Developing a quality evaluation indicator system for lung cancer diagnosis and treatment: a modified delphi method study

Developing a quality evaluation indicator system for lung cancer diagnosis and treatment: a modified delphi method study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing a quality evaluation indicator system for lung cancer diagnosis and treatment: a modified delphi method study Liming Shi, Kailun Fei, Meicen Liu, Chengcheng Zhou, Juan Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6166642/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Objective: To establish a quality evaluation index system for the diagnosis and treatment of lung cancer. Method: The Donabedian health assessment model and Delphi method were used to construct an indicator system, and the Analytic Hierarchy Process was used to determine the weights of each level of indicators. Result: The final indicator system constructed includes 3 primary indicators: structure, process and outcome, 15 secondary indicators, and 55 tertiary indicators. Structural indicators include staff, regulations, and facilities; Process indicators include diagnosis, multidisciplinary team (MDT), Neoadjuvant therapy, surgical treatment, adjuvant treatment, radiation therapy, systematic treatment, patient follow-up and patient-centered; Outcome indicators include effectiveness, safety and timeliness. Of the two rounds of Delphi experts consulting, the Expert Enthusiasm Coefficient were respectively 100.0% and 88.46%, the Expert Authority Coefficient were respectively 0.818 and 0.825, and Expert Coordination Coefficient was between 0.476~0.748. Conclusion: The quality evaluation indicator system of lung cancer has high credibility and can be used as a tool for evaluating the quality of lung cancer care. Quality evaluation Lung Cancer Delphi Method Figures Figure 1 Introduction According to the latest data released by the International Agency for Research on Cancer (IARC) in the 2022 Global Cancer Statistics (GLOBCAN2022), there were approximately 20 million new cancer cases worldwide in 2022, with lung cancer being the most common, accounting for 12.4% of new cases. In China, Lung cancer is the most prevalent and deadly cancer, with a standardized incidence rate of 40.78 per 100,000, accounting for 42.8% of the global lung cancer new cases. The standardized mortality rate is 26.66 per 100,000, representing 40.3% of the global lung cancer deaths. The situation of cancer prevention and control in China is severe, with cancer diagnosis and treatment imposing a dual burden on patients both physically and financially [ 1 – 2 ] . The Chinese government place great emphasis on cancer prevention and treatment and have released a series of policies since 2009. In 2020, the National Health Commission issued the Notice on Further Strengthening the Quality Management and Control of Single Disease Diagnosis and Treatment, proposing the establishment of a quality control and evaluation system based on the entire process of disease diagnosis and treatment for medical quality management. Medical quality evaluation serves as both a tool for external quality verification to ensure safety and quality, and as an internal review tool for continuous quality improvement within institutions. By identifying key problems, the medical care quality can be enhanced. The National Cancer Center (NCC) has undertaken a series of initiatives for single-disease quality control, including organizing expert committees for various cancer types, developing quality control indicators, conducting single disease standardized diagnosis and treatment and quality control pilot project. However, a quality evaluation system based has not yet been established. This study aims to construct a comprehensive quality evaluation system for lung cancer diagnosis and treatment by learning from domestic and international experiences and employing scientific research methods. Method Data Resource The construction of the initial indicators for this study was derived from: (1) academic literature searches using keywords such as "tumor/cancer," "lung cancer," "quality assessment/evaluation," and "quality indicators/measures/ metrics" in Chinese and English databases including CNKI, Wanfan Database, PubMed, and Web of Science; (2) searches of international websites, including organizations involved in quality control and improvement projects in the United States, such as the National Cancer Institute (NCI), the Surveillance, Epidemiology, and End Results Program (SEER) database, the National Cancer Database (NCDB), the American Society of Clinical Oncology (ASCO), and the National Comprehensive Cancer Network (NCCN); and (3) searches of domestic policy documents, including Chinese Lung Cancer Diagnosis and Treatment Quality Control Indicators (Version 2022) and the National Performance Evaluation Indicators for Tertiary Public Hospitals. These materials were integrated to serve as primary indicator pool for lung cancer diagnosis and treatment quality evaluation system. Research Framework Donabedian Healthcare Evaluation Model : The model was proposed by Avedis Donabedian with a three-dimensional framework for evaluating healthcare quality: structure, process, and outcome. Due to its flexibility and practicality the model is widely used in healthcare quality assessment. To conduct a comprehensive evaluation, this study adopts the three modules of the model as the primary indicators for the quality evaluation system [ 3 ] . IOM Quality Evaluation Framework : In 2001, the Institute of Medicine (IOM) proposed six dimensions of healthcare quality in its report “Crossing the Quality Chasm: A New Health System for the 21st Century” which were later adopted by the World Health Organization (WHO). These six dimensions are safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity. This study partially references this framework for secondary indicators [ 4 ] . Indicator Screening Principles The selection of evaluation indicators also follows the SMART principles (Specific, Measurable, Attainable, Result-based, and Time-bound) from health management performance evaluation theory, emphasizing the scientific and practical value of the indicators. Research Methods Expert Interviews Experts with extensive experience in internal medicine, surgery, pathology, and quality management were invited to discuss the content and feasibility of the preliminary indicator system. Modified Delphi Method : A modified Delphi method was used to solicit opinions and suggestions from experts in the field of oncology. The selection criteria for experts included: (1) approximately 10 members of the Lung Cancer Single-Disease Quality Control Expert Committee of NCC; (2) approximately 10 clinical experts and administrators from the first batch of lung cancer standardized diagnosis and treatment quality control pilots; and (3) approximately 5 experts in medical quality management. The experts were required to hold associate senior titles or higher, have a strong interest and extensive experience in oncology diagnosis and treatment quality control, and be able to provide comprehensive and objective opinions and suggestions. The selection of experts also considered regional representation