Response to “The effects of global budget payments on hospital utilization in rural Maryland”

Roberts, Eric T.

doi:10.1111/1475-6773.13161

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…This led to a long period of unmet demand for financial resources from the poor. In the long run, without financial support, the poor will become poorer [ 15 ]. In order to change this situation, Professor Yunus established the Village Bank of Bangladesh by implementing microfinance.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Construction of Rural Financial Organization Spatial Structure and Service Management Model Based on Deep Convolutional Neural Network

Liu

2021

Computational Intelligence and Neuroscience

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Local credit cooperatives have long played an important role in local financial services. It has made a significant contribution to agricultural production, farmers’ incomes, and the economic development of rural areas. In particular, as a financial instrument serving farmers, microfinance management by local credit cooperatives plays a key role in pursuing profits and fulfilling social responsibility. It was therefore important to obtain effective instruments for combating poverty in rural areas from all walks of society. This paper first outlines the development of microfinance loans in Germany and other countries and describes the current situation and some of the challenges facing local credit cooperatives in financial management. Next, we present the basic concepts of data mining, describe the common methods and key techniques of data mining, analyze and compare the properties of the individual data, and show how the associated mining can actually be performed. Next, we will explain the basic model of microfinance for farmers and some risks in detail and analyze and evaluate the characteristics of these risks in the context of local credit cooperatives. As a result, this paper proposes an improved deep convolutional neural network. The optimized algorithm selects the optimal weight threshold value and different iteration times. The results are fewer errors, the results are closer to the correct data, and the efficiency is better than before. The algorithm is more efficient because errors have been greatly reduced and the time spent on them has been slightly reduced.

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Section: Related Workmentioning

confidence: 99%

[Retracted] Construction of Rural Financial Organization Spatial Structure and Service Management Model Based on Deep Convolutional Neural Network

Liu

2021

Computational Intelligence and Neuroscience

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“…5 While Maryland has successfully been able to decrease the growth of health care costs and reduce overall health care utilization across the state, subsequent analyses focused on rural hospitals have demonstrated conflicting results. 6 Perhaps demonstrating the limitations of this model, 1 rural hospital in Maryland closed in 2020 despite being under a global budget model since 2010. 1 The Pennsylvania Rural Health Model was implemented across 5 rural hospitals in 2019 and has since scaled to 18 hospitals by 2021.…”

Section: Prior Experience With Global Budgetsmentioning

confidence: 99%

“…Maryland piloted this policy in 10 rural hospitals in 2010 before expanding to all hospitals in Maryland in 2014 . While Maryland has successfully been able to decrease the growth of health care costs and reduce overall health care utilization across the state, subsequent analyses focused on rural hospitals have demonstrated conflicting results . Perhaps demonstrating the limitations of this model, 1 rural hospital in Maryland closed in 2020 despite being under a global budget model since 2010…”

Section: Prior Experience With Global Budgetsmentioning

confidence: 99%