Do the effects of the DIP payment reform work for patients in the same city who are not yet covered by the reform? Evidence from Guangzhou, China

Do the effects of the DIP payment reform work for patients in the same city who are not yet covered by the reform? 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Evidence from Guangzhou, China Yuhao Wang, Xiaoqing Huang, Xin Xu, Lina Wang, Nana Lu, Wei Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2932479/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The Reform of medical insurance payment methods is one of the crucial measures for controlling irrational medical practices. In China, the National Health Insurance Administration requires a full switch from the existing fee-for-service payment to DRG or DIP payments by 2025. Guangzhou, a city with a more developed economy and abundant medical resources in China, has completed the DIP payment reform for local-insured patients since 2018. However, patients from other-insured-region have not yet been included in the reform and still retain fee-for-service payment. This paper intends to prove that if DIP payment reform for local patients has made some effects and can these effects be simultaneously applied to the other-insured-region patients who are not covered by the payment reform. Methods The data in this paper were obtained from the database of basic medical insurance for urban employees of local and the other-insured-region patients in Guangzhou provided by the Guangzhou Healthcare Security Administration from 2017 to 2019. We used the average inpatient cost and the average rate of cost belonging to the medical insurance catalog to evaluate the changes in inpatient costs and reimbursement levels for both groups of patients before and after implementing the DIP payment reform. Single-group and multi-group interrupted time series were used to analyze the indicators. Results (1) After the DIP payment reform, local patients’ average inpatient costs fluctuated less between months compared with those before the policy implementation. (2) The average rate of cost belonging to the medical insurance catalog for local patients reflected a short- and long-term decreasing trend after the policy implementation, suggesting that patients’ burden increased and patients failed to enjoy the reform dividends. (3) The average inpatient costs of the other-insured-region patients were higher than those of local patients before and after the policy. The restraining effect on medical costs of the other-insured-region patients was not significant. (4) The average rate of cost belonging to the medical insurance catalog of the other-insured-region patients was lower than that of local patients, and after the implementation of the policy, although this indicator showed a significant downward trend in the long-term trend, but for some diseases was equal to or even higher than that of local patients. Conclusions On one hand, DIP payment reform for local patients has achieved some success, but still need further strengthening of regulation. On the other hand, the reform’s effect has not been synchronized with the effect on the other-insuerd-region patients. There is a risk that hospitals will pass on the payment reform cost to the other-insuerd-region patients. DIP payment reform Policy effectiveness evaluation The other-insured-region patients Interrupted time series analysis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Background The reform of medical insurance payment methods is one of the crucial measures for controlling irrational medical practices. [ 1 ] The medical insurance payment methods can be divided into retrospective and prospective payment systems. Fee-for-services (FFS) is the most typical and traditional retrospective payment method. [ 2 ] But practices have shown that FFS is prone to moral risks of medical service providers such as over-care due to “information asymmetry”. [ 3 – 5 ] The prospective payment systems mainly include capitation, per-diem payment, and Diagnosis Related Groups (DRG) payments. [ 6 – 8 ] Among these, DRG is a globally recognized method that can guarantee the quality of medical care while providing better cost control. [ 9 , 10 ] DRG originated in the United States and is used to group patients into different diagnostic groups based on factors such as age, gender, number of days in the hospital, clinical diagnosis, severity of illness, comorbidities and complications, and regression. The medical insurance department calculates the medical insurance payment standard for each DRG group and pays the hospitals according to this standard. [ 11 , 12 ] Compared to FFS, DRG contributes more effectively to reducing unreasonable medical expenditures, improving the efficiency of medical insurance fund utilization, and enhancing the management capabilities of hospitals, but it also harbors the risk of patient shirking and medical service quality reduction. [ 13 , 14 ] In China, the National Healthcare Security Administration has independently designed the Diagnosis-Intervention Packet (DIP) payment method, referring to the DRG. DIP expands the disease grouping analysis method based on big data technology, using mathematical models to exhaust and cluster the data of diseases, diagnostic and treatment methods, quickly forming groups for management and payment. [ 15 ] Its overall approach, characteristics, and payment models are similar to the DRG. The main difference between the two lies in the grouping method: In contrast, the DRG emphasizes clinical experience as the basis, relying on clinical pathway selection and artificial judgment of experts, with the characteristics of “one group of multiple diseases” or “one group of multiple operations”, while the DIP emphasizes on a statistical analysis of objective and real-world data, through exhaustive clustering of disease diagnosis and surgical operations of cases in historical data, with the characteristics of “one group of one operation for one disease”. [ 16 – 18 ] According to the National Healthcare Security Administration’s regulations, DRG/DIP payment method reforms will be implemented in all regions of China by 2024. By the end of 2025, the DRG/DIP payment methods will cover all eligible hospitals providing inpatient services, achieving full coverage of diseases and medical insurance funds. [ 19 ] However, in China, some patients are temporarily unaffected by the medical insurance payment method reform, which are patients seeking medical treatment outside their insured region (later abbreviated as “the other-insured-region patients”). [ 20 ] This type of patients is similar to cross-border medical care in the European Union. Essentially, two types of patients seek medical treatment outside their insured region. First are those who reside outside their insured region long-term due to work or family reasons; their medical needs are similar to those of local insured residents, and their disease progression is relatively mild. Second are those who pursue better medical resources, including patients with relatively severe disease progression who cannot be well treated locally and a small number of patients who seek medical care in large cities due to distrust of local hospital resources. [ 21 ] In terms of medical cost coverage, the medical costs of the other-insured-region patients are reimbursed on the FFS payment basis according to the medical insurance catalog of their treatment region and the coverage policy of their insured region. [ 22 ] This sets the opportunity for this paper: in the same city, there are both local patients who implement DIP payment reform and the other-insured-region patients who retain FFS payment, so does DIP payment reform have an impact on doctors’ treatment behavior? Can these effects be simultaneously applied to the other-insured-region patients who are not covered by the payment reform? This paper will address these two questions through empirical studies. Guangzhou is the capital city of China's largest economic province, Guangdong. Its GDP ranks fourth in China and its economy is relatively developed. Guangzhou has a frequent population flow, with a resident population of about 18.68 million, of which about 9.38 million are non-domiciled residents. Guangzhou is one of the cities with the best medical resources in China and is also among the primary destinations for the other-insured-region patients. Furthermore, Guangzhou is an early adopter of the DIP payment method reform in China. Since January 2018, local insured residents have been using the DIP payment method for inpatient expenses, making the city highly representative. Consequently, this paper will use Guangzhou as a case example for related research. 2 Data and Methods 2.1 Sample data 2.1.1 Data source The data in this paper were obtained from the database of basic medical insurance for urban employees of local and the other-insured-region patients in Guangzhou provided by the Guangzhou Healthcare Security Administration from 2017 to 2019. Since the other-insured-region patients were more likely to seek medical treatment in tertiary hospitals, this paper further selected the sample scope for hospitals and finally decided to use the three diseases with a high number of the other-insured-region patients, namely, I63.9, I25.1, and Z51.1, as the blueprint for analysis. We selected the data of local patients with the same standards for comparison. 2.2 Indicators This paper will focus on the changes in inpatient costs and reimbursement levels for both types of patients before and after implementing the DIP payment reform. Inpatient costs are evaluated using the average inpatient cost, which is the average of the inpatient costs of patients admitted for I63.9, I25.1, and Z51.1 each month. Since local patients and patients from other places have the same reimbursement catalog but different reimbursement levels, this paper will use the average rate of cost belonging to the medical insurance catalog, which is the average ratio of the cost of each patient admitted each month that falls within the reimbursable scope of medical insurance to the total cost of hospitalization. Table 1 shows the baseline data for this paper. Table 1 detail of the sample data Disease Patient group Number of cases Inpatient cost (Unit: Yuan) The rate of cost belonging to the medical insurance catalog Mean SD Mean SD I25.1 Local patient 56349 26037.20 32256.64 92.97% 5.75% The other-insured-region patient 5896 41281.83 48692.29 94.12% 10.30% I63.9 Local patient 52165 18686.62 24367.65 94.16% 5.03% The other-insured-region patient 4066 31489.14 38505.07 91.86% 10.21% Z51.1 Local patient 30641 13265.94 11223.47 91.59% 11.10% The other-insured-region patient 55479 15428.48 15593.33 85.47% 20.04% 2.2 Methods We used single-group and multi-group interrupted time series to compare the changes in various study indicators among local patients, the other-insured-region patients who still use the FFS method, and the difference between the two types of patients before and after implementing DIP payment policy for the three diseases, respectively, in order to dissect the effect of DIP payment reform for local patients and whether it has an impact on the medical behavior of the other-insured-region patients. [ 23 – 25 ] This paper constructs interruption time series models in months, with January 2018 as the policy intervention time, involving 36-time points in three years from 2017–2019, including 12 before and 24 after the intervention. The single-group interrupted time series model is as follows: \({Y}_{t}={\beta }_{0}+{\beta }_{1}{T}_{\text{t}}+{\beta }_{2}{X}_{\text{t}}+{\beta }_{3}{T}_{\text{t}}{X}_{\text{t}}+{\epsilon }_{\text{t}}\) In this model, \({Y}_{t}\) represents the indicators of this paper, and \({T}_{\text{t}}\) is the time unit variable, with months as the unit, assigning values of 1 to 36 from January 2017 to December 2019. \({X}_{\text{t}}\) is the policy intervention variable, with January 2018 as the boundary. The 12 months before the intervention are assigned a value of 0, and the 24 months after the intervention are assigned a value of 1. \({T}_{\text{t}}{X}_{\text{t}}\) is a continuous variable, indicating the time variable after the implementation of the DIP payment reform, with a value of 0 before the intervention and a value of time after the intervention. \({\beta }_{0}\) - \({\beta }_{3}\) reflect the parameter estimates before and after the policy. Among them, \({\beta }_{0}\) represents the baseline level of the indicators, \({\beta }_{1}\) reflects the changing trend of each indicator before the implementation of the DIP payment reform, \({\beta }_{2}\) represents the difference between the first time point after the implementation of the DIP payment reform (i.e., January 2018) and the predicted point of the pre-intervention time series trend, indicating the short-term effect of the policy. \({\beta }_{3}\) represents the slope change variable of the indicator changes after the implementation of the DIP policy, reflecting the long-term effect of the policy. This paper uses a single-group interrupted time series to analyze the changes in each indicator for local patients. The multi-group interrupted time series expands on the above model as follows: $${Y}_{t}={\beta }_{0}+{\beta }_{1}{T}_{\text{t}}+{\beta }_{2}{X}_{\text{t}}+{\beta }_{3}{T}_{\text{t}}{X}_{\text{t}}+{\beta }_{4}Z+{\beta }_{5}{T}_{\text{t}}Z+{\beta }_{6}{X}_{\text{t}}Z+{\beta }_{7}{T}_{\text{t}}{X}_{\text{t}}Z+{\epsilon }_{\text{t}}$$ \({Y}_{t}\) , \({\beta }_{0}\) - \({\beta }_{3}\) have the same meaning as the single-group interrupted time series. \({\beta }_{4}\) - \({\beta }_{7}\) reflect the differences between the control group and the experimental group. Among them, \({\beta }_{4}\) shows the differences in the indicators in baseline levels between the two types of patients before the DIP payment reform, \({\beta }_{5}\) represents the difference in the change trends of the two groups before the DIP payment reform, \({\beta }_{6}\) , \({\beta }_{7}\) separately represents the difference in short- and long-term effects of the two groups before and after the DIP payment reform. In this study, the control group was the local patients, and the experimental group was the other-insured-region patients. On this basis, the study introduces \({\beta }_{8}\) and \({\beta }_{9}\) to separately test the significance of the model fitting results after implementing the reform. 