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When features x10 and x8 are added continually, the effect rebounds, and the performance of the model is unsatisfactory. Based on the above analysis, this article finally selects the six features of x17, x3, x16, x7, x4, and x5. Figures 2 , 3 are performance descriptions of the XG-Boost model built on these six features.
It can be seen from Figure 2 that with the increase of the number of iterations, the errors of the model in both the training set and the test set are continuously reduced, and finally tend to be stable. Figure 3 compares the population mortality predicted by the test set model with the true population mortality, and the difference between the two is acceptable in most samples.
The two most important parameters of the random forest model are the number of variables selected by the node branches of the decision tree and the number of decision trees. Similarly, after determining the optimal parameters, this paper does not adjust them to ensure a fair comparison between the model constructed using some features and the full feature model, and to ensure the importance of features is analyzed under the same parameters.
Other detailed parameters are shown in Table 5. The random forest algorithm uses OOB error to calculate and rank feature variables' relative importance. When many features participate in classification, this method is very suitable because the high correlation between many features will cause high-dimensional problems, significantly reducing extraction accuracy. At this stage, the feature space of machine learning models is often huge and complex, showing complex characteristics such as high-dimensionality and non-linearity.
For such massive high-dimensional data, eliminating redundant features for feature screening has become one of the important issues faced by information technology today. The basic idea and process of random forest algorithm for ranking the importance of feature variables are as follows:. For each variable, the out-of-bag data error corresponding to each tree is calculated and recorded as Err OOB1.
Noise interference is added to the variables of the data outside the bag. That is, the sequence is changed randomly, and the error of the data outside the bag is calculated again, which is recorded as Err OOB2.
The importance of a variable can be estimated by analyzing the increase in the error of the data outside the bag when the data sequence changes outside the bag. M can explain the importance of variables, because the accuracy of out-of-bag data drops significantly after adding random noise.
That is, Err OOB2 rises. This shows that this variable has a great influence on the prediction results of the sample. In other words, the degree of importance is relatively high. As shown in Figure 4 , this paper uses the random forest algorithm to rank the importance of features that affect the health of Chinese residents. The horizontal axis of Figure 4 represents the variable importance scores, and the vertical axis variables are arranged in descending order of importance.
In order to obtain the optimal model and ensure the stability of the experimental results, this paper uses the five-fold cross-validation method to evaluate the predictive ability and robustness of the model. The results of the five-fold crossover experiment are shown in Table 6. From Table 6 , when using full range features for analysis, the root mean square errorsof training and test samples was 0. Take all feature modeling and analysis as the baseline, and use different feature numbers for comparison and analysis.
When only the top four important feature variables are selected to train the model, the root mean square errors of the training samples and test samples are 0. The results of the root mean square error corresponding to all features models are quite different. This shows that only four features are not enough to support the prediction of the whole model, and the loss of information is relatively large. In the same way, until the analysis of the first seven feature variables, there is a small gap between the root mean square error of training samples and test samples and the root mean square error corresponding to the full feature.
Therefore, this article finally chooses seven features of x16, x4, x17, x8, x5, x12, x7. Figures 5 , 6 depict the predictive effect of the random forest model on population mortality. The model stabilizes after about 50 iterations, and the prediction error in the test set is also in an acceptable range. From the above analysis, we can use the XG-boost algorithm to screen out the six characteristic variables of x17, x3, x16, x7, x4 and x5, which have a relatively high impact on the health level of residents.
Using the Random forest algorithm, we screened out the seven characteristic variables, x16, x4, x17, x8, x5, x12 and x7. In order to better grasp the influencing factors of Chinese residents' health level from a macro perspective, this paper combines the characteristic variables selected by the two methods.
Finally, eight characteristic variables of x3, x4, x5, x7, x8, x12, x16, x17 are determined. That is, among economic factors, the number of industrial enterprises above designated size, industrial added value, population density, and per capita GDP have a greater impact on the health of residents. Among the environmental factors, coal consumption, energy consumption, total wastewater discharge and solid waste discharge have a greater impact on the health level of residents.
