
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

MD-D-23-09521
00047
10.1097/MD.0000000000039421
3
6600
Research Article
Systematic Review and Meta-Analysis
Study on the cost-control effect of diagnosis-related groups based on meta-analysis
Feng Xin a
https://orcid.org/0009-0005-3945-2877
Cheng Lulu a
Wei Hua ab*
a School of Medicine Economics and Management, Anhui University of Chinese Medicine, Hefei, Anhui Province, China
b Key Laboratory of Data Science and Innovative Development of Chinese Medicine in Anhui Province Philosophy and Social, Hefei, Anhui Province, China.
* Correspondence: Hua Wei, Key Laboratory of Data Science and Innovative Development of Chinese Medicine in Anhui Province Philosophy and Social, Hefei 230012, Anhui Province, China (e-mail: 15240020480@163.com).
13 9 2024
13 9 2024
103 37 e3942104 11 2023
01 8 2024
02 8 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Objective:

To evaluate the effect of diagnosis-related group (DRG) payment method systematically before and after implementation in terms of average hospitalization day, cost and care quality.

Method:

Restricted the period from 2019 to May 31, 2023, we use 6 databases from CNKI, Wipu, Wanfang, PubMed, ScienceDirect, and web of science. With the related study, we extract the data about DRG, then we conducted meta-analysis of the data about length of stay (LOS) and cost by RevMan 5.4 and Stata 12.0 software. Care quality is in conjunction with literature reports.

Result:

About 24 articles were included, covering 2 indicators: average hospitalization expenses and days. Meta-analysis shows that implementing DRG payment method has an advantage in terms of average hospital stay (pooled effect: −1.13%, 95% CI: −1.42 to −0.84, P = .00), and the difference is statistically significant. There is also an advantage in average hospitalization expenses (pooled effect: −2.58, 95% CI: −3.38 to −1.79, P = .00), and the difference is statistically significant.

Conclusion:

The use of DRG payment method can effectively reduce LOS and average hospitalization expenses. However, quality of care may decline with DRG adoption.

average hospitalization expenses
LOS
meta-analysis
quality of care
Social Science Planning Project of Anhui Province2022AH050424 Hua WeiSocial Science of Anhui ProvinceAHSKY2021D143 Hua WeiOPEN-ACCESSTRUE
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pmc1. Introduction

Diagnosis-related groups (DRGs) are crucial for medical insurance payments and quality assessment in contemporary healthcare systems, which can effectively manage medical expenses and maintain the sustainability of medical insurance system.[1] DRGs are defined as the groups of hospital cases according to the principles of similar clinical course and similar resource consumption based on the type of disease, treatment and individual patient characteristics.[2] Under DRGs-based payment system, hospitals receive an anticipant set fixed amount for each admission in conformity with its DRG. Driven by the operational demands of medical insurance, DRG has been more and more widely used around the world. Originating in the United States during the 1980s, DRGs have demonstrated efficacy in reducing healthcare costs and improving resource efficiency. This success has led to their implementation in countries like Germany, Japan, and South Korea, each adapting the system to their unique national healthcare contexts.[3]

Compared to traditional the cost-based payment systems, although prepaid-based DRG payments can theoretically reduce medical costs and improve the quality of medical care, existing research presents mixed outcomes.[4,5] Through a systematic literature search, we can find that the meta-analysis by Meng evaluates the effectiveness of DRGs from 1988 to 2018 and demonstrates that DRGs have an impact on hospital costs by reducing average hospital days, though lacking average cost data analysis.[6] Yin meta-analysis indicates that the payment effects of DRGs change over time, highlighting short-term versus long-term impacts.[7,8] Therefore, our study focuses on DRG payment effects from 2019 to May 2023, includes a meta-analysis of average hospital days and costs. Deeply, we also assess care quality pre- and post-DRG implementation. In order to minimize study variability and clarify DRG cost-control efficacy, this study based on meta-analysis aims to incorporate the relevant literature and systematically analyzes the cost-control effect in different countries before and after the implementation of DRG on length of stay (LOS) and total inpatient spending per admission. Given the growing interest in DRG-based payment systems, it is important to create a sound evidence base that reveals the impact of such payment systems on patient healthcare utilization and serving as a reference for the policymakers in formulating policy.

