A narrative review of time consumption index in diagnosis-related groups-based department efficiency evaluation

Introduction

Background

Since the late 1980s, China started the study and promotion of diagnosis-related groups (DRGs) (1). In 2019, the National Healthcare Security Administration issued the “National Healthcare Security DRG Grouping and Payment Technical Specifications” and the “National Healthcare Security DRG (CHS-DRG) Grouping Plan”, forming a DRG payment system with Chinese characteristics. The cost-based settlement model established on the DRG payment system is conducive to promoting high-quality development of medical institutions and improving the efficiency of medical services (1-3). Recently, the General Office of the Guangdong Provincial Government proposed that enhancing new efficiencies for high-quality development of public hospitals requires improving operational management systems, establishing disease combination systems using big data methods, and forming quantified standards for each disease group. The “Opinions of the General Office of the State Council on Promoting the High-Quality Development of Public Hospitals” clearly defines the time consumption index (TCI) as one of the indicators for evaluating improvements in the capabilities of tertiary public hospitals. By comparing the time consumed by the institution in treating specific diseases with the average time consumed by similar cases in the region, TCI can scientifically guide departments to reasonably adjust medical efficiency (4).

Rationale and knowledge gap

Existing literatures on healthcare time efficiency primarily focuses on isolated drivers of average length of stay (ALOS) or general DRG performance metrics. Few studies specifically differentiate TCI from ALOS in the context of standardized efficiency evaluation, nor do they integrate process-level drivers into a cohesive framework specific to TCI (5,6). Consequently, there is a lack of systematic evidence on how specific management interventions impact TCI across different clinical scenarios. Cost Consumption Index (CCI) could also assess hospital performance by directly measuring economic burden, but often a downstream outcome of hospitalization duration.

Objective

To address this gap, this study reviews research linking directly to TCI, contrasting it with traditional ALOS metrics. It aims to clarify the application of TCI in the context of high-quality development and provide actionable, data-driven insights for guiding the rational turnover of hospital departments. We present this article in accordance with the Narrative Review reporting checklist (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-88/rc).

Methods

The literature screening was performed independently by three reviewers in a double-blind manner. Before the formal screening work, all reviewers have reached an agreement on the pre-specified criteria, as well as the workflow, to ensure the consistency of the implementation of literature screening. Original researches (cross-sectional studies, retrospective analyses, cohort studies), review articles, policy documents, and methodological papers related to TCI or DRG-based time efficiency evaluation were enrolled for analysis (Table 1).

For studies with inconsistent screening conclusions, all reviewers jointly conducted a full-text re-evaluation of the target literature, and strictly carried out in-depth discussions until a unanimous consensus was reached. For any residual disagreements, a fourth senior author with extensive experience was invited to make the final adjudication.

Retrieval timeframe

All literature was retrieved with no restriction on the start year, up to July 31, 2025—ensuring coverage of the latest research progress after the formal implementation of China’s CHS-DRG system [2019] and the update of tertiary hospital evaluation standards [2022].

Search terms

For CNKI (China National Knowledge Infrastructure), combined core concepts including “diagnosis-related groups”, “time consumption index”, “average length of stay”, “departmental efficiency”, and “DRG payment”. Boolean operators (“AND”/”OR”) were used for combination: (DRG) AND (TCI) AND (departmental efficiency OR average length of stay).

For PubMed, corresponding English keywords were used to retrieve international literature on DRG efficiency and risk adjustment: “Diagnosis-Related Groups (DRGs)”, “Time Consumption Index (TCI)”, “Average Length of Stay (ALOS)”, “hospital efficiency”, and “departmental performance”. Combination strategy: (DRG OR “Diagnosis-Related Groups”) AND (“Time Consumption Index” OR TCI) AND (“hospital efficiency” OR “departmental efficiency”).

After initial retrieval, 386 Chinese records (from CNKI) and 124 English records (from PubMed) were obtained. Following duplicate removal (n=62) and title/abstract screening (n=298 excluded for irrelevant themes), 149 full-text articles were assessed for eligibility.

For included articles, quality was evaluated and a total of 57 high-quality studies (45 Chinese, 12 English) were retained as the core evidence base, ensuring the reliability of this study.

