Optimization referral rate design for hierarchical diagnosis and treatment system based on accessibility-utilization efficiency bi-objective collaboration: A case study of China.

The hierarchical diagnosis and treatment reform of China can guide residents to seek medical treatment in an orderly manner and improve access to medical treatment. Most existing studies on hierarchical diagnosis and treatment used accessibility as the evaluation index to determine the referral rate between hospitals. However, the blind pursuit of accessibility will cause the problem of uneven utilization efficiency of hospitals at different levels. In response to this, we constructed a bi-objective optimization model based on the perspective of residents and medical institutions. This model can give the optimal referral rate for each province considering the accessibility of residents and the utilization efficiency of hospitals, to improve the utilization efficiency and equality of access for hospitals. The results showed that the applicability of bi-objective optimization model is good, and the optimal referral rate based on the model can ensure the maximum benefit of the two optimization goals. In the optimal referral rate model, residents' medical accessibility is relatively balanced overall. In terms of obtaining high-grade medical resources, the accessibility is better in the eastern and central regions, but poorer in the western China. According to the current allocation of medical resources in China, the medical tasks undertaken by high-grade hospitals account for 60%-78%, which are still the main force of medical services. In this way, there is a big gap in realizing the "serious diseases do not leave the county" goal of hierarchical diagnosis and treatment reform.

Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China.

Urban ecological environment is the basis of citizens' survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECIIMP), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECIIMP, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECIIMP was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECIIMP fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECIIMP in Hangzhou > Hefei ≈ Nanjing, spring ≈ autumn > summer â‰« winter. From 2009 to 2019, UECIIMP has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECIIMP in 2014-2019 is 4.3 times than that in 2009-2014. The correlation between UECIIMP and economic index indicates that economic development has a positive impact on the urban ecological environment. The significant degradation of UECIIMP in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.