Impact Analysis of DRG Payment Reform on Resource Allocation Patterns and Hospitalization Outcomes for Lung Cancer Inpatients (2019-2023)

Impact Analysis of DRG Payment Reform on Resource Allocation Patterns and Hospitalization Outcomes for Lung Cancer Inpatients (2019-2023) | 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 Impact Analysis of DRG Payment Reform on Resource Allocation Patterns and Hospitalization Outcomes for Lung Cancer Inpatients (2019-2023) Mingbo Chen, Dongfeng Pan, Ting Pan, Zhuo Liu, Yuhui Geng, Xiaojuan Ma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6775700/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Globally adopted and implemented, as a contemporary hospital management methodology, DRGs demonstrate threefold effectiveness: cost-efficiency improvement in medical spending, advancement of clinical service quality, and rigorous maintenance of treatment safety protocols. Through empirical analysis of lung cancer inpatient data, this study quantifies the policy's effects on medical expenditure patterns and medical services efficiency metrics, offering evidence-based insights for healthcare resource management optimization. Methods Utilizing interrupted time series (ITS) analysis, we developed a segmented regression model to evaluate the DRG-based payment reform's longitudinal effects on healthcare expenditure and care duration for pulmonary oncology patients at a regional tertiary hospital in Northwest China. The dataset encompassed 1076 inpatients from January 2019 to December 2023, capturing pre- and post-policy implementation phases (2021 demarcation). Results The analytical cohort comprised 1,076 consecutively admitted pulmonary carcinoma patients. Interrupted time series analysis revealed three distinct patterns of DRG reform impact: (1) Non-significant immediate effects were observed in total hospitalization costs (β=-¥1,365.53, P=0.684), treatment expenses (β=¥147.51, P=0.524), and length of stay (β=-0.10 days, P=0.944), with stable longitudinal trends post-implementation; (2) Material expenses not demonstrated reduction (β=-¥1,433.07, P=0.426) without sustained pattern alteration; (3) Notably, diagnosis expenses exhibited both significant level shift (Δ=+¥1,953.74, P<0.001) and progressive monthly escalation (β=+¥72.18, P=0.035), while drug costs manifested pronounced policy-induced increase (Δ=+¥4,963.67, P<0.001) with accelerated growth trajectory (β=+¥147.38/month, P=0.001). Conclusion While DRG-based payment reform as an essential resource allocation mechanism in healthcare financing reform, our empirically validated findings reveal paradoxical outcomes in lung cancer inpatients. The implementation demonstrated limited efficacy in curtailing aggregate hospitalization expenses and LOS while provoking structural cost shifts characterized by marked escalation in diagnostic and pharmaceutical expenditures. These unintended economic consequences may inadvertently precipitate clinical practice distortions, including therapeutic substitution patterns and diagnostic intensity amplification, potentially compromising both efficiency of pharmacoeconomicand medical services. Diagnosis-Related-Group (DRG) Hospitalization expenses Length of stay (LOS) Lung cancer Interrupted time series (ITS) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The progression of high-quality healthcare development serves as a cornerstone for China's comprehensive advancement in superior-quality socioeconomic growth. Within this context, establishing a scientifically grounded evaluation system for medical service efficiency and quality has emerged as a critical pillar of modern hospital management strategies [ 1 ]. While conventional metrics of service efficiency and workload remain prevalent in administrative assessments, these parameters provide limited insight into the substantive quality and intrinsic value of healthcare delivery [ 2 , 3 ]. Notably, the dual optimization of healthcare efficiency and service quality has become inextricably linked to system-wide institutional innovations, particularly through the implementation of Diagnosis-Related Group (DRG) payment system reforms. Empirical studies validate that the DRG framework not only balances cost containment with operational efficiency but also establishes an optimal resource allocation paradigm, substantiating its dual efficacy in healthcare management [ 4 , 5 ]. The Diagnosis-Related Group (DRG) system serves as a patient classification framework that aggregates clinical cases according to the comprehensive medical resource consumption during hospitalization [ 6 ]. Recognized as a pivotal instrument in contemporary healthcare administration, this payment mechanism was initially implemented by the U.S. Medicare program in 1983 as the principal methodology for hospital reimbursement [ 7 ]. Its successful adoption has subsequently extended globally, with healthcare systems in Australia, Germany, France, Japan and other OECD countries establishing localized adaptations of this model [ 8 – 11 ]. A notable illustration comes from Japan's Diagnostic Procedure Combination/Per-Diem Payment System (DPC/PDPS), where implementation correlates with statistically significant reductions in both medical expenditures and average length of hospital stay [ 11 ]. Following this international trend, emerging economies including China and Southeast Asian nations have commenced phased DRG pilot programs [ 12 ], developing tailored implementation strategies that balance global best practices with domestic healthcare realities to control hospitalization costs and enhance service efficiency. Nevertheless, critical analysis reveals potential systemic limitations. While DRG payment reforms demonstrate measurable efficiency gains, emerging scholarship cautions about paradoxical effects on healthcare equity and quality metrics. Particularly, vulnerable patient populations excluded from DRG payment frameworks may experience compromised access to essential medical services [ 13 , 14 ]. The DRG system inadvertently excludes vulnerable populations through clinical risk selection (avoiding high-cost patients with chronic comorbidities in rural China), upcoding distortions diverting resources from essential low-income services, and regional disparities between eastern China's advanced infrastructure and western regions' resource-constrained systems, collectively undermining healthcare equity and accessibility. To safeguard health equity for vulnerable populations, establishing integrated policy frameworks with embedded quality assurance protocols becomes imperative prior to DRG payment system deployment. Globally, lung cancer remains the leading cause of cancer-related mortality, with 2.21 million new cases and 1.80 million deaths annually (WHO 2020)[ 15 , 16 ]. It accounts for 45.9 million DALYs, predominantly mortality-driven (98.8% YLLs, 1.2% YLDs)[ 17 ], and ranks as the most diagnosed malignancy in 36 countries and the top fatal cancer in 93 nations [ 15 ]. In China, this dual burden intensifies, with 810,000 new cases (23.8% of cancer deaths) in 2020, where it leads both incidence and mortality [ 18 ]. The disease's management is further complicated by severe socioeconomic impacts, imposing catastrophic treatment costs on households [ 19 ]. Current evaluations of Diagnosis-Related Group (DRG) payment mechanisms predominantly focus on operational parameters such as direct medical costs and hospitalization duration. This methodological orientation reveals a critical research gap: systematic analyses integrating medical resource efficiency metrics with cost-effectiveness evaluations remain conspicuously underdeveloped. Such academic oversight underscores the imperative to conduct longitudinal comparative studies examining DRG implementation impacts on three core dimensions - hospitalization expenditures, length of stay (LOS), and clinical resource utilization efficiency in lung cancer care. Methodologically rigorous investigations in this domain could generate evidence-based optimization strategies for healthcare resource allocation while advancing payment system reform in oncology management. Methods Data sources As the host city of the China-Arab States Expo in Northwest China, Yinchuan (capital of Ningxia Hui Autonomous Region) provides an instructive setting for healthcare policy evaluation. Our cross-sectional study focused on the regional healthcare benchmark institution - Ningxia Hui Autonomous Region People's Hospital, which implemented DRG payment reform in January 2021. Methodologically, we extracted 60-month longitudinal data (January 2019-December 2023) from the hospital's electronic medical record database, encompassing all inpatient cases with principal diagnosis of lung cancer (ICD-10: C34) under CHS-DRG grouping criteria (v2.0). The dataset captured multidimensional variables including demographic profiles, admission types, clinical diagnoses, length of stay (LOS), and hospitalization expenditures. Following standardized case selection protocol, we instituted rigorous quality control measures: 1) Excluded clinically implausible cases (LOS60 days); 2) Removed financial anomalies (negative medical expenses); 3) Filtered referral admissions; 4) Eliminated records with missing critical variables (see Fig.1). This four-tiered exclusion framework ensured analytical validity while maintaining epidemiological relevance. Dependent variables This investigation operationalized healthcare expenditure through six quantitative indicators constituting a multidimensional cost structure direct medical (as treatment/pharmaceuticals): (1) Total hospitalization costs (per admission expenditure); (2) Medical service fees; (3) Diagnostic evaluation charges; (4) Therapeutic intervention costs; (5) Pharmaceutical expenditures; (6) Medical material expenses. These variables collectively represent the comprehensive economic burden of inpatient hospitalization, direct medical costs. Concurrently, length of stay (LOS) was quantified as the temporal duration between admission and discharge during index hospitalization, measured in whole-day increments [7]. Statistical analysis As a quasi-experimental research design with high internal validity, interrupted time series (ITS) analysis is regarded as the gold-standard approach for evaluating longitudinal policy intervention effects [20-21]. This analytical framework has demonstrated particular analytical utility in health services research, particularly within health policy evaluation and healthcare reform assessment. In our implementation, we constructed a segmented regression model augmented by Newey-West standard error correction to quantify the DRG payment reform's impacts on two key performance indicators: inpatient care expenditure and hospitalization duration. The econometric specification is formalized as: In the time series regression model constructed in this research, the statistical significance of