3 Results 3.1 The changes in the average inpatient cost for local patients Table 2 and Fig. 1 show the changes in inpatient costs for local patients after implementing the DIP reform. First, the short-term effect of the policy, except for a significant decrease in the I25.1 disease, the changes in the other two diseases are not statistically significant. Second, the long-term effect of the policy, all three diseases were not significant at the 5% significance level. Third, in terms of the overall trend, the cost variation of the three diseases fluctuated significantly between months before January 2018 while after implementing the DIP reform, the cost variation of patients showed a more stable trend between months. Table 2 The changes in the average inpatient cost for local patients Coefficient I25.1 I63.9 Z51.1 Coef. P Coef. P Coef. P β1 71.16 0.312 -52.92 0.559 -12.36 0.838 β2 -3460.05 0.000 -853.58 0.196 380.85 0.390 β3 20.81 0.798 158.57 0.090 75.58 0.241 β0 27035.74 0.000 19010.07 0.000 12318.12 0.000 β9 91.97 0.043 105.66 0.000 63.22 0.000 3.2 The changes in the average rate of cost belonging to the medical insurance catalog for local patients Table 3 and Fig. 2 show the changes in the average rate of cost belonging to the medical insurance catalog after implementing the DIP reform. The three diseases show a significant downward trend in the policy implementation's short-term and long-term effects. Table 3 The changes in the average rate of cost belonging to the medical insurance catalog for local patients Coefficient I25.1 I63.9 Z51.1 Coef. P Coef. P Coef. P β1 0.0000 0.756 0.0006 0.261 0.0006 0.594 β2 -0.0611 0.000 -0.0235 0.000 -0.0223 0.013 β3 -0.0009 0.000 -0.0018 0.017 -0.0022 0.049 β0 0.9790 0.000 0.9614 0.000 0.9492 0.000 β9 -0.0009 0.000 -0.0011 0.000 -0.0017 0.000 3.3 The difference in changes in the average inpatient costs between the two groups of patients Table 4 and Fig. 3 show the difference in changes in the average inpatient costs between the two groups of patients before and after policy implementation. We first focus on whether the average inpatient costs of the other-insured-region patients are affected by the implementation of the DIP policy for local patients. First, only for the Z51.1 disease, the average inpatient costs of the other-insured-region patients exhibited a significant decreasing trend of 327.37 yuan per month during 2017 (P < 0.019), whereas the changing trends for the other two diseases were not statistically significant. Secondly, the short-term changes in the average inpatient costs of the other-insured-region patients lacked consistency. The average inpatient costs for I63.9 patients decreased instantaneously by 5815.67 yuan (P < 0.010), while those for I25.1 patients increased instantaneously by 10229.26 yuan (P < 0.001), and the instantaneous change for the Z51.1 disease was not statistically significant. Additionally, in terms of long-term effects, the decreasing trend of average inpatient costs for Z51.1 disease significantly slowed down after policy implementation (P < 0.004). The long-term changing trends for the other two diseases were not statistically significant. When comparing the changes in average inpatient costs of local patients before and after the implementation of the DIP payment method, three points can be observed. Firstly, the baseline levels of average inpatient costs for the other-insured-region patients for all three diseases were significantly higher than those for local patients (P < 0.001). Secondly, the degree of change in the number of short-term trends after policy implementation varied among the three diseases, the average inpatient costs were higher for the other-insured-region patients than for local patients. In contrast, only the I63.9 disease showed a significant \({\beta }_{8}\) , and the other two diseases of the other-region-insured patients had larger differences in average inpatient costs between months. On this basis, it was seen that there was a statistically significant difference in the long-term trend change between the other-insured-region and local patients in the I63.9 disease, with a more significant upward trend in average inpatient costs for the other-insured-region patients. Table 4 The difference in changes in the average inpatient costs between the two groups of patients Coefficient I25.1 I63.9 Z51.1 Coef. P Coef. P Coef. P β1 -379.64 0.120 202.98 0.249 -320.37 0.019 β2 10229.26 0.000 -5815.67 0.010 -87.16 0.929 β3 353.54 0.188 127.57 0.525 317.04 0.042 β4 -10063.56 0.000 -11782.26 0.000 -6535.77 0.000 β5 477.50 0.056 -275.85 0.183 323.06 0.033 β6 -13953.09 0.000 5179.06 0.028 304.85 0.780 β7 -356.83 0.193 49.52 0.828 -255.79 0.138 β0 36956.73 0.000 30831.48 0.000 18803.41 0.000 β8 -26.11 0.822 330.55 0.002 -3.33 0.905 β9 94.57 0.004 105.66 0.000 63.22 0.004 3.4 The difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients Table 5 and Fig. 4 show the difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients before and after policy implementation. First, we focus on the changes in the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients before and after the policy. Initially, for the I25.1 disease, the indicator significantly decreased by 0.42% per month before the policy implementation (P < 0.001), while the Z51.1 disease significantly increased by 0.57% per month (P < 0.009). The changing trend before the policy for I63.9 disease was not statistically significant. Subsequently, for the I25.1 and I63.9 diseases, the indicators both significantly increased at the time of policy implementation, rising by 2.22% (P < 0.001) and 3.75% (P < 0.001) respectively, while the Z51.1 disease did not show short-term effects. Additionally, from a long-term perspective, the decreasing trend in the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients with the I25.1 slightly eased after the policy implementation, with a weak downward trend of 0.09% per month (β3 = 0.32%, P < 0.001). In contrast, the Z51.1 group changed from increasing to decreasing, and its significantly dropped by 1.00% compared to before policy implementation (P < 0.001), with a downward trend of 0.43% per month (P < 0.001). Second, a comparison of the average rate of cost belonging to the medical insurance catalog between local and the other-insured-region patients before and after January 2018 is made. Firstly, the baseline levels of the indicator for the other-insured-region patients with I63.9 and Z51.1 diseases were significantly lower than those for local patients, with differences of 7.03% (P < 0.001) and 13.12% (P < 0.001), respectively, while there was no significant difference in the I25.1 disease. Subsequently, the indicator for local patients with I63.9, I25.1, and Z51.1 diseases all decreased after the policy implementation. The I25.1 disease showed the most significant decrease for local patients, dropping below the level for the other-insured-region patients. The I63.9 disease decreased for local patients to a level similar to the other-insured-region patients, and the Z51.1 disease remained higher for local patients than for the other-insured-region after the decrease. Additionally, from a long-term perspective, the indicator for local and the other-insured-region patients with the three diseases all maintained a slight downward trend. Table 5 The difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients Coefficient I25.1 I63.9 Z51.1 Coef. P Coef. P Coef. P β1 -0.42% 0.000 0.11% 0.419 0.57% 0.009 β2 2.22% 0.001 3.75% 0.001 2.31% 0.351 β3 0.32% 0.000 -0.22% 0.115 -1.00% 0.000 β4 0.17% 0.691 7.03% 0.000 13.12% 0.000 β5 0.41% 0.000 -0.06% 0.655 -0.50% 0.044 β6 -8.27% 0.000 -5.53% 0.000 -4.75% 0.074 β7 -0.41% 0.000 0.05% 0.721 0.77% 0.008 β0 97.75% 0.000 88.99% 0.000 81.77% 0.000 β8 -0.09% 0.000 -0.11% 0.001 -0.43% 0.000 β9 -0.09% 0.000 -0.11% 0.000 -0.17% 0.000 4 Discussion 4.1 DIP payment reform for local patients has achieved some success, but still need further strengthening of regulation. Evaluating the effectiveness of the DIP reform is a complex issue. The DIP payment method is similar to DRG, with the primary difference being the classification of disease groups. As such, it is worthwhile to look at the effectiveness of DIP reform using the same indicators used to evaluate the effectiveness of DRG. Theoretically, DIP should improve the efficiency of medical services and reduce inpatient costs and patients’ financial burdens. Choi’s study on the policy effects after South Korea transitioned from FFS to DRG payment revealed that implementing DRG payments significantly reduced patients’ overall medical expenses. [ 26 ] Similarly, Schuetza's research on the treatment quality of Swiss patients with community-acquired pneumonia found that there were no significant differences in clinical outcomes between the DRG and FFS, indicating a certain degree of moral hazard in the FFS method. [ 27 ] In Taiwan, employing DRG payments effectively reduced the length of hospital stays without compromising patient treatment quality. [ 28 ] Payment methods similar to DRG, such as DPC/PDPS in Japan, have also reduced patients’ medical burden and shortened the average length of hospital stay compared to FFS. [ 29 ] Research conducted by some Chinese scholars has likewise confirmed that DRG payment methods can produce cost-reduction and efficiency-enhancing effects compared to FFS method. [ 30 – 33 ] However, it is essential to recognize that the effectiveness of the DIP payment method in reducing patients' medical burden is based on the premise that doctors tend to over-treat patients under the FFS method. For regions with stricter medical practice control and higher professional ethical awareness among doctors, over-treatment may be less prevalent even before the implementation of DIP payment methods. In such cases, the DIP payment method may not significantly reduce inpatient costs but rather guide hospitals to control their own costs by adopting the “retain surplus, share overruns” to achieve long-term stability of inpatient costs for each DIP group. As one of the cities with the most abundant medical resources and highest medical service management levels in China, the DIP payment method reform in Guangzhou has not shown an immediately or significantly decreasing trend in inpatient costs, as seen in Fig. 1 . However, the changes in inpatient costs across different months have been relatively stable, indicating that the DIP payment method reform has achieved some of its intended effects for local patients. But, as seen in Section 3.2, the average rate of cost belonging to the medical insurance catalog has significantly decreased after the policy implementation. This section further analyzes the changes in out-of-pocket expenses for local patients with the three diseases before and after the implementation of DIP payment. Table 6 shows the result. It can be seen that, firstly, for the I25.1 disease, the out-of-pocket expenses had a significantly increasing trend before policy implementation (P < 0.001). Although these expenses decreased by 885.69 yuan in the short term after the policy implementation (P < 0.000), the long-term trend remained unchanged (P < 0.000). After policy implementation, the monthly increase in out-of-pocket expenses remained at a significant rate of 50.12 yuan. Second, for the I63.9 and Z51.1 diseases, patients’ out-of-pocket expenses did not exhibit statistical significance in both the pre-implementation period and short-term effects of the policy, but both showed a significant long-term increasing trend (P < 0.000). Therefore, from the patient's perspective, their financial burden has not decreased with the implementation of DIP payment. The overall trend is still increasing, which means they have not enjoyed the benefits of the DIP payment method reform. Table 6 The changes in the out-of-pocket expenses for local patient Coefficient I25.1 I63.9 Z51.1 Coef. P Coef. P Coef. P β1 46.546 0.001 -13.031 0.583 -22.643 0.383 β2 -885.690 0.000 -204.379 0.237 27.925 0.901 β3 3.570 0.829 68.411 0.008 56.589 0.046 β0 5638.457 0.000 3180.307 0.000 2729.990 0.000 β8 50.116 0.000 55.381 0.000 32.588 