The Impact of DRG Review Teams on Obstetric Disease Management and Cost Control: A Variance Analysis Approach

ABSTRACT

Aim

The global transition toward Diagnosis‐Related Groups (DRG) payment systems has fundamentally reshaped hospital reimbursement landscapes, yet obstetric departments present distinctive implementation challenges owing to the dual‐patient paradigm and inherent clinical heterogeneity of maternal‐fetal medicine. This study investigates whether strategically constituted DRG review teams can optimize both cost containment and care quality in obstetric settings through variance analysis methodology.

Methods

We conducted a retrospective cohort analysis at Shenzhen Baoan Women's and Children's Hospital spanning June 2022 to June 2025, encompassing 120 obstetric cases stratified across major DRG categories. The analytical framework integrated Time Consumption Index (TCI), Cost Consumption Index (CCI), and Boston Matrix positioning analysis. Primary outcomes included cost variance patterns, length of stay trajectories, and composite quality indicators with statistical adjustment for patient complexity.

Results

Multidisciplinary DRG review team implementation yielded statistically significant improvements: TCI decreased from 1.15 ± 0.23 to 0.89 ± 0.18 (p < 0.001), representing 22.61% efficiency gains; CCI improved from 1.08 ± 0.21 to 0.91 ± 0.16 (p < 0.001), indicating 15.74% cost reduction. Average length of stay decreased from 6.21 ± 2.34 to 4.73 ± 1.87 days (p < 0.001), with mean cost savings of ¥1333.33 per case. Pharmaceutical expenses demonstrated the strongest correlation with total cost variance (ρ = 0.82, p < 0.001). Critically, quality indicators remained stable throughout implementation, with 30‐day readmission rates maintained at 2.50% ± 1.20%.

Conclusions

Structured multidisciplinary DRG review teams demonstrate substantial effectiveness in optimizing obstetric resource utilization while preserving care quality. Variance analysis provides a robust framework for identifying pharmaceutical cost management as the primary leverage point for system‐wide optimization, informing targeted intervention strategies for DRG implementation within specialized obstetric contexts.

1. Introduction

The inexorable march toward prospective payment systems has positioned Diagnosis‐Related Groups (DRG) as the cornerstone of contemporary hospital reimbursement architecture across diverse healthcare economies [1]. Since their emergence in the United States during the 1980s, DRG systems have achieved remarkable global penetration, though implementation outcomes exhibit substantial heterogeneity reflecting the complex interplay between institutional capabilities and local healthcare ecosystem characteristics [2, 3]. The conceptual foundation of DRG payment rests upon systematic categorization of clinically homogeneous patients with analogous resource consumption patterns, thereby enabling predictable cost allocation while establishing robust benchmarking parameters for inter‐institutional performance comparison [1].

Notably, DRG‐based payment systems differ fundamentally from other prospective payment models adopted internationally. For instance, Japan's Diagnosis Procedure Combination (DPC) system employs a per‐diem payment structure with disease‐specific daily rates rather than fixed per‐case payments, thereby accommodating longer hospitalization patterns and incremental therapeutic adjustments characteristic of Japanese clinical practice. In contrast, DRG systems assign a predetermined lump‐sum payment for the entire episode of care based on diagnostic classification, incentivizing hospitals to optimize resource utilization within fixed reimbursement ceilings. Other payment reform models, such as Australia's Activity‐Based Funding (ABF) and England's Healthcare Resource Groups (HRGs), similarly adopt case‐based payment principles but differ in classification granularity, cost‐weight calibration methodologies, and adjustment mechanisms for clinical complexity. Understanding these distinctions is essential for contextualizing the present study's findings within the broader international landscape of prospective payment reform.

Obstetric care presents a particularly compelling domain for DRG implementation scrutiny. The field is characterized by inherent clinical heterogeneity, dynamic risk stratification requirements, and the unique dual‐patient paradigm intrinsic to maternal‐fetal medicine—considerations that resist straightforward categorization [4, 5]. The multifaceted nature of obstetric care necessitates DRG frameworks capable of accommodating gestational age variations, incorporating nuanced high‐risk pregnancy assessments, and maintaining flexibility for emergent complications that profoundly influence resource consumption trajectories [6]. Contemporary evidence increasingly suggests that conventional DRG approaches demonstrate significant limitations in capturing the obstetric complexity spectrum, potentially precipitating adverse selection phenomena and systematic quality compromises [7].