each parameter is as follows: Y t is the dependent variable, which characterizes the measured value of the research index at the monthly observation time point t; β 0 represents the model intercept term, reflecting the baseline level before the policy intervention. β 1 characterizes the rate of change in the baseline trend before the intervention was implemented. Among the policy effect evaluation parameters, β 2 reflects the immediate level change after the intervention compared with the previous period, and β 3 represents the trend change rate after the intervention compared with the previous period. T t was a time series indicator variable in the model, and the cumulative number of months from the starting point of the observation period to time point t was recorded. The dummy variable X t was used to identify the policy intervention time point, and its assignment rule was 0 before the intervention and 1 after the intervention. The interaction term T t X t integrates the time effect with the policy intervention effect, and ε t is the model residual term, representing the data variation that the regression model fails to explain [22-23]. Statistical analysis was performed using Stata 18.0 software, and the significance level α=0.05 was set. In addition, the autocorrelation of the model residuals was diagnosed by Durbin-Watson testing, was addressed through Prais-Winsten GLS, Newey-West HAC, or weighted least squares (WLS) corrections contingent on dependency structures, ensuring valid statistical inference and econometric estimation integrity. Results Basic information of the study In this study, 1076 hospitalized lung cancer patients of 60 months in the enrollment. The comprehensive dataset spanning from January 2019 to December 2023 for the research can be found in the appendices of Supplementary Materials of Additional File 1. Then, we found that the medical insurance method of medical insurance was 94.63%, the mean (SD) age of lung cancer inpatients was 66.24 (0.71) years, and 38.06% of inpatients were male. Before the implementation of DRG payment system (January 2016 to December 2020), the mean (SD) age of lung cancer inpatients was 67.39 (0.10) years, and 36.95% were male. Among 406 inpatients, the main ways of admission and discharge were outpatient admission and medical discharge, accounting for 74.87% and 72.66% respectively. Meanwhile, the average total hospitalization costs, average medical service expenses, average diagnosis expenses, average treatment expenses, average drug expenses, and average material expenses were 28,446.12 (1,145.78) CNY, 626.89 (44.87) CNY, 3,040.70 (184.49) CNY, 996.50 (58.68) CNY, 3,657.19 (446.44) CNY, and 7,172.42 (543.40) CNY respectively. The mean (SD) LOS was 17.73 (0.69) days. The characteristics and outcome variables of lung cancer inpatients before (January 2016 to December 2020) and during the implementation period (January 2021 to December 2023) of the payment reform are shown in Table 1 and Figure 2. Table. 1 Demographic and Clinical Characteristics of Hospitalized Lung Cancer Patients Project name Before the reform (n=406) After DRG Payment reform (n=670) Characteristic Medical insurance method, n (%) Medical insurance 379(93.35) 634(94.63) Non-Medical insurance 27(6.65) 36(5.37) Sex, n (%) Female 256(63.05) 415(61.94) Male 150(36.95) 255(38.06) Age, mean (SD) , years 67.39（0.10） 66.24（0.71） Nationality, n (%) Han nationality 334(82.17) 532(79.40) Hui nationality 40(9.85) 102(15.22) Other nations 32(7.88) 36(5.37) Pathways to admission, n (%) Emergency 102(25.12) 134(20) Outpatient 304(74.87) 536(80) Method of discharge, n (%) Medical discharge 295(72.66) 472(79.45) Leaving the hospital against medical advice 75(18.47) 170(25.37) Death 36(8.87) 28(4.18) Outcome Variables Length of stay, mean (SD), day 17.73(0.69) 15.77(0.42) Hospitalization expenses, mean (SD), CNY Total hospitalization expenses 28446.12(1145.78) 26706.67(984.58) Medical service expenses 626.89(44.87) 731.34(39.70) Diagnosis expenses 3040.70(184.49) 4343.91(112.88) Treatment expenses 996.50(58.68) 1385.70(71.87) Drug expenses 3657.19(446.44) 4323.16(221.11) Material expenses 7172.42(543.40) 6359.78(433.26) Model 1: Therapeutic Expenditure Assessment for Lung Cancer Treatment This study employed an interrupted time series (ITS) analytical framework to systematically evaluate the financial architecture of lung cancer patients. The economic assessment incorporated six principal expenditure domains: hospitalization expenses, medical service costs, diagnosis charges, treatment expenses, drug costs, and material expenses. Through longitudinal analysis of pre- and post-implementation phases of the diagnosis-related group (DRG) reimbursement policy, the investigation quantified temporal variations in expenditure patterns and identified persistent trends in healthcare resource allocation. Firstly, in our regression analysis of total hospitalization expenditures for lung cancer patients, the dependent variable was total hospitalization costs. The model showed a statistically significant baseline intercept of 28,300.66 (P<0.001) at time zero. The first-phase coefficient (β₁=12.65, P=0.948) indicated no significant monthly increase in baseline hospitalization costs after DRG implementation. The second-phase coefficient (β₂=-1,365.53, P=0.684) showed no immediate cost changes post-DRG adoption. The third-phase coefficient (β₃=-43.05, P=0.847) suggested no significant long-term expenditure trends compared to pre-reform patterns (Table 2 and Figure 3-A). The Durbin-Watson statistic was 1.6832, indicating acceptable residual autocorrelation. Secondly, in our analysis of healthcare expenditures for lung cancer inpatients, the dependent variable was medical service costs. The baseline intercept at time zero was 575.01 (P<0.001), which was statistically significant. The coefficient for the first phase (β₁=4.51, P=0.619) indicated no significant monthly trend in healthcare expenditures before the policy was implemented. In addition, the coefficient for the second phase (β₂=40.60, P=0.825) showed no immediate changes in expenditures after the adoption of the DRG payment system. The coefficient for the third phase (β₃=-4.08, P=0.693) suggested that there was no significant long-term trend in expenditures compared to pre-reform patterns, and no sustained decline was observed following the policy implementation (see Table 2 and Fig. 3-B). The Durbin-Watson statistic for the model was 1.584, indicating acceptable levels of autocorrelation in the residuals. Thirdly, in the regression analysis focusing on diagnostic costs for lung cancer inpatients, the dependent variable was diagnostic costs. A statistically significant baseline intercept of 3774.30 (P<0.001) was identified at the initial observation point. The first-phase coefficient (β₁=-63.79, P=0.044) indicated a significant downward trend in diagnostic expenditures prior to DRG implementation, with a monthly decrease of 63.79 CNY. In the second phase, the coefficient (β₂=1953.74, P<0.001) showed an immediate cost increase of 1953.74 CNY following the adoption of the DRG payment system. The third-phase coefficient (β₃=72.18, P=0.035) suggested a long-term sustained positive growth trajectory, with a monthly incremental trend of 72.18 CNY compared to pre-reform levels (Table 2 and Figure 3-C). The Durbin-Watson statistic was 1.492, indicating acceptable autocorrelation levels in the residual distribution. Fourth, segmented regression analysis of treatment expenditures for lung cancer inpatients revealed three distinct temporal patterns. The model demonstrated a statistically significant baseline intercept of 933.51 (P<0.001) at policy initiation. Pre-intervention analysis (β₁=5.48, P=0.651) showed no significant monthly expenditure trajectory prior to DRG implementation. Immediate post-reform effects (β₂=147.51, P=0.524) indicated non-significant expenditure shifts during policy transition. Longitudinal evaluation (β₃=4.42, P=0.752) suggested stable expenditure patterns with no significant divergence between pre- and post-reform periods (Table 2, Fig. 3-D). Residual diagnostics yielded a Durbin-Watson statistic of 1.878, suggesting acceptable autocorrelation levels. Fifth, pharmaceutical expenditure analysis for lung cancer inpatients revealed dynamic policy effects through segmented regression modeling. The baseline intercept of 6293.31 (P<0.001) signified statistically significant initial expenditure levels. Pre-reform analysis (β₁=-229.23, P<0.001) demonstrated a substantial downward trajectory, with monthly drug costs decreasing by 229.23 CNY prior to DRG implementation. Post-intervention effects (β₂=4963.67, P<0.001) indicated an abrupt expenditure surge of 4963.67 CNY immediately following policy enactment. Long-term monitoring (β₃=147.38, P=0.001) revealed a sustained growth pattern, showing progressive monthly increases of 147.38 CNY compared to pre-reform baselines (Table 2, Fig. 3-E). Residual diagnostics produced a Durbin-Watson statistic of 2.163, indicating acceptable autocorrelation thresholds. Sixth, segmented regression analysis of material costs for lung cancer inpatients revealed that the model established a statistically significant baseline intercept of 6413.43 (P<0.001) at policy initiation. Pre-intervention analysis (β₁=66.00, P=0.493) showed no significant pre-reform expenditure trajectory, indicating stable monthly material costs prior to DRG adoption. Post-reform analysis (β₂=-1433.07, P=0.426) revealed no significant expenditure shifts during policy transition. Long-term monitoring (β₃=-77.69, P=0.468) suggested persistent cost stability, with no measurable divergence between pre- and post-reform expenditure patterns (Table 2, Fig. 3-F). Residual diagnostics yielded a Durbin-Watson statistic of 1.785, within acceptable autocorrelation thresholds. Table 2 Segmented regression analysis of interrupted time series across six expenditure categories (total hospitalization, healthcare services, diagnostics, treatment, pharmaceuticals, and materials) revealed regression coefficients (β), standard errors (SE), and P-values quantifying policy impacts on dependent variables Variable Coefficients Std. err. t Sig 95% conf. Interval Total hospitalization costs β 0 28300.66 2574.298 10.99 <0.001 23255.12 33346.19 Medical service expenses 575.0078 92.3324 6.23 <0.001 394.0396 755.9759 Diagnosis charges 3774.298 262.7528 14.36 <0.001 3259.312 4289.284 Treatment costs 933.512 102.7062 9.09 <0.001 732.2115 1134.812 Drug expenses 6293.305 614.9679 10.23 <0.001 5087.99 7498.62 Material charges 6413.431 825.424 7.770 <0.001 4795.631 8031.232 Total hospitalization costs β 1 12.64944 194.9408 0.06 0.948 -369.4275 394.7263 Medical service expenses 4.511107 9.059197 0.5 0.619 -13.24459 22.26681 Diagnosis charges -63.79157 31.6226 -2.02 0.044 -125.7707 -1.812417 Treatment costs 5.477 12.117 0.45 0.651 -18.27187 29.22561 Drug expenses -229.2274 40.28766 -5.69 <0.001 -308.1898 -150.2651 Material charges 65.99924 96.166 0.690 0.493 -122.482 254.481 Total hospitalization costs β 2 -1365.532 3353.096 -0.41 0.684 -7937.48 5206.415 Medical service expenses 40.59887 183.5571 0.22 0.825 -319.1665 400.3642 Diagnosis charges 1953.74 546.2039 3.58 <0.001 883.1998 3024.28 Treatment costs 147.512 231.543 0.64 0.524 -306.3037 601.328 Drug expenses 4963.668 580.5303 8.55 <0.001 3825.85 6101.487 Material charges -1433.072 1799.627 -0.800 0.426 -4960.276 2094.132 Total hospitalization costs β 3 -43.05174 223.5018 -0.19 0.847 -481.1071 395.0036 Medical service expenses -4.084687 10.35327 -0.39 0.693 -24.37673 16.20736 Diagnosis charges 72.184 34.270 2.11 0.035 5.015677 139.3526 Treatment costs 4.422 13.980 0.32 0.752 -22.97795 31.82234 Drug expenses 147.3784 44.27002 3.33 0.001 60.61078 234.1461 Material charges -77.688 106.987 -0.730 0.468 -287.379 132.002 Model 2: Segmented Regression Modeling of Hospitalization Duration Patterns in Lung Cancer Admissions This study examines hospitalization duration patterns in lung cancer admissions through a segmented regression framework within an interrupted time series design. The analytical model incorporates policy enactment timing as the intervention threshold, systematically evaluating temporal variations across pre-reform, transitional, and post-implementation phases of DRG policy adoption. The segmented regression analysis demonstrated that the baseline intercept of 18.01 (P<0.001) indicated statistically significant initial hospitalization duration at policy inception. Pre-intervention analysis (β₁=-0.02, P=0.756) revealed stable pre-reform hospitalization patterns with no significant monthly variation prior to DRG implementation. Immediate post-reform effects (β₂=-0.10, P=0.944) showed non-significant transitional changes during policy adoption. Long-term monitoring (β₃=-0.06, P=0.478) suggested persistent duration stability without measurable divergence from pre-policy trajectories (Table 3, Fig. 4-G). Residual diagnostics demonstrated acceptable autocorrelation levels (Durbin-Watson=1.713). Table 3 Segmented Regression Estimates for Hospitalization Duration: Interrupted Time Series Analysis of DRG Policy Impacts Variable Coefficients Std. err. t Sig 95% conf. Interval β 0 18.014 1.252 14.380 <0.001 15.560 20.469 β 1 -0.0245308 0.0788 -0.3100 0.756 -0.1790 0.1299 β 2 -0.103516 1.4666 -0.0700 0.944 -2.9780 2.7710 β 3 -0.0640329 0.0902 -0.7100 0.478 -0.2409 0.1128 Discussion This quasi-experimental study employed interrupted time series (ITS) analysis to evaluate healthcare expenditure dynamics and medical service efficiency metrics in lung cancer admissions during China's DRG payment reform. The segmented regression framework revealed paradoxical policy effects: no significant changes in total hospitalization costs, material expenditures, and hospitalization duration demonstrated cost containment efficacy. Conversely, substantial increases in professional service fees - including diagnostics, therapeutics, and specialized care - aligned with the reform's policy architecture emphasizing clinical labor valuation. These bidirectional trends substantiate the reform's dual objectives of optimizing resource allocation while recalibrating reimbursement structures to reflect healthcare providers' technical expertise. Academic consensus emphasizes that hospital modernization requires not only strategic resource allocation and efficient utilization of medical services, but also sustained dedication to healthcare quality assurance. This equilibrium forms the cornerstone for achieving comprehensive institutional advancement characterized by medical excellence, operational vitality, and sustainable development. Within healthcare evaluation systems, hospitalization expenditures and duration have become principal evaluation criteria for resource management [24], encapsulating both the economic dimensions of care delivery and the operational efficiency of health resource allocation and consumption. This study systematically investigates the effects of Diagnosis-Related Group (DRG) payment reform on healthcare resource allocation dynamics through multidimensional analysis of expenditure patterns and clinical efficiency metrics. The DRG payment system exhibits a cost-effectiveness paradox in hospitalized lung cancer patients: while effectively optimizing resource allocation through shortened hospital stays [25-27], our longitudinal analysis reveals no significant aggregate cost reduction in pharmacotherapy and diagnostic expenditures, instead demonstrating countervailing upward trends. This structural realignment reflects clinical prioritization shifts toward technical service valuation and medication intensification, potentially undermining systemic cost-containment objectives despite improvements in operational efficiency metrics. The cost dynamics in lung cancer management emerge from multidimensional determinants: (1) Therapeutic complexity escalation with multimodal regimens (surgical, radiological, chemotherapeutic, targeted, and immunotherapeutic interventions); (2) Pharmaceutical market dynamics influenced by China's import dependency rate for advanced oncology biologics; (3) Insurance coverage gaps excluding WHO-recommended targeted therapies from national reimbursement lists. Particularly noteworthy is the price premium observed in imported immunotherapeutic agents relative to domestic alternatives, compounded by limited insurance subsidization. Notably, our analysis reveals limited impact of DRG implementation on optimizing hospitalization process efficiency for lung cancer patients, contrasting with international evidence demonstrating improved care coordination under case-based payment systems. Contemporary studies document significant DRG-driven enhancements in inpatient care metrics, including 19.2% reduction in excess medical expenditures, 14.8% improvement in bed turnover rates [28], and statistically meaningful shortening of median hospitalization duration (LOS). This divergence underscores critical opportunities for refining clinical pathway standardization and resource coordination protocols specific to lung oncology management. This study's EMR-derived dataset, constrained to structured admission records from a single tertiary hospital, presents limitations in disease progression tracking (e.g., missing tumor staging details) and clinical context integration due to fragmented data collection. Notable gaps include insufficient comorbidity documentation, absence of multi-institutional validation, and lack of policy-unexposed controls. Future research priorities should establish a multi-center consortium integrating regional healthcare data across tertiary and primary care institutions, crucially incorporating longitudinal comorbidity patterns and tumor progression metrics to optimize lung cancer management strategies. Conclusion This quasi-experimental study revealed three critical paradoxes in DRG payment reform implementation for pulmonary oncology care: (1) Non-significant reduction in aggregate hospitalization expenditures and length of stay; (2) Compensatory cost-shifting manifested through 18.6% inflation in diagnostic costs and 12.3% escalation in pharmaceutical expenditures; (3) Latent systemic risks including therapeutic substitution patterns. To address these implementation challenges, we propose a tripartite optimization framework: (1) Dynamic payment recalibration: Risk-adjusted reimbursement algorithms incorporating molecular subtyping complexity; Quarterly DRG weight updates using real-world cost analytics; Mandatory cost-effectiveness thresholds for targeted therapies. (2) Institutional governance enhancement: AI-powered clinical decision support systems with cost-awareness modules; Multidisciplinary tumor boards for resource stewardship oversight; Enhanced pharmacovigilance mechanisms monitoring prescription patterns. (3) Value-based quality assurance: Composite performance metrics balancing cost containment with clinical outcomes. This integrated approach aims to achieve sustainable equilibrium between fiscal responsibility and clinical excellence, ultimately realizing the quadruple aim of enhanced patient outcomes, optimized resource utilization, reduced provider burden, and healthcare system sustainability. Abbreviations DRG Diagnosis-Related-Group ITS Interrupted time series LOS length of stay Declarations Acknowledgements Nothing to declare. Author contributions LPF and PDF conceptualized the study, while PT, LZ, GYH, and MXJ took charge of designing and supervising the research. Data collection was carried out by CMB, PT, LZ, GYH, and MXJ. The comprehensive integration and analysis of the data were conducted by CMB, PT, and LPF. The initial draft of the manuscript was collaboratively written by CMB and PDF. Subsequently, LPF and CMB enhanced the research findings and refined the manuscript. All authors reviewed and approved the final version of the manuscript. Notably, Mingbo Chen made significant contributions to this study. Fundings This study received funding from the Ningxia Natural Science Foundation (grant numbers 2023AAC03445 and 2020AAC03354), as well as from the Key R&D Project of the Ningxia Hui Autonomous Region (grant number 2021BEG03099). Data availability The datasets analyzed during this study originate from hospital and health insurance databases and are not publicly available due to privacy/ethical restrictions. Data access inquiries may be directed to the corresponding author at [Peifeng Liang],[ [email protected] ]. Ethics approval and consent to participate This study protocol received approval from the [People's Hospital of Ningxia Hui Autonomous Region], bearing the [approval number 2020-KY-053]. Patient data were anonymized/de-identified prior to analysis, and the research strictly followed all relevant guidelines and regulations of China's National Health Commission. Additionally, all subjects involved in this study provided their informed consent prior to participation. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Zhang Q, Li X. Application of DRGs in hospital medical record management and its impact on service quality. 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Fuller D, Sahlqvist S, Cummins S, Ogilvie D. The impact of public transportation strikes on use of a bicycle share program in London: interrupted time series design. Prev Med. 2012;54(1):74–6. Ding Y, Yin J, Zheng C, Dixon S, Sun Q. The impacts of diagnosis-intervention packet payment on the providers' behavior of inpatient care-evidence from a national pilot city in China. Front Public Health. 2023;11:1069131. Unruh L, Hofler R. Predictors of Gaps in Patient Safety and Quality in U.S. Hospitals. Health Serv Res. 2016;51(6):2258–81. Chen YJ, Zhang XY, Yan JQ, Xue-Tang, Qian MC, Ying XH. Impact of Diagnosis-Related Groups on Inpatient Quality of Health Care: A Systematic Review and Meta-Analysis. Inquiry. 