from eastern, central, and western China [ 5 ] . A questionnaire was designed based on the preliminary lung cancer quality evaluation indicator system, and the Delphi method was conducted in two rounds. Experts scored the importance, familiarity, and judgment basis of the indicators and provided suggestions for revision. The inclusion criteria for indicators were: (1) an arithmetic mean score of importance ≥ 7.0; and (2) a coefficient of variation for importance ≤ 0.20. The English version of the two round questionnaires of Delphi are provided as supplementary files (Supplementary File 1 and 2 ). Analytic Hierarchy Process (AHP) The weights of primary indicators were determined using the expert direct weighting method, while the weights of secondary and tertiary indicators were calculated using the AHP, the relative importance of all indicators was assigned using Saaty's 1–9 scale. Pairwise comparison matrices are construct for weight calculation, the consistency ratio (CR) of the matrix was tested. The combined weight of a tertiary indicator is the product of the weights of its associated primary, secondary, and tertiary indicators [ 6 ] . Statistical Analysis Microsoft Excel 2019 was used to calculate the expert Enthusiasm coefficient, authority coefficient, and coordination coefficient, with a significance level of 0.05. The expert Enthusiasm coefficient was measured by the effective response rate and suggestion given. The expert authority coefficient (Cr) was calculated as the arithmetic mean of the expert familiarity coefficient (Cs) and the judgment basis coefficient (Ca). Expert familiarity was measured using a Likert 5-point scale, ranging from "not familiar" to "very familiar," with scores of 0.2, 0.4, 0.6, 0.8, and 1, respectively. The judgment basis coefficient was calculated based on the expert judgment criteria in table 1. A Cr ≥ 0.7 was considered reliable. Kendall's W coefficient was used to measure the coordination of expert opinions, with values ranging from 0 to 1, higher values indicated greater coordination among experts. SPSSPRO software was used to determine the weights of the indicators using AHP. Table.1 Expert judgement basis criteria Category Impact level for expert judgement High Medium Low Practical experience 0.40 0.20 0 Theoretical analysis 0.30 0.15 0 Literature knowledge 0.20 0.10 0 Intuition 0.10 0.05 0 Result Expert Information and Enthusiasm Coefficient A total of 26 experts were selected for the two rounds of the Delphi method. The basic information of the experts is shown in Table 2. In the first round, 26 questionnaires were distributed and 26 were returned, with a 100% effective response rate. In the second round, 26 questionnaires were distributed and 23 were returned, with a 88.46% effective response rate, indicating a high level of enthusiasm among the experts. Four experts provided question and suggestion in the first round, and one provided question in the second round. Table.2 Expert information(n = 26) Category Expert Number Proportion(%) Education Doctor and above 15 57.69% Master 6 23.08% Bachelor 5 19.23% Professional title Senior 22 84.62% Associate senior 4 15.38% Years of work ≥ 40 5 19.23% 30 ~ 39 10 38.46% 20 ~ 29 3 11.54% 10 ~ 19 8 30.77% Research area Clinical 20 76.92% Medical technology 2 7.69% Management 4 15.38% Work time direct to patients ≥ 50% 17 65.38% 10%~49% 7 26.92% <10% 2 7.70% Expert Authority Coefficient and Coordination Coefficient In the first round, the Ca, Cs and Cr values were 0.946, 0.690, and 0.818, respectively, indicating reliable and authoritative results. The Kendall's W coefficients for the primary, secondary, and tertiary indicators in the first round were respectively 0.748, 0.498, and 0.510, with statistically significant results (P < 0.05). In the second round, the Ca, Cs, and Cr values were respectively 0.942, 0.708, and 0.825 and the Kendall's W coefficient for the tertiary indicators was 0.476, with statistically significant results (P < 0.05), indicating good coordination among the experts. Indicator Screening Results In both rounds of the Delphi method, all indicators met the criteria of an arithmetic mean score of importance ≥ 7.0 and a coefficient of variation for importance ≤ 0.20. The main feedback from the first round included suggestions to clarify the requirements for pulmonary nodules in the clinical TNM staging examination strategies before initial treatment and the standardization of lymph node dissection in lung cancer surgery, and indicator nutrition assessment before treatment was suggested. After discussion, a preoperative nutritional assessment indicator was added in the second round. No additional indicators were proposed or removed in the second round. The final indicator system included 3 primary indicators, 15 secondary indicators, and 57 tertiary indicators. Weight Setting The results of the second round of the Delphi method were entered into SPSSPRO software to construct pairwise comparison matrices for weight calculation. The combined weights of the tertiary indicators were calculated using the product method. The CR values of all levels of indicators were < 0.1, passing the consistency test. The final lung cancer diagnosis and treatment quality evaluation framework (Fig. 1 ) and the three-level indicator system with weights (Table 3) were established. Table.3 Lung cancer diagnosis and treatment quality evaluation indicator and weight Primary indicator Secondary indicator Tertiary indicator Comprehensive weight 1. Structure (0.250) 1.1 Staff (0.333) 1.1.1 Two-level work team of quality control (hospital and department) 0.021 1.1.2 Clinician educational certification 0.021 1.1.3 Clinical pharmacist of Oncology 0.021 1.1.4 Nurse educational certification 0.021 1.2 Regulation (0.333) 1.2.1 Quality control related regulations 0.021 1.2.2 Functional department quality audit 0.021 1.2.3 Clinical department quality self-examination 0.021 1.2.4 Quality related training 0.021 1.3 Facilities (0.333) 1.3.1 Molecular Pathology Quality Certification 0.083 2. Process (0.500) 2.1 Diagnosis (0.138) 2.1.1 Pathological diagnosis 0.014 2.1.2 Histological subtype diagnosis 0.014 2.1.3 Molecular type diagnosis 0.014 2.1.4 Clinical TNM stage diagnosis 0.028 2.2 MDT (0.113) 2.2.1 Multidisciplinary discussion 0.019 2.2.2 Appropriate multidisciplinary treatment strategy 0.019 2.2.3 Execution of multidisciplinary treatment strategy 0.019 2.3 Neoadjuvant therapy (0.071) 2.3.1 Complete and standardized neoadjuvant therapy plan 0.007 2.3.2 Appropriate neoadjuvant therapy plan 0.007 2.3.3 Clinical TNM staging after neoadjuvant therapy 0.007 2.3.4 pathological staging after neoadjuvant therapy 0.007 2.3.5 pathological remission evaluation 0.007 2.4 Surgical therapy (0.138) 2.4.1Standardized record of surgical plan 0.004 2.4.2 Appropriate surgical regimen 0.009 2.4.3 Preoperative nutritional risk screening 0.004 2.4.4 Preoperative VTE risk assessment 0.004 2.4.5 Preoperative bleeding risk assessment 0.004 2.4.6 Complete and standardized surgical records 0.009 2.4.7 lymph node dissection performed during lung cancer resection surgery 0.009 2.4.8 Sufficient lymph node dissection (sampling) 0.009 2.4.9 Complete and standardized pathological report 0.009 2.4.10 Postoperative pTN