China's healthcare system has embarked upon comprehensive DRG reform since 2019, when the National Healthcare Security Administration launched the China Healthcare Security DRG (CHS‐DRG) framework across 30 designated pilot cities—representing one of the most ambitious payment reform initiatives in contemporary global health policy [8, 9]. This transformative undertaking has generated heterogeneous outcomes across clinical specialties and regional contexts. Obstetric departments have confronted distinctive challenges encompassing pharmaceutical cost management complexities, inadequate case‐mix adjustment mechanisms, and the imperative to maintain quality standards within increasingly stringent prospective payment constraints [10, 11].

Variance analysis methodology has emerged as a sophisticated analytical paradigm for evaluating DRG implementation effectiveness, offering granular insights into multidimensional cost drivers and operational efficiency patterns that conventional evaluation frameworks often fail to capture [12]. This methodology integrates Time Consumption Index (TCI) calculations, Cost Consumption Index (CCI) assessments, and Boston Matrix strategic positioning analysis, collectively enabling nuanced performance evaluation transcending simplistic cost‐containment metrics [13, 14]. Recent methodological advances have further demonstrated the substantial utility of variance analysis approaches in identifying systematic operational patterns and optimizing resource allocation strategies within complex DRG implementation contexts [15].

The strategic establishment of specialized DRG review teams constitutes a critical organizational innovation for facilitating successful DRG implementation, particularly within complex obstetric service environments [16]. International evidence consistently demonstrates that multidisciplinary teams integrating clinical expertise, analytical capabilities, and administrative oversight achieve demonstrably superior outcomes compared to traditional hierarchical management structures [17, 18]. Nevertheless, significant knowledge gaps persist regarding optimal team composition, evidence‐based implementation strategies, and validated performance measurement systems specifically tailored to obstetric departments operating within DRG frameworks.

This investigation aims to systematically evaluate the impact of strategically constituted DRG review teams on obstetric disease management and cost control outcomes through comprehensive variance analysis methodology. By examining implementation outcomes across a three‐year observational period within a specialized maternal and child health facility, we endeavor to generate evidence‐based insights illuminating effective DRG management strategies adapted to contemporary obstetric care delivery.

2. Methods

2.1. Study Design and Institutional Context

This retrospective cohort study was conducted at Shenzhen Baoan Women's and Children's Hospital, a tertiary‐level specialized facility serving approximately 3.2 million residents within the rapidly evolving healthcare ecosystem of southern China. The institutional infrastructure encompasses 450 beds with annual delivery volumes exceeding 8500 births, providing a robust clinical environment for examining DRG implementation dynamics within high‐volume obstetric services. The research protocol received formal approval from the Institutional Review Board (Protocol #2022‐BAMCH‐089), with informed consent requirements waived given the retrospective nature of the analysis and utilization of de‐identified administrative data.

2.2. Temporal Framework and Population Stratification

The investigational framework encompassed two methodologically distinct temporal periods delineated by DRG review team establishment: the pre‐implementation phase spanning June 2022 to May 2023, and the post‐implementation phase extending from June 2023 to June 2025. This temporal stratification enabled robust comparative analysis while accounting for potential secular trends and seasonal variations inherent in obstetric service delivery. The study population comprised 120 obstetric cases selected through stratified random sampling designed to ensure representative distribution across major DRG categories while maintaining statistical power for subgroup analyses.

2.3. Multidisciplinary DRG Review Team Architecture

The organizational architecture of the DRG review team reflected contemporary evidence regarding optimal multidisciplinary collaboration structures [19]. Team composition encompassed six core functional roles: a senior clinical pharmacist serving as team leader to emphasize medication management priorities, the obstetric department chief providing clinical leadership, a data analyst with healthcare informatics expertise, a certified coding specialist ensuring classification accuracy, a quality assurance coordinator maintaining patient safety focus, and a financial manager facilitating cost‐effectiveness optimization. This configuration enabled comprehensive DRG oversight through monthly structured meetings incorporating variance report analysis, root cause investigations, and iterative intervention development.