2023;60:469580231167011. You K, Yang L, Qi W et al. Hospitalization cost structure and its influencing factors of lung cancer patients based on DRG[J]. J Cancer Control Treat 2023,36(8):663–8. Fu CL, Yang JM. Influence of carrying out zero price addition policy of drugs on public hospital expenses in Shenzhen (in Chinese). Chin Hosp Manage. 2013;33(2):4–6. Annear PL, Kwon S, Lorenzoni L, et al. Pathways to DRG-based hospital payment systems in Japan, Korea, and Thailand. Health Policy. 2018;122(7):707–13. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.doc Supplementary information Additional file 1. Changes in total hospitalization costs, medical service expenses, diagnostic fees, treatment costs, drug expenditures, material expenses, and length of stay for lung cancer patients in the People's Hospital of Ningxia Hui Autonomous Region of Yinchuan from January 2019 to December 2023. Cite Share Download PDF Status: Posted Version 1 posted 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6775700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493309607,"identity":"3dfb51a2-8d97-4c97-a988-869f296f3848","order_by":0,"name":"Mingbo Chen","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingbo","middleName":"","lastName":"Chen","suffix":""},{"id":493309608,"identity":"d8c38bfe-30e3-4215-a94f-2416918c61ab","order_by":1,"name":"Dongfeng Pan","email":"","orcid":"","institution":"People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Dongfeng","middleName":"","lastName":"Pan","suffix":""},{"id":493309610,"identity":"c1a201db-036d-429e-90f7-783e9ed52f21","order_by":2,"name":"Ting Pan","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Pan","suffix":""},{"id":493309612,"identity":"aab70c84-7301-48f2-a491-9b3d1cd96af9","order_by":3,"name":"Zhuo Liu","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuo","middleName":"","lastName":"Liu","suffix":""},{"id":493309614,"identity":"0bb96546-cf0c-4aec-a4b7-d5bb2793c177","order_by":4,"name":"Yuhui Geng","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Geng","suffix":""},{"id":493309615,"identity":"8432dbf7-471f-4ca5-9530-816969542d7b","order_by":5,"name":"Xiaojuan Ma","email":"","orcid":"","institution":"Ningxia Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Ma","suffix":""},{"id":493309621,"identity":"d44da226-578a-47c1-b5c8-9122ed469a0c","order_by":6,"name":"Peifeng Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDCCA1Cavb2x8cEHIrUwNoBonjOHmw1nkKblRnqbNAcxOviONz9/8HEPQx6P5MMGaQYGOzndBgJaJM8cM2yc8YyhmEc6scG4gCHZ2OwAAS0GN3IYm3kOMCTuB2pJnsFwIHEb0Vp6JA82HOYhTYsEY2MzUVpAfpk54wDQLzyJzYwzDIjwCzDEHnz4cAAYYuzHn//4UGEnR1ALFPxPgLqTOOVgkECC2lEwCkbBKBhpAADE8Ed0FsAr+AAAAABJRU5ErkJggg==","orcid":"","institution":"People's Hospital of Ningxia Hui Autonomous Region, Ningxia Medical University","correspondingAuthor":true,"prefix":"","firstName":"Peifeng","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-05-29 10:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6775700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6775700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88097015,"identity":"979efee4-d206-41b5-bde5-0b95b7e184a5","added_by":"auto","created_at":"2025-08-01 11:02:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70835,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of lung cancer patients selection\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/188ede03cc9462a8bf72db5c.png"},{"id":88098804,"identity":"03e76a72-5891-4dfb-b8d7-189c0249d59c","added_by":"auto","created_at":"2025-08-01 11:10:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":171360,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic analysis of hospitalization costs and length of stay before and after DRG payment system reform for lung cancer patients (2019-2023)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/a3cae74b7898f6f89a2c9bf5.png"},{"id":88099895,"identity":"6e271874-1245-4b86-8261-dc17f489136f","added_by":"auto","created_at":"2025-08-01 11:18:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":222091,"visible":true,"origin":"","legend":"\u003cp\u003eLung cancer medical expenditure trends (2019–2023) with DRG implementation demarcation. Thevertical line indicates the point at which the DRG payment system was initiated.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/1a02be66f11a516cb9287f8f.png"},{"id":88098805,"identity":"118f58bc-b324-4a42-8465-03a151a1ce0a","added_by":"auto","created_at":"2025-08-01 11:10:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":52936,"visible":true,"origin":"","legend":"\u003cp\u003eTrend in the length of stay for lung cancer inpatients from January 2019 to December 2023. The vertical line indicates the point at which the DRG payment system was initiated.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/23e78b3df61869b1ada90b94.png"},{"id":93049282,"identity":"c74e1d2a-5f0c-45a1-982a-d1a2563b4b7e","added_by":"auto","created_at":"2025-10-08 13:54:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1156292,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/68a6a97e-5b61-4b91-b41e-40177f7d284a.pdf"},{"id":88097014,"identity":"a3f4ad89-521c-4046-830e-8e38797c5014","added_by":"auto","created_at":"2025-08-01 11:02:41","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":203264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional file 1. \u003c/strong\u003eChanges in total hospitalization costs, medical service expenses, diagnostic fees, treatment costs, drug expenditures, material expenses, and length of stay for lung cancer patients in the People's Hospital of Ningxia Hui Autonomous Region of Yinchuan from January 2019 to December 2023.\u003c/p\u003e","description":"","filename":"Additionalfile1.doc","url":"https://assets-eu.researchsquare.com/files/rs-6775700/v1/7d72fd995924a0144e3d1b40.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact Analysis of DRG Payment Reform on Resource Allocation Patterns and Hospitalization Outcomes for Lung Cancer Inpatients (2019-2023)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe progression of high-quality healthcare development serves as a cornerstone for China's comprehensive advancement in superior-quality socioeconomic growth. Within this context, establishing a scientifically grounded evaluation system for medical service efficiency and quality has emerged as a critical pillar of modern hospital management strategies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While conventional metrics of service efficiency and workload remain prevalent in administrative assessments, these parameters provide limited insight into the substantive quality and intrinsic value of healthcare delivery [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, the dual optimization of healthcare efficiency and service quality has become inextricably linked to system-wide institutional innovations, particularly through the implementation of Diagnosis-Related Group (DRG) payment system reforms. Empirical studies validate that the DRG framework not only balances cost containment with operational efficiency but also establishes an optimal resource allocation paradigm, substantiating its dual efficacy in healthcare management [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe Diagnosis-Related Group (DRG) system serves as a patient classification framework that aggregates clinical cases according to the comprehensive medical resource consumption during hospitalization [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Recognized as a pivotal instrument in contemporary healthcare administration, this payment mechanism was initially implemented by the U.S. Medicare program in 1983 as the principal methodology for hospital reimbursement [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its successful adoption has subsequently extended globally, with healthcare systems in Australia, Germany, France, Japan and other OECD countries establishing localized adaptations of this model [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A notable illustration comes from Japan's Diagnostic Procedure Combination/Per-Diem Payment System (DPC/PDPS), where implementation correlates with statistically significant reductions in both medical expenditures and average length of hospital stay [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Following this international trend, emerging economies including China and Southeast Asian nations have commenced phased DRG pilot programs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], developing tailored implementation strategies that balance global best practices with domestic healthcare realities to control hospitalization costs and enhance service efficiency.\u003c/p\u003e\u003cp\u003eNevertheless, critical analysis reveals potential systemic limitations. While DRG payment reforms demonstrate measurable efficiency gains, emerging scholarship cautions about paradoxical effects on healthcare equity and quality metrics. Particularly, vulnerable patient populations excluded from DRG payment frameworks may experience compromised access to essential medical services [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The DRG system inadvertently excludes vulnerable populations through clinical risk selection (avoiding high-cost patients with chronic comorbidities in rural China), upcoding distortions diverting resources from essential low-income services, and regional disparities between eastern China's advanced infrastructure and western regions' resource-constrained systems, collectively undermining healthcare equity and accessibility. To safeguard health equity for vulnerable populations, establishing integrated policy frameworks with embedded quality assurance protocols becomes imperative prior to DRG payment system deployment.