2. Materials and methods

The systematic reviews and meta-analyses adhered to the systematic review principles for interventions as outlined in the Cochrane Handbook. The findings were reported following the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Additionally, the protocol for this study was registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD42023451842).

2.1. Inclusion criteria

2.1.1. Study design

The studies include controlled studies, cohort studies, and randomized controlled studies before and after the implementation of the DRG payment method.

2.1.2. Study subjects

Payment is made on a prepaid basis. Diseases have clearly diagnostic codes, and DRG payment methods are implemented in different countries.

2.1.3. Intervention measures

The control group is the cost-control effect that did not use DRG payment or used non-DRG payment; the experimental group is the cost-control effect that used DRG.

2.1.4. Outcome indicators

In this review, LOS and total inpatient spending per admission were selected as outcome indicators of inpatient healthcare utilization associated with costs.

2.2. Exclusion criteria mainly include

unclear research type or mixed reimbursement system;

lack of complete data on mean, standard deviation, and sample size used for meta-analysis in the literature;

duplicate publications and literature that cannot obtain full texts;

literature that does not meet DRG intervention measures;

literature with unclear intervention time period.

2.3. Search strategy

Restricted the period from 2019 to May 31, 2023, we used 6 databases from CNKI, Wipu, Wanfang, PubMed, ScienceDirect, and Web of Science, without language restrictions. Using the following key words for search: “Diagnosis-Related Groups”, “Group, Diagnosis-Related”, “Groups, Diagnosis-Related”, “Diagnosis-Related Group”, “drg or drgs”, “length of day”, “average charge”, “LOS,” and 1255 articles were obtained by searching subject word and free word.

2.4. Literature screening and data extraction

NoteExpressV3.9.0.9636 (Beijing Aegean Lezhi Technology Co.) was used to facilitate literature management. Two qualified researchers independently screened the literature using predefined criteria. All potentially relevant studies are deserved to full-text review. When disagreements arose, 2 researchers resolved them by discussion. If necessary, with a third researcher input, a third researcher will assist in the judgment. The final inclusion was based on full-text evaluation. Data extraction, also performed independently by 2 researchers. The summarized baseline characteristics and information include the type of study, the country of study, the time before and after the implementation of DRGs, patient demographics (age, gender, diagnosis), and outcomes data on LOS and average hospitalization costs.

2.5. Literature quality assessment

Newcastle-Ottawa Scale (NOS) for 21 cohort studies and the Cochrane Risk of Bias Assessment Tool for 3 randomized controlled trials (RCTs) to assess the risk of 24 studies. The NOS is widely used for evaluating case–control and cohort studies, comprising 3 domains (selection, comparability, exposure/outcome) across 8 items. It utilizes a star rating system for quality assessment, with a maximum of 2 stars for comparability and 1 star for other items, totaling 9 stars. Higher scores indicate superior study quality.

2.6. Statistical analysis

For statistical analysis, RevMan version 5.4.0 (Cochrane Collaboration, Copenhagen, Denmark) and Stata version 12.0 (Stata Corp LP, College Station, TX) were utilized. Data aggregation and analysis were feasible with at least 2 homogeneous studies. Heterogeneity in meta-analyses was determined using P values and I2 values. Firstly, a P-value over 0.05, indicating no heterogeneity between studies and vice versa; Secondly, I2 scores were categorized as: 0% to 39% (unimportant), 40% to 60% (moderate), 60% to 75% (substantial), and >75% (considerable) heterogeneity.[9] Additionally, non-overlapping 95% confidence intervals in forest plots signaled study heterogeneity. In cases of no heterogeneity post-sensitivity analysis, a fixed-effect model was applied; unresolved heterogeneity necessitated reexamination of original data and methods to assess study combinability.