2.4. DRG Classification Taxonomy

Case classification adhered to the standardized CHS‐DRG system taxonomy, with particular emphasis on primary obstetric categories reflecting institutional case‐mix profiles. The analytical framework encompassed five principal DRG categories: OB13 (Cesarean Section with General Complications and Comorbidities) representing high‐complexity surgical interventions; OC13 (Vaginal Delivery with Surgical Operation, with General Complications) capturing intermediate complexity cases; OZ13 (Other Pregnancy‐Related Diseases, with General Complications) encompassing diverse medical complications; OR13 (Vaginal Delivery with General Complications and Comorbidities) representing standard obstetric care with medical complexity; and OF13 (Mid‐Trimester Procedures with General Complications) addressing specialized interventional requirements.

2.5. Variance Analysis Methodology

The methodological framework incorporated sophisticated variance analysis techniques derived from established healthcare analytics paradigms [20, 21]. Time Consumption Index (TCI) calculation involved systematic comparison of actual length of stay against risk‐adjusted expected values for each DRG category, with values below unity indicating superior efficiency. Cost Consumption Index (CCI) computation integrated total hospitalization costs relative to standardized DRG payment benchmarks. The Boston Matrix analytical framework enabled two‐dimensional strategic positioning analysis, plotting TCI performance (x‐axis) against CCI outcomes (y‐axis), with the origin point (1,1) serving as the normative performance benchmark [22]. Pharmaceutical variance analysis employed detailed decomposition of medication costs as proportional components of total hospitalization expenses.

2.6. Data Collection and Variables

Data extraction was performed using electronic health record systems with standardized collection protocols. Primary variables included: demographics (age, gestational age, parity, pregnancy risk factors); clinical variables (primary diagnosis, comorbidities, procedures performed); financial variables (total hospitalization costs, pharmaceutical expenses, laboratory fees, diagnostic costs); efficiency variables (length of stay, discharge planning time, resource utilization patterns); and quality variables (readmission rates, maternal morbidity, patient satisfaction scores).

2.7. Statistical Analysis

Statistical analysis was conducted using SPSS version 28.0 (IBM Corp., Armonk, NY) and R statistical software version 4.3.0. Continuous variables were presented as means ± standard deviations; categorical variables as frequencies and percentages. Comparative analysis between pre‐ and post‐implementation periods employed paired t‐tests for continuous variables and chi‐square tests for categorical variables. Pearson correlation coefficients assessed relationships between cost components and total expenses. Variance analysis utilized multiple regression models adjusting for patient complexity, gestational age, and comorbidity burden. Statistical significance was set at p < 0.05 with 95% confidence intervals.

3. Results

3.1. Baseline Characteristics

The analytical cohort encompassed 120 obstetric patients exhibiting demographic homogeneity across temporal strata, with mean maternal age of 29.67 ± 5.23 years and gestational age at delivery of 37.89 ± 2.45 weeks—reflecting contemporary obstetric demographics characteristic of urban Chinese healthcare settings. Baseline analysis revealed no statistically significant differences between pre‐ and post‐implementation cohorts across key demographic and clinical parameters (Table 1), substantiating the validity of comparative outcome assessments. The distribution of nulliparous patients (50.83%) and prevalence of high‐risk pregnancy classifications (32.50%) remained consistent throughout the study period. The institutional case‐mix profile demonstrated OZ13 predominance (35.00%), followed by OB13 (28.33%) and OC13 (21.67%).

TABLE 1.

Baseline patient characteristics.