staging 0.009 2.5 Adjuvant therapy (0.138) 2.5.1 Complete and standardized adjuvant therapy 0.037 2.5.2 Sufficient adjuvant treatment cycle 0.002 2.6 Radiotherapy (0.138) 2.6.1 Complete and standardized radiotherapy plan 0.014 2.6.2 Appropriate radiotherapy regimen 0.014 2.6.3 Standardized radiotherapy record 0.014 2.6.4 Radiation Dose Completion 0.014 2.6.5 Radiotherapy efficacy evaluation implementation 0.014 2.7 Systematic therapy (0.138) 2.7.1 Complete and standardized systematic treatment plan 0.015 2.7.2 Complete systematic treatment orders/prescriptions 0.008 2.7.3 Appropriate systematic regimen 0.015 2.7.4 Physical score before chemotherapy 0.008 2.7.5 Pain assessment before chemotherapy 0.008 2.7.6 Toxicity assessment before chemotherapy 0.008 2.7.7 Systematic therapy efficacy evaluation implementation 0.008 2.8 Patient follow-up (0.077) 2.8.1 5 year follow-up after treatment 0.039 2.9 Patient-centered (0.049) 2.9.1 Patient experience survey 0.025 3. Outcome (0.250) 3.1 Effectiveness (0.400) 3.1.1 Result of systematic therapy efficacy evaluation 0.050 3.1.2 Result of radiotherapy efficacy evaluation 0.025 3.1.3 R0 surgical resection 0.025 3.2 Safety (0.400) 3.2.1 In-hospital death of patients undergoing elective surgery 0.033 3.2.2 Occurrence of surgical serious complications 0.033 3.2.3 Serious adverse reactions of anti-tumor drugs 0.017 3.2.4 Serious adverse reactions of radiotherapy 0.017 3.3 Timeliness (0.200) 3.3.1 Time from first diagnosis to first treatment 0.050 Discussion Selection and Trade-offs of Quality Control Indicators for Lung Cancer Diagnosis and Treatment The quality assessment indicator system for lung cancer diagnosis and treatment constructed in this study includes 57 specific indicators and reflects key elements in the lung cancer treatment process in a professional and systematic manner. The selection of indicators underwent multiple rounds of expert evaluations to ensure their scientific validity and practicality. Special attention was given to structural indicators, considering the professional capacity of the medical team, system establishment, and facility completeness—all fundamental to ensuring quality in lung cancer treatment. In terms of process indicators, the importance of Multidisciplinary Team (MDT) discussions was particularly emphasized. Studies have shown that MDT can enhance information exchange among different specialties, leading to comprehensive and targeted treatment plans, ultimately improving patient quality of life [ 7 ] . Additionally, standardization of pre-operative and post-operative follow-up management was suggested to ensure continuous monitoring and intervention for patients throughout the treatment cycle, reflecting a commitment to holistic patient health management [ 8 ] . It is worth noting that individual differences among professionals may result in discrepancies in understanding and applying indicators across regions. Therefore, it is recommended that future research continues to track the application of these indicators in various hospitals and make timely adjustments to optimize the relevant indicator system to ensure its applicability and effectiveness [ 9 ] . Quality Throughout the Full Chain of Lung Cancer Diagnosis and Treatment The comprehensive, full cycle quality oversight system proposed in this study can provide scientific and systematic management for lung cancer patients. By integrating various stages into a comprehensive monitoring system, a closed-loop evaluation system is formed, which not only focuses on short-term effectiveness but also gives adequate attention to long-term follow-up and quality of life. In particular, the importance of patient experiences surveys and regular follow-up was emphasized in the outcome indicators, allowing for the collection of long-term data to support quality improvement initiatives [ 9 – 13 ] . Essentially, this quality monitoring system requires healthcare institutions to deliver more targeted comprehensive treatment strategies for lung cancer patients, ensuring that they receive high-level medical services at different stages of treatment. In the future, it is recommended to enhance the real-time collection and analysis of data using information technology to better evaluate the effectiveness of the indicators [ 14 – 15 ] . Limitations Despite the high scientific and practical value of the lung cancer diagnosis and treatment quality indicator system constructed in this study, several limitations remain. Firstly, while the selection of indicators has undergone expert review, the literature and data used may be insufficiently comprehensive, failing to fully account for the disparities in practice across different regions. Secondly, although the Delphi method was employed to strengthen expert consensus, the diversity of the expert panel may not encompass all practice areas in lung cancer diagnosis and treatment. Lastly, empirical validation of the indicator system is still lacking, which may present challenges during practical application. Future studies need to focus on large-scale, multicenter validations, and timely revisions of the indicator system based on feedback obtained during implementation. Conclusion The quality assessment indicator system for lung cancer diagnosis and treatment constructed in this study includes 55 indicators, aimed at enhancing the overall quality of early diagnosis, standardized treatment, and patient management for lung cancer. Through multiple rounds of expert consultation using the Delphi method, the scientific validity and practicality of the indicators were ensured. The system balances structural, process, and outcome indicators, particularly emphasizing the importance of multidisciplinary collaboration and standardized treatment. Furthermore, the study clarified a comprehensive quality oversight mechanism that includes postoperative follow-up and patient experience, ensuring that each stage optimizes the medical experience and outcomes for patients. Nevertheless, the study recognizes the potential disparities in implementation and the challenges that may arise in practical applications, necessitating empirical validation in multicenter and large-scale samples looking forward, the research suggests further exploration of new technologies (such as artificial intelligence) in the quality control of lung cancer diagnosis and treatment and calls for the continuous updating and refinement of the indicator system to adapt to the evolving clinical needs. Only through these efforts can we provide more precise, efficient, and comprehensive medical services for lung cancer patients, ultimately achieving the goals of improving survival rates and quality of life. Declarations Ethics approval and consent to participate As the study involves a survey on obtaining consensus on quality assessment of lung cancer, ethics approval and informed consent is deemed unnecessary according to “Notice on Issuing the Measures for Ethical Review of Life Sciences and Medical Research Involving Humans (Guo Wei Ke Jiao Fa [2023] No.4)” issued by National Health Commission (NHC) of China. Additionally, the study was performed in accordance with the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding Chinese Academy of Medical Sciences