Analysis of Drug Medical Insurance Access Pricing Based on the Perspective of Binary Equilibrium

Objective To study the influencing factors in the process of national medical insurance negotiation and drug pricing from the dualistic equilibrium perspective,and to provide reference for the harmonious management of drug pricing in China.Methods Through the literature analysis and policy review,the pricing subject,pricing basis and price control system in the pricing process of medical-accessed medicines were analyzed from the perspective of binary equilibrium and harmonious management.Results and Conclusion It is found that four balances in the drug pricing process,two balances in pricing basis and three balances in price control system need to be considered,respectively.Drug pricing is the key content of national medical insurance access,which is also the hotspot of the policy in the pharmaceutical fields in recent years.Drug pricing not only reflects the value of drugs,but also reflects a lot of top-level designs of binary equilibriums in medical insurance policy.While the rational design of drug pricing requires the joint efforts of the government,pharmaceutical companies and relevant experts to comprehensively consider many equilibriums,so as to improve the relevant systems.

Associative Tasks Computing Offloading Scheme in Internet of Medical Things with Deep Reinforcement Learning

The Internet of Medical Things(Io MT) is regarded as a critical technology for intelligent healthcare in the foreseeable 6G era. Nevertheless, due to the limited computing power capability of edge devices and task-related coupling relationships, Io MT faces unprecedented challenges. Considering the associative connections among tasks, this paper proposes a computing offloading policy for multiple-user devices(UDs) considering device-to-device(D2D) communication and a multi-access edge computing(MEC)technique under the scenario of Io MT. Specifically,to minimize the total delay and energy consumption concerning the requirement of Io MT, we first analyze and model the detailed local execution, MEC execution, D2D execution, and associated tasks offloading exchange model. Consequently, the associated tasks’ offloading scheme of multi-UDs is formulated as a mixed-integer nonconvex optimization problem. Considering the advantages of deep reinforcement learning(DRL) in processing tasks related to coupling relationships, a Double DQN based associative tasks computing offloading(DDATO) algorithm is then proposed to obtain the optimal solution, which can make the best offloading decision under the condition that tasks of UDs are associative. Furthermore, to reduce the complexity of the DDATO algorithm, the cacheaided procedure is intentionally introduced before the data training process. This avoids redundant offloading and computing procedures concerning tasks that previously have already been cached by other UDs. In addition, we use a dynamic ε-greedy strategy in the action selection section of the algorithm, thus preventing the algorithm from falling into a locally optimal solution. Simulation results demonstrate that compared with other existing methods for associative task models concerning different structures in the Io MT network, the proposed algorithm can lower the total cost more effectively and efficiently while also providing a tradeoff between delay and energy consumption tolerance.

Inspiration of Foreign Innovative Drug Pricing Methods and Medical Insurance Payment Standards to China

Objective To study the innovative drug pricing methods and medical insurance payment standards in foreign countries and to provide reference for China’s government.Methods The official websites were searched for information and related literature,and literature review was used.Results and Conclusion In foreign countries,the clinical value of innovative drugs and their impact on medical insurance funds were comprehensively evaluated based on factors such as quality-adjusted life years,clinical benefit,and improvement of clinical benefit.Then,the evaluation results were taken as an important basis for whether innovative drugs were admitted to the medical insurance catalog and establishing medical insurance payment standards.By using international experience for reference,innovative drug pricing methods and medical insurance payment standards for China’s national conditions can be improved by establishing a basic database of clinical value and drug economic evaluation of innovative drugs,as well as innovative drug payment models based on decision thresholds.

The Impact of Population Aging on the Expenditure of Medical Insurance Fund for Urban Workers in China

Objective To explore the impact of population aging on the expenditures of medical insurance funds against the background that great changes in population structure influences economic development.Methods Through analyzing the impact of the population aging,the income and accumulated balance of the medical insurance fund,and other related factors on the expenditure of the medical insurance fund,development status of the medical insurance fund for urban employees in China since 2003 was obtained.Stata 16.0 was used to perform multiple linear regression analysis on related factors to determine the correlation between population aging and the change in medical insurance expenditures.Results and Conclusion The factors that have a greater impact on the expenditure of the medical insurance fund are the amount of income from the medical insurance,followed by the number of people over the age of 65 in China and the urban retired employees participating in medical insurance.We should focus on the sustainable development of the urban employee medical insurance fund to deal with the threat of aging.