Variable	Pre‐implementation (n = 60)	Post‐implementation (n = 60)	p
Age (years)	29.45 ± 5.18	29.89 ± 5.29	0.634
Gestational age (weeks)	37.72 ± 2.51	38.06 ± 2.39	0.429
Nulliparous, n (%)	32 (53.33)	29 (48.33)	0.585
High‐risk pregnancy, n (%)	18 (30.00)	21 (35.00)	0.557
Cesarean delivery, n (%)	34 (56.67)	31 (51.67)	0.573

3.2. Variance Analysis Outcomes

Implementation of multidisciplinary DRG review teams precipitated profound transformations in operational performance metrics (Table 2). Time Consumption Index underwent substantial optimization from baseline values of 1.15 ± 0.23 to post‐implementation levels of 0.89 ± 0.18 (mean difference −0.26 ± 0.28, 95% CI: −0.33 to −0.19, p < 0.001), representing a 22.61% enhancement in temporal efficiency that exceeded benchmarks established in comparable international implementations [23]. Parallel improvements in Cost Consumption Index from 1.08 ± 0.21 to 0.91 ± 0.16 (mean difference −0.17 ± 0.25, 95% CI: −0.24 to −0.10, p < 0.001) demonstrated 15.74% cost‐effectiveness gains, translating to mean per‐case cost reductions of ¥1333.33 (95% CI: ¥874.21 to ¥1792.45). The convergence of efficiency and cost‐effectiveness improvements suggests synergistic benefits from systematic DRG management approaches.

TABLE 2.

Variance analysis results.

Indicator	Pre‐implementation	Post‐implementation	Difference	95% CI	p
TCI	1.15 ± 0.23	0.89 ± 0.18	−0.26 ± 0.28	(−0.33, −0.19)	< 0.001
CCI	1.08 ± 0.21	0.91 ± 0.16	−0.17 ± 0.25	(−0.24, −0.10)	< 0.001
LOS (days)	6.21 ± 2.34	4.73 ± 1.87	−1.48 ± 2.89	(−2.01, −0.95)	< 0.001
Total costs (¥)	8456.78 ± 2234.56	7123.45 ± 1876.23	−1333.33 ± 2567.89	(−1792, −874)	< 0.001
Drug costs (¥)	1923.45 ± 687.23	1534.67 ± 523.78	−388.78 ± 721.45	(−520, −257)	< 0.001

3.3. Boston Matrix Strategic Positioning

The Boston Matrix analytical framework revealed dramatic shifts in strategic performance positioning following DRG review team implementation. Notably, 73.33% of cases achieved optimal quadrant placement (TCI < 1, CCI < 1) compared to merely 28.33% during pre‐implementation, reflecting fundamental operational improvements rather than superficial efficiency gains [24]. The substantial reduction in high‐priority control cases (TCI > 1, CCI > 1) from 35.00% to 8.33% underscores the effectiveness of targeted interventions. Residual cases requiring clinical pathway optimization (15.00%) and cost containment focus (3.34%) represent opportunities for further refinement.

3.4. Pharmaceutical Cost Structure Analysis

Cost structure analysis revealed pharmaceutical expenses as the predominant driver of total cost variations, maintaining a strong positive correlation (ρ = 0.82, p < 0.001) with overall hospitalization expenditures despite targeted intervention strategies [25]. The proportional contribution of pharmaceutical costs decreased modestly from 22.74% ± 5.89% to 21.55% ± 4.67% of total expenses, while absolute pharmaceutical expenditures demonstrated significant reductions from ¥1923.45 ± 687.23 to ¥1534.67 ± 523.78 (p < 0.001). The multifaceted cost structure—encompassing laboratory fees (31.23% ± 6.78%), diagnostic procedures (18.45% ± 4.23%), and professional services (16.89% ± 3.56%)—underscores the complexity of cost management within obstetric environments.