Among economic factors, the number of industrial enterprises above designated size, industrial added value, and per capita GDP represents the economic development level of a region On the one hand, the rapid development of industry has promoted the improvement of China's economic level.
The improvement of the national economic level is conducive to increasing health investment and promoting the development of medical and health care, which creates better material conditions for preventing, controlling, and eliminating certain diseases On the other hand, the process of industrialization has caused a large amount of vegetation damage.
The rapid economic development also increases the discharge of waste, waste gas and wastewater, all of which have brought serious harm to the health of residents. Therefore, the number of industrial enterprises above designated size, industrial added value, and per capita GDP have a greater impact on the health of residents.
The population density reflects the spatial agglomeration characteristics of the population that is, the number of people per unit land area in different regions The increase in population density promotes the coverage of health resources to a larger population, effectively avoids the waste of medical resources, and promotes the effective utilization of medical resources and the health output of residents.
But at the same time, the high residential population density also brings a large amount of traffic flow and automobile exhaust emissions, which indirectly causes air pollution and further increases the risk of illness. Even if there is no significant change in air quality, the growth and migration of the urban population lead to an increase in the number of people exposed to air pollution.
High exposure brings about changes in the health of residents Therefore, population density has a greater impact on the health of residents. Among environmental factors, coal consumption and energy consumption are general indicators that reflect the level of energy consumption.
Energy consumption has a dual impact. On the one hand, energy plays a key role in social and economic development. On the other hand, it has brought serious environmental pollution problems In particular, the primary energy consumption and emissions based on fossil fuels such as coal have released various air pollution, such as CO, SO 2 , soot particles, and PM 2.
Air pollution can cause and aggravate various diseases, such as respiratory diseases and lung cancer. Therefore, coal consumption and energy consumption have a more significant impact on the health of residents. The total discharge of wastewater and solid waste represent the discharge of pollutants. The problem of water pollution and solid waste has always been one of the important factors restricting the economic development of many regions in China.
At the same time, it seriously affects public health and social welfare. Wastewater contains a large number of pathogenic microorganisms, many of which can spread through water and cause various diseases.
Wastewater also contains various types of heavy metal pollutants, which are carcinogenic, mutagenic and teratogenic to the human body Solid waste can cause serious harm to water, atmosphere and soil. If water, the atmosphere, and the soil are all polluted, the pollutants can enter the human body through the respiratory tract and digestive tract, causing serious effects on human health.
Therefore, the total amount of wastewater discharge and solid waste discharge have a greater impact on the health of residents. Although the existing studies have comprehensively analyzed the factors affecting the health level of residents, considering the impact of only one factor may underestimate the interaction or combination of various factors. Therefore, based on China's provincial panel data from to , this paper selects 17 characteristic variables from three levels of economy, environment and society, and uses the XG-boost algorithm and random forest algorithm based on recursive feature elimination to select the variables.
The results show that at the economic level, the number of industrial enterprises above designated size, industrial added value, population density and per capita GDP have a greater impact on the health of residents; At the environmental level, coal consumption, energy consumption, total wastewater discharge and solid waste discharge have a greater impact on the health level of residents.
Based on the above conclusions, this article puts forward the following suggestions:. At the economic level, we should continue to promote the development of industrial green and low-carbon recycling firstly. In , China proposed to achieve a carbon peak by and achieve carbon neutrality by Therefore, to achieve the dual-carbon goal, industrial enterprises should accelerate green transformation, change their development methods, and continuously improve the quality contribution of green total factor productivity to industrial economic growth On the one hand, we should vigorously develop strategic emerging industries and high-tech industries.
On the other hand, we should accelerate the elimination of outdated production capacity, rationally allocate scarce resources, constantly improve the quality of economic growth, and finally realize the green transformation of the industrial economy.