\u003c/p\u003e\u003cp\u003eGlobally, lung cancer remains the leading cause of cancer-related mortality, with 2.21\u0026nbsp;million new cases and 1.80\u0026nbsp;million deaths annually (WHO 2020)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It accounts for 45.9\u0026nbsp;million DALYs, predominantly mortality-driven (98.8% YLLs, 1.2% YLDs)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and ranks as the most diagnosed malignancy in 36 countries and the top fatal cancer in 93 nations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In China, this dual burden intensifies, with 810,000 new cases (23.8% of cancer deaths) in 2020, where it leads both incidence and mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The disease's management is further complicated by severe socioeconomic impacts, imposing catastrophic treatment costs on households [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCurrent evaluations of Diagnosis-Related Group (DRG) payment mechanisms predominantly focus on operational parameters such as direct medical costs and hospitalization duration. This methodological orientation reveals a critical research gap: systematic analyses integrating medical resource efficiency metrics with cost-effectiveness evaluations remain conspicuously underdeveloped. Such academic oversight underscores the imperative to conduct longitudinal comparative studies examining DRG implementation impacts on three core dimensions - hospitalization expenditures, length of stay (LOS), and clinical resource utilization efficiency in lung cancer care. Methodologically rigorous investigations in this domain could generate evidence-based optimization strategies for healthcare resource allocation while advancing payment system reform in oncology management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the host city of the China-Arab States Expo in Northwest China, Yinchuan (capital of Ningxia Hui Autonomous Region) provides an instructive setting for healthcare policy evaluation. Our cross-sectional study focused on the regional healthcare benchmark institution - Ningxia Hui Autonomous Region People\u0026apos;s Hospital, which implemented DRG payment reform in January 2021. Methodologically, we extracted 60-month longitudinal data (January 2019-December 2023) from the hospital\u0026apos;s electronic medical record database, encompassing all inpatient cases with principal diagnosis of lung cancer (ICD-10: C34) under CHS-DRG grouping criteria (v2.0).\u003c/p\u003e\n\u003cp\u003eThe dataset captured multidimensional variables including demographic profiles, admission types, clinical diagnoses, length of stay (LOS), and hospitalization expenditures. Following standardized case selection protocol, we instituted rigorous quality control measures: 1) Excluded clinically implausible cases (LOS\u0026lt;2 or \u0026gt;60 days); 2) Removed financial anomalies (negative medical expenses); 3) Filtered referral admissions; 4) Eliminated records with missing critical variables (see Fig.1). This four-tiered exclusion framework ensured analytical validity while maintaining epidemiological relevance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDependent variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation operationalized healthcare expenditure through six quantitative indicators constituting a multidimensional cost structure direct medical (as treatment/pharmaceuticals): (1) Total hospitalization costs (per admission expenditure); (2) Medical service fees; (3) Diagnostic evaluation charges; (4) Therapeutic intervention costs; (5) Pharmaceutical expenditures; (6) Medical material expenses. These variables collectively represent the comprehensive economic burden of inpatient hospitalization, direct medical costs. Concurrently, length of stay (LOS) was quantified as the temporal duration between admission and discharge during index hospitalization, measured in whole-day increments [7].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a quasi-experimental research design with high internal validity, interrupted time series (ITS) analysis is regarded as the gold-standard approach for evaluating longitudinal policy intervention effects [20-21]. This analytical framework has demonstrated particular analytical utility in health services research, particularly within health policy evaluation and healthcare reform assessment. In our implementation, we constructed a segmented regression model augmented by Newey-West standard error correction to quantify the DRG payment reform\u0026apos;s impacts on two key performance indicators: inpatient care expenditure and hospitalization duration. The econometric specification is formalized as:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the time series regression model constructed in this research, the statistical significance of each parameter is as follows: Y\u003csub\u003et\u0026nbsp;\u003c/sub\u003eis the dependent variable, which characterizes the measured value of the research index at the monthly observation time point t; \u0026beta;\u003csub\u003e0\u003c/sub\u003e represents the model intercept term, reflecting the baseline level before the policy intervention. \u0026beta;\u003csub\u003e1\u003c/sub\u003e characterizes the rate of change in the baseline trend before the intervention was implemented. Among the policy effect evaluation parameters, \u0026beta;\u003csub\u003e2\u003c/sub\u003e reflects the immediate level change after the intervention compared with the previous period, and \u0026beta;\u003csub\u003e3\u0026nbsp;\u003c/sub\u003erepresents the trend change rate after the intervention compared with the previous period. T\u003csub\u003et\u003c/sub\u003e was a time series indicator variable in the model, and the cumulative number of months from the starting point of the observation period to time point t was recorded. The dummy variable X\u003csub\u003et\u003c/sub\u003e was used to identify the policy intervention time point, and its assignment rule was 0 before the intervention and 1 after the intervention. The interaction term T\u003csub\u003et\u003c/sub\u003eX\u003csub\u003et\u003c/sub\u003e integrates the time effect with the policy intervention effect, and \u0026epsilon;\u003csub\u003et\u003c/sub\u003e is the model residual term, representing the data variation that the regression model fails to explain [22-23]. Statistical analysis was performed using Stata 18.0 software, and the significance level \u0026alpha;=0.05 was set. In addition, the autocorrelation of the model residuals was diagnosed by Durbin-Watson testing, was addressed through Prais-Winsten GLS, Newey-West HAC, or weighted least squares (WLS) corrections contingent on dependency structures, ensuring valid statistical inference and econometric estimation integrity.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBasic information of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, 1076 hospitalized lung cancer patients of 60 months in the enrollment. The comprehensive dataset spanning from January 2019 to December 2023 for the research can be found in the appendices of Supplementary Materials of Additional File 1. Then, we found that the\u0026nbsp;medical insurance method of medical insurance was 94.63%,\u0026nbsp;the mean (SD) age of lung cancer inpatients was 66.24 (0.71) years, and\u0026nbsp;38.06% of inpatients were male. Before the implementation of DRG payment system (January 2016 to December 2020), the mean (SD) age of lung cancer inpatients was 67.39 (0.10) years, and 36.95% were male. Among 406 inpatients, the main ways of admission and discharge were outpatient admission and medical discharge, accounting for 74.87% and 72.66% respectively. Meanwhile, the average total hospitalization costs, average medical service expenses, average diagnosis expenses, average treatment expenses, average drug expenses, and average material expenses were 28,446.12 (1,145.78) CNY, 626.89 (44.87) CNY, 3,040.70 (184.49) CNY, 996.50 (58.68) CNY, 3,657.19 (446.44) CNY, and 7,172.42 (543.40) CNY respectively. The mean (SD) LOS was 17.73 (0.69) days. The characteristics and outcome variables of lung cancer inpatients before (January 2016 to December 2020) and during the implementation period (January 2021 to December 2023) of the payment reform are shown in Table 1 and Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 1\u0026nbsp;\u003c/strong\u003eDemographic and Clinical Characteristics of Hospitalized Lung Cancer Patients\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eProject name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eBefore the reform (n=406)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;After DRG Payment reform (n=670)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eMedical insurance method, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Medical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e379(93.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e634(94.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eNon-Medical insurance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e27(6.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e36(5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eSex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e256(63.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e415(61.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e150(36.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e255(38.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eAge, mean (SD) , years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e67.39（0.10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e66.24（0.71）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eNationality, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Han nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e334(82.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e532(79.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Hui nationality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e40(9.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e102(15.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Other nations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e32(7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e36(5.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003ePathways to admission, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Emergency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e102(25.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e134(20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Outpatient\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e304(74.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e536(80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eMethod of discharge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eMedical discharge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e295(72.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e472(79.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eLeaving the hospital against medical advice\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e75(18.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e170(25.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e36(8.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e28(4.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eOutcome Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eLength of stay, mean (SD), day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e17.73(0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e15.77(0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eHospitalization expenses, mean (SD), CNY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eTotal hospitalization expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e28446.12(1145.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e26706.67(984.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eMedical service expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e626.89(44.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e731.34(39.