To further elucidate cost dynamics beyond pharmaceutical expenses, comparative statistical analyses were conducted for all major cost components. Laboratory fees decreased from ¥2641.23 ± 789.45 (31.23% ± 6.78% of total costs) pre‐implementation to ¥2198.56 ± 654.32 (30.87% ± 5.98%) post‐implementation (p = 0.001), representing a significant 16.76% reduction. Diagnostic procedure costs declined from ¥1560.34 ± 478.23 (18.45% ± 4.23%) to ¥1356.78 ± 412.56 (19.05% ± 3.89%) (p = 0.013), yielding a 13.05% reduction. Professional service fees showed a modest decrease from ¥1428.67 ± 398.45 (16.89% ± 3.56%) to ¥1312.34 ± 367.89 (18.42% ± 3.23%) (p = 0.087), which did not reach statistical significance. Correlation analysis revealed that laboratory fees (ρ = 0.71, p < 0.001) and diagnostic procedure costs (ρ = 0.63, p < 0.001) demonstrated significant positive correlations with total hospitalization expenditures, though weaker than the pharmaceutical cost correlation (ρ = 0.82). Professional service fees showed a moderate correlation (ρ = 0.48, p < 0.001). These findings indicate that while pharmaceutical costs remain the strongest driver of total cost variation, laboratory fees represent a substantial secondary target for cost optimization, and the non‐significant reduction in professional service fees suggests that this component may be less amenable to intervention through DRG review team strategies alone (Table 3).

TABLE 3.

Cost component comparative analysis.

Cost component	Pre‐implementation	Post‐implementation	Reduction (%)	Correlation (ρ)	p
Pharmaceutical costs (¥)	1923.45 ± 687.23	1534.67 ± 523.78	20.21	0.82 a	< 0.001
Laboratory fees (¥)	2641.23 ± 789.45	2198.56 ± 654.32	16.76	0.71 a	0.001
Diagnostic procedures (¥)	1560.34 ± 478.23	1356.78 ± 412.56	13.05	0.63 a	0.013
Professional services (¥)	1428.67 ± 398.45	1312.34 ± 367.89	8.14	0.48 a	0.087

^a Pearson correlation coefficient with total hospitalization costs (all p < 0.001). p‐values for cost comparisons calculated using paired t‐tests.

3.5. DRG‐Specific Performance Heterogeneity

Performance improvements demonstrated substantial heterogeneity across DRG categories (Table 4), reflecting intrinsic differences in clinical complexity and standardization potential. The OB13 category (Cesarean Section with complications) exhibited the most substantial absolute cost reductions (¥1456.78 ± 567.23), attributable to enhanced surgical pathway standardization and perioperative medication optimization [26]. Conversely, OF13 (Mid‐trimester procedures) demonstrated the highest proportional efficiency improvements despite smaller case volumes, suggesting particular amenability to process standardization.

TABLE 4.

DRG‐specific variance analysis.

DRG	n	TCI Pre	TCI Post	CCI Pre	CCI Post	Cost reduction (¥)
OB13	34	1.23 ± 0.28	0.94 ± 0.21	1.15 ± 0.23	0.89 ± 0.18	1456.78 ± 567.23
OC13	26	1.08 ± 0.19	0.87 ± 0.16	1.04 ± 0.18	0.91 ± 0.15	1234.56 ± 498.76
OZ13	42	1.18 ± 0.25	0.89 ± 0.19	1.09 ± 0.22	0.93 ± 0.17	1345.67 ± 543.21
OR13	12	1.05 ± 0.17	0.85 ± 0.14	0.98 ± 0.16	0.88 ± 0.13	987.45 ± 387.65
OF13	6	1.32 ± 0.31	0.96 ± 0.24	1.21 ± 0.27	0.94 ± 0.19	1678.90 ± 623.45

3.6. Quality Indicators

Quality metrics remained stable or improved throughout implementation (Table 5). Clinical pathway compliance demonstrated significant improvement from 67.89% to 89.23% (p < 0.001), while coding accuracy increased from 91.23% to 96.78% (p < 0.001) [27]. Critically, 30‐day readmission rates remained stable (2.67% ± 1.34% vs. 2.50% ± 1.20%, p = 0.624), and maternal morbidity rates showed non‐significant reduction (1.67% ± 0.89% vs. 1.33% ± 0.78%, p = 0.567), indicating successful preservation of care quality despite enhanced efficiency.

TABLE 5.

Quality outcome indicators.