3. Results

3.1. Literature search results

A search of 6 databases yielded 1255 articles, reduced to 994 records after duplicate removal. Initial screening based on titles, abstracts, and keywords excluded 908 articles, remaining 86 articles for full review. Although some studies might appear to meet the inclusion criteria, which were excluded for lacking mean and standard deviation, such as Li Xiaohui, Zhang Jingqiu, and Zhang Xin.[10–12] In line with EPOC study design requirements for policy evaluation system reviews, studies that met the inclusion criteria were selected, and meta-analysis was performed on 2 indicators: average LOS and average hospitalization cost, and the flow chart of the included literature is shown in Figure 1.

Figure 1. Flow chart of literature screening. Using 6 databases from CNKI, Wipu, Wanfang, PubMed, ScienceDirect, and Web of Science, without language restrictions. 24 studies were obtained for meta-analysis, containing 21 cohort studies and 3 randomized controlled trails.

3.2. Basic information of the included studies

The 24 studies analyzed all compared DRG implementation effects before and after its adoption in various countries, including China, Switzerland, and Korea. Given the variability in sample characteristics, payment methods, number of groups, case types, disease complexity, and DRG reimbursement rates, we used random-effect models for an immediate impact assessment of DRG-based payments. Specific details of DRG-based payment policies in these studies are presented in Table 1.

Table 1 Characteristics of the included studies.

Study ID	Region	Study design	Age, years	%, female	Research cycle	Diagnosis/procedures	Quality	
Kutz (2019)[13]	Swiss	ITS	C: 69.00
E: 70.00	C: 47.80
E: 49.10	C: 2011
E: 2012	CAP, COPD, AMI, AHF, PE	High	
Yuan (2019)[14]	China	ITS	C: 62.18
E: 61.99	C: 22.15
E: 21.98	C: January 2008 to June 2010
E: July 2010 to December 2014	Acute myocardial infarction	High	
Xiaoxuan (2019)[15]	China	CBA	C: 70.57
E: 70.57	C: 49.80
E: 60.00	C: January 2018 to June 2018
C: January 2018 to June 2018	Gallstones	Low	
Jeon (2019)[16]	Korea	ITS	C: 65.69
E: 66.53	—	C: 2011 to 2013
E: 2013 to 2016	Hysterectomy	High	
Shiwei (2020)[17]	China	ITS	C: 55.90
E: 56.22	C: 59.67
E: 54.73	C: March to October 2018
E: November 2018 to June 2019	Herniated lumbar disk	High	
Qingsheng (2020)[18]	China	ITS	C: 38.31
E: 39.03	C: 50.85
E: 45.57	C: 2018
E: 2019	Acute appendicitis	High	
Jiali (2021)[19]	China	ITS	C: 72.10
E: 71.90	C: 48.33
E: 50.88	C: January 2016 to June 2017
E: July 2017 to December 2018	Cerebral infarction	High	
Yanping (2021)[20]	China	CBA	C: 30 to 75
E: 30 to 75	C: 47.67
E: 47.67	C: May 2018 to June 2019
C: May 2018 to June 2019	Cardiology	High	
Jing (2021)[21]	China	ITS	C: 31 to 77
E: 31 to 77	C: 42.67
E: 42.67	C: May 2017 to December 2018
E: May 2017 to December 2018	Type 2 diabetes	High	
Yan (2021)[22]	China	ITS	C: 39.70
E: 37.60	C: 60.78
E: 61.92	C: January to June 2018
E: July to December 2018	Acute appendicitis	High	
Tarantino (2021)[23]	Swiss	CBA	C: 55.00
E: 55.00	C: 47.7
E: 45.8	June to December 2019 C and E concurrently	Neurosurgery	High	
Xiaobao (2021)[24]	China	CBA	C: 43.56
E: 49.25	C: 37.14
E: 34.29	C: January to March 2020
E: April to July 2020	Internal medicine	Low	
Yue (2021)[25]	China	ITS	C: 49.76
E: 49.93	C: 69.3
E: 68.1	C: January to June 2020
E: January to June 2021	Head and Neck Surgery	High	
Yanhui (2021)[26]	China	CBA	C: 38.45
E: 39.04	C: 40%
E: 43.3%	C: July 2016 to July 2018
E: August 2018 to August 2020	30 clinical departments	High	
Zhuo (2022)[27]	China	ITS	C: 46.63
E: 46.20	C: 25.83
E: 24.17	C: September 2018 to August 2019
E: September 2019 to August 2020	Cervical spinal stenosis	High	
Shuyang (2022)[4]	China	CBA	C: 47.63
E: 44.53	C: 74.40
E: 75.98	C: January to June 2021
E: January to June 2021	Thyroid gland	High	
Shuyang (2022)[28]	China	CBA	C: 54.57
E: 56.33	C: 55.39
E: 62.55	January to December 2021
C and E concurrently	Radiotherapy	Low	
Huili (2022)[29]	China	ITS	C: 58.56
E: 58.56	C: 52.43
E: 49.91	C: January to December 2020
E: January to October 2021	Lumbar fusion	High	
Lin (2022)[30]	China	CBA	C: 56.63
E: 55.98	C: 46.51
E: 48.74	C: January 2018 to January 2019
E: February 2019 to January 2021	Critically ill patients	Low	
Jinsong (2023)[5]	China	CBA	—	C: 50.21
E: 50.88	October 2021 to October 2022 C and E concurrently	Tumours	Low	
Yi (2023)[31]	China	CBA	C: 64.25
E: 63.40	C: 23.71
E: 30.49	June 2018 to June 2022 C and E concurrently	Patients with drug-coated balloons	Low	
Yanrong (2023)[32]	China	ITS	C: 30.69
E: 30.64	C: 41.67
E: 43.45	C: July 2020 to June 2021
E: July 2021 to June 2022	Femoral neck Fracture	High	
Zhenying (2023)[33]	China	ITS	C: 68.56
E: 69.60	C: 57.90
E: 61.60	C: January to June 2021
E: July 2021 to January 2022	Cataract	High	
Le (2023)[34]	China	ITS	NR	C: 10.38
E: 11.86	C: 2018 to 2019
E: 2019 to 2020	Acquired immune Deficiency Syndrome	High	
AHF = acute heart failure, AMI = acute myocardia infarction, C = control group, CAP = community-acquired pneumonia, CBA = controlled before-and-after, COPD = China healthcare security diagnosis related groups, E = experimental group, ITS = interrupted time series, NR = not reported, PE = pulmonary embolism.