0.001 We need to explain this phenomenon by focusing on the calculation method of hospitals’ income within the DIP payment in Guangzhou. Under the DIP payment framework in Guangzhou, the income from the DIP payment for a hospital can be represented by the following formula [ 34 ] : $$\begin{array}{c}{x}_{i}={s}_{h} \times up\times {apr}_{h}\end{array}$$ Where \({x}_{i}\) represents a hospital’s medical insurance fund income under DIP payment, \({s}_{h}\) represents the sum score of all cases in the hospital, \(up\) represents the payment standard of unit score in DIP payment, and \({apr}_{h}\) represents the actual medical insurance reimbursement ratio calculated by FFS for the current year in this hospital. Among them, \(up\) is calculated as follows: $${cb}_{t}=mifb÷{apr}_{t}$$ $$\begin{array}{c}up = {cb}_{t}÷{s}_{t}\end{array}$$ The \({cb}_{t}\) represents the total DIP paid cost budget for the current year, which is obtained by dividing the \(mifb\) (representing the annual DIP payment medical insurance fund budget) with the \({apr}_{t}\) (representing the citywide actual medical insurance reimbursement ratio for the current year), and the \({s}_{t}\) represents the sum score of the citywide for all cases for the current year. Combining the above equations with a series of simplifications, the following equation is finally obtained: $$\begin{array}{c}{x}_{i}=\frac{{s}_{h}}{{s}_{t}}\times \frac{{apr}_{h}}{{apr}_{t}}\times mifb\end{array}$$ From this, it is clear that the income of the hospital is mainly influenced by \(\frac{{s}_{h}}{{s}_{t}}\) and \(\frac{{apr}_{h}}{{apr}_{t}}\) . Relative to the \({s}_{h}\) , hospitals can more easily achieve the regulation of \({apr}_{h}\) . Under the assumption that the total hospital inpatient cost \({c}_{h}\) , the total citywide inpatient cost \({c}_{t}\) and other conditions remain unchanged, and only ∆p reimbursable amounts are changed, the final income impact for a hospital can be calculated as $${\varDelta x}_{i}=\frac{{s}_{h}}{{s}_{t}}\times mifb\times (\frac{\frac{{p}_{h}+\varDelta p}{{c}_{h}}}{\frac{{p}_{t}+\varDelta p}{{c}_{t}}}-\frac{\frac{{p}_{h}}{{c}_{h}}}{\frac{{p}_{t}}{{c}_{t}}})$$ $$=\frac{{s}_{h}}{{s}_{t}}\times \text{m}\text{i}\text{b}\times \left(\frac{{c}_{t}\varDelta p({p}_{t}-{p}_{h})}{{p}_{t}{c}_{h}({p}_{t}+\varDelta p)}\right)$$ Since \({c}_{h}\) , \({c}_{t}\) , \({p}_{h}\) , \({p}_{t}\) are all constants greater than 0, and \({p}_{t}\ge {p}_{h}\) , the relationship between \({\varDelta x}_{i}\) and \(\varDelta p\) can be simplified as $${\varDelta x}_{i}\propto \frac{\varDelta p}{\varDelta p+{p}_{t}}$$ Taking practical factors into account, we know that \(\varDelta p\) is always less than \({p}_{t}\) . As a result, the equation represents a continuously increasing function. When \(\varDelta p0\) , \({\varDelta x}_{i}\) is positive, indicating an increase in the income of hospitals, and the larger \(\varDelta p\) , the more \({\varDelta x}_{i}\) increases. Therefore, theoretically, hospitals should aim to obtain more compensation from the health insurance fund by reducing the rate of patients’ out-of-pocket expenses and increasing the average rate of cost belonging to the medical insurance catalog. However, this contradicts the actual data results. This may seem illogical, but when we look at the calculation rules for \({\varDelta x}_{i}\) , firstly, the product of coefficients \(\frac{{s}_{h}}{{s}_{t}}\) and \(\frac{{c}_{t}({p}_{t}-{p}_{h})}{{p}_{t}{c}_{h}}\) is relatively stable, with certain constraints between them. This causes the variation of \(\frac{\varDelta p}{\varDelta p+{p}_{t}}\) not to be significantly scaled. Secondly, since \({p}_{t}\gg \left|\varDelta p\right|\) , the final value of \(\frac{\varDelta p}{\varDelta p+{p}_{t}}\) is too small, and changes in \(\varDelta p\) within a certain range have minimal impact on the actual medical insurance fund income that hospitals can obtain. Due to the characteristics of DIP payment, as long as patients’ inpatient costs are less than the product of the DIP group's score and the unit price, hospitals can get profit from the medical insurance fund. Thus, from the perspective of hospitals, it is appropriate to moderately increase patients’ out-of-pocket expenses and utilize more items outside the medical insurance catalog. On one hand, it has less impact on the amount of DIP medical insurance payment received at the end of the year. [ 35 , 36 ] On the other hand, the out-of-pocket expenses are fully paid by patients upon discharge, easing the financial pressure on hospitals and facilitating the operation of their cash flow. Therefore, for local patients who have experienced the DIP payment method reform, medical insurance management departments should place particular emphasis on assessing the proportion of patients' out-of-pocket expenses and further standardizing doctors’ clinical practices. 4.2 DIP payment reform for local patients fails to affect the other-insured-region patients at the same time, and there is still some over-treatment phenomenon From the results in sections 3.3 –3.4, the average inpatient costs of the three diseases for the other-insured-region patients are higher than those for local patients, particularly for I25.1 and I63.9. In addition, the average inpatient costs for the other-insured-region patients fluctuate more between months, we failed to find the impact of DIP payment reform for local patients on the other-insured-region patients. Is this due to the fact that the other-insured-region patients have relatively “more” severe conditions? [ 20 ] To investigate this, the paper further analyzed the total number of diagnoses and the proportion of patients who did not undergo surgery among the other-insured-region and local patients in 2018 and 2019. The results after the Mann-Whitney U test are shown in Table 6 . It can be seen that the total number of diagnoses for the other-insured-region patients in both years was significantly lower than that for local patients. Relatively speaking, the proportion of the other-insured-region patients who did not receive surgical is higher. Only in 2019, the proportion of the other-insured-region patients with I25.1 disease who underwent surgery was higher than that of local patients. Table 6 The difference in average numbers of diagnoses and the percentage of patients not receiving surgery in the two patient groups Disease Year Average numbers of diagnoses Percentage of patients not receiving surgery The other-insured-region patients Local patients P The other-insured-region patients Local patients I25.1 2018 4.65 7.42 0.000 47.05% 44.56% 2019 4.39 7.52 0.000 15.13% 38.04% I63.9 2018 4.80 5.97 0.000 89.05% 80.19% 2019 5.15 6.53 0.000 63.89% 33.51% Z51.1 2018 1.90 5.28 0.000 85.40% 53.57% 2019 2.50 5.45 0.000 71.18% 39.29% Considering the number and difficulty level of surgeries performed on patients who received surgical (see Table 7 ), on one hand, there is a certain pattern in the comparison of the number of surgeries performed on the other-insured-region and local patients for the three diseases over the two years. Specifically, the other-insured-region patients with I25.1 and Z51.1 diseases had a significantly higher total number of surgeries per person compared to local patients, while there was no significant difference in the total number of surgeries performed on I63.9 patients between the two groups. On the other hand, except for the Z51.1 group in 2019, the proportion of level 3 and 4 surgeries performed on the other-insured-region patients was higher than that for local patients in the same period, especially for I25.1 and I63.9 groups, where the number of level 3 and 4 surgeries performed on the other-insured-region patients was higher than that for local patients. Table 7 The difference of the number and difficulty level of surgeries in the two patient groups Disease Year Average number of surgeries Proportion of level 3 and 4 surgeries performed Average number of level 3 and 4 surgery per case The other-insured-region patients Local patients P The other-insured-region patients Local patients The other-insured-region patients Local patients I25.1 2018 3.23 3.04 0.004 50.88% 47.30% 3.68 3.33 2019 3.59 3.18 0.000 60.76% 50.14% 4.07 3.39 I63.9 2018 2.10 2.09 0.671 17.21% 14.69% 2.28 2.1 2019 2.16 1.98 0.654 21.90% 9.92% 2.64 2.28 Z51.1 2018 1.55 1.38 0.000 8.35% 7.13% 1.28 1.37 2019 1.43 1.42 0.000 3.59% 5.64% 1.19 1.29 These two sets of data can better explain the following issues: firstly, they objectively demonstrate the characteristics of the other-insured-region patients, i.e. compared with local patients, the other-insured-region patients have a greater degree of disease differentiation, which causes a greater fluctuation of the mean value of off-site patients by month in Fig. 3 ; secondly, relatively speaking, for patients who undergo surgical treatment, the other-insured-region patients receive a higher number of surgeries and face more complex surgeries, indicating more severe disease progression than local patients. However, this does not mean that the relatively higher costs for the other-insured-region patients are completely justified. The average inpatient costs of the other-insured-region and local patients for the three types of diseases in 2018 and 2019, classified by the highest level of surgery performed, are shown in Fig. 5 . As can be seen, firstly, the other-insured-region patients have higher average inpatient costs than local patients in all three diseases, but the difference in costs between the two types of patients varies among different diseases. I25.1 and I63.9 diseases have a more considerable difference in average inpatient costs between the other-insured-region and local patients, while the difference between Z51.1 patient groups is relatively more minor, which is consistent with the phenomenon shown in Fig. 1 , wherein the I25.1 and I63.9 diseases, the other-insured-region patients have significantly higher average inpatient costs than local patients each month, while in the Z51.1 disease is slightly higher than local patients. Secondly, it can be observed that the inpatient costs of local patients increase gradually with the difficulty of the surgery they undergo, while for the other-insured-region patients, especially those in I25.1 and I63.9 diseases, the average inpatient costs of patients without surgery are higher than those of patients who underwent lower-level surgeries and are significantly higher than local patients who did not receive surgical treatment, accounting for 2.56 times and 1.89 times of the corresponding reference, respectively. Moreover, as shown in Fig. 4 , before the implementation of the DIP payment reform, the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients of the three diseases was significantly lower than those of local patients, indicating that doctors prescribed more services outside the insurance scope for the other-insured-region patients. Notably, after the implementation of the DIP payment reform for local patients, the other-insured-region patients still retain the FFS payment method, and hospitals may transfer the costs of the payment method reform to the other-insured-region patients. [ 13 , 37 , 38 ] In China, for some high-level hospitals, the scale of the other-insured-region patients has exceeded that of local patients, and the source of income of the hospital's medical insurance fund has gradually changed from local to nationwide, which makes the medical insurance payment reform for the other-insured-region patients an urgent issue. By building a DRG/DIP payment system for the other-insured-region patients with the “same disease, same group, same score, same treatment, and same price” as local patients, we can achieve the purpose of guiding doctors’ behavior by using medical insurance payment policy, and thus reduce the moral risk of over-care by doctors. We can realize the use of medical insurance payment policies to restrain doctors’ behavior, thus reducing the moral risk of over-treatment by doctors. 