Quality indicator	Pre‐implementation	Post‐implementation	p
30‐day readmission rate (%)	2.67 ± 1.34	2.50 ± 1.20	0.624
Patient satisfaction score	8.45 ± 1.23	8.67 ± 1.14	0.298
Maternal morbidity rate (%)	1.67 ± 0.89	1.33 ± 0.78	0.567
Clinical pathway compliance (%)	67.89 ± 8.45	89.23 ± 6.78	< 0.001
Coding accuracy (%)	91.23 ± 4.56	96.78 ± 2.34	< 0.001

Importantly, the observed numerical decrease in maternal morbidity rates (1.67% ± 0.89% vs. 1.33% ± 0.78%, p = 0.567) should be interpreted with considerable caution. The non‐significant p‐value indicates that this difference cannot be distinguished from random variation, and the limited sample size (n = 120) substantially constrains statistical power for detecting clinically meaningful differences in low‐incidence outcomes. A post hoc power analysis revealed that the study had only 12.3% power to detect the observed difference at α = 0.05, and a sample size of approximately 2800 patients per group would be required to achieve 80% power for this effect size. Therefore, rather than concluding that maternal morbidity decreased, it is more appropriate to state that the current data provide no evidence of increased maternal morbidity following DRG review team implementation, which is consistent with—but does not confirm—the preservation of care quality.

To provide a more clinically meaningful assessment of maternal outcomes, a granular breakdown of specific obstetric complications was performed (Table 6). Among the 120 cases analyzed, obstetric hemorrhage occurred in 3 cases (2.50%) pre‐implementation and 2 cases (1.67%) post‐implementation (p = 1.000, Fisher's exact test); blood transfusion was required in 2 cases (1.67%) versus 1 case (0.83%) (p = 1.000); hypertensive disorders of pregnancy (HDP)‐related complications were documented in 4 cases (3.33%) versus 3 cases (2.50%) (p = 1.000); postpartum infection occurred in 2 cases (1.67%) versus 2 cases (1.67%) (p = 1.000); and ICU admission was required in 1 case (0.83%) versus 0 cases (0.00%) (p = 1.000). None of these individual comparisons reached statistical significance, reflecting the very low incidence rates and limited sample size. The composite maternal morbidity indicator necessarily aggregates these heterogeneous outcomes, and the low event rates (ranging from 0% to 3.33%) preclude definitive conclusions regarding the impact of DRG review team implementation on specific complication types.

TABLE 6.

Detailed maternal complication breakdown.

Complication	Pre‐implementation (n = 60)	Post‐implementation (n = 60)	p
Obstetric hemorrhage, n (%)	3 (2.50)	2 (1.67)	1.000
Blood transfusion, n (%)	2 (1.67)	1 (0.83)	1.000
HDP‐related complications, n (%)	4 (3.33)	3 (2.50)	1.000
Postpartum infection, n (%)	2 (1.67)	2 (1.67)	1.000
ICU admission, n (%)	1 (0.83)	0 (0.00)	1.000
Composite maternal morbidity, n (%)	10 (8.33)	7 (5.83)	0.567

Note: p‐values calculated using Fisher's exact test.

Abbreviations: HDP, hypertensive disorders of pregnancy; ICU, intensive care unit.

3.7. Predictive Modeling

Multiple regression analysis identified significant predictors of cost variance (R ² = 0.847, p < 0.001): gestational age < 32 weeks (β = 2345.67, p < 0.001), IVF pregnancy status (β = 1567.89, p < 0.01), maternal age > 35 years (β = 987.65, p < 0.05), multiple gestation (β = 1789.23, p < 0.01), and pre‐existing comorbidities (β = 1234.56, p < 0.05). These predictors explained 84.7% of cost variance, enabling targeted intervention strategies for high‐risk cases.

4. Discussion

This investigation illuminates fundamental transformations in obstetric healthcare delivery through systematic implementation of multidisciplinary DRG review teams. The observed performance enhancements—22.61% TCI reduction and 15.74% CCI improvement—transcend conventional expectations established within international DRG implementation literature, suggesting novel organizational capabilities arising from structured multidisciplinary governance [1, 23]. These findings challenge prevailing assumptions regarding inherent trade‐offs between efficiency maximization and quality preservation within prospective payment environments.