Initiative for Innovative Medicine, grant number: 2021-I2M-1-001. Author Contribution LS conceived the study. LS, KF and ZW drafted the manuscript. CZ, JY and JY contributed to the implementation process. ML contributed to the statistical analysis and interpretation of data.All authors contributed to the preparation of the manuscript and approved for the final version. JW and WY guarantee for the work. Acknowledgements The authors would like to thank all the experts who contributed to this study. Data Availability Data is provided within the manuscript or supplementary information files References Chhikara BS, Parang K. Global Cancer Statistics 2022: the trends projection analysis[J]. Chem Biology Lett. 2023;10(1):451–451. Han B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent. 2024;4(1):47–53. Donabedian A. The quality of care. How can it be assessed? JAMA. Sep. 1988;23–30(12):1743–8. Crossing the quality chasm. A new system for the 21st century[M]. Washington(DC): National academies; 2001. Shi J, Sun X, Meng K. Identifying organisational capability of hospitals amid the new healthcare reform in China: a Delphi study[J]. BMJ open. 2021;11(1):e042447. Shen L, Tang Q, Wang Z, et al. Construction and application of quality evaluation index system in oncology hospital. Chin Health Qual Manage. 2024;31(4):32–26. Basse C, Chabanol H, Bonte PE, et al. Management of cutaneous toxicities under amivantamab (anti MET and anti EGFR bispecific antibody) in patients with metastatic non-small cell lung cancer harboring EGFR Exon20ins: towards a proactive, multidisciplinary approach. Lung Cancer. 2022;173:116–23. Colice GL, Rubins J, Unger M. American College of Chest Physicians. Follow-up and surveillance of the lung cancer patient following curative-intent therapy. Chest. 2003;123(1 Suppl):S272–83. GBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100–32. Fonseca A, Antunes M, Firmino-Machado J, et al. Characteristics and patient-reported outcomes of long-term lung cancer survivors. J Thorac Dis. 2024;16(2):1087–96. Collier A, Spruijt O, Minton O et al. Patient-reported outcome measurement in palliative care: A hermeneutic narrative review. Palliat Support Care 2023 Jun 23:1–8. Dingemans AC, Früh M, Ardizzoni A, et al. ESMO Guidelines Committee. Electronic address: [email protected] . Small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up☆. Ann Oncol. 2021;32(7):839–53. Cram P, Wachter RM, Landon BE. Readmission Reduction as a Hospital Quality Measure: Time to Move on to More Pressing Concerns? JAMA. 2022;328(16):1589–90. Bail K, Gibson D, Acharya P, Blackburn J, Kaak V, Kozlovskaia M, Turner M, Redley B. Using health information technology in residential aged care homes: An integrative review to identify service and quality outcomes. Int J Med Inf. 2022;165:104824. Yang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Healthcare. Comput Math Methods Med. 2021;2021:5812499. 10.1155/2021/5812499 . Retraction in: Comput Math Methods Med. 2023;2023:9854236. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile1.docx SupplementaryFile2.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Jul, 2025 Reviews received at journal 01 Jul, 2025 Reviewers agreed at journal 23 Jun, 2025 Reviews received at journal 15 May, 2025 Reviews received at journal 01 May, 2025 Reviewers agreed at journal 22 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 13 Apr, 2025 Editor assigned by journal 07 Apr, 2025 Editor invited by journal 18 Mar, 2025 Submission checks completed at journal 17 Mar, 2025 First submitted to journal 17 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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In China, Lung cancer is the most prevalent and deadly cancer, with a standardized incidence rate of 40.78 per 100,000, accounting for 42.8% of the global lung cancer new cases. The standardized mortality rate is 26.66 per 100,000, representing 40.3% of the global lung cancer deaths. The situation of cancer prevention and control in China is severe, with cancer diagnosis and treatment imposing a dual burden on patients both physically and financially\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Chinese government place great emphasis on cancer prevention and treatment and have released a series of policies since 2009. In 2020, the National Health Commission issued the Notice on Further Strengthening the Quality Management and Control of Single Disease Diagnosis and Treatment, proposing the establishment of a quality control and evaluation system based on the entire process of disease diagnosis and treatment for medical quality management.\u003c/p\u003e \u003cp\u003e Medical quality evaluation serves as both a tool for external quality verification to ensure safety and quality, and as an internal review tool for continuous quality improvement within institutions. By identifying key problems, the medical care quality can be enhanced. The National Cancer Center (NCC) has undertaken a series of initiatives for single-disease quality control, including organizing expert committees for various cancer types, developing quality control indicators, conducting single disease standardized diagnosis and treatment and quality control pilot project. However, a quality evaluation system based has not yet been established. This study aims to construct a comprehensive quality evaluation system for lung cancer diagnosis and treatment by learning from domestic and international experiences and employing scientific research methods.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Resource\u003c/h2\u003e \u003cp\u003eThe construction of the initial indicators for this study was derived from: (1) academic literature searches using keywords such as \"tumor/cancer,\" \"lung cancer,\" \"quality assessment/evaluation,\" and \"quality indicators/measures/ metrics\" in Chinese and English databases including CNKI, Wanfan Database, PubMed, and Web of Science; (2) searches of international websites, including organizations involved in quality control and improvement projects in the United States, such as the National Cancer Institute (NCI), the Surveillance, Epidemiology, and End Results Program (SEER) database, the National Cancer Database (NCDB), the American Society of Clinical Oncology (ASCO), and the National Comprehensive Cancer Network (NCCN); and (3) searches of domestic policy documents, including Chinese Lung Cancer Diagnosis and Treatment Quality Control Indicators (Version 2022) and the National Performance Evaluation Indicators for Tertiary Public Hospitals. These materials were integrated to serve as primary indicator pool for lung cancer diagnosis and treatment quality evaluation system.