5 Conclusions The fact that a portion of the population in the same city has implemented payment reform while another portion has not led to the two central questions that this paper seeks to argue: First, do the implemented payment reforms have some effect? Second, do physicians treat the two groups of patients differently, i.e., do the effects of the payment reform work in tandem for patients who still need to implement the new policy? The results show that, on the one hand, the payment reform for local patients has had some expected effects. However, the patient's out-of-pocket payment has increased significantly, and the medical insurance department must still strengthen its supervision. On the other hand, the reform’s effect has not been synchronized with the effect on the other-insuerd-region patients. There is a risk that hospitals will pass on the payment reform cost to the other-insuerd-region patients. The findings of this paper have implications for China and other countries. The arguments in this paper fill in the Chinese evidence for the study of the effects of prospective payment methods, implying that the implementation of DRG-like payment methods is feasible and effective in developing countries. At the same time, this study reveals that macro medical insurance policy reforms need to be considered holistically. It has been empirically confirmed that providers will respond strategically to the reforms (e.g., payment method reform) and that reforms targeting only some of the population will increase the risk of over-care for those not covered by the reforms. [ 38 – 40 ] The findings of this paper also have some limitations. First, due to the limitation of collecting information fields in the health insurance database, some demographic variables such as wage income and marital status are missing from the data selection criteria, and other factors that may affect patients’ medical behavior are not considered for the time being. Second, the study was conducted in a more economically developed region of China, and further validation is needed to determine whether the findings are also applicable to economically disadvantaged areas. Third, further exploration is needed on how to implement DRG/DIP payment reform for the other-insuerd-region patients. Declarations Ethics approval and consent to participate All methods in our study were carried out in accordance with the Declaration of Helsinki. Guangzhou Healthcare Security Administration and China Pharmaceutical University gave approval for this study, and all participants gave informed consent. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding Financial supports for the study was provided by the general program of the National Natural Science Foundation of China: Study on the influence of medical insurance management and payment policy of medical treatment beyond pooling regions on recurrent population’s health-seeking behavior, health outcome and medical expenditure (Grant No. 72074220) and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX220834). Authors' contributions W.X., X.H., and X.X. collected and organized data regarding medical visits of the sample population, and provided analysis tools. Y.W. performed statistical analysis of data and was a major contributor to writing the manuscript. N.L. and L.W. were responsible for the quality control of the study and assisted in writing the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors are grateful to the National Natural Science Foundation of China (Grant No. 72074220) and the Guangzhou Healthcare Security Administration for supporting the research. And then sincerely thanks to the whole research group at China Pharmaceutical University for collecting and organizing data. Author Details Yuhao Wang 1 , Xiaoqing Huang 1 , Xin Xu 1 , Lina Wang 1 , Nana Lu 1 , Wei Xu 1 1. School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, China. Wei Xu is the corresponding author of this article. References Levine R: Getting health reform right: A guide to improving performance and equity. Health Affairs 2005, 24: 1370-1371. Baker LC: The effect of HMOs on fee-for-service health care expenditures: Evidence from Medicare. Journal of Health Economics 1997, 16: 453-481. Lee C, Kim JM, Kim YS, Shin E: The Effect of Diagnosis-Related Groups on the Shift of Medical Services From Inpatient to Outpatient Settings: A National Claims-Based Analysis. Asia Pac J Public Health 2019, 31: 499-509. Newhouse JP: Medical-Care Costs - How Much Welfare Loss. Journal of Economic Perspectives 1992, 6: 3-21. Manning WG, Marquis MS: Health insurance: The tradeoff between risk pooling and moral hazard. Journal of Health Economics 1996, 15: 609-639. Quentin W, Scheller-Kreinsen D, Blumel M, Geissler A, Busse R: Hospital payment based on diagnosis-related groups differs in Europe and holds lessons for the United States. Health Aff (Millwood) 2013, 32: 713-723. Gosden T, Forland F, Kristiansen IS, Sutton M, Leese B, Giuffrida A, Sergison M, Pedersen L: Capitation, salary, fee-for-service and mixed systems of payment: effects on the behaviour of primary care physicians. Cochrane Database Syst Rev 2000, 2000: CD002215. 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Med Care 1980, 18: iii, 1-53. Zou K, Li HY, Zhou D, Liao ZJ: The effects of diagnosis-related groups payment on hospital healthcare in China: a systematic review. BMC Health Serv Res 2020, 20: 112. Boes S, Napierala C: Assessment of the introduction of DRG-based reimbursement in Switzerland: Evidence on the short-term effects on length of stay compliance in university hospitals. Health Policy 2021, 125: 739-750. B Y: Reform Practice and Development Connotation of DRG and DIP. Health Economics Research 2021, 38: 6. Yazhen Y: DIP & DRG ： Similarities And Differences. China Health Insurance 2021 : 4. Fu Wei JQ, Yu Lihua, et al: Comparison of DRG and DIP and Analysis of Their Impacts on Medical Facilities. Chinese Health Economics 2020, 39: 4. Baorong Y: Differences and similarities between DRG and DIP. China Health 2020 : 2. 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Linden A: Conducting interrupted time-series analysis for single- and multiple-group comparisons. Stata Journal 2015, 15: 480-500. Linden A: A comprehensive set of postestimation measures to enrich interrupted time-series analysis. The Stata Journal 2017, 17: 73-88. Choi JW, Kim SJ, Park HK, Jang SI, Kim TH, Park EC: Effects of a mandatory DRG payment system in South Korea: Analysis of multi-year nationwide hospital claims data. Bmc Health Services Research 2019, 19 . Schuetz P, Albrich WC, Suter I, Hug BL, Christ-Crain M, Holler T, Henzen C, Krause M, Schoenenberger R, Zimmerli W, Mueller B: Quality of care delivered by fee-for-service and DRG hospitals in Switzerland in patients with community-acquired pneumonia. Swiss Medical Weekly 2011, 141 . Tsai YW, Chuang YC, Huang WF, See LC, Yang CL, Chen PF: The effect of changing reimbursement policies on quality of in-patient care, from fee-for-service to prospective payment. International Journal for Quality in Health Care 2005, 17: 421-426. Hamada H, Sekimoto M, Imanaka Y: Effects of the per diem prospective payment system with DRG-like grouping system (DPC/PDPS) on resource usage and healthcare quality in Japan. Health Policy 2012, 107: 194-201. Zhang YH, He GP, Liu JW: Comparison of Medical Costs and Care of Appendectomy Patients between Fee-for-Service and Set Fee for Diagnosis-Related Group Systems in 20 Chinese Hospitals. Southeast Asian Journal of Tropical Medicine and Public Health 2016, 47: 1055-1061. Zhang JL: The impact of a diagnosis-related group-based prospective payment experiment: the experience of Shanghai. Applied Economics Letters 2010, 17: 1797-1803. Liu R, Shi J, Yang B, Jin C, Sun P, Wu L, Yu D, Xiong L, Wang Z: Charting a path forward: policy analysis of China's evolved DRG-based hospital payment system. International health 2017, 9: 317-324. Jian W, Lu M, Chan KY, Poon AN, Han W, Hu M, Yip W: The impact of a pilot reform on the diagnosis-related-groups payment system in China: a difference-in-difference study. The Lancet 2015, 386: S26. Notice on the implementation of the Guangzhou social medical insurance inpatient medical expenses according to DIP payment method [https://www.gz.gov.cn/gzybj/gkmlpt/content/7/7097/post_7097028.html?jump=false#14609] Kim TH, Park EC, Jang SI, Jang SY, Lee SA, Choi JW: Effects of diagnosis-related group payment system on appendectomy outcomes. Journal of Surgical Research 2016, 206: 347-354. Gay EG, Kronenfeld JJ: Regulation, Retrenchment - the Drg Experience - Problems from Changing Reimbursement Practice. Social Science & Medicine 1990, 31: 1103-1118. Barouni M, Ahmadian L, Anari HS, Mohsenbeigi E: Investigation of the impact of DRG based reimbursement mechanisms on quality of care, capacity utilization, and efficiency- A systematic review. International Journal of Healthcare Management 2021, 14: 1463-1474. Clemens J, Gottlieb JD: Do Physicians' Financial Incentives Affect Medical Treatment and Patient Health? American Economic Review 2014, 104: 1320-1349. Shigeoka H, Fushimi K: Supplier-induced demand for newborn treatment: Evidence from Japan. Journal of Health Economics 2014, 35: 162-178. Chan MK, Zeng GH: Unintended consequences of supply-side cost control? Evidence from China's new cooperative medical scheme. Journal of Health Economics 2018, 61: 27-46. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2932479","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":200355717,"identity":"571d02b2-908b-44b1-8476-646120013623","order_by":0,"name":"Yuhao Wang","email":"","orcid":"","institution":"China Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Yuhao","middleName":"","lastName":"Wang","suffix":""},{"id":200355719,"identity":"2d6138df-0a29-4a2a-a89e-a3fa053c0137","order_by":1,"name":"Xiaoqing Huang","email":"","orcid":"","institution":"China Pharmaceutical 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03:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2932479/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2932479/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37149131,"identity":"63da8a78-a529-45f7-913b-87e9a0e24a76","added_by":"auto","created_at":"2023-05-17 18:02:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75616,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes in the average inpatient cost for local patients\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/e591b54174e7e53b25abeaad.png"},{"id":37148331,"identity":"8c79eace-5507-4351-b7cb-5765176e4cc2","added_by":"auto","created_at":"2023-05-17 17:54:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71029,"visible":true,"origin":"","legend":"\u003cp\u003eThe changes in the average rate of cost belonging to the medical insurance catalog for local patients\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/ff9b0c3b6fd3c727fa23f367.png"},{"id":37149129,"identity":"e786bf3b-da06-46cf-aab9-57fdd1dadb1f","added_by":"auto","created_at":"2023-05-17 18:02:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109307,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference in changes in the average inpatient costs between the two groups of patients\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/4ac9e2ed81266f031a15e4a5.png"},{"id":37149801,"identity":"fc4d4412-20b4-4d46-a182-9b11e486ed7a","added_by":"auto","created_at":"2023-05-17 18:10:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":106990,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/04445223d51d6842412eaf5c.png"},{"id":37148328,"identity":"6ea7bd97-f500-44a7-984e-b69fd1289318","added_by":"auto","created_at":"2023-05-17 17:54:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":52816,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of average inpatient costs between the other-insured-region and local patients according to the highest level of surgery performed (Unit: Yuan)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/98b442b2b5805b531cfb610d.png"},{"id":37174316,"identity":"38426128-4880-452e-a1f6-dd9e0d3e7a53","added_by":"auto","created_at":"2023-05-18 07:14:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1816927,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2932479/v1/f7e6d5b5-37d5-45e4-aff9-1bce81b176b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do the effects of the DIP payment reform work for patients in the same city who are not yet covered by the reform? Evidence from Guangzhou, China","fulltext":[{"header":"1 Background","content":"\u003cp\u003eThe reform of medical insurance payment methods is one of the crucial measures for controlling irrational medical practices.\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e The medical insurance payment methods can be divided into retrospective and prospective payment systems. Fee-for-services (FFS) is the most typical and traditional retrospective payment method.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e But practices have shown that FFS is prone to moral risks of medical service providers such as over-care due to \u0026ldquo;information asymmetry\u0026rdquo;.\u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe prospective payment systems mainly include capitation, per-diem payment, and Diagnosis Related Groups (DRG) payments.\u003csup\u003e[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e Among these, DRG is a globally recognized method that can guarantee the quality of medical care while providing better cost control.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e DRG originated in the United States and is used to group patients into different diagnostic groups based on factors such as age, gender, number of days in the hospital, clinical diagnosis, severity of illness, comorbidities and complications, and regression. The medical insurance department calculates the medical insurance payment standard for each DRG group and pays the hospitals according to this standard.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e Compared to FFS, DRG contributes more effectively to reducing unreasonable medical expenditures, improving the efficiency of medical insurance fund utilization, and enhancing the management capabilities of hospitals, but it also harbors the risk of patient shirking and medical service quality reduction.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn China, the National Healthcare Security Administration has independently designed the Diagnosis-Intervention Packet (DIP) payment method, referring to the DRG. DIP expands the disease grouping analysis method based on big data technology, using mathematical models to exhaust and cluster the data of diseases, diagnostic and treatment methods, quickly forming groups for management and payment.