Connotation of TCI in the context of high-quality development

On September 14, 2021, the National Health Commission and the National Administration of Traditional Chinese Medicine issued a notice regarding the “Action Plan for Promoting High-Quality Development of Public Hospitals (2021-2025)”, proposing an initiative to enhance patient experience, emphasizing the continuous improvement of medical service indicators, and integrating patient safety management into all aspects of hospital management. Additionally, in December 2022, the “Tertiary Hospital Evaluation Standards (2022 Edition)” and its implementation rules released by the National Health Commission included the DRG time index as part of the evaluation criteria for medical service capabilities. This aims to reflect the time efficiency of medical institutions in treating similar diseases through TCI, providing a more accurate assessment of the institution’s medical efficiency within the region (7).

Background of the proportion of TCI

Since the 14th Five-Year Plan and the approval on the “Opinions on Promoting the High-Quality Development of Public Hospitals” by the 18th meeting of the Central Committee for Deepening Overall Reform, from the establishment of 17 national medical centers across ten categories to pilot constructions of 26 national regional medical centers, to the improvement of cross-provincial medical settlement and enhancement of primary healthcare service levels to continuously solving problems like “difficult and expensive access to medical care”, deepened reforms promoting high-quality development in the medical and health sector is a critical step towards building Healthy China. The “Trial Evaluation Indicators for High-Quality Development of Public Hospitals” issued by the National Health Commission on June 29, 2022, identifies TCI as one of five indicators assessing the degree of capability enhancement in tertiary public hospitals. Furthermore, the “Operation Manual for Trial Evaluation Indicators for High-Quality Development of Public Hospitals (2022 Edition)” released by the National Health Commission provides a detailed explanation of TCI, advocating the use of big data methods to establish standards for disease combination systems, forming quantified treatment standards, medication standards, and material standards for each disease group according to the severity of diseases and resource consumption. This aims to guide hospitals back to their functional positioning, improve efficiency, save costs, and reduce patient medical expenses.

To address issues such as “difficult and expensive access to medical care”, besides gradually advancing the tiered diagnosis and treatment mechanism and improving the quality of primary healthcare services, it is crucial to enhance the diagnostic and therapeutic efficiency of public hospitals, accelerate turnover, and increase the number of surgical patients admitted to tertiary public hospitals (8). Based on this, the proportion of discharged patients undergoing surgery and their medical service income have become important evaluation indicators for tertiary public hospitals (9). To optimize these indicators, many studies have begun exploring TCI as guidance to set goals for reducing ALOS, identifying nodes that can improve the efficiency of diagnostic processes, thereby gradually enhancing hospital operational efficiency.

Calculation formula of TCI

In a 2018 study focusing on evaluation indicators of DRGs, scholars had already proposed the TCI to quantify patient hospitalization time based on the relevant diagnostic grouping conditions (10). In 2020, the National Health Commission officially listed TCI as one of the criteria for evaluating medical service capabilities of medical institutions in the “Notice on Issuing Tertiary Hospital Evaluation Standards (2020 Edition)”.

The calculation formula for the TCI is: Σ (Average Length of Stay for a Specific DRG Group/Average Length of Stay for All DRG Groups) (11). As such, ALOS directly influences TCI. When TCI equals 1, it indicates that the time spent by the medical institution treating similar diseases is equivalent to the regional average level. Low TCI (<1) indicates that the hospital’s ALOS for specific DRG groups is shorter than the regional average which reflects higher operational efficiency, faster bed turnover, and optimized diagnosis and treatment processes (12). A low TCI does not imply compromised medical quality—instead, it often correlates with improved resource utilization without increasing adverse event rates (13). Reversely, high TCI (>1) signifies that the hospital’s ALOS exceeds the regional average for similar DRG cases. This may stem from the specialization of hospital in treating more severe cases (14) or suboptimal operational efficiency such as delayed preoperative examinations (15).

Differences in connotation between TCI and ALOS

Unlike TCI, ALOS refers to the mean duration of hospitalization during a specific period for discharged patients. It serves as a comprehensive indicator for assessing the efficiency and effectiveness of medical institutions, medical quality and technology levels, and healthcare resource allocation (16). Studies have shown that reducing ALOS by 1.7 days can increase outpatient visits, discharge volumes, bed turnover rates, and other metrics by more than 14%, highlighting its significant role in improving medical efficiency (17). ALOS was also used to measure the effectiveness of health services received by patients, thereby evaluating the work of inpatient physicians and their associated medical teams (18).

