The public-private partnerships in healthcare sector in China

This manuscript is a narrative review on experience in the healthcare public-private partnerships(PPP)field project in China.The PPP model allows healthcare officials to share the risk of building new facilities with the private sector.The objective of this study is to evaluate and to review the PPP of healthcare sector in China,and to investigate the critical success factors and best practice of PPP.We adapted the PPP evaluation framework of the World Bank Independent Evaluation Group as our conceptual framework to summarize the literatures.The current study systematically reviewed the evolution and current status of public and private hospitals development in China,and to investigate factors related to the successful and less successful deployment and performance of PPP in the healthcare sector of China,and to develop best practice models of PPP among hospitals of China.We found that the PPP organizations providing finance and political risk coverage,thus enabling specific PPP transactions to reach financial closure-potentially setting demonstration effects.Such PPPs may then contribute to improving access to infrastructure and social services,which drives economic growth and other optimal outcomes.

Leveraging Retinal Fundus Images with Deep Learning for Diabetic Retinopathy Grading and Classification

Recently,there has been a considerable rise in the number of diabetic patients suffering from diabetic retinopathy(DR).DR is one of the most chronic diseases and makes the key cause of vision loss in middle-aged people in the developed world.Initial detection of DR becomes necessary for decreasing the disease severity by making use of retinal fundus images.This article introduces a Deep Learning Enabled Large Scale Healthcare Decision Making for Diabetic Retinopathy(DLLSHDM-DR)on Retinal Fundus Images.The proposed DLLSHDM-DR technique intends to assist physicians with the DR decision-making method.In the DLLSHDM-DR technique,image preprocessing is initially performed to improve the quality of the fundus image.Besides,the DLLSHDM-DR applies HybridNet for producing a collection of feature vectors.For retinal image classification,the DLLSHDM-DR technique exploits the Emperor Penguin Optimizer(EPO)with a Deep Recurrent Neural Network(DRNN).The application of the EPO algorithm assists in the optimal adjustment of the hyperparameters related to the DRNN model for DR detection showing the novelty of our work.To assuring the improved performance of the DLLSHDMDR model,a wide range of experiments was tested on the EyePACS dataset.The comparison outcomes assured the better performance of the DLLSHDM-DR approach over other DL models.

Optimizing Healthcare Big Data Processing with Containerized PySpark and Parallel Computing: A Study on ETL Pipeline Efficiency

In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.

A Unified Decision-Making Technique for Analysing Treatments in Pandemic Context

The COVID-19 pandemic has triggered a global humanitarian disaster that has never been seen before.Medical experts,on the other hand,are undecided on the most valuable treatments of therapy because people ill with this infection exhibit a wide range of illness indications at different phases of infection.Further,this project aims to undertake an experimental investigation to determine which treatments for COVID-19 disease is the most effective and preferable.The research analysis is based on vast data gathered from professionals and research journals,making this study a comprehensive reference.To solve this challenging task,the researchers used the HF AHPTOPSIS Methodology,which is a well-known and highly effective MultiCriteria Decision Making(MCDM)technique.The technique assesses the many treatment options identified through various research papers and guidelines proposed by various countries,based on the recommendations of medical practitioners and professionals.The review process begins with a ranking of different treatments based on their effectiveness using the HF-AHP approach and then evaluates the results in five different hospitals chosen by the authors as alternatives.We also perform robustness analysis to validate the conclusions of our analysis.As a result,we obtained highly corroborative results that can be used as a reference.The results suggest that convalescent plasma has the greatest rank and priority in terms of effectiveness and demand,implying that convalescent plasma is the most effective treatment for SARS-CoV-2 in our opinion.Peepli also has the lowest priority in the estimation.

Feature Subset Selection with Artificial Intelligence-Based Classification Model for Biomedical Data

Recently,medical data classification becomes a hot research topic among healthcare professionals and research communities,which assist in the disease diagnosis and decision making process.The latest developments of artificial intelligence(AI)approaches paves a way for the design of effective medical data classification models.At the same time,the existence of numerous features in the medical dataset poses a curse of dimensionality problem.For resolving the issues,this article introduces a novel feature subset selection with artificial intelligence based classification model for biomedical data(FSS-AICBD)technique.The FSS-AICBD technique intends to derive a useful set of features and thereby improve the classifier results.Primarily,the FSS-AICBD technique undergoes min-max normalization technique to prevent data complexity.In addition,the information gain(IG)approach is applied for the optimal selection of feature subsets.Also,group search optimizer(GSO)with deep belief network(DBN)model is utilized for biomedical data classification where the hyperparameters of the DBN model can be optimally tuned by the GSO algorithm.The choice of IG and GSO approaches results in promising medical data classification results.The experimental result analysis of the FSS-AICBD technique takes place using different benchmark healthcare datasets.The simulation results reported the enhanced outcomes of the FSS-AICBD technique interms of several measures.

Magnetic Resonance Imaging Department Cost Study： A Glance from Cyprus

MRI （Magnetic Resonance Imaging） usage tends to be rapidly increasing over the last decades in clinical applications. Although MRI service is a powerful and very useful diagnostic tool for the doctors, it is very expensive. This research paper concerns the economic cost of the NGH （Nicosia General Hospital） MRI Department for the calendar year 2015. Data were collected and analysed through triangulate data collection methods. Research methods include empirical observation, interviews and secondary data analysis. Calculations were made by the bottom-up method. The total cost for the MRI department for the year 2015 was 434,987.52. The external services bought from private sector in order to eliminate waiting list cost 1,071,803. In case NHIS （National Health Insurance System） was implemented in 2015 possible incomes should be 1,412,152. Nicosia General Hospital MRI department balance in case NHIS was implemented in 2015 was 94,638.52 loss. The negative result strongly supports that there is space for improvement in the MRI department. Our research suggests that changes must be done in the management of the MRI Department. Implementation of TQM （Total Quality Management） method could reduce the cost of the aforementioned diagnostic department. This work is the first cost study for the NGH MRI Department and could be used as a reference and give basic costing data for MRI service to healthcare policy makers.