The second is to reasonably adjust the population density. By optimizing the land use structure of the urban area and increasing land for public service facilities, green areas and squares, and road traffic, we promote the coordinated development of the residential and employment populations. At the environmental level, it is necessary to accelerate energy transformation and energy technology innovation, so as to reduce the damage of pollutants from energy consumption to residents' public health.
For a long time, the Chinese energy consumption structure has been dominated by coal Therefore, the coal-based energy structure needs to be changed. The Chinese government can increase the proportion of clean energy consumption through energy transformation, reduce the consumption of traditional energy such as coal as much as possible, and promote the development of energy production and consumption toward a cleaner, low-carbon, and high-efficiency direction.
At the same time, we should increase investment in science and technology innovation and policy support, and promote the innovation, application and promotion of industrial low-carbon equipment. Reduce the discharge of CO, SO 2 , wastewater, solid waste, and other pollutants through technological innovation. Although this article has deeply analyzed the influencing factors of residents' health level in China, there are still some shortcomings, which should be further improved in the following two aspects in the future.
Firstly, this article does not divide the region to study the factors affecting the health level of residents, and the research area should be expanded in the future. Second, given the availability of data, this article uses a single indicator to measure the health of residents, and constructing a reasonable and objective health indicator system will be our future research direction.
WeP and MX contributed to study design and wrote the manuscript. HX interpreted results. All authors have read and agreed to the published version of the manuscript. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Dynamic analysis of health-related factors with its impacts on economic growth.
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The nexus between urbanization and PM 2. Environ Pollut. In response, insurers may raise premiums to meet expected costs of the remaining, sicker enrollees on exchanges, which in turn will accelerate exits of more healthy people, and eventually insurers, from the exchange.
Creating policies that address the potential for adverse selection will increase insurer participation, competition, and the overall stability of insurance exchanges. Incentivesóboth positive and negativeócan be harnessed to improve health. A public health triumph in the U. Incentives matter. As we continue to debate how to achieve a high performing health care system and a healthier population, we need to remember how economics shapes health insurance, health care, and health outcomes.
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Thus, one of the hospitals is highly affected by decreasing reimbursements from insurance providers and escalating demands for medical services. I as the administrator of this hospital will conduct the environmental analysis, and in the context of this paper, I will define the most powerful external and internal forces and their impact on the competitive edge of the hospitals and propose a relevant method to support the implementation of the strategic plan.
The first step of the environmental analysis is to determine key external forces that have a vehement influence on the organizational competitive edge and financial performance. One of the major aspects pertains to changes in legislation such as development and continuous revision of the Affordable Care Act ACA.
It is widely known that the ACA triggered higher demand for healthcare services due to the expansion of Medicaid coverage Spetz, When referring to the laws of supply and demand, the ACA contributes to a dramatic growth of the demand side of the market while supply tends to shrink. It creates a shortage of qualified professionals due to the lack of available education and continuously escalating burnout rates Spetz, Along with that, the ACA and related initiatives restrict requirements for competences that nurses are obliged to have while creating penalties for poor performance Spetz, These matters have to be considered of paramount importance at my workplace since the management cherishes its employees as the most important assets while the ACA has a direct effect on their know-how, working conditions, attitudes, and well-being.
Thus, another matter is linked to economic forces that define tendencies in the healthcare market. For example, one of the most influential factors is a decline in reimbursement rates and an overall transition to a flat reimbursement rate model. It has a direct impact on organizational revenues and financial solvency. For instance, it negatively affects the values ROE, ROA, and current and acid ratios while having an adverse influence on liquidity.
As a consequence, a substantial drop in these values less than one will make the organization less attractive to investors and FDIs. Along with that, this change can affect the satisfaction of nurses and their attitude towards patients while a combination of these reasons emphasizes the need to pay careful attention to these forces simultaneously.
Meanwhile, the last one is linked to the external factors mentioned above including low reimbursement rates and a shortage of medical professionals. The major consequences of ineffective leadership, the absence of motivation, and burnout may trigger the dropout of nurses. This matter will have a direct impact on a nurse-to-patient ratio and affect the quality of the provided services negatively while decreasing readmission rates. Today, in hospitals, the number of outpatient activities continues to increase since the medical institutions are highly concerned about the overall health of the community Noh et al.