\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e Its overall approach, characteristics, and payment models are similar to the DRG. The main difference between the two lies in the grouping method: In contrast, the DRG emphasizes clinical experience as the basis, relying on clinical pathway selection and artificial judgment of experts, with the characteristics of \u0026ldquo;one group of multiple diseases\u0026rdquo; or \u0026ldquo;one group of multiple operations\u0026rdquo;, while the DIP emphasizes on a statistical analysis of objective and real-world data, through exhaustive clustering of disease diagnosis and surgical operations of cases in historical data, with the characteristics of \u0026ldquo;one group of one operation for one disease\u0026rdquo;.\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e According to the National Healthcare Security Administration\u0026rsquo;s regulations, DRG/DIP payment method reforms will be implemented in all regions of China by 2024. By the end of 2025, the DRG/DIP payment methods will cover all eligible hospitals providing inpatient services, achieving full coverage of diseases and medical insurance funds.\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, in China, some patients are temporarily unaffected by the medical insurance payment method reform, which are patients seeking medical treatment outside their insured region (later abbreviated as \u0026ldquo;the other-insured-region patients\u0026rdquo;).\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e This type of patients is similar to cross-border medical care in the European Union. Essentially, two types of patients seek medical treatment outside their insured region. First are those who reside outside their insured region long-term due to work or family reasons; their medical needs are similar to those of local insured residents, and their disease progression is relatively mild. Second are those who pursue better medical resources, including patients with relatively severe disease progression who cannot be well treated locally and a small number of patients who seek medical care in large cities due to distrust of local hospital resources.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn terms of medical cost coverage, the medical costs of the other-insured-region patients are reimbursed on the FFS payment basis according to the medical insurance catalog of their treatment region and the coverage policy of their insured region.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e This sets the opportunity for this paper: in the same city, there are both local patients who implement DIP payment reform and the other-insured-region patients who retain FFS payment, so does DIP payment reform have an impact on doctors\u0026rsquo; treatment behavior? Can these effects be simultaneously applied to the other-insured-region patients who are not covered by the payment reform? This paper will address these two questions through empirical studies.\u003c/p\u003e \u003cp\u003eGuangzhou is the capital city of China's largest economic province, Guangdong. Its GDP ranks fourth in China and its economy is relatively developed. Guangzhou has a frequent population flow, with a resident population of about 18.68\u0026nbsp;million, of which about 9.38\u0026nbsp;million are non-domiciled residents. Guangzhou is one of the cities with the best medical resources in China and is also among the primary destinations for the other-insured-region patients. Furthermore, Guangzhou is an early adopter of the DIP payment method reform in China. Since January 2018, local insured residents have been using the DIP payment method for inpatient expenses, making the city highly representative. Consequently, this paper will use Guangzhou as a case example for related research.\u003c/p\u003e"},{"header":"2 Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample data\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Data source\u003c/h2\u003e \u003cp\u003eThe data in this paper were obtained from the database of basic medical insurance for urban employees of local and the other-insured-region patients in Guangzhou provided by the Guangzhou Healthcare Security Administration from 2017 to 2019. Since the other-insured-region patients were more likely to seek medical treatment in tertiary hospitals, this paper further selected the sample scope for hospitals and finally decided to use the three diseases with a high number of the other-insured-region patients, namely, I63.9, I25.1, and Z51.1, as the blueprint for analysis. We selected the data of local patients with the same standards for comparison.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Indicators\u003c/h2\u003e \u003cp\u003eThis paper will focus on the changes in inpatient costs and reimbursement levels for both types of patients before and after implementing the DIP payment reform. Inpatient costs are evaluated using the average inpatient cost, which is the average of the inpatient costs of patients admitted for I63.9, I25.1, and Z51.1 each month. Since local patients and patients from other places have the same reimbursement catalog but different reimbursement levels, this paper will use the average rate of cost belonging to the medical insurance catalog, which is the average ratio of the cost of each patient admitted each month that falls within the reimbursable scope of medical insurance to the total cost of hospitalization. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline data for this paper.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003edetail of the sample data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePatient group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of cases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eInpatient cost\u003c/p\u003e \u003cp\u003e(Unit: Yuan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eThe rate of cost belonging to the medical insurance catalog\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26037.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32256.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e92.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe other-insured-region patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41281.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48692.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18686.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24367.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe other-insured-region patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31489.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38505.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.21%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocal patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13265.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11223.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe other-insured-region patient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15428.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15593.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\u003e \u003cp\u003eWe used single-group and multi-group interrupted time series to compare the changes in various study indicators among local patients, the other-insured-region patients who still use the FFS method, and the difference between the two types of patients before and after implementing DIP payment policy for the three diseases, respectively, in order to dissect the effect of DIP payment reform for local patients and whether it has an impact on the medical behavior of the other-insured-region patients.\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e This paper constructs interruption time series models in months, with January 2018 as the policy intervention time, involving 36-time points in three years from 2017\u0026ndash;2019, including 12 before and 24 after the intervention.\u003c/p\u003e \u003cp\u003eThe single-group interrupted time series model is as follows:\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{t}={\\beta }_{0}+{\\beta }_{1}{T}_{\\text{t}}+{\\beta }_{2}{X}_{\\text{t}}+{\\beta }_{3}{T}_{\\text{t}}{X}_{\\text{t}}+{\\epsilon }_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eIn this model, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the indicators of this paper, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the time unit variable, with months as the unit, assigning values of 1 to 36 from January 2017 to December 2019. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({X}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is the policy intervention variable, with January 2018 as the boundary. The 12 months before the intervention are assigned a value of 0, and the 24 months after the intervention are assigned a value of 1. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{\\text{t}}{X}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e is a continuous variable, indicating the time variable after the implementation of the DIP payment reform, with a value of 0 before the intervention and a value of time after the intervention.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e \u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e reflect the parameter estimates before and after the policy. Among them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the baseline level of the indicators, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{1}\\)\u003c/span\u003e\u003c/span\u003e reflects the changing trend of each indicator before the implementation of the DIP payment reform, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{2}\\)\u003c/span\u003e\u003c/span\u003e represents the difference between the first time point after the implementation of the DIP payment reform (i.e., January 2018) and the predicted point of the pre-intervention time series trend, indicating the short-term effect of the policy. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e represents the slope change variable of the indicator changes after the implementation of the DIP policy, reflecting the long-term effect of the policy. This paper uses a single-group interrupted time series to analyze the changes in each indicator for local patients.\u003c/p\u003e \u003cp\u003eThe multi-group interrupted time series expands on the above model as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${Y}_{t}={\\beta }_{0}+{\\beta }_{1}{T}_{\\text{t}}+{\\beta }_{2}{X}_{\\text{t}}+{\\beta }_{3}{T}_{\\text{t}}{X}_{\\text{t}}+{\\beta }_{4}Z+{\\beta }_{5}{T}_{\\text{t}}Z+{\\beta }_{6}{X}_{\\text{t}}Z+{\\beta }_{7}{T}_{\\text{t}}{X}_{\\text{t}}Z+{\\epsilon }_{\\text{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{t}\\)\u003c/span\u003e \u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{3}\\)\u003c/span\u003e\u003c/span\u003e have the same meaning as the single-group interrupted time series.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{4}\\)\u003c/span\u003e\u003c/span\u003e-\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{7}\\)\u003c/span\u003e\u003c/span\u003e reflect the differences between the control group and the experimental group. Among them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{4}\\)\u003c/span\u003e\u003c/span\u003e shows the differences in the indicators in baseline levels between the two types of patients before the DIP payment reform, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{5}\\)\u003c/span\u003e\u003c/span\u003e represents the difference in the change trends of the two groups before the DIP payment reform, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{6}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{7}\\)\u003c/span\u003e\u003c/span\u003e separately represents the difference in short- and long-term effects of the two groups before and after the DIP payment reform. In this study, the control group was the local patients, and the experimental group was the other-insured-region patients.\u003c/p\u003e \u003cp\u003eOn this basis, the study introduces \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{8}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{9}\\)\u003c/span\u003e\u003c/span\u003e to separately test the significance of the model fitting results after implementing the reform.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The changes in the average inpatient cost for local patients\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e show the changes in inpatient costs for local patients after implementing the DIP reform. First, the short-term effect of the policy, except for a significant decrease in the I25.1 disease, the changes in the other two diseases are not statistically significant. Second, the long-term effect of the policy, all three diseases were not significant at the 5% significance level. Third, in terms of the overall trend, the cost variation of the three diseases fluctuated significantly between months before January 2018 while after implementing the DIP reform, the cost variation of patients showed a more stable trend between months.