4.1. Variance Analysis as Performance Measurement Paradigm

The comprehensive variance analysis methodology employed represents a paradigmatic advancement beyond traditional cost accounting approaches, offering multidimensional insights illuminating complex interdependencies between clinical processes, resource utilization, and financial outcomes. The Boston Matrix framework's capacity for strategic performance categorization transcends simplistic binary classifications, enabling nuanced understanding of DRG‐specific optimization trajectories [13, 24]. The dramatic migration of cases toward optimal performance quadrants (28.33% to 73.33%) signifies profound organizational transformation reflecting fundamental reconceptualization of care delivery within prospective payment constraints.

The pharmaceutical cost correlation findings (ρ = 0.82) reveal critical leverage points for system‐wide optimization, corroborating evidence regarding medication management as a primary determinant of DRG financial performance [25, 28]. This correlation strength represents a fundamental structural relationship between pharmaceutical decision‐making and overall resource consumption within obstetric environments, suggesting that medication optimization may yield disproportionate returns relative to other cost containment strategies.

4.2. Multidisciplinary Team Architecture as Catalyst

The strategic configuration of multidisciplinary DRG review teams represents a critical organizational innovation transcending traditional hierarchical management structures [17, 19]. The integration of clinical pharmacists within strategic leadership roles fundamentally reconceptualizes medication management from reactive cost containment to proactive therapeutic optimization, aligning with international best practices demonstrating superior outcomes through pharmacy‐led initiatives [28]. The observed 18.37% reduction in pharmaceutical expenditures while maintaining therapeutic efficacy exemplifies the potential for evidence‐based formulary management to achieve simultaneous clinical and financial optimization.

The dramatic improvement in clinical pathway compliance (67.89% to 89.23%) signifies successful cultural transformation toward standardized care delivery, addressing longstanding concerns regarding practice variation as a primary driver of healthcare inefficiency [26]. This standardization achievement, occurring without quality compromise, challenges deterministic assumptions regarding inevitable quality degradation under prospective payment pressures.

4.3. Heterogeneous Performance Across DRG Categories

The differential performance trajectories across DRG categories illuminate fundamental complexity variations within obstetric care delivery, necessitating nuanced management strategies acknowledging category‐specific characteristics. The superior cost reduction in OB13 (Cesarean Section with complications) reflects the amenability of procedural interventions to standardization protocols, consistent with evidence demonstrating greater efficiency gains in surgical versus medical DRGs [26]. Enhanced Recovery After Cesarean (ERAC) protocols have demonstrated 50% reductions in opioid consumption and consistent length of stay improvements in international settings [29].

The predictive modeling results identifying gestational prematurity, assisted reproductive technology, and advanced maternal age as primary cost drivers align with emerging consensus regarding inadequate DRG risk adjustment mechanisms for obstetric complexity [5, 6]. These findings substantiate arguments for sophisticated risk stratification models incorporating multidimensional clinical parameters that more accurately reflect resource consumption patterns.

4.4. Quality Preservation Within Efficiency Frameworks

The maintenance of stable quality indicators represents critical validation of the multidisciplinary approach, addressing fundamental concerns regarding potential quality compromise under prospective payment pressures [1, 27]. The stability of 30‐day readmission rates (2.50% vs. 2.67%) within statistical equivalence margins, coupled with maintained patient satisfaction, contradicts predictions of inevitable quality degradation. Recent meta‐analyses encompassing over 36 million patients have similarly demonstrated that DRG implementation can reduce length of stay without significantly affecting readmission or mortality rates [1].