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Framework\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eDonabedian Healthcare Evaluation Model\u003c/b\u003e: The model was proposed by Avedis Donabedian with a three-dimensional framework for evaluating healthcare quality: structure, process, and outcome. Due to its flexibility and practicality the model is widely used in healthcare quality assessment. To conduct a comprehensive evaluation, this study adopts the three modules of the model as the primary indicators for the quality evaluation system\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIOM Quality Evaluation Framework\u003c/b\u003e: In 2001, the Institute of Medicine (IOM) proposed six dimensions of healthcare quality in its report \u0026ldquo;Crossing the Quality Chasm: A New Health System for the 21st Century\u0026rdquo; which were later adopted by the World Health Organization (WHO). These six dimensions are safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity. This study partially references this framework for secondary indicators\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eIndicator Screening Principles\u003c/strong\u003e \u003cp\u003eThe selection of evaluation indicators also follows the SMART principles (Specific, Measurable, Attainable, Result-based, and Time-bound) from health management performance evaluation theory, emphasizing the scientific and practical value of the indicators.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eResearch Methods\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eExpert Interviews\u003c/strong\u003e \u003cp\u003eExperts with extensive experience in internal medicine, surgery, pathology, and quality management were invited to discuss the content and feasibility of the preliminary indicator system.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eModified Delphi Method\u003c/b\u003e: A modified Delphi method was used to solicit opinions and suggestions from experts in the field of oncology. The selection criteria for experts included: (1) approximately 10 members of the Lung Cancer Single-Disease Quality Control Expert Committee of NCC; (2) approximately 10 clinical experts and administrators from the first batch of lung cancer standardized diagnosis and treatment quality control pilots; and (3) approximately 5 experts in medical quality management. The experts were required to hold associate senior titles or higher, have a strong interest and extensive experience in oncology diagnosis and treatment quality control, and be able to provide comprehensive and objective opinions and suggestions. The selection of experts also considered regional representation from eastern, central, and western China\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA questionnaire was designed based on the preliminary lung cancer quality evaluation indicator system, and the Delphi method was conducted in two rounds. Experts scored the importance, familiarity, and judgment basis of the indicators and provided suggestions for revision. The inclusion criteria for indicators were: (1) an arithmetic mean score of importance\u0026thinsp;\u0026ge;\u0026thinsp;7.0; and (2) a coefficient of variation for importance\u0026thinsp;\u0026le;\u0026thinsp;0.20. The English version of the two round questionnaires of Delphi are provided as supplementary files (Supplementary File 1 and 2 ).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAnalytic Hierarchy Process (AHP)\u003c/strong\u003e \u003cp\u003eThe weights of primary indicators were determined using the expert direct weighting method, while the weights of secondary and tertiary indicators were calculated using the AHP, the relative importance of all indicators was assigned using Saaty's 1\u0026ndash;9 scale. Pairwise comparison matrices are construct for weight calculation, the consistency ratio (CR) of the matrix was tested. The combined weight of a tertiary indicator is the product of the weights of its associated primary, secondary, and tertiary indicators\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eMicrosoft Excel 2019 was used to calculate the expert Enthusiasm coefficient, authority coefficient, and coordination coefficient, with a significance level of 0.05. The expert Enthusiasm coefficient was measured by the effective response rate and suggestion given. The expert authority coefficient (Cr) was calculated as the arithmetic mean of the expert familiarity coefficient (Cs) and the judgment basis coefficient (Ca). Expert familiarity was measured using a Likert 5-point scale, ranging from \"not familiar\" to \"very familiar,\" with scores of 0.2, 0.4, 0.6, 0.8, and 1, respectively. The judgment basis coefficient was calculated based on the expert judgment criteria in table 1. A Cr\u0026thinsp;\u0026ge;\u0026thinsp;0.7 was considered reliable. Kendall's W coefficient was used to measure the coordination of expert opinions, with values ranging from 0 to 1, higher values indicated greater coordination among experts. SPSSPRO software was used to determine the weights of the indicators using AHP.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.1 Expert judgement basis criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eImpact level for expert judgement\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractical experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheoretical analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiterature knowledge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntuition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eExpert Information and Enthusiasm Coefficient\u003c/h2\u003e \u003cp\u003eA total of 26 experts were selected for the two rounds of the Delphi method. The basic information of the experts is shown in Table\u0026nbsp;2. In the first round, 26 questionnaires were distributed and 26 were returned, with a 100% effective response rate. In the second round, 26 questionnaires were distributed and 23 were returned, with a 88.46% effective response rate, indicating a high level of enthusiasm among the experts. Four experts provided question and suggestion in the first round, and one provided question in the second round.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2 Expert information(n\u0026thinsp;=\u0026thinsp;26)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpert Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProportion(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDoctor and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProfessional title\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84.62%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociate senior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYears of work\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.23%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026thinsp;~\u0026thinsp;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026thinsp;~\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResearch area\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical technology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWork time direct to patients\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.38%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10%~49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpert Authority Coefficient and Coordination Coefficient\u003c/h3\u003e\n\u003cp\u003eIn the first round, the Ca, Cs and Cr values were 0.946, 0.690, and 0.818, respectively, indicating reliable and authoritative results. The Kendall's W coefficients for the primary, secondary, and tertiary indicators in the first round were respectively 0.748, 0.498, and 0.510, with statistically significant results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In the second round, the Ca, Cs, and Cr values were respectively 0.942, 0.708, and 0.825 and the Kendall's W coefficient for the tertiary indicators was 0.476, with statistically significant results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating good coordination among the experts.