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe changes in the average inpatient cost for local patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-52.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3460.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-853.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e380.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e158.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e75.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27035.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19010.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12318.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 The changes in the average rate of cost belonging to the medical insurance catalog for local patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the changes in the average rate of cost belonging to the medical insurance catalog after implementing the DIP reform. The three diseases show a significant downward trend in the policy implementation's short-term and long-term effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe changes in the average rate of cost belonging to the medical insurance catalog for local patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The difference in changes in the average inpatient costs between the two groups of patients\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show the difference in changes in the average inpatient costs between the two groups of patients before and after policy implementation. We first focus on whether the average inpatient costs of the other-insured-region patients are affected by the implementation of the DIP policy for local patients. First, only for the Z51.1 disease, the average inpatient costs of the other-insured-region patients exhibited a significant decreasing trend of 327.37 yuan per month during 2017 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.019), whereas the changing trends for the other two diseases were not statistically significant. Secondly, the short-term changes in the average inpatient costs of the other-insured-region patients lacked consistency. The average inpatient costs for I63.9 patients decreased instantaneously by 5815.67 yuan (P\u0026thinsp;\u0026lt;\u0026thinsp;0.010), while those for I25.1 patients increased instantaneously by 10229.26 yuan (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the instantaneous change for the Z51.1 disease was not statistically significant. Additionally, in terms of long-term effects, the decreasing trend of average inpatient costs for Z51.1 disease significantly slowed down after policy implementation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.004). The long-term changing trends for the other two diseases were not statistically significant.\u003c/p\u003e \u003cp\u003eWhen comparing the changes in average inpatient costs of local patients before and after the implementation of the DIP payment method, three points can be observed. Firstly, the baseline levels of average inpatient costs for the other-insured-region patients for all three diseases were significantly higher than those for local patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Secondly, the degree of change in the number of short-term trends after policy implementation varied among the three diseases, the average inpatient costs were higher for the other-insured-region patients than for local patients. In contrast, only the I63.9 disease showed a significant \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{8}\\)\u003c/span\u003e\u003c/span\u003e, and the other two diseases of the other-region-insured patients had larger differences in average inpatient costs between months. On this basis, it was seen that there was a statistically significant difference in the long-term trend change between the other-insured-region and local patients in the I63.9 disease, with a more significant upward trend in average inpatient costs for the other-insured-region patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference in changes in the average inpatient costs between the two groups of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-379.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-320.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10229.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5815.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-87.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e317.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10063.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-11782.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6535.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e477.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-275.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e323.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13953.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5179.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e304.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-356.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-255.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36956.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30831.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18803.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-26.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e330.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e94.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e63.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4 The difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients before and after policy implementation. First, we focus on the changes in the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients before and after the policy. Initially, for the I25.1 disease, the indicator significantly decreased by 0.42% per month before the policy implementation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while the Z51.1 disease significantly increased by 0.57% per month (P\u0026thinsp;\u0026lt;\u0026thinsp;0.009). The changing trend before the policy for I63.9 disease was not statistically significant. Subsequently, for the I25.1 and I63.9 diseases, the indicators both significantly increased at the time of policy implementation, rising by 2.22% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 3.75% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) respectively, while the Z51.1 disease did not show short-term effects. Additionally, from a long-term perspective, the decreasing trend in the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients with the I25.1 slightly eased after the policy implementation, with a weak downward trend of 0.09% per month (β3\u0026thinsp;=\u0026thinsp;0.32%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, the Z51.1 group changed from increasing to decreasing, and its significantly dropped by 1.00% compared to before policy implementation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a downward trend of 0.43% per month (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSecond, a comparison of the average rate of cost belonging to the medical insurance catalog between local and the other-insured-region patients before and after January 2018 is made. Firstly, the baseline levels of the indicator for the other-insured-region patients with I63.9 and Z51.1 diseases were significantly lower than those for local patients, with differences of 7.03% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 13.12% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), respectively, while there was no significant difference in the I25.1 disease. Subsequently, the indicator for local patients with I63.9, I25.1, and Z51.1 diseases all decreased after the policy implementation. The I25.1 disease showed the most significant decrease for local patients, dropping below the level for the other-insured-region patients. The I63.9 disease decreased for local patients to a level similar to the other-insured-region patients, and the Z51.1 disease remained higher for local patients than for the other-insured-region after the decrease. Additionally, from a long-term perspective, the indicator for local and the other-insured-region patients with the three diseases all maintained a slight downward trend.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference in changes in the average rate of cost belonging to the medical insurance catalog between the two groups of patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.41%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e81.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e4.1 DIP payment reform for local patients has achieved some success, but still need further strengthening of regulation.\u003c/p\u003e \u003cp\u003eEvaluating the effectiveness of the DIP reform is a complex issue. The DIP payment method is similar to DRG, with the primary difference being the classification of disease groups. As such, it is worthwhile to look at the effectiveness of DIP reform using the same indicators used to evaluate the effectiveness of DRG. Theoretically, DIP should improve the efficiency of medical services and reduce inpatient costs and patients\u0026rsquo; financial burdens. Choi\u0026rsquo;s study on the policy effects after South Korea transitioned from FFS to DRG payment revealed that implementing DRG payments significantly reduced patients\u0026rsquo; overall medical expenses.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e Similarly, Schuetza's research on the treatment quality of Swiss patients with community-acquired pneumonia found that there were no significant differences in clinical outcomes between the DRG and FFS, indicating a certain degree of moral hazard in the FFS method.\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e In Taiwan, employing DRG payments effectively reduced the length of hospital stays without compromising patient treatment quality.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Payment methods similar to DRG, such as DPC/PDPS in Japan, have also reduced patients\u0026rsquo; medical burden and shortened the average length of hospital stay compared to FFS.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e Research conducted by some Chinese scholars has likewise confirmed that DRG payment methods can produce cost-reduction and efficiency-enhancing effects compared to FFS method.\u003csup\u003e[\u003cspan additionalcitationids=\"CR31 CR32\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, it is essential to recognize that the effectiveness of the DIP payment method in reducing patients' medical burden is based on the premise that doctors tend to over-treat patients under the FFS method. For regions with stricter medical practice control and higher professional ethical awareness among doctors, over-treatment may be less prevalent even before the implementation of DIP payment methods. In such cases, the DIP payment method may not significantly reduce inpatient costs but rather guide hospitals to control their own costs by adopting the \u0026ldquo;retain surplus, share overruns\u0026rdquo; to achieve long-term stability of inpatient costs for each DIP group. As one of the cities with the most abundant medical resources and highest medical service management levels in China, the DIP payment method reform in Guangzhou has not shown an immediately or significantly decreasing trend in inpatient costs, as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. However, the changes in inpatient costs across different months have been relatively stable, indicating that the DIP payment method reform has achieved some of its intended effects for local patients.\u003c/p\u003e \u003cp\u003eBut, as seen in Section 3.2, the average rate of cost belonging to the medical insurance catalog has significantly decreased after the policy implementation. This section further analyzes the changes in out-of-pocket expenses for local patients with the three diseases before and after the implementation of DIP payment. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the result. It can be seen that, firstly, for the I25.1 disease, the out-of-pocket expenses had a significantly increasing trend before policy implementation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although these expenses decreased by 885.69 yuan in the short term after the policy implementation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.000), the long-term trend remained unchanged (P\u0026thinsp;\u0026lt;\u0026thinsp;0.000). After policy implementation, the monthly increase in out-of-pocket expenses remained at a significant rate of 50.12 yuan. Second, for the I63.9 and Z51.1 diseases, patients\u0026rsquo; out-of-pocket expenses did not exhibit statistical significance in both the pre-implementation period and short-term effects of the policy, but both showed a significant long-term increasing trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.000). Therefore, from the patient's perspective, their financial burden has not decreased with the implementation of DIP payment. The overall trend is still increasing, which means they have not enjoyed the benefits of the DIP payment method reform.