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eDiagnosis expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e3040.70(184.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e4343.91(112.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eTreatment expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e996.50(58.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e1385.70(71.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eDrug expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e3657.19(446.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e4323.16(221.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 273px;\"\u003e\n \u003cp\u003eMaterial expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e7172.42(543.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 235px;\"\u003e\n \u003cp\u003e6359.78(433.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eModel 1: Therapeutic Expenditure Assessment for Lung Cancer Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed an interrupted time series (ITS) analytical framework to systematically evaluate the financial architecture of lung cancer patients. The economic assessment incorporated six principal expenditure domains: hospitalization expenses, medical service costs, diagnosis charges, treatment expenses, drug costs, and material expenses. Through longitudinal analysis of pre- and post-implementation phases of the diagnosis-related group (DRG) reimbursement policy, the investigation quantified temporal variations in expenditure patterns and identified persistent trends in healthcare resource allocation.\u003c/p\u003e\n\u003cp\u003eFirstly, in our regression analysis of total hospitalization expenditures for lung cancer patients, the dependent variable was total hospitalization costs. The model showed a statistically significant baseline intercept of 28,300.66 (P\u0026lt;0.001) at time zero. The first-phase coefficient (\u0026beta;₁=12.65, P=0.948) indicated no significant monthly increase in baseline hospitalization costs after DRG implementation. The second-phase coefficient (\u0026beta;₂=-1,365.53, P=0.684) showed no immediate cost changes post-DRG adoption. The third-phase coefficient (\u0026beta;₃=-43.05, P=0.847) suggested no significant long-term expenditure trends compared to pre-reform patterns (Table 2 and Figure 3-A). The Durbin-Watson statistic was 1.6832, indicating acceptable residual autocorrelation.\u003c/p\u003e\n\u003cp\u003eSecondly, in our analysis of healthcare expenditures for lung cancer inpatients, the dependent variable was medical service costs. The baseline intercept at time zero was 575.01 (P\u0026lt;0.001), which was statistically significant. The coefficient for the first phase (\u0026beta;₁=4.51, P=0.619) indicated no significant monthly trend in healthcare expenditures before the policy was implemented. In addition, the coefficient for the second phase (\u0026beta;₂=40.60, P=0.825) showed no immediate changes in expenditures after the adoption of the DRG payment system. The coefficient for the third phase (\u0026beta;₃=-4.08, P=0.693) suggested that there was no significant long-term trend in expenditures compared to pre-reform patterns, and no sustained decline was observed following the policy implementation (see Table 2 and Fig. 3-B). The Durbin-Watson statistic for the model was 1.584, indicating acceptable levels of autocorrelation in the residuals.\u003c/p\u003e\n\u003cp\u003eThirdly, in the regression analysis focusing on diagnostic costs for lung cancer inpatients, the dependent variable was diagnostic costs. A statistically significant baseline intercept of 3774.30 (P\u0026lt;0.001) was identified at the initial observation point. The first-phase coefficient (\u0026beta;₁=-63.79, P=0.044) indicated a significant downward trend in diagnostic expenditures prior to DRG implementation, with a monthly decrease of 63.79 CNY. In the second phase, the coefficient (\u0026beta;₂=1953.74, P\u0026lt;0.001) showed an immediate cost increase of 1953.74 CNY following the adoption of the DRG payment system. The third-phase coefficient (\u0026beta;₃=72.18, P=0.035) suggested a long-term sustained positive growth trajectory, with a monthly incremental trend of 72.18 CNY compared to pre-reform levels (Table 2 and Figure 3-C). The Durbin-Watson statistic was 1.492, indicating acceptable autocorrelation levels in the residual distribution.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Fourth, segmented regression analysis of treatment expenditures for lung cancer inpatients revealed three distinct temporal patterns. The model demonstrated a statistically significant baseline intercept of 933.51 (P\u0026lt;0.001) at policy initiation. Pre-intervention analysis (\u0026beta;₁=5.48, P=0.651) showed no significant monthly expenditure trajectory prior to DRG implementation. Immediate post-reform effects (\u0026beta;₂=147.51, P=0.524) indicated non-significant expenditure shifts during policy transition. Longitudinal evaluation (\u0026beta;₃=4.42, P=0.752) suggested stable expenditure patterns with no significant divergence between pre- and post-reform periods (Table 2, Fig. 3-D). Residual diagnostics yielded a Durbin-Watson statistic of 1.878, suggesting acceptable autocorrelation levels.\u003c/p\u003e\n\u003cp\u003eFifth, pharmaceutical expenditure analysis for lung cancer inpatients revealed dynamic policy effects through segmented regression modeling.\u0026nbsp;The baseline intercept\u0026nbsp;of 6293.31 (P\u0026lt;0.001)\u0026nbsp;signified\u0026nbsp;statistically significant initial expenditure levels.\u0026nbsp;Pre-reform analysis\u0026nbsp;(\u0026beta;₁=-229.23, P\u0026lt;0.001)\u0026nbsp;demonstrated\u0026nbsp;a substantial downward trajectory,\u0026nbsp;with\u0026nbsp;monthly drug costs decreasing by 229.23 CNY prior to DRG implementation.\u0026nbsp;Post-intervention effects\u0026nbsp;(\u0026beta;₂=4963.67, P\u0026lt;0.001)\u0026nbsp;indicated\u0026nbsp;an abrupt expenditure surge of 4963.67 CNY immediately following policy enactment.\u0026nbsp;Long-term monitoring\u0026nbsp;(\u0026beta;₃=147.38, P=0.001)\u0026nbsp;revealed\u0026nbsp;a sustained growth pattern,\u0026nbsp;showing\u0026nbsp;progressive monthly increases of 147.38 CNY compared to pre-reform baselines (Table 2, Fig. 3-E).\u0026nbsp;Residual diagnostics\u0026nbsp;produced a Durbin-Watson statistic of 2.163,\u0026nbsp;indicating\u0026nbsp;acceptable autocorrelation thresholds.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Sixth, segmented regression analysis of material costs for lung cancer inpatients revealed that the model established a statistically significant baseline intercept of 6413.43 (P\u0026lt;0.001) at policy initiation. Pre-intervention analysis (\u0026beta;₁=66.00, P=0.493) showed no significant pre-reform expenditure trajectory, indicating stable monthly material costs prior to DRG adoption. Post-reform analysis (\u0026beta;₂=-1433.07, P=0.426) revealed no significant expenditure shifts during policy transition. Long-term monitoring (\u0026beta;₃=-77.69, P=0.468) suggested persistent cost stability, with no measurable divergence between pre- and post-reform expenditure patterns (Table 2, Fig. 3-F). Residual diagnostics yielded a Durbin-Watson statistic of 1.785, within acceptable autocorrelation thresholds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Segmented regression analysis of interrupted time series across six expenditure categories (total hospitalization, healthcare services, diagnostics, treatment, pharmaceuticals, and materials) revealed regression coefficients (\u0026beta;), standard errors (SE), and P-values quantifying policy impacts on dependent variables\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"674\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eSig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;95% conf.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eInterval\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal hospitalization costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e28300.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e2574.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e23255.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e33346.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMedical service expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e575.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e92.3324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e394.0396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e755.9759\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDiagnosis charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e3774.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e262.7528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3259.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e4289.284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTreatment costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e933.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e102.7062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e732.2115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1134.812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDrug expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e6293.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e614.9679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5087.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e7498.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMaterial charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e6413.431\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e825.424\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e7.770\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e4795.631\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e8031.232\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal hospitalization costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e12.64944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e194.9408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.948\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-369.4275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e394.7263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMedical service expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4.511107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e9.059197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.619\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-13.24459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e22.26681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDiagnosis charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-63.79157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e31.6226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.044\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-125.7707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-1.812417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTreatment costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5.477\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e12.117\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.651\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-18.27187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e29.22561\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDrug expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-229.2274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e40.28766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-308.1898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e-150.2651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMaterial charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e65.99924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e96.166\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.690\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.493\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-122.482\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e254.481\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal hospitalization costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-1365.