However, due to variations in disease types and complexity, ALOS differs for different diseases. Additionally, social factors, healthcare management models, clinical factors, and individual patient factors are important indicators affecting the length of stay (13). Therefore, blindly shortening the length of stay without considering regional and disease-specific overall levels cannot scientifically guide improvements in medical service levels and may even lead to negative impacts. TCI standardizes ALOS and enables scientific comparisons of medical treatment durations across different DRG groups. By benchmarking values, hospitals can measure differences from regional averages in handling specific diseases, allowing for scientifically formulated goals and implementation plans.

Factors correlated to the TCI

As one of the indicators for high-quality development, TCI is influenced by multiple factors, thus requiring a multidimensional and comprehensive consideration of improvement factors based on actual circumstances.

Patient-level drivers

Patient age

Age is a well-documented predictor of ALOS due to age-related physiological differences and comorbidity burdens (19,20). Elderly patients often exhibit declined organ function such as reduced cardiac output and higher rates of chronic comorbidities. These burdens may prolong postoperative recovery and increase the risk of in-hospital complications. A retrospective study of 3,216 surgical patients in DRG groups for orthopedic fractures found that patients aged ≥70 years had an average ALOS of 12.3 days—37.1% longer than younger patients (50–69 years: 9.0 days; <50 years: 7.8 days) (21).

Disease severity

Disease severity directly determines the complexity of diagnosis and treatment, which in turn affects ALOS (22). Within the same DRG group, patients requiring ICU admission or treatment for multiple organ dysfunction often need more invasive interventions, longer monitoring periods, and multidisciplinary collaborative management (23). In the DRG group for community-acquired pneumonia, patients with severe sepsis had an ALOS of 18.7 days, compared to 8.2 days for non-severe cases (10). Notably, since DRG grouping may not fully capture nuanced severity, such unobserved severity can further prolong ALOS and inflate TCI. DRG grouping relies on observable clinical attributes (diagnoses, procedures, documented comorbidities) but fails to capture nuanced unobserved severity-such as subclinical dysfunction, frailty, or rare complications (21). Dafny’s seminal economic analysis confirms that unobserved patient characteristics (e.g., unrecorded comorbidities) explain 15–20% of cross-hospital LOS variation, directly translating to higher TCI for institutions treating such complex cases (24).

Patient education

Recent studies have proposed that patient education on health knowledge, regulation of drug use and adjustment of hospital cost structures were new approaches to optimize TCI. However, these studies still lack discussion of how they impact TCI (25-27).

Process-level drivers

Preoperative hospital stay

Preoperative hospitalization duration is a critical process factor affecting ALOS. Prolonged preoperative stays typically stem from inefficient coordination (e.g., delayed surgical scheduling, uncoordinated preoperative examinations) or resource shortages such as limited operating room capacity and unavailable diagnostic equipment (28,29). A study of patients with extra-long length of stay found that additional day of preoperative stay could significantly increase total ALOS (30). Notably, prolonged preoperative waiting delays the initiation of definitive treatment and increases the risk of hospital-acquired infections, further extending ALOS (31). Conversely, optimizing preoperative processes such as implementing pre-hospitalization examinations can reduce preoperative stay to the regional average (32).

Waiting time for initial tests

The time from patient admission to completion of initial diagnostic tests (e.g., laboratory tests, imaging studies) directly impacts the speed of diagnosis and treatment planning, thereby affecting ALOS (33). Delays in initial tests often result from inefficient test appointment systems, insufficient equipment capacity, or poor communication between clinical departments and medical technology departments (34). Optimizing test appointment workflows by digital pre-admission test scheduling and equipment resource pooling may shorten initial test waiting time, accelerate diagnosis and ultimately reduce ALOS (15,35).

TCI-driven incentives

To contextualize the association between TCI and management of hospitals, we first clarify the core incentives shaped by TCI within the DRG payment and tertiary hospital evaluation framework—consistent with China’s healthcare policy goals (National Health Commission, 2022) and international DRG-based incentive logic (24).