3.3. Quality assessment of included studies

Two qualified researchers independently assessed the included studies. When disagreements arose, 2 researchers resolved them by discussion. Studies scoring 6 or fewer stars were deemed low-quality, while others were classified as high-quality in Figure 2. The 3 RCTs were categorized as high-quality in Figure 3. Overall, eighteen studies qualified as high-quality and 6 as low-quality.

Figure 2. Newcastle-Ottawa Scale for 21 cohort studies quality assessment. Studies scoring 6 or fewer stars were deemed low-quality, while others were classified as high-quality. About 15 studies were considered as high-quality.

Endogeneity in our study could lead to biased results since DRG-based patient payments are influenced by various factors like personal, socio-medical, and treatment modalities, rather than being randomly assigned. This could result in higher heterogeneity, for which we plan to use a random-effects model, along with subgroup analysis for cost-benefit analysis (CBA) and interrupted time series (ITS). Sensitivity analyses were also conducted to further assess these influences.

3.4. Meta-analysis

3.4.1. Effect of DRGs-based payment system on LOS

About 22 studies provided the necessary data in LOS (14 ITS studies and 8 pre–post comparison studies with control groups) for meta-analysis. As shown in Figure 4, the heterogeneity testing (I2 = 98.60%, P = .000) indicated significant differences between groups, so we use a random-effects model. Sensitivity analysis confirmed persistent heterogeneity despite excluding individual studies. Given that LOS was uniformly measured in days and was a continuous variable, we opted for the Weighted Mean Difference (WMD) as the effect measure. The statistical result was WMD = −1.13 (95% CI: −1.42, −0.84, P = .00), indicating a significant reduction. This suggests the DRG payment system effectively shortened the LOS by 1.13 days. Egger test (P = .097), shown in Figure 5, found no publication bias.