\u003c/p\u003e\n\u003ch3\u003eIndicator Screening Results\u003c/h3\u003e\n\u003cp\u003eIn both rounds of the Delphi method, all indicators met the criteria of an arithmetic mean score of importance\u0026thinsp;\u0026ge;\u0026thinsp;7.0 and a coefficient of variation for importance\u0026thinsp;\u0026le;\u0026thinsp;0.20. The main feedback from the first round included suggestions to clarify the requirements for pulmonary nodules in the clinical TNM staging examination strategies before initial treatment and the standardization of lymph node dissection in lung cancer surgery, and indicator nutrition assessment before treatment was suggested. After discussion, a preoperative nutritional assessment indicator was added in the second round. No additional indicators were proposed or removed in the second round. The final indicator system included 3 primary indicators, 15 secondary indicators, and 57 tertiary indicators.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWeight Setting\u003c/h2\u003e \u003cp\u003eThe results of the second round of the Delphi method were entered into SPSSPRO software to construct pairwise comparison matrices for weight calculation. The combined weights of the tertiary indicators were calculated using the product method. The CR values of all levels of indicators were \u0026lt;\u0026thinsp;0.1, passing the consistency test. The final lung cancer diagnosis and treatment quality evaluation framework (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and the three-level indicator system with weights (Table\u0026nbsp;3) were established.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.3 Lung cancer diagnosis and treatment quality evaluation indicator and weight\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertiary indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComprehensive\u003c/p\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e1. Structure (0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.1 Staff\u003c/p\u003e \u003cp\u003e(0.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1.1 Two-level work team of quality control (hospital and department)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1.2 Clinician educational certification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1.3 Clinical pharmacist of Oncology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1.4 Nurse educational certification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e1.2 Regulation (0.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2.1 Quality control related regulations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2.2 Functional department quality audit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2.3 Clinical department quality self-examination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.2.4 Quality related training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3 Facilities (0.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3.1 Molecular Pathology Quality Certification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"37\" rowspan=\"38\"\u003e \u003cp\u003e2. Process (0.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.1 Diagnosis (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1.1 Pathological diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1.2 Histological subtype diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1.3 Molecular type diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1.4 Clinical TNM stage diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2.2 MDT\u003c/p\u003e \u003cp\u003e(0.113)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2.1 Multidisciplinary discussion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2.2 Appropriate multidisciplinary treatment strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2.3 Execution of multidisciplinary treatment strategy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2.3 Neoadjuvant therapy (0.071)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3.1 Complete and standardized neoadjuvant therapy plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3.2 Appropriate neoadjuvant therapy plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3.3 Clinical TNM staging after neoadjuvant therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3.4 pathological staging after neoadjuvant therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.3.5 pathological remission evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e2.4 Surgical therapy (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.1Standardized record of surgical plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.2 Appropriate surgical regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.3 Preoperative nutritional risk screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.4 Preoperative VTE risk assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.5 Preoperative bleeding risk assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.6 Complete and standardized surgical records\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.7 lymph node dissection performed during lung cancer resection surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.8 Sufficient lymph node dissection (sampling)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.9 Complete and standardized pathological report\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4.10 Postoperative pTN staging\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.5 Adjuvant therapy (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5.1 Complete and standardized adjuvant therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5.2 Sufficient adjuvant treatment cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e2.6 Radiotherapy (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6.1 Complete and standardized radiotherapy plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6.2 Appropriate radiotherapy regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6.3 Standardized radiotherapy record\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6.4 Radiation Dose Completion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.6.5 Radiotherapy efficacy evaluation implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e2.7 Systematic therapy (0.138)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.1 Complete and standardized systematic treatment plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.2 Complete systematic treatment orders/prescriptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.3 Appropriate systematic regimen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.4 Physical score before chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.5 Pain assessment before chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.6 Toxicity assessment before chemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7.7 Systematic therapy efficacy evaluation implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 Patient follow-up (0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8.1 5 year follow-up after treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.9 Patient-centered (0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9.1 Patient experience survey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e3. Outcome (0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.1 Effectiveness (0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1.1 Result of systematic therapy efficacy evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1.2 Result of radiotherapy efficacy evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1.3 R0 surgical resection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e3.2 Safety (0.