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe changes in the out-of-pocket expenses for local patient\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-22.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-885.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-204.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5638.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3180.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2729.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eβ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e\u003cp\u003eWe need to explain this phenomenon by focusing on the calculation method of hospitals\u0026rsquo; income within the DIP payment in Guangzhou. Under the DIP payment framework in Guangzhou, the income from the DIP payment for a hospital can be represented by the following formula \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e:\u003c/p\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{x}_{i}={s}_{h} \\times up\\times {apr}_{h}\\end{array}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({x}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents a hospital\u0026rsquo;s medical insurance fund income under DIP payment, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s}_{h}\\)\u003c/span\u003e\u003c/span\u003e represents the sum score of all cases in the hospital, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(up\\)\u003c/span\u003e\u003c/span\u003e represents the payment standard of unit score in DIP payment, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({apr}_{h}\\)\u003c/span\u003e\u003c/span\u003e represents the actual medical insurance reimbursement ratio calculated by FFS for the current year in this hospital.\u003c/p\u003e \u003cp\u003eAmong them, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(up\\)\u003c/span\u003e\u003c/span\u003e is calculated as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$${cb}_{t}=mifb\u0026divide;{apr}_{t}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}up = {cb}_{t}\u0026divide;{s}_{t}\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({cb}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the total DIP paid cost budget for the current year, which is obtained by dividing the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(mifb\\)\u003c/span\u003e\u003c/span\u003e (representing the annual DIP payment medical insurance fund budget) with the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({apr}_{t}\\)\u003c/span\u003e\u003c/span\u003e (representing the citywide actual medical insurance reimbursement ratio for the current year), and the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the sum score of the citywide for all cases for the current year.\u003c/p\u003e \u003cp\u003eCombining the above equations with a series of simplifications, the following equation is finally obtained:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{x}_{i}=\\frac{{s}_{h}}{{s}_{t}}\\times \\frac{{apr}_{h}}{{apr}_{t}}\\times mifb\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFrom this, it is clear that the income of the hospital is mainly influenced by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{s}_{h}}{{s}_{t}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{apr}_{h}}{{apr}_{t}}\\)\u003c/span\u003e\u003c/span\u003e. Relative to the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({s}_{h}\\)\u003c/span\u003e\u003c/span\u003e, hospitals can more easily achieve the regulation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({apr}_{h}\\)\u003c/span\u003e\u003c/span\u003e. Under the assumption that the total hospital inpatient cost \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{h}\\)\u003c/span\u003e\u003c/span\u003e, the total citywide inpatient cost \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{t}\\)\u003c/span\u003e\u003c/span\u003e and other conditions remain unchanged, and only ∆p reimbursable amounts are changed, the final income impact for a hospital can be calculated as\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${\\varDelta x}_{i}=\\frac{{s}_{h}}{{s}_{t}}\\times mifb\\times (\\frac{\\frac{{p}_{h}+\\varDelta p}{{c}_{h}}}{\\frac{{p}_{t}+\\varDelta p}{{c}_{t}}}-\\frac{\\frac{{p}_{h}}{{c}_{h}}}{\\frac{{p}_{t}}{{c}_{t}}})$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$=\\frac{{s}_{h}}{{s}_{t}}\\times \\text{m}\\text{i}\\text{b}\\times \\left(\\frac{{c}_{t}\\varDelta p({p}_{t}-{p}_{h})}{{p}_{t}{c}_{h}({p}_{t}+\\varDelta p)}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSince\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{h}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({c}_{t}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{h}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{t}\\)\u003c/span\u003e\u003c/span\u003e are all constants greater than 0, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{t}\\ge {p}_{h}\\)\u003c/span\u003e\u003c/span\u003e, the relationship between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\\)\u003c/span\u003e\u003c/span\u003e can be simplified as\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$${\\varDelta x}_{i}\\propto \\frac{\\varDelta p}{\\varDelta p+{p}_{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTaking practical factors into account, we know that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\\)\u003c/span\u003e\u003c/span\u003e is always less than \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{t}\\)\u003c/span\u003e\u003c/span\u003e. As a result, the equation represents a continuously increasing function. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\u0026lt;0\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is negative, which means a reduction in the income of hospitals, and the larger \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\left|\\varDelta p\\right|\\)\u003c/span\u003e\u003c/span\u003e, the more \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e decreases. When \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is positive, indicating an increase in the income of hospitals, and the larger \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\\)\u003c/span\u003e\u003c/span\u003e, the more \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e increases. Therefore, theoretically, hospitals should aim to obtain more compensation from the health insurance fund by reducing the rate of patients\u0026rsquo; out-of-pocket expenses and increasing the average rate of cost belonging to the medical insurance catalog. However, this contradicts the actual data results.\u003c/p\u003e \u003cp\u003eThis may seem illogical, but when we look at the calculation rules for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varDelta x}_{i}\\)\u003c/span\u003e\u003c/span\u003e, firstly, the product of coefficients\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{s}_{h}}{{s}_{t}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{c}_{t}({p}_{t}-{p}_{h})}{{p}_{t}{c}_{h}}\\)\u003c/span\u003e\u003c/span\u003e is relatively stable, with certain constraints between them. This causes the variation of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\varDelta p}{\\varDelta p+{p}_{t}}\\)\u003c/span\u003e\u003c/span\u003e not to be significantly scaled. Secondly, since \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{t}\\gg \\left|\\varDelta p\\right|\\)\u003c/span\u003e\u003c/span\u003e, the final value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\varDelta p}{\\varDelta p+{p}_{t}}\\)\u003c/span\u003e\u003c/span\u003e is too small, and changes in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\varDelta p\\)\u003c/span\u003e\u003c/span\u003e within a certain range have minimal impact on the actual medical insurance fund income that hospitals can obtain.\u003c/p\u003e \u003cp\u003eDue to the characteristics of DIP payment, as long as patients\u0026rsquo; inpatient costs are less than the product of the DIP group's score and the unit price, hospitals can get profit from the medical insurance fund. Thus, from the perspective of hospitals, it is appropriate to moderately increase patients\u0026rsquo; out-of-pocket expenses and utilize more items outside the medical insurance catalog. On one hand, it has less impact on the amount of DIP medical insurance payment received at the end of the year.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e On the other hand, the out-of-pocket expenses are fully paid by patients upon discharge, easing the financial pressure on hospitals and facilitating the operation of their cash flow.\u003c/p\u003e \u003cp\u003eTherefore, for local patients who have experienced the DIP payment method reform, medical insurance management departments should place particular emphasis on assessing the proportion of patients' out-of-pocket expenses and further standardizing doctors\u0026rsquo; clinical practices.\u003c/p\u003e \u003cp\u003e4.2 DIP payment reform for local patients fails to affect the other-insured-region patients at the same time, and there is still some over-treatment phenomenon\u003c/p\u003e \u003cp\u003eFrom the results in sections \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e\u0026ndash;3.4, the average inpatient costs of the three diseases for the other-insured-region patients are higher than those for local patients, particularly for I25.1 and I63.9. In addition, the average inpatient costs for the other-insured-region patients fluctuate more between months, we failed to find the impact of DIP payment reform for local patients on the other-insured-region patients. Is this due to the fact that the other-insured-region patients have relatively \u0026ldquo;more\u0026rdquo; severe conditions?\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e To investigate this, the paper further analyzed the total number of diagnoses and the proportion of patients who did not undergo surgery among the other-insured-region and local patients in 2018 and 2019. The results after the Mann-Whitney U test are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e. It can be seen that the total number of diagnoses for the other-insured-region patients in both years was significantly lower than that for local patients. Relatively speaking, the proportion of the other-insured-region patients who did not receive surgical is higher. Only in 2019, the proportion of the other-insured-region patients with I25.1 disease who underwent surgery was higher than that of local patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference in average numbers of diagnoses and the percentage of patients not receiving surgery in the two patient groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAverage numbers of diagnoses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003ePercentage of patients not receiving surgery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe other-insured-region patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe other-insured-region patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLocal patients\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e44.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.51%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.29%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConsidering the number and difficulty level of surgeries performed on patients who received surgical (see Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e), on one hand, there is a certain pattern in the comparison of the number of surgeries performed on the other-insured-region and local patients for the three diseases over the two years. Specifically, the other-insured-region patients with I25.1 and Z51.1 diseases had a significantly higher total number of surgeries per person compared to local patients, while there was no significant difference in the total number of surgeries performed on I63.9 patients between the two groups. On the other hand, except for the Z51.1 group in 2019, the proportion of level 3 and 4 surgeries performed on the other-insured-region patients was higher than that for local patients in the same period, especially for I25.1 and I63.9 groups, where the number of level 3 and 4 surgeries performed on the other-insured-region patients was higher than that for local patients.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe difference of the number and difficulty level of surgeries in the two patient groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eAverage number of surgeries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eProportion of level 3 and 4 surgeries performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eAverage number of level 3 and 4 surgery per case\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThe other-insured-region patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe other-insured-region patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLocal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eThe other-insured-region patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLocal patients\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e50.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eI63.