Figure 4. Forest plot of average hospital stays. 22 studies provided the necessary data in LOS (14 ITS studies and 8 pre–post comparison studies with control groups) for meta-analysis. Stata version 12.0 (Stata Corp LP, College Station, TX) was utilized. The statistical result was WMD = −1.13 (95% CI: −1.42, −0.84), P = .00, indicating a significant reduction.

Figure 5. Egger test for publication bias in LOS. Stata version 12.0 (Stata Corp LP, College Station, TX) was utilized. Egger test (P = .097) found no publication bias.

3.4.2. Effect of DRG payment system on total inpatient spending per admission

About 15 studies provided the necessary data about average charge (10 ITS studies and 5 pre–post comparison studies with control groups) for meta-analysis. As shown in Figure 6, the heterogeneity test (I2 = 99.8%, P = .000) showed significant heterogeneity between groups, necessitating the use of a random-effects model. Sensitivity analysis confirmed heterogeneity remained after excluding individual studies. Although the average charge is a continuous variable, there are different units and the average charge is expressed in dollars in Yuan.[14] The Standardized Mean Difference (SMD) was selected as the effect measure, using the 2014 conversion rate of 1$=7.13￥. The SMD result was −2.58 (95% CI: −3.38, −1.79), P = .00), signifying a statistically significant decrease. This suggests the DRG payment system effectively reduced average charges by 2.58, lowering average hospitalization costs. Egger test (P = .095) found no publication bias, as indicated in Figure 7.

Figure 6. Forest plot of total inpatient spending per admission. About 15 studies provided the necessary data about average charge (10 ITS studies and 5 pre–post comparison studies with control groups) for meta-analysis. Stata version 12.0 (Stata Corp LP, College Station, TX) was utilized. The SMD result was −2.58 (95% CI: −3.38, −1.79), P = .000, signifying a statistically significant decrease.

Figure 7. Egger’s test for publication bias in total inpatient spending per admission. Stata version 12.0 (Stata Corp LP, College Station, TX) was utilized. Egger’s test (P = .095) found no publication bias.

3.5. Quality of care

The concept of quality of care encompasses the professionalism and outcome effectiveness of healthcare organizations and caregivers. It includes the quality of nursing staff, the care environment, nursing process management, and patient involvement and satisfaction. The DRG payment system has addressed the issue of varying costs for identical diseases seen in traditional health insurance models, thereby controlling medical expenses. Nonetheless, variations in diagnostic and treatment resources and techniques have led to significant disparities in health insurance payments, underscoring the inequity in medical services.[35] Research indicates that in some affluent countries, DRGs can pressure healthcare providers into prematurely discharging patients to cut costs, raising concerns about releasing medically unstable patients post-DRG implementation.[36] To mitigate these negative impacts, Switzerland has implemented 2 strategies. Firstly, it has established a policy where hospitals self-finance re-hospitalization costs within 18 days, incentivizing quality care. Secondly, the introduction of Acute and Transitional Care as a discharge option, now part of the federal health insurance law, offers patients essential transitional care and rehabilitation post-hospitalization.[37]

4. Discussion

4.1. DRG cost-control effect

The systematic review indicates that DRGs-based payment mechanisms has significantly affect LOS and total inpatient expenditure per admission, effectively reducing hospital costs compared to conventional payment models. Prior research, including Xiao et al, has shown that DRG-based payments enhance medical resource efficiency, reduce medical risks, and improve quality without substantially increasing insurance expenses.[38] Wang et al“s analysis of 5 hospitals” billing data, using a 2-tailed paired t-test, demonstrated notable reductions in average case cost and LOS.[39] The implementation of DRG can not only clarify the cost criteria for medical insurance payment, but also establish a clear risk-sharing mechanism between medical insurance institutions and medical institutions through DRG classification and payment, which makes hospitals pay more attention to the actual condition, treatment effect and improve the efficiency of services.[40]