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2.1 In-hospital death of patients undergoing elective surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2.2 Occurrence of surgical serious complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2.3 Serious adverse reactions of anti-tumor drugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.2.4 Serious adverse reactions of radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.3 Timeliness (0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3.1 Time from first diagnosis to first treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSelection and Trade-offs of Quality Control Indicators for Lung Cancer Diagnosis and Treatment\u003c/h2\u003e \u003cp\u003eThe quality assessment indicator system for lung cancer diagnosis and treatment constructed in this study includes 57 specific indicators and reflects key elements in the lung cancer treatment process in a professional and systematic manner. The selection of indicators underwent multiple rounds of expert evaluations to ensure their scientific validity and practicality. Special attention was given to structural indicators, considering the professional capacity of the medical team, system establishment, and facility completeness\u0026mdash;all fundamental to ensuring quality in lung cancer treatment.\u003c/p\u003e \u003cp\u003eIn terms of process indicators, the importance of Multidisciplinary Team (MDT) discussions was particularly emphasized. Studies have shown that MDT can enhance information exchange among different specialties, leading to comprehensive and targeted treatment plans, ultimately improving patient quality of life\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Additionally, standardization of pre-operative and post-operative follow-up management was suggested to ensure continuous monitoring and intervention for patients throughout the treatment cycle, reflecting a commitment to holistic patient health management \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is worth noting that individual differences among professionals may result in discrepancies in understanding and applying indicators across regions. Therefore, it is recommended that future research continues to track the application of these indicators in various hospitals and make timely adjustments to optimize the relevant indicator system to ensure its applicability and effectiveness\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuality Throughout the Full Chain of Lung Cancer Diagnosis and Treatment\u003c/h2\u003e \u003cp\u003eThe comprehensive, full cycle quality oversight system proposed in this study can provide scientific and systematic management for lung cancer patients. By integrating various stages into a comprehensive monitoring system, a closed-loop evaluation system is formed, which not only focuses on short-term effectiveness but also gives adequate attention to long-term follow-up and quality of life. In particular, the importance of patient experiences surveys and regular follow-up was emphasized in the outcome indicators, allowing for the collection of long-term data to support quality improvement initiatives \u003csup\u003e[\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEssentially, this quality monitoring system requires healthcare institutions to deliver more targeted comprehensive treatment strategies for lung cancer patients, ensuring that they receive high-level medical services at different stages of treatment. In the future, it is recommended to enhance the real-time collection and analysis of data using information technology to better evaluate the effectiveness of the indicators \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eDespite the high scientific and practical value of the lung cancer diagnosis and treatment quality indicator system constructed in this study, several limitations remain. Firstly, while the selection of indicators has undergone expert review, the literature and data used may be insufficiently comprehensive, failing to fully account for the disparities in practice across different regions. Secondly, although the Delphi method was employed to strengthen expert consensus, the diversity of the expert panel may not encompass all practice areas in lung cancer diagnosis and treatment. Lastly, empirical validation of the indicator system is still lacking, which may present challenges during practical application. Future studies need to focus on large-scale, multicenter validations, and timely revisions of the indicator system based on feedback obtained during implementation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe quality assessment indicator system for lung cancer diagnosis and treatment constructed in this study includes 55 indicators, aimed at enhancing the overall quality of early diagnosis, standardized treatment, and patient management for lung cancer. Through multiple rounds of expert consultation using the Delphi method, the scientific validity and practicality of the indicators were ensured. The system balances structural, process, and outcome indicators, particularly emphasizing the importance of multidisciplinary collaboration and standardized treatment.\u003c/p\u003e \u003cp\u003eFurthermore, the study clarified a comprehensive quality oversight mechanism that includes postoperative follow-up and patient experience, ensuring that each stage optimizes the medical experience and outcomes for patients. Nevertheless, the study recognizes the potential disparities in implementation and the challenges that may arise in practical applications, necessitating empirical validation in multicenter and large-scale samples looking forward, the research suggests further exploration of new technologies (such as artificial intelligence) in the quality control of lung cancer diagnosis and treatment and calls for the continuous updating and refinement of the indicator system to adapt to the evolving clinical needs. Only through these efforts can we provide more precise, efficient, and comprehensive medical services for lung cancer patients, ultimately achieving the goals of improving survival rates and quality of life.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e As the study involves a survey on obtaining consensus on quality assessment of lung cancer, ethics approval and informed consent is deemed unnecessary according to \u0026ldquo;Notice on Issuing the Measures for Ethical Review of Life Sciences and Medical Research Involving Humans (Guo Wei Ke Jiao Fa [2023] No.4)\u0026rdquo; issued by National Health Commission (NHC) of China. Additionally, the study was performed in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eChinese Academy of Medical Sciences Initiative for Innovative Medicine, grant number: 2021-I2M-1-001.