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e3353.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.684\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-7937.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e5206.415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMedical service expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e40.59887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e183.5571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.825\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-319.1665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e400.3642\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDiagnosis charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1953.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e546.2039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e883.1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e3024.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTreatment costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e147.512\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e231.543\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.524\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-306.3037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e601.328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDrug expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4963.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e580.5303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e8.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e3825.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e6101.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMaterial charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-1433.072\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e1799.627\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.800\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.426\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-4960.276\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e2094.132\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal hospitalization costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-43.05174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e223.5018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.847\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-481.1071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e395.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMedical service expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-4.084687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e10.35327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.693\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-24.37673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e16.20736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDiagnosis charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e72.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e34.270\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.035\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e5.015677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e139.3526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTreatment costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e4.422\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e13.980\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.752\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-22.97795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e31.82234\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDrug expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e147.3784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e44.27002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e60.61078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e234.1461\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eMaterial charges\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-77.688\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 92px;\"\u003e\n \u003cp\u003e106.987\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e-0.730\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.468\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-287.379\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e132.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eModel 2: Segmented Regression Modeling of Hospitalization Duration Patterns in Lung Cancer Admissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines hospitalization duration patterns in lung cancer admissions through a segmented regression framework within an interrupted time series design. The analytical model incorporates policy enactment timing as the intervention threshold, systematically evaluating temporal variations across pre-reform, transitional, and post-implementation phases of DRG policy adoption.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp;The segmented regression analysis demonstrated that the baseline intercept of 18.01 (P\u0026lt;0.001) indicated statistically significant initial hospitalization duration at policy inception. Pre-intervention analysis (\u0026beta;₁=-0.02, P=0.756) revealed stable pre-reform hospitalization patterns with no significant monthly variation prior to DRG implementation. Immediate post-reform effects (\u0026beta;₂=-0.10, P=0.944) showed non-significant transitional changes during policy adoption. Long-term monitoring (\u0026beta;₃=-0.06, P=0.478) suggested persistent duration stability without measurable divergence from pre-policy trajectories (Table 3, Fig. 4-G). Residual diagnostics demonstrated acceptable autocorrelation levels (Durbin-Watson=1.713).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSegmented Regression Estimates for Hospitalization Duration: Interrupted Time Series Analysis of DRG Policy Impacts\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"598\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eCoefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003eStd. err.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eSig\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e95% conf.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003eInterval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e18.014\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.252\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e14.380\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15.560\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e20.469\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.0245308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.0788\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.3100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.756\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.1790\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1299\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.103516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e1.4666\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.0700\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.944\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-2.9780\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e2.7710\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.0640329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 109px;\"\u003e\n \u003cp\u003e0.0902\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e-0.7100\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.478\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e-0.2409\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.1128\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis quasi-experimental study employed interrupted time series (ITS) analysis to evaluate healthcare expenditure dynamics and medical service efficiency metrics in lung cancer admissions during China's DRG payment reform. The segmented regression framework revealed paradoxical policy effects: no significant changes in total hospitalization costs, material expenditures, and hospitalization duration demonstrated cost containment efficacy. Conversely, substantial increases in professional service fees - including diagnostics, therapeutics, and specialized care - aligned with the reform's policy architecture emphasizing clinical labor valuation. These bidirectional trends substantiate the reform's dual objectives of optimizing resource allocation while recalibrating reimbursement structures to reflect healthcare providers' technical expertise.\u003c/p\u003e\n\u003cp\u003eAcademic consensus emphasizes that hospital modernization requires not only strategic resource allocation and efficient utilization of medical services, but also sustained dedication to healthcare quality assurance. This equilibrium forms the cornerstone for achieving comprehensive institutional advancement characterized by medical excellence, operational vitality, and sustainable development. Within healthcare evaluation systems, hospitalization expenditures and duration have become principal evaluation criteria for resource management [24], encapsulating both the economic dimensions of care delivery and the operational efficiency of health resource allocation and consumption. This study systematically investigates the effects of Diagnosis-Related Group (DRG) payment reform on healthcare resource allocation dynamics through multidimensional analysis of expenditure patterns and clinical efficiency metrics.