However, studies indicate that DRG-based payment impacts evolve over time, reflecting immediate and long-term effects. A finding from Chen subgroup analysis is the different effects of DRG implementation based on its duration. Specifically, there was a significant change in the impact on LOS for implementations shorter than 2 years compared to those extending beyond 2 years.[8] This variation underscores the dynamic nature of DRG-based payments and their evolving influence on healthcare outcomes. In a contrasting study, the effect of DRG payments on LOS was more pronounced in the second year postimplementation, showing a reduction of 0.93 days. This contrasts with a mere 0.04 day reduction observed in the first year, highlighting the evolving nature of DRG impacts over time.[41] Therefore, the need for more high-quality studies to investigate the effects of DRG-based payment systems is evident.

4.2. Impact of DRG on quality of care

The DRG system, primarily focused on inpatient services, can lead to cost-shifting to outpatient or home care by some medical institutions, circumventing DRG-imposed limitations on bed occupancy and costs.[42] The effects of the DRG payment model vary globally, with different countries experiencing diverse impacts and addressing resultant quality issues. Continuous quality assessment and improvement are key to enhancing care quality. Scientific evaluation methods and effective indicators are essential for monitoring and improving nursing services. Medical institutions and staff should strive to upgrade professionalism, improve working conditions, manage nursing processes effectively, and emphasize patient participation and feedback for better care services.

5. Conclusion

Meta-analysis shows that the DRG payment system effectively reduces LOS and total inpatient expenditure per admission, aligning with policy goals and confirming its efficiency in enhancing health insurance fund sustainability. However, prioritizing cost reduction over care quality can lead to issues like understaffing, reduced resource allocation, and limited use of essential medications and therapies, adversely affecting patient outcomes. Therefore, balancing cost containment with quality care is crucial. Policymakers and care managers are increasingly focused on improving healthcare quality to ensure patient safety and high-quality care.

Author contributions

Conceptualization: Xin Feng, Lulu Cheng, Hua Wei

Data curation: Xin Feng, Lulu Cheng, Hua Wei

Formal analysis: Xin Feng, Lulu Cheng, Hua Wei.

Funding acquisition: Xin Feng, Lulu Cheng, Hua Wei.

Investigation: Xin Feng, Lulu Cheng, Hua Wei.

Methodology: Xin Feng, Lulu Cheng, Hua Wei.

Project administration: Xin Feng, Lulu Cheng.

Resources: Xin Feng, Lulu Cheng.

Software: Xin Feng, Lulu Cheng.

Supervision: Xin Feng, Lulu Cheng.

Validation: Xin Feng, Lulu Cheng.

Visualization: Xin Feng, Lulu Cheng.

Writing – original draft: Xin Feng, Lulu Cheng.

Writing – review & editing: Xin Feng, Lulu Cheng.

Abbreviations:

AHF acute heart failure

AMI acute myocardia infarction

CAP community-acquired pneumonia

CBA controlled before–after studies

COPD China healthcare security diagnosis-related groups

DRG diagnosis-related groups

ITS interrupted time series studies

LOS length of stay

NR not reported

PE pulmonary embolism

RCTs randomized controlled trials

SMD standardized mean difference

WMD weighted mean difference

This study was funded by the Social Science Planning Project of Anhui Province under Grant number 2022AH050424, Philosophy and Social Science of Anhui Province under Grant number AHSKY2021D143 and Scientific Research Team for Innovative Development of Chinese Medicine (No: 2022AH010039).

Ethics approval and participants consent are not required because this study is a meta-analysis based on the published studies.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Feng X, Cheng L, Wei H. Study on the cost-control effect of diagnosis-related groups based on meta-analysis. Medicine 2024;103:37(e39421).

XF and LLC equally contributed to this article.
==== Refs
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