| Literature DB >> 33142790 |
Shuduo Zhou1, Jin Xu2, Xiaochen Ma2, Beibei Yuan2, Xiaoyun Liu2, Hai Fang2, Qingyue Meng1,2.
Abstract
How one can reshape the current healthcare sector into a tiered healthcare system with clarified division of functions between primary care facilities and hospitals, and improve the utilization of primary care, is a worldwide problem, especially for the low and middle-income countries (LMICs). This paper aimed to evaluate the impact of the Beijing Reform on healthcare-seeking behavior and tried to explain the mechanism of the change of patient flow. In this before and after study, we evaluated the changes of outpatient visits and inpatient visits among different levels of health facilities. Using the monitored and statistical data of 373 healthcare institutions 1-year before and 1-year after the Beijing Reform, interrupted time series analysis was applied to evaluate the impact of the reform on healthcare-seeking behavior. Semi-structured interviews were used to further explore the mechanisms of the changes. One year after the reform, the flow of outpatients changed from tertiary hospitals to community health centers with an 11.90% decrease of outpatients in tertiary hospitals compared to a 15.01% increase in primary healthcare facilities. The number of ambulatory care visits in primary healthcare (PHC) showed a significant upward trend (P < 0.10), and the reform had a significant impact on the average number of ambulatory care visits per institution in Beijing's tertiary hospitals (p < 0.10). We concluded that the Beijing Reform has attracted a substantial number of ambulatory care visits from hospitals to primary healthcare facilities in the short-term. Comprehensive reform policies were necessary to align incentives among relative stakeholders, which was a critical lesson for other provinces in China and other LMICs.Entities:
Keywords: China; health system reform; tiered healthcare system
Mesh:
Year: 2020 PMID: 33142790 PMCID: PMC7663312 DOI: 10.3390/ijerph17218040
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
The details of the main strategies for the Beijing Reform in April 2017.
| Reform Measures | Descriptions of Reform Measures |
|---|---|
| Zero mark-up of drug sales and hierarchical medical service fee |
15% mark-up of drug sales were removed in all public healthcare facilities; A hierarchical medical service fees with higher level hospitals and senior physicians charging higher service fees; The junior service fees was increased from Y3.5 toY20 in PHC, from Y4 to Y30 in secondary and from Y5 to Y50 in tertiary, and for senior physicians, the new medical service fee is Y60, Y80, Y100 and Y50, Y70,Y90 for tertiary and secondary, respectively; Out-of-pocket expenditures were reimbursed by the Beijing Health Insurance with Y19, Y28, Y40 for primary, secondary and tertiary, respectively. |
| Changes of drug catalogues in PHC |
105 kinds of drugs for chronic diseases are available at PHC, and the drug catalogues are the same as the secondary or tertiary hospitals The long prescription policy is designed for chronic patients with two months prescription for once. |
| Prices adjustment of 435 medical service items |
Prices for surgical operations and traditional Chinese medicine services are increased. Prices for diagnostic tests (CT and MRI) are decreased. All the services changes are covered by the Beijing Health Insurance, and the Out-of-pocket expenditures remain the same. |
The levels and classifications of monitored public medical institutions.
| Levels | Classifications | Numbers |
|---|---|---|
| Tertiary hospitals | 89 | |
| Traditional Chinese Medicine Hospitals | 23 | |
| Specialist Hospitals | 20 | |
| General Hospitals | 46 | |
| Secondary hospitals | 78 | |
| Maternal and Child Health Hospitals | 13 | |
| Traditional Chinese Medicine Hospitals | 12 | |
| Specialist Hospitals | 11 | |
| General Hospitals | 42 | |
| Primary health institutions | 206 | |
| In total | 373 |
The outpatient changes at medical institutions of each level (thousand person-times).
| Medical | Year | Second Quarter | Third Quarter | Fourth Quarter | First Quarter * | Total |
|---|---|---|---|---|---|---|
| Tertiary hospitals | 2016 | 309.2 | 313.4 | 331.5 | 288.5 | 1242.6 |
| 2017 | 272.1 | 278.0 | 287.7 | 256.9 | 1094.7 | |
| Percent increase | −12.01% | −11.29% | −13.23% | −10.94% | 11.90% | |
| Secondary hospitals | 2016 | 97.8 | 97.9 | 107.6 | 87.0 | 390.2 |
| 2017 | 94.5 | 97.1 | 110.5 | 90.3 | 392.4 | |
| Percent increase | −3.35% | −0.79% | 2.72% | 3.79% | 0.55% | |
| Primary health centers | 2016 | 24.6 | 25.7 | 29.7 | 22.2 | 102.2 |
| 2017 | 26.9 | 29.0 | 33.7 | 27.9 | 117.6 | |
| Percent increase | 9.40% | 13.11% | 13.35% | 25.60% | 15.01% |
* The figures of first quarter for the 2016 line are actually for the first quarter of 2017; the 2017 line refers to the first quarter of 2018.