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.92%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese two sets of data can better explain the following issues: firstly, they objectively demonstrate the characteristics of the other-insured-region patients, i.e. compared with local patients, the other-insured-region patients have a greater degree of disease differentiation, which causes a greater fluctuation of the mean value of off-site patients by month in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; secondly, relatively speaking, for patients who undergo surgical treatment, the other-insured-region patients receive a higher number of surgeries and face more complex surgeries, indicating more severe disease progression than local patients.\u003c/p\u003e \u003cp\u003eHowever, this does not mean that the relatively higher costs for the other-insured-region patients are completely justified. The average inpatient costs of the other-insured-region and local patients for the three types of diseases in 2018 and 2019, classified by the highest level of surgery performed, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs can be seen, firstly, the other-insured-region patients have higher average inpatient costs than local patients in all three diseases, but the difference in costs between the two types of patients varies among different diseases. I25.1 and I63.9 diseases have a more considerable difference in average inpatient costs between the other-insured-region and local patients, while the difference between Z51.1 patient groups is relatively more minor, which is consistent with the phenomenon shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, wherein the I25.1 and I63.9 diseases, the other-insured-region patients have significantly higher average inpatient costs than local patients each month, while in the Z51.1 disease is slightly higher than local patients. Secondly, it can be observed that the inpatient costs of local patients increase gradually with the difficulty of the surgery they undergo, while for the other-insured-region patients, especially those in I25.1 and I63.9 diseases, the average inpatient costs of patients without surgery are higher than those of patients who underwent lower-level surgeries and are significantly higher than local patients who did not receive surgical treatment, accounting for 2.56 times and 1.89 times of the corresponding reference, respectively.\u003c/p\u003e \u003cp\u003eMoreover, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, before the implementation of the DIP payment reform, the average rate of cost belonging to the medical insurance catalog for the other-insured-region patients of the three diseases was significantly lower than those of local patients, indicating that doctors prescribed more services outside the insurance scope for the other-insured-region patients. Notably, after the implementation of the DIP payment reform for local patients, the other-insured-region patients still retain the FFS payment method, and hospitals may transfer the costs of the payment method reform to the other-insured-region patients.\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e In China, for some high-level hospitals, the scale of the other-insured-region patients has exceeded that of local patients, and the source of income of the hospital's medical insurance fund has gradually changed from local to nationwide, which makes the medical insurance payment reform for the other-insured-region patients an urgent issue.\u003c/p\u003e \u003cp\u003eBy building a DRG/DIP payment system for the other-insured-region patients with the \u0026ldquo;same disease, same group, same score, same treatment, and same price\u0026rdquo; as local patients, we can achieve the purpose of guiding doctors\u0026rsquo; behavior by using medical insurance payment policy, and thus reduce the moral risk of over-care by doctors. We can realize the use of medical insurance payment policies to restrain doctors\u0026rsquo; behavior, thus reducing the moral risk of over-treatment by doctors.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThe fact that a portion of the population in the same city has implemented payment reform while another portion has not led to the two central questions that this paper seeks to argue: First, do the implemented payment reforms have some effect? Second, do physicians treat the two groups of patients differently, i.e., do the effects of the payment reform work in tandem for patients who still need to implement the new policy? The results show that, on the one hand, the payment reform for local patients has had some expected effects. However, the patient's out-of-pocket payment has increased significantly, and the medical insurance department must still strengthen its supervision. On the other hand, the reform\u0026rsquo;s effect has not been synchronized with the effect on the other-insuerd-region patients. There is a risk that hospitals will pass on the payment reform cost to the other-insuerd-region patients.\u003c/p\u003e \u003cp\u003eThe findings of this paper have implications for China and other countries. The arguments in this paper fill in the Chinese evidence for the study of the effects of prospective payment methods, implying that the implementation of DRG-like payment methods is feasible and effective in developing countries. At the same time, this study reveals that macro medical insurance policy reforms need to be considered holistically. It has been empirically confirmed that providers will respond strategically to the reforms (e.g., payment method reform) and that reforms targeting only some of the population will increase the risk of over-care for those not covered by the reforms.\u003csup\u003e[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe findings of this paper also have some limitations. First, due to the limitation of collecting information fields in the health insurance database, some demographic variables such as wage income and marital status are missing from the data selection criteria, and other factors that may affect patients\u0026rsquo; medical behavior are not considered for the time being. Second, the study was conducted in a more economically developed region of China, and further validation is needed to determine whether the findings are also applicable to economically disadvantaged areas. Third, further exploration is needed on how to implement DRG/DIP payment reform for the other-insuerd-region patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods in our study were carried out in accordance with the Declaration of Helsinki. Guangzhou Healthcare Security Administration and China Pharmaceutical University gave approval for this study, and all participants gave informed consent.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial supports for the study was provided by the general program of the National Natural Science Foundation of China: Study on the influence of medical insurance management and payment policy of medical treatment beyond pooling regions on recurrent population\u0026rsquo;s health-seeking behavior, health outcome and medical expenditure (Grant No. 72074220) and Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (Grant No. KYCX220834).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.X., X.H., and X.X. collected and organized data regarding medical visits of the sample population, and provided analysis tools. Y.W. performed statistical analysis of data and was a major contributor to writing the manuscript. N.L. and L.W. were responsible for the quality control of the study and assisted in writing the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the National Natural Science Foundation of China (Grant No. 72074220) and the Guangzhou Healthcare Security Administration for supporting the research. And then sincerely thanks to the whole research group at China Pharmaceutical University for collecting and organizing data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthor Details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuhao Wang\u003csup\u003e1\u003c/sup\u003e, Xiaoqing Huang\u003csup\u003e1\u003c/sup\u003e, Xin Xu\u003csup\u003e1\u003c/sup\u003e, Lina Wang\u003csup\u003e1\u003c/sup\u003e, Nana Lu\u003csup\u003e1\u003c/sup\u003e, Wei Xu\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e1. School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, China.\u003c/p\u003e\n\u003cp\u003eWei Xu is the corresponding author of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLevine R: \u003cstrong\u003eGetting health reform right: A guide to improving performance and equity.\u003c/strong\u003e \u003cem\u003eHealth Affairs \u003c/em\u003e2005, \u003cstrong\u003e24:\u003c/strong\u003e1370-1371.\u003c/li\u003e\n\u003cli\u003eBaker LC: \u003cstrong\u003eThe effect of HMOs on fee-for-service health care expenditures: Evidence from Medicare.\u003c/strong\u003e \u003cem\u003eJournal of Health Economics \u003c/em\u003e1997, \u003cstrong\u003e16:\u003c/strong\u003e453-481.\u003c/li\u003e\n\u003cli\u003eLee C, Kim JM, Kim YS, Shin E: \u003cstrong\u003eThe Effect of Diagnosis-Related Groups on the Shift of Medical Services From Inpatient to Outpatient Settings: A National Claims-Based Analysis.\u003c/strong\u003e 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Evidence from China\u0026apos;s new cooperative medical scheme.\u003c/strong\u003e\u003cem\u003eJournal of Health Economics \u003c/em\u003e2018, \u003cstrong\u003e61:\u003c/strong\u003e27-46.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DIP payment reform, Policy effectiveness evaluation, The other-insured-region patients, Interrupted time series analysis.","lastPublishedDoi":"10.21203/rs.3.rs-2932479/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2932479/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Reform of medical insurance payment methods is one of the crucial measures for controlling irrational medical practices. In China, the National Health Insurance Administration requires a full switch from the existing fee-for-service payment to DRG or DIP payments by 2025. Guangzhou, a city with a more developed economy and abundant medical resources in China, has completed the DIP payment reform for local-insured patients since 2018. However, patients from other-insured-region have not yet been included in the reform and still retain fee-for-service payment. This paper intends to prove that if DIP payment reform for local patients has made some effects and can these effects be simultaneously applied to the other-insured-region patients who are not covered by the payment reform.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe data in this paper were obtained from the database of basic medical insurance for urban employees of local and the other-insured-region patients in Guangzhou provided by the Guangzhou Healthcare Security Administration from 2017 to 2019. We used the average inpatient cost and the average rate of cost belonging to the medical insurance catalog to evaluate the changes in inpatient costs and reimbursement levels for both groups of patients before and after implementing the DIP payment reform. Single-group and multi-group interrupted time series were used to analyze the indicators.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e(1) After the DIP payment reform, local patients\u0026rsquo; average inpatient costs fluctuated less between months compared with those before the policy implementation. (2) The average rate of cost belonging to the medical insurance catalog for local patients reflected a short- and long-term decreasing trend after the policy implementation, suggesting that patients\u0026rsquo; burden increased and patients failed to enjoy the reform dividends. (3) The average inpatient costs of the other-insured-region patients were higher than those of local patients before and after the policy. The restraining effect on medical costs of the other-insured-region patients was not significant. (4) The average rate of cost belonging to the medical insurance catalog of the other-insured-region patients was lower than that of local patients, and after the implementation of the policy, although this indicator showed a significant downward trend in the long-term trend, but for some diseases was equal to or even higher than that of local patients.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOn one hand, DIP payment reform for local patients has achieved some success, but still need further strengthening of regulation. On the other hand, the reform\u0026rsquo;s effect has not been synchronized with the effect on the other-insuerd-region patients. There is a risk that hospitals will pass on the payment reform cost to the other-insuerd-region patients.\u003c/p\u003e","manuscriptTitle":"Do the effects of the DIP payment reform work for patients in the same city who are not yet covered by the reform? Evidence from Guangzhou, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-17 17:54:28","doi":"10.21203/rs.3.rs-2932479/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c311f8a5-c63f-4ea0-aee7-3a156a2de4f2","owner":[],"postedDate":"May 17th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-05-18T07:14:10+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-17 17:54:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2932479","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2932479","identity":"rs-2932479","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}