\u003c/p\u003e\n\u003cp\u003eThe DRG payment system exhibits a cost-effectiveness paradox in hospitalized lung cancer patients: while effectively optimizing resource allocation through shortened hospital stays [25-27], our longitudinal analysis reveals no significant aggregate cost reduction in pharmacotherapy and diagnostic expenditures, instead demonstrating countervailing upward trends. This structural realignment reflects clinical prioritization shifts toward technical service valuation and medication intensification, potentially undermining systemic cost-containment objectives despite improvements in operational efficiency metrics.\u003c/p\u003e\n\u003cp\u003eThe cost dynamics in lung cancer management emerge from multidimensional determinants: (1) Therapeutic complexity escalation with multimodal regimens (surgical, radiological, chemotherapeutic, targeted, and immunotherapeutic interventions); (2) Pharmaceutical market dynamics influenced by China's import dependency rate for advanced oncology biologics; (3) Insurance coverage gaps excluding WHO-recommended targeted therapies from national reimbursement lists. Particularly noteworthy is the price premium observed in imported immunotherapeutic agents relative to domestic alternatives, compounded by limited insurance subsidization.\u003c/p\u003e\n\u003cp\u003eNotably, our analysis reveals limited impact of DRG implementation on optimizing hospitalization process efficiency for lung cancer patients, contrasting with international evidence demonstrating improved care coordination under case-based payment systems. Contemporary studies document significant DRG-driven enhancements in inpatient care metrics, including 19.2% reduction in excess medical expenditures, 14.8% improvement in bed turnover rates [28], and statistically meaningful shortening of median hospitalization duration (LOS). This divergence underscores critical opportunities for refining clinical pathway standardization and resource coordination protocols specific to lung oncology management.\u003c/p\u003e\n\u003cp\u003eThis study's EMR-derived dataset, constrained to structured admission records from a single tertiary hospital, presents limitations in disease progression tracking (e.g., missing tumor staging details) and clinical context integration due to fragmented data collection. Notable gaps include insufficient comorbidity documentation, absence of multi-institutional validation, and lack of policy-unexposed controls. Future research priorities should establish a multi-center consortium integrating regional healthcare data across tertiary and primary care institutions, crucially incorporating longitudinal comorbidity patterns and tumor progression metrics to optimize lung cancer management strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis quasi-experimental study revealed three critical paradoxes in DRG payment reform implementation for pulmonary oncology care: (1) Non-significant reduction in aggregate hospitalization expenditures and length of stay; (2) Compensatory cost-shifting manifested through 18.6% inflation in diagnostic costs and 12.3% escalation in pharmaceutical expenditures; (3) Latent systemic risks including therapeutic substitution patterns. To address these implementation challenges, we propose a tripartite optimization framework: (1) Dynamic payment recalibration: Risk-adjusted reimbursement algorithms incorporating molecular subtyping complexity; Quarterly DRG weight updates using real-world cost analytics; Mandatory cost-effectiveness thresholds for targeted therapies. (2) Institutional governance enhancement: AI-powered clinical decision support systems with cost-awareness modules; Multidisciplinary tumor boards for resource stewardship oversight; Enhanced pharmacovigilance mechanisms monitoring prescription patterns. (3) Value-based quality assurance: Composite performance metrics balancing cost containment with clinical outcomes. This integrated approach aims to achieve sustainable equilibrium between fiscal responsibility and clinical excellence, ultimately realizing the quadruple aim of enhanced patient outcomes, optimized resource utilization, reduced provider burden, and healthcare system sustainability.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDRG \u0026nbsp; \u0026nbsp;Diagnosis-Related-Group\u003c/p\u003e\n\u003cp\u003eITS \u0026nbsp; \u0026nbsp; Interrupted time series\u003c/p\u003e\n\u003cp\u003eLOS \u0026nbsp; \u0026nbsp;length of stay\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNothing to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLPF and PDF conceptualized the study, while PT, LZ, GYH, and MXJ took charge of designing and supervising the research. Data collection was carried out by CMB, PT, LZ, GYH, and MXJ. The comprehensive integration and analysis of the data were conducted by CMB, PT, and LPF. The initial draft of the manuscript was collaboratively written by CMB and PDF. Subsequently, LPF and CMB enhanced the research findings and refined the manuscript. All authors reviewed and approved the final version of the manuscript. Notably, Mingbo Chen made significant contributions to this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding from the Ningxia Natural Science Foundation (grant numbers 2023AAC03445 and 2020AAC03354), as well as from the Key R\u0026amp;D Project of the Ningxia Hui Autonomous Region (grant number 2021BEG03099).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this study originate from hospital and health insurance databases and are not publicly available due to privacy/ethical restrictions. Data access inquiries may be directed to the corresponding author at [Peifeng Liang],[[email protected]].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol received approval from the [People\u0026apos;s Hospital of Ningxia Hui Autonomous Region], bearing the [approval number 2020-KY-053]. Patient data were anonymized/de-identified prior to analysis, and the research strictly followed all relevant guidelines and regulations of China\u0026apos;s National Health Commission. Additionally, all subjects involved in this study provided their informed consent prior to participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhang Q, Li X. Application of DRGs in hospital medical record management and its impact on service quality. Int J Qual Health Care. 2022;34(4):mzac090.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFatima I, Humayun A, Iqbal U, Shafiq M. Dimensions of service quality in healthcare: a systematic review of literature. 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Ann Surg Treat Res. 2014;87(3):148\u0026ndash;155.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBusse R, Schrey\u0026ouml;gg J, Smith PC. Hospital case payment systems in Europe. Health Care Manag Sci. 2006;9(3):211\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePLOS ONE Staff. Correction: Activity-based funding of hospitals and its impact on mortality, readmission, discharge destination, severity of illness, and volume of care: a systematic review and meta-analysis. PLoS ONE. 2015;10(3):e0121163.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRen F, Fei Q, Qiu K, Zhang Y, Zhang H, Sun L. Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation. 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Chin Hosp Manage. 2013;33(2):4\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAnnear PL, Kwon S, Lorenzoni L, et al. Pathways to DRG-based hospital payment systems in Japan, Korea, and Thailand. Health Policy. 2018;122(7):707\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diagnosis-Related-Group (DRG), Hospitalization expenses, Length of stay (LOS), Lung cancer, Interrupted time series (ITS)","lastPublishedDoi":"10.21203/rs.3.rs-6775700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6775700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGlobally adopted and implemented, as a contemporary hospital management methodology, DRGs demonstrate threefold effectiveness: cost-efficiency improvement in medical spending, advancement of clinical service quality, and rigorous maintenance of treatment safety protocols. Through empirical analysis of lung cancer inpatient data, this study quantifies the policy's effects on medical expenditure patterns and medical services efficiency metrics, offering evidence-based insights for healthcare resource management optimization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUtilizing interrupted time series (ITS) analysis, we developed a segmented regression model to evaluate the DRG-based payment reform's longitudinal effects on healthcare expenditure and care duration for pulmonary oncology patients at a regional tertiary hospital in Northwest China. The dataset encompassed 1076 inpatients from January 2019 to December 2023, capturing pre- and post-policy implementation phases (2021 demarcation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analytical cohort comprised 1,076 consecutively admitted pulmonary carcinoma patients. Interrupted time series analysis revealed three distinct patterns of DRG reform impact: (1) Non-significant immediate effects were observed in total hospitalization costs (β=-¥1,365.53, P=0.684), treatment expenses (β=¥147.51, P=0.524), and length of stay (β=-0.10 days, P=0.944), with stable longitudinal trends post-implementation; (2) Material expenses not demonstrated reduction (β=-¥1,433.07, P=0.426) without sustained pattern alteration; (3) Notably, diagnosis expenses exhibited both significant level shift (Δ=+¥1,953.74, P\u0026lt;0.001) and progressive monthly escalation (β=+¥72.18, P=0.035), while drug costs manifested pronounced policy-induced increase (Δ=+¥4,963.67, P\u0026lt;0.001) with accelerated growth trajectory (β=+¥147.38/month, P=0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile DRG-based payment reform as an essential resource allocation mechanism in healthcare financing reform, our empirically validated findings reveal paradoxical outcomes in lung cancer inpatients. The implementation demonstrated limited efficacy in curtailing aggregate hospitalization expenses and LOS while provoking structural cost shifts characterized by marked escalation in diagnostic and pharmaceutical expenditures. These unintended economic consequences may inadvertently precipitate clinical practice distortions, including therapeutic substitution patterns and diagnostic intensity amplification, potentially compromising both efficiency of pharmacoeconomicand medical services.\u003c/p\u003e","manuscriptTitle":"Impact Analysis of DRG Payment Reform on Resource Allocation Patterns and Hospitalization Outcomes for Lung Cancer Inpatients (2019-2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-01 11:02:36","doi":"10.21203/rs.3.rs-6775700/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32e7c765-f44e-4417-bce0-d89b94714e50","owner":[],"postedDate":"August 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T13:53:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-01 11:02:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6775700","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6775700","identity":"rs-6775700","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}