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLS conceived the study. LS, KF and ZW drafted the manuscript. CZ, JY and JY contributed to the implementation process. ML contributed to the statistical analysis and interpretation of data.All authors contributed to the preparation of the manuscript and approved for the final version. JW and WY guarantee for the work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors would like to thank all the experts who contributed to this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChhikara BS, Parang K. Global Cancer Statistics 2022: the trends projection analysis[J]. Chem Biology Lett. 2023;10(1):451\u0026ndash;451.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan B, Zheng R, Zeng H, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent. 2024;4(1):47\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDonabedian A. The quality of care. How can it be assessed? JAMA. Sep. 1988;23\u0026ndash;30(12):1743\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrossing the quality chasm. A new system for the 21st century[M]. Washington(DC): National academies; 2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi J, Sun X, Meng K. Identifying organisational capability of hospitals amid the new healthcare reform in China: a Delphi study[J]. BMJ open. 2021;11(1):e042447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen L, Tang Q, Wang Z, et al. Construction and application of quality evaluation index system in oncology hospital. Chin Health Qual Manage. 2024;31(4):32\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasse C, Chabanol H, Bonte PE, et al. Management of cutaneous toxicities under amivantamab (anti MET and anti EGFR bispecific antibody) in patients with metastatic non-small cell lung cancer harboring EGFR Exon20ins: towards a proactive, multidisciplinary approach. Lung Cancer. 2022;173:116\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColice GL, Rubins J, Unger M. American College of Chest Physicians. Follow-up and surveillance of the lung cancer patient following curative-intent therapy. Chest. 2003;123(1 Suppl):S272\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Causes of Death Collaborators. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2024;403(10440):2100\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFonseca A, Antunes M, Firmino-Machado J, et al. Characteristics and patient-reported outcomes of long-term lung cancer survivors. J Thorac Dis. 2024;16(2):1087\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollier A, Spruijt O, Minton O et al. Patient-reported outcome measurement in palliative care: A hermeneutic narrative review. Palliat Support Care 2023 Jun 23:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDingemans AC, Fr\u0026uuml;h M, Ardizzoni A, et al. ESMO Guidelines Committee. Electronic address: [email protected]. Small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up☆. Ann Oncol. 2021;32(7):839\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCram P, Wachter RM, Landon BE. Readmission Reduction as a Hospital Quality Measure: Time to Move on to More Pressing Concerns? JAMA. 2022;328(16):1589\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBail K, Gibson D, Acharya P, Blackburn J, Kaak V, Kozlovskaia M, Turner M, Redley B. Using health information technology in residential aged care homes: An integrative review to identify service and quality outcomes. Int J Med Inf. 2022;165:104824.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang YC, Islam SU, Noor A, Khan S, Afsar W, Nazir S. Influential Usage of Big Data and Artificial Intelligence in Healthcare. Comput Math Methods Med. 2021;2021:5812499. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2021/5812499\u003c/span\u003e\u003cspan address=\"10.1155/2021/5812499\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Retraction in: Comput Math Methods Med. 2023;2023:9854236.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Quality evaluation, Lung Cancer, Delphi Method","lastPublishedDoi":"10.21203/rs.3.rs-6166642/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6166642/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To establish a quality evaluation index system for the diagnosis and treatment of lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The Donabedian health assessment model and Delphi method were used to construct an indicator system, and the Analytic Hierarchy Process was used to determine the weights of each level of indicators.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The final indicator system constructed includes 3 primary indicators: structure, process and outcome, 15 secondary indicators, and 55 tertiary indicators. Structural indicators include staff, regulations, and facilities; Process indicators include diagnosis, multidisciplinary team (MDT), Neoadjuvant therapy, surgical treatment, adjuvant treatment, radiation therapy, systematic treatment, patient follow-up and patient-centered; Outcome indicators include effectiveness, safety and timeliness. Of the two rounds of Delphi experts consulting, the Expert Enthusiasm Coefficient were respectively 100.0% and 88.46%, the Expert Authority Coefficient were respectively 0.818 and 0.825, and Expert Coordination Coefficient was between 0.476~0.748.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The quality evaluation indicator system of lung cancer has high credibility and can be used as a tool for evaluating the quality of lung cancer care.\u003c/p\u003e","manuscriptTitle":"Developing a quality evaluation indicator system for lung cancer diagnosis and treatment: a modified delphi method study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 09:28:07","doi":"10.21203/rs.3.rs-6166642/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-02T07:14:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-01T17:13:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-06-23T12:29:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-15T06:07:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T06:59:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305191639651040218812052540067961417786","date":"2025-04-22T06:09:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"105618528467692763481515004412974006727","date":"2025-04-21T20:37:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-13T17:44:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T03:47:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-18T04:01:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-18T02:04:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2025-03-18T02:03:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9285f4c3-b06c-4841-a23d-b97399660a91","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T14:38:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 09:28:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6166642","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6166642","identity":"rs-6166642","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}