會. 關于發布大型醫用設備配置許可管理目錄的通知(2018年).
2. 楊光, 楊麗亞, 董蓬玉, 等. 我國公立醫院住院病人醫藥費用分析. 中國公共衛生管理, 2020, 36(1): 24-27.
3. 梁斐, 胡獻之, 劉佳琦, 等. 2005年和2009年全國CT、MRI檢查總費用估算與分析. 中國衛生資源, 2012, 15(6): 462-464.
4. 陳英耀, 董恒進, 劉佳琦, 等. 我國乙類大型醫用設備配置、利用現狀與政策建議. 中國衛生資源, 2012, 15(1): 42-45.
5. 段勝楠, 吳媛, 梁斐, 等. 大型醫用設備使用合理性分析. 中國衛生資源, 2012, 15(3): 228-230.
6. 劉佳琦, 陳英耀. 三種大型醫用設備使用適宜性評價及分析. 中國衛生政策研究, 2013, 6(2): 40-43.
7. Huang B, Law MW, Khong PL. Whole-body PET/CT scanning: estimation of radiation dose and cancer risk. Radiology, 2009, 251(1): 166-174.
8. Brix G, Lechel U, Glatting G, et al. Radiation exposure of patients undergoing whole-body dual-modality 18F-FDG PET/CT examinations. J Nucl Med, 2005, 46(4): 608-613.
9. 衛生部辦公廳. 衛生部辦公廳關于規范健康體檢應用放射檢查技術的通知.
10. Framework for FDA's real world evidence program.
11. 吳博生, 陳英耀, 耿勁松, 等. 大型醫用設備配置規劃與配置許可證制度的政策評價. 中國醫院管理, 2016, 36(1): 25-27.
12. 張宇斐, 柴建軍, 胡冰水, 等. 基于德爾菲法和層次分析法的現代醫院門診醫療質控指標體系構建與應用. 中國醫院, 2021, 25(3): 36-39.
13. Hasson F, Keeney S, Mckenna H. Research guidelines for the Delphi survey technique. J Adv Nurs, 2008, 32(4): 1008-1015.
14. 曾光. 現代流行病學. 北京: 氣象出版社, 2002.
15. 陶文娟, 文進, 嚴玲, 等. 基于分級診療的患者信任度評價指標體系的構建. 中國循證醫學雜志, 2018, 18(8): 802-806.
16. Ijzerman MJ, van Til JA, Bridges JF. A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation. Patient, 2012, 5(1): 45-56.
17. Diaz-Ledezma C, Parvizi J. Surgical approaches for cam femoroacetabular impingement: the use of multicriteria decision analysis. Clin Orthop Relat Res, 2013, 471(8): 2509-2516.
18. 馬玉, 惠文, 鄧可, 等. 缺血性腦卒中中成藥綜合評價指標體系構建研究. 中國循證醫學雜志, 2022, 22(9): 1055-1061.
19. 孫欣然, 孫金海. 運用德爾菲法和層次分析法構建基于基本能力和基本狀態的養老照護評估指標體系. 實用心腦肺血管病雜志, 2018, 26(2): 58-62.
20. 董君. 層次分析法權重計算方法分析及其應用研究. 科技資訊, 2015, 13(29): 218, 20.
21. 鄧雪, 李家銘, 曾浩健, 等. 層次分析法權重計算方法分析及其應用研究. 數學的實踐與認識, 2012, 42(7): 93-100.
22. 孫振球, 徐勇勇. 醫學統計學. 北京: 人民衛生出版社, 2014.
23. 關于發布2018-2020年大型醫用設備配置規劃的通知.
24. Moriarity AK, Klochko C, O'Brien M, et al. The effect of clinical decision support for advanced inpatient imaging. J Am Coll Radiol, 2015, 12(4): 358-363.
25. Tapper EB, Sengupta N, Lai M, et al. A decision support tool to reduce overtesting for ceruloplasmin and improve adherence with clinical guidelines. JAMA Intern Med, 2015, 175(9): 1561-1562.
26. Comprehensive clinical audits of diagnostic radiology practices: a tool for quality improvement. Vienna: International Atomic Energy Agency, 2010.
27. European Society of Radiology (ESR). The ESR Audit Tool (Esperanto): genesis, contents and pilot. Insights Imaging, 2018, 9(6): 899-903.
28. 楊玉潔, 毛阿燕, 邱五七, 等. 醫療機構大型醫用設備使用管理評價指標體系構建. 中國醫院管理, 2022, 42(4): 26-30.
29. 衛生部. 衛生部關于印發《2011-2015年全國正電子發射型斷層掃描儀配置規劃》的通知.
30. 國家衛生健康委三級公立醫院績效考核工作領導小組. 國家三級公立醫院績效考核操作手冊(2020版).

Chinese Journal of Evidence-Based Medicine

Development and application of a real-world-data-based indicator system to evaluate the use of diagnostic large medical equipment

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