Currently, the hospital experiences a decline in inpatient admissions while outpatient activities continue to grow. These changes have a direct effect on the financial performance of the company. For example, short stays at the hospitals are very costly and increase the overall expenses of the medical institutions, and due to the low number of inpatient admissions, they continue to escalate Noh et al.
The matters create more pressure while requiring medical entities to focus on hospitalization, but the number of outpatients continues to grow and triggers the development of home-nursing care Noh et al.
The previous analysis showed that internal and external factors tended to be interdependent. For example, as it was mentioned earlier, the major consequences of the ACA were associated with the shortage of nurses and the low quality of the provided care Spetz, It could be said that one cannot underestimate a correlation between these internal and external factors, competitive edge, and financial performance. It was depicted in the previous sections that decreasing reimbursement rates hurt the motivation and satisfaction of nurses while increasing the number of outpatients raised costs significantly.
The ability of the company to survive in the continuously intensifying rivalry is highly dependent on these trends and its financial performance.
For example, when forecasting a continuous drop in reimbursement rates, it could be said that its solvency and liquidity ratios will also decrease in their values. This matter will create obstacles for technological development and research, as the medical institutions will not be attractive to investors due to the lack of financial stability. This factor implies that the market will be occupied by the companies with well-developed brand images, and small firms will have to face high barriers when entering the industry or surviving on it.
Another factor that has a direct impact on the competitiveness of the hospital is the attitudes of nurses. In this case, low levels of motivation accompanied by the insufficient leadership style will question the ability of the hospital to offer high-quality and patient-centered services.
It will not only damage its reputation but also develop a wrong attitude towards the medical institution among potential patients. Overall, it could be said that it is vital to pay attention to both internal and external factors due to their interdependence. Nonetheless, focusing on internal forces such as motivation of nurses and organizational culture has to be discovered as a priority since these matters have a stronger impact on the competitive edge and brand image than external ones.
Based on the environmental analysis conducted above, it is possible to make a particular decision concerning the most appropriate strategic initiatives. In this instance, to maintain the momentum of the strategic plan, the organization should focus on ensuring the fast responsiveness of internal culture to external forces.
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The causes of burnout among healthcare workers are well-documented, including consolidation and other factors driving the current labor shortage. Yet many are leaving these jobs for a variety of reasons including burnout , which merely increases the strain including burnout on the existing workforce.
As a stopgap measure, digital health tools can decrease the outward flow of people leaving the industry by automating some tasks and reducing administrative burdens. This, in turn, decreases the overall workload on clinicians.
Other digital technologies can help clinicians manage their own mental and physical health. In the midst of infrastructure difficulties, care providers are increasingly concerned about maintaining patient safety. This concern is particularly acute for the maternity population after the recent Supreme Court ruling in Dobbs vs. A predicted increase in high-risk pregnancies as a result of abortion access restrictions puts more pressure on a strained maternal health system, as OB wards are shuttering across low-income and rural areas.
Digital health solutions that collect biometric data and monitor risk remotely can ensure that patients are cared for between appointments, even as they face increased issues of access. Digital tools support patient safety by empowering them to track their own health, with the security of knowing they are connected to a care provider. Inflation is putting increased financial pressure on hospitals, which were struggling with rising labor costs even before this year.
Now, the traditional means for a hospital to boost its razor-thin profit margins are drying up. Elective and outpatient surgeries experience historic declines during economic downturns.
WebJul 24, †∑ We will write a custom Essay on Healthcare Services: Internal and External Factors specifically for you. for only $ $11/page. certified writers online. Learn . WebMar 4, †∑ Deliver quality care and services. Quality of care remains a pressing initiative with the overarching goal of continually improving the health of patients and society at . WebMay 24, †∑ External factors Affecting Healthcare Organizations. Weather, natural disasters, loss of utilities, and flooding stand out as obvious potential hazards and .