DRG grouping by machine learning: from expert-oriented to data-based method

Influencing factors and mechanism of physicians strategic behavior under the DRG payment system

OBJECTIVES

Reforming medical insurance payment methods is a key part of deepening the healthcare system reform. Understanding the influencing factors and underlying mechanisms of physicians' strategic behaviors under the diagnosis-related groups (DRG) payment system is crucial for reducing medical resource waste and improving the efficiency of health insurance fund utilization.

METHODS

Based on the Theory of Planned Behavior, this study used grounded theory to construct a questionnaire encompassing belief, behavioral attitude, subjective norm, perceived behavioral control, behavioral intention, and behavior measurement items. Structural equation modeling was then used for empirical analysis.

RESULTS

Physicians' behavioral intention had the most significant impact on their strategic behavior (β=0.606, P<0.001). Physician's attitude toward strategic behavior (β=-0.159, P<0.01), subjective norm (β=-0.093, P<0.05), and perceived behavioral control (β=-0.120, P<0.05) were major influencing factors of behavioral intention. Physicians' behavioral beliefs, normative beliefs, and control beliefs were significantly correlated with behavioral attitude (β=0.554, P<0.001), subjective norm (β=0.383, P<0.001), and perceived behavioral control (β=0.274, P<0.001), respectively.

CONCLUSIONS

Behavioral intention is the primary predictor driving physicians to engage in strategic behavior. Attitudes toward the behavior, subjective norms, and perceived behavioral control all significantly affect physicians' behavioral intentions.

The influence of policy advocacy and education on medical staff's adaptation to diagnosis related groups payment reform in China: an analysis of the mediating effect of policy cognition

Introduction

In recent years, China has been carrying out the Diagnosis Related Groups (DRGs) payment reform, which has an impact not only on payment methods and medical expenses, but also on the behaviors of medical staff. Some of these behaviors are unexpected by policymakers, such as turning away critically ill patients, disaggregating hospitalization costs, setting up disease groups with higher points, and so on. This phenomenon attracted the attention of some scholars, who put forward a few positive intervention measures, mainly including policy advocacy and system improvement. The scholars believed that the former was more feasible. However, there is a lack of research on the effects and influencing processes of these interventions. Therefore, this study aims to explore the influence of policy advocacy and education on medical staff's adaptation to DRGs payment reform in China and the role of policy cognition in this process, in order to provide experiences for the smooth implementation and sustainable development of DRGs payment system.

Methods

A questionnaire survey was conducted among 650 medical staff in five large general hospitals in Zhejiang Province, China, to understand their participation and feedback on policy advocacy and education, their adaptation to the current DRGs payment reform, and their cognition of relevant policies. After descriptive statistical analysis, partial correlation analysis, multiple linear regression models and bias correction Bootstrap sampling method were used to analyze the mediating effect of policy cognition factors.

Results

All respondents had participated in organized collective policy advocacy and education activities in the past year, but the degree of satisfaction and recognition was not very high. 59.5 percent said their adaptation to the DRGs payment reform was average. Nearly half did not regularly pay attention to and participate in the management of the medical costs of patients with DRGs through compliance. And they had a low understanding of the specific rules of DRGs payment and did not form a high policy identity. The mediating effect values of policy cognition were 0.148, 0.152, 0.108, and 0.057, respectively, when the frequency and quality of policy advocacy and education influenced medical staff's adaptive perception and adaptive behaviors.

Discussion

The organized collective policy advocacy and education can promote medical staff's adaptation to DRGs payment reform by improving their policy cognition, and the action paths are diverse. Policymakers and hospital managers need pay attention to this phenomenon, and formulate demand-centered, value-oriented whole-process advocacy and education strategies while constantly improving the DRGs payment system. All of these provided a basis for further research and practice of positive intervention in DRGs payment reform.

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website. We divided the evaluation indicators into four categories, including (i) medical service quality; (ii) medical service efficiency; (iii) profitability and sustainability; (iv) case grouping ability. Then we carried out descriptive analysis and comprehensive scoring on outcome measurements performance, improvement strategy and specialty performance. At last, the DRGKB finally contains 297 entries. It was found that DRGs generally have a considerable impact on hospital operations, including average length of stay, medical quality and use of medical resources. At the same time, the current DRGs also have many deficiencies, including insufficient reimbursement rates and the ability to classify complex cases. We analyzed these underperforming parts by domain. In conclusion, this research innovatively constructed a knowledgebase to quantify the practice effects of DRGs, analyzed and visualized the development trends and area performance from a comprehensive perspective. This study provides a data-driven research paradigm for following DRGs-related work along with a proposed DRGs evolution model. Availability and implementation: DRGKB is freely available at http://www.sysbio.org.cn/drgkb/. Database URL: http://www.sysbio.org.cn/drgkb/.

Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model

Background

Uterine leiomyoma (UL) is one of the most common benign tumors in women, and its incidence is gradually increasing in China. The clinical complications of UL have a negative impact on women's health, and the cost of treatment poses a significant burden on patients. Diagnosis-related groups (DRG) are internationally recognized as advanced healthcare payment management methods that can effectively reduce costs. However, there are variations in the design and grouping rules of DRG policies across different regions. Therefore, this study aims to analyze the factors influencing the hospitalization costs of patients with UL and optimize the design of DRG grouping schemes to provide insights for the development of localized DRG grouping policies.

Methods

The Mann-Whitney U-test or the Kruskal-Wallis H-test was employed for univariate analysis, and multiple stepwise linear regression analysis was utilized to identify the primary influencing factors of hospitalization costs for UL. Case combination classification was conducted using the exhaustive chi-square automatic interactive detection (E-CHAID) algorithm within a decision tree framework.

Results

Age, occupation, number of hospitalizations, type of medical insurance, Transfer to other departments, length of stay (LOS), type of UL, admission condition, comorbidities and complications, type of primary procedure, other types of surgical procedures, and discharge method had a significant impact on hospitalization costs (P<0.05). Among them, the type of primary procedure, other types of surgical procedures, and LOS were the main factors influencing hospitalization costs. By incorporating the type of primary procedure, other types of surgical procedures, and LOS into the decision tree model, patients were divided into 11 DRG combinations.

Conclusion

Hospitalization costs for UL are mainly related to the type of primary procedure, other types of surgical procedures, and LOS. The DRG case combinations of UL based on E-CHAID algorithm are scientific and reasonable.