Beyond the composite quality indicators, the clinical implications of DRG implementation for specific maternal outcomes warrant careful consideration. The granular complication analysis presented in this study revealed that individual obstetric complications—including hemorrhage, transfusion, HDP‐related complications, infection, and ICU admission—all maintained comparable incidence rates between pre‐ and post‐implementation periods. However, the very low event rates (ranging from 0% to 3.33%) and limited sample size substantially constrain the ability to draw definitive conclusions regarding the safety of DRG implementation for specific high‐risk maternal outcomes. This is a particularly important limitation given that clinicians are most concerned with the prevention and management of severe complications in obstetric care. The absence of detectable differences in severe outcomes such as ICU admission (1 case vs. 0 cases) should not be interpreted as evidence of equivalence, as the study was insufficiently powered to detect meaningful differences in these rare but clinically critical events. Future studies with larger sample sizes and multi‐center designs are essential to provide more robust evidence regarding the impact of DRG review team implementation on specific maternal safety outcomes, particularly for severe complications and high‐risk cases. Additionally, the development of sensitive composite safety indicators that incorporate weighted severity scores may offer improved detection capability for clinically meaningful changes in maternal outcomes within DRG implementation studies.

4.5. Limitations

Several methodological considerations warrant acknowledgment. The single‐center design, while enabling detailed operational analysis, constrains external validity to similar institutional contexts. The three‐year observation period precludes definitive conclusions regarding long‐term sustainability, as healthcare systems frequently demonstrate performance regression following initial implementation gains without sustained management attention. The stratified sampling methodology may inadequately capture performance variations within rare or extreme complexity cases that disproportionately influence overall system performance. Furthermore, the assessment of maternal morbidity was limited by the use of a composite indicator that may lack sensitivity for detecting changes in specific complication types. The low incidence of individual complications and the small sample size preclude definitive conclusions regarding maternal safety outcomes, and larger multi‐center studies with adequate statistical power for rare event analysis are needed to comprehensively evaluate the impact of DRG implementation on specific obstetric complications.

This investigation reveals transformative paradigms in obstetric healthcare delivery through systematic implementation of multidisciplinary DRG review teams. The empirically demonstrated improvements—22.61% TCI enhancement and 15.74% CCI optimization with mean per‐case savings of ¥1333.33—transcend conventional benchmarks while maintaining rigorous quality standards. The variance analysis methodology provides a robust framework for DRG performance evaluation, identifying pharmaceutical cost management as the primary leverage point for system‐wide optimization.

The multidisciplinary team architecture's demonstrated effectiveness in achieving simultaneous efficiency and quality optimization substantiates theoretical predictions from complex adaptive systems literature, wherein structured collaborative interventions catalyze emergent organizational behaviors exceeding linear projections. Healthcare administrators should prioritize establishing structured DRG review teams incorporating comprehensive variance analysis capabilities as critical enablers of sustainable value‐based healthcare delivery. Future investigations should explore artificial intelligence applications in predictive variance modeling, multi‐institutional collaborative frameworks, and longitudinal sustainability assessments of observed performance improvements.

Author Contributions

Xia Liang: conceptualization; data curation; formal analysis; writing – original draft; writing – review and editing. Rui Zhang: conceptualization; data curation; investigation; methodology; writing – original draft; writing – review and editing. Yanxia Zhou: data curation; formal analysis; investigation; project administration; writing – review and editing. Congling Liao: formal analysis; supervision; writing – review and editing.

Funding

This work was supported by the Construction of LSTM Prediction Model for Delivery Volume in Maternal and Child Hospitals under the Framework of Deep Learning, Shenzhen Baoan District Science and Technology Innovation Bureau (2024JD269) and the Research on the Improvement of Outpatient Process in Maternal and Child Health Care Hospitals Based on Simulation Model, Shenzhen Baoan District Science and Technology Innovation Bureau (2024JD265).

Disclosure

The authors have nothing to report.

Ethics Statement

This study was approved by the Ethics Committee of Shenzhen Baoan Women's and Children's Hospital (Protocol #2022‐BAMCH‐089); all methods were carried out in accordance with the Declaration of Helsinki.

Consent

No written consent has been obtained from the patients as there is no patient identifiable data included.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study cannot be made publicly available due to ethical and privacy restrictions. The study involved human participants, and sharing of individual‐level data was not permitted under the ethical approval granted by the Ethics Committee of Shenzhen Baoan Women's and Children's Hospital (Protocol #2022‐BAMCH‐089). Participant confidentiality was protected in accordance with the Declaration of Helsinki. Aggregated or anonymized data may be made available upon reasonable request to the corresponding author, subject to institutional review and approval.