The inpatient changes at each level of medical institution (person-times).
| Medical | Year | Second Quarter | Third Quarter | Fourth Quarter | First Quarter * | Total |
|---|---|---|---|---|---|---|
| Tertiary hospitals | 2016 | 7787.46 | 7827.49 | 7922.44 | 7486.42 | 31,023.81 |
| 2017 | 7951.04 | 8207.90 | 7986.67 | 7706.35 | 31,851.97 | |
| Percent increase | 2.10% | 4.86% | 0.81% | 2.94% | 2.67% | |
| Secondary hospitals | 2016 | 1718.15 | 1748.47 | 1846.82 | 1670.46 | 6983.90 |
| 2017 | 1718.69 | 1738.51 | 1771.39 | 1710.48 | 6939.07 | |
| Percent increase | 0.03% | −0.57% | −4.08% | 2.40% | −0.64% |
* The figures of first quarter for the 2016 line are actually for the first quarter of 2017; the 2017 line refers to the first quarter of 2018.
Figure 1The ITS analysis of monthly outpatient visits in per primary healthcare (PHC) institution. Model adjusted for seasonality and autocorrelation. Solid line: Predicted trend based on the seasonally adjusted regression model. Dashed line: De-seasonalized trend. Vertical line: Intervention began. Blue dots: Actual number.
The impacts of the Beijing Reform on monthly outpatient visits per healthcare facility type.
| Level/Trend | PHC | Secondary | Tertiary |
|---|---|---|---|
| Baseline trend | 0.135 (0.149) | 0.367 (0.534) | 1.294 (1.300) |
| Level change | −0.939 (1.656) | −5.453 (5.729) | −27.423 (14.358) * |
| Trend change | 0.108 (0.084) | 0.223 (0.322) | −0.078 (0.775) |
| Post-reform trend | 0.244 (0.139) * | 0.590 (0.458) | 1.216 (1.201) |
The model was adjusted for seasonality and autocorrelation with the Fourier terms and Newey-West for regression and standard error, respectively. * p < 0.10.
Figure 2The ITS analysis of monthly outpatient visits per secondary hospital. Model adjusted for seasonality and autocorrelation. Solid line: predicted trend based on the seasonally adjusted regression model. Dashed line: de-seasonalized trend. Vertical line: intervention began. Blue dots: actual number.
Figure 3The ITS analysis of monthly outpatient visits per tertiary hospital. Model adjusted for seasonality and autocorrelation. Solid line: predicted trend based on the seasonally adjusted regression model. Dashed line: de-seasonalized trend. Vertical line: intervention began. Blue dots: actual number.
Figure 4The ITS analysis of monthly inpatient visits in per tertiary hospital. Model adjusted for seasonality and autocorrelation. Solid line: predicted trend based on the seasonally adjusted regression model. Dashed line: de-seasonalized trend. Vertical line: intervention began. Blue dots: actual number.
Figure 5The ITS analysis of monthly inpatient visits per secondary hospital. Model adjusted for seasonality. Solid line: predicted trend based on the seasonally adjusted regression model and autocorrelation. Dashed line: de-seasonalized trend. Vertical line: intervention began. Blue dots: actual number.
The impacts of the Beijing Reform on monthly inpatient visits per healthcare facilities.
| Level/Trend | Secondary | Tertiary |
|---|---|---|
| Baseline trend | 0.005 (0.007) | 0.034 (0.037) |
| Level change | −0.052 (0.076) | −0.334 (0.410) |
| Trend change | −0.003 (0.004) | −0.001 (0.019) |
| Post-reform trend | 0.002 (0.006) | 0.033 (0.032) |
The model was adjusted for seasonality and autocorrelation with the Fourier terms and Newey-West regression and standard error, respectively. * p < 0.10.