Economic incentives

DRG’s prospective payment system (PPS) reimburses hospitals a fixed amount per DRG group, regardless of actual hospitalization duration (2). A higher TCI (reflecting longer ALOS) increases marginal costs (e.g., labor, consumables, bed occupancy) without additional reimbursement, creating a cost-control incentive (36). Conversely, a TCI ≤1 aligns with regional average efficiency, ensuring cost neutrality or surplus under DRG payment—reinforcing the economic rationale for optimizing TCI (37).

Performance evaluation incentives

As a key indicator for tertiary public hospital capability assessments, TCI directly impacts hospitals’ rankings, policy support, and public reputation (38). Poor TCI performance may result in reduced resource allocations or regulatory scrutiny, creating a performance accountability incentive. Hospitals must align TCI with regional benchmarks to meet evaluation requirements and maintain institutional competitiveness (39).

Resource allocation incentives

TCI is tightly linked to bed turnover rate. A lower TCI enables faster patient turnover, increasing the number of treatable cases within a given period. This creates a resource efficiency incentive and hospitals have strong motivation to optimize bed capacity, medical staff productivity according to TCI, as improved turnover directly enhances service accessibility and operational throughput (14,40).

Application of TCI in hospital high-quality development management

Optimizing the TCI

According to the calculation formula of TCI, the primary measure for optimizing TCI is to optimize ALOS, which was proposed in early 1980s China. In recent years, with the increased emphasis on high-quality development in medical institutions, novel hospital management concepts have emerged across various regions, such as Six Sigma management, clinical pathway management, and departmental optimization and restructuring (41). These management models can effectively optimize TCI by enhancing the efficiency of medical services (42,43).

Additionally, effective measures to optimize TCI include implementing multidisciplinary team (MDT) consultations, organizing discussions of complex cases across departments to determine treatment plans, focusing on quality control of the first page of medical records, regularly rectifying and providing feedback on medical records (44). Therefore, establishing scientific management models based on the application of TCI will be an important direction for future research on managing high-quality medical development.

Using the TCI to evaluate medical quality

In recent years, most studies related to hospital management have used the Case-Mix Index (CMI) of DRGs to guide adjustments in length of stay and costs (45-47). Some studies utilize quadrant diagrams and Boston matrix charts to comprehensively analyze the service efficiency and capacity of hospital departments, thereby directing their development (48,49). However, there are relatively fewer studies focused on TCI.

As mentioned before, low TCI typically reflects optimized clinical workflows, high resource utilization efficiency, and rapid patient turnover. Liu et al. used quadrant diagrams to analyze indicators such as CMI, total DRG admissions, TCI, and CCI in orthopedic diseases at 43 tertiary general hospitals in Hebei Province, proposing rational suggestions for optimizing hospital management (11). The decrease of TCI in the orthopedic department suggested streamlined preoperative preparation and optimized medical service efficiency (50). Notably, Overemphasis on TCI reduction may lead to premature discharge, increasing readmission rates.

High TCI may have dual interpretations. Some hospital specializes in treating severe cases within the DRG group where longer ALOS is clinically necessary. Suboptimal workflows may also lead to operational inefficiency and unnecessary hospitalization time. High TCI linked to inefficiency which may cause resource waste and financial pressure. Under DRG payment, longer hospitalization time increases marginal costs without additional reimbursement, leading to financial losses for high-TCI DRG groups.

Based on the specific implication and effect of TCI, empirical studies have validated its practical value in medical quality evaluation. Han et al. referenced benchmark data from different DRGs in Beijing to compare the relationship between nosocomial infection cases and their TCI and CCI at a tertiary public hospital, elucidating the burden of nosocomial infections at the hospital (10). Additionally, other studies implemented clinical nursing for specific surgical patients under the DRG payment model, using TCI as a primary indicator to compare and analyze the effects before and after adjustments, thereby confirming its effectiveness (51).

Significance of TCI in promoting high-quality medical development

Optimize diagnostic and treatment processes to enhance service efficiency

Scientific and effective adjustments to TCI require cooperation among various hospital departments and related functional departments. For example, a public hospital has adopted a “frontline research + process improvement” model based on TCI, which can promptly identify bottlenecks in the patient admission-to-discharge process. Additionally, leveraging the “pre-hospitalization” policy allows patients to complete all preoperative examinations before admission, not only improving bed utilization rates but also aiding in promoting clinical pathway management to enhance the quality of hospital diagnosis and treatment (52,53). Besides optimizing admission and discharge processes, strengthening cooperation between clinical departments and platform departments and promoting the improvement of hospital information systems can better enhance the quality and efficiency of medical services, thereby promoting high-quality medical development.

Strengthen medical quality management and improve diagnostic and therapeutic capabilities

Using TCI as a benchmark, conducting regular training and evaluations, and rewarding outstanding medical staff can enhance the overall diagnostic and therapeutic level of the hospital while motivating medical personnel to learn new technologies more actively. By comparing with peers and implementing diagnostic and therapeutic techniques that help optimize TCI, such as enhanced recovery after surgery (ERAS) technology to accelerate postoperative recovery, significant improvements in diagnostic and therapeutic capabilities can be achieved, leading to high-quality management of medical resources.

Calibration issues of the TCI

Relevant studies have shown that DRGs, as tools for evaluating medical services, ensure the homogeneity of disease complexity and resource consumption (54-56). Similarly, as one of the service indicators under DRGs, TCI should also exhibit homogeneity when evaluating different cases (21). However, some studies suggest that the DRG evaluation system may unfairly assess certain diseases. For instance, provincial-level medical institutions treating patients with “five failures” provide extensive medical equipment, consume more time, incur higher medical costs, and face greater expenses, potentially resulting in greater losses compared to other institutions (57). Furthermore, the current number of disease categories within the grouping system may no longer meet the needs of today’s diverse disease types (21).

DRG data originates from the first page of medical records, directly affecting the accuracy and timeliness of TCI (58). The first page of medical records serves as the core data source for DRG classification, containing key information such as primary or secondary diagnoses, complications, surgical procedures, patient demographics, and treatment processes (59,60). The continuous emergence of new medical technologies and diseases poses new challenges for accurate classification under DRGs. Moreover, some hospitals face shortages of formally employed physicians, leading to parts of medical records being completed by visiting or training physicians, which may result in lower quality of the first page of medical records (61,62). Common documentation flaws including inaccurate coding, underreporting of comorbidities, and incomplete surgical descriptions directly lead to biased ALOS statistics and distorted TCI values. Notably, coding errors are not random, as training physicians may overcode mild conditions to higher-weight DRGs or undercode comorbidities due to insufficient clinical experience (63,64). Therefore, training clinical physicians to properly fill out the first page of medical records is a crucial prerequisite for achieving accurate DRG classification.

Limitation

Several limitations must be acknowledged. First, regarding the included literature, the majority of studies are retrospective cross-sectional analyses or policy interpretations. There is a paucity of multi-center, prospective randomized controlled trials investigating the causal relationship between specific management interventions and TCI reduction. Second, as noted in the discussion, the validity of TCI is heavily dependent on the quality of the “first page of medical records”. Existing studies often lack rigorous control for unobserved severity, which may account for significant variation. More robust methodological approaches are needed to ensure that TCI reflects true efficiency rather than coding proficiency, such as advanced risk-adjustment models and data quality auditing algorithms. Finally, as a narrative review, we did not statistically aggregate effect sizes for specific TCI optimization measures.

Conclusions

Under the DRG payment reforms, TCI acts as a pivotal lever for uncovering operational inefficiencies that raw ALOS data may obscure. To effectively enhance departmental efficiency in future practice, hospitals must shift from passive monitoring to active process re-engineering. Specifically, this entails fostering synergy between clinical and platform departments to accelerate diagnostic turnover, and ensuring the data accuracy of medical record for fair evaluation. By embedding TCI into refined management models, hospitals can achieve a scientific balance between cost control and medical quality, ultimately driving high-quality development.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-88/rc

Peer Review File: Available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-88/prf

Funding: This work was supported by the National Key Clinical Discipline (2012) and Key Research Projects of Guangdong Province Health Economics Association (No. 2023-SJZD-03).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jhmhp.amegroups.com/article/view/10.21037/jhmhp-25-88/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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