Deep Forest-Based Fall Detection in Internet of Medical Things Environment

This article introduces a new medical internet of things(IoT)framework for intelligent fall detection system of senior people based on our proposed deep forest model.The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks.Moreover,the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer.The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset,which is acquired from three-axis accelerometer in a smartwatch.It includes 92781 training samples and 91025 testing samples with two labeled classes,namely non-fall and fall.Classification results of our deep forest classifier demonstrated a superior performance with the best accuracy score of 98.0%compared to three machine learning models,i.e.,K-nearest neighbors,decision trees and traditional random forest,and two deep learning models,which are dense neural networks and convolutional neural networks.By considering security and privacy aspects in the future work,our proposed medical IoT framework for fall detection of old people is valid for real-time healthcare application deployment.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Efficient Certificateless Authenticated Key Agreement for Blockchain-Enabled Internet of Medical Things

Internet of Medical Things(IoMT)plays an essential role in collecting and managing personal medical data.In recent years,blockchain technology has put power in traditional IoMT systems for data sharing between different medical institutions and improved the utilization of medical data.However,some problems in the information transfer process between wireless medical devices and mobile medical apps,such as information leakage and privacy disclosure.This paper first designs a cross-device key agreement model for blockchain-enabled IoMT.This model can establish a key agreement mechanism for secure medical data sharing.Meanwhile,a certificateless authenticated key agreement(KA)protocol has been proposed to strengthen the information transfer security in the cross-device key agreement model.The proposed KA protocol only requires one exchange of messages between the two parties,which can improve the protocol execution efficiency.Then,any unauthorized tampering of the transmitted signed message sent by the sender can be detected by the receiver,so this can guarantee the success of the establishment of a session key between the strange entities.The blockchain ledger can ensure that the medical data cannot be tampered with,and the certificateless mechanism can weaken the key escrow problem.Moreover,the security proof and performance analysis are given,which show that the proposed model and KA protocol are more secure and efficient than other schemes in similar literature.

Machine Learning-Enabled Communication Approach for the Internet of Medical Things

The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.

Optimized Identification with Severity Factors of Gastric Cancer for Internet of Medical Things

The Internet of Medical Things (IoMT) emerges with the visionof the Wireless Body Sensor Network (WBSN) to improve the health monitoringsystems and has an enormous impact on the healthcare system forrecognizing the levels of risk/severity factors (premature diagnosis, treatment,and supervision of chronic disease i.e., cancer) via wearable/electronic healthsensor i.e., wireless endoscopic capsule. However, AI-assisted endoscopy playsa very significant role in the detection of gastric cancer. Convolutional NeuralNetwork (CNN) has been widely used to diagnose gastric cancer based onvarious feature extraction models, consequently, limiting the identificationand categorization performance in terms of cancerous stages and gradesassociated with each type of gastric cancer. This paper proposed an optimizedAI-based approach to diagnose and assess the risk factor of gastric cancerbased on its type, stage, and grade in the endoscopic images for smarthealthcare applications. The proposed method is categorized into five phasessuch as image pre-processing, Four-Dimensional (4D) image conversion,image segmentation, K-Nearest Neighbour (K-NN) classification, and multigradingand staging of image intensities. Moreover, the performance of theproposed method has experimented on two different datasets consisting ofcolor and black and white endoscopic images. The simulation results verifiedthat the proposed approach is capable of perceiving gastric cancer with 88.09%sensitivity, 95.77% specificity, and 96.55% overall accuracy respectively.

Determinants of internet addiction among medical students in Casablanca: a cross-sectional study

Objective:This study aimed to estimate the prevalence and determinants of Internet addiction among medical students at the Faculty of Medicine and Pharmacy of Casablanca,Morocco.Methods:This was a cross-sectional study conducted among students at the Faculty of Medicine and Pharmacy in Casablanca between October and March 2020.An online questionnaire was administered to students to collect data and internet addiction was assessed by the Young questionnaire.A score threshold≥50 was adopted to define addiction.Univariate and multivariate logistic regression analyses were used to identify factors associated with internet addiction.Results:Out of a total of 4093 FMPC students enrolled in the 2020-2021 academic year,506 agreed to participate in this study,including 303 females and 203 males.The mean addiction score assessed on the Young scale was(49.08±16.11).The prevalence of Internet addiction was 44.5%(225/506,95% CI:40% to 49%).Multiple regression analysis showed that being older than 20 years(OR=0.17,95% CI:0.40 to 0.64),being female(OR=1.70,95% CI:1.04 to 2.78),being in the dissertation year(6th year)(OR=5.17,95% CI:2.23 to 11.44),having a history of psychiatric consultation(OR=2.64,95% CI:1.34 to 5.21),having divorced parents(OR=2.64,95% CI:1.05 to 5.87),use of sleeping medication(OR=2.9,95% CI:1.05 to 3.70),sleep disorders(OR=2.06,95% CI:1.25 to 3.79),sleep deprivation(OR=2.26,95% CI:1.39 to 3.65),excessive daytime sleepiness(OR=5.39,95% CI:2.19 to 13.24),anxiety disorders(OR=1.47,95% CI:1.18 to 2.30),duration of internet connection(>4 h)(OR=11.43,95% CI:4.85 to 27.66),and having frequent conflicts with parents(OR=2.37,95% CI:1.49 to 3.79)and friends(OR=0.26,95% CI:0.11 to 0.65)were independently associated with internet addiction.Conclusion:The prevalence of Internet addiction among medical students in Casablanca remains high.Targeted action on the determinants would be of great value in prevention.

Encryption with User Authentication Model for Internet of Medical Things Environment

Internet of Medical Things(IoMT)enabled e-healthcare has the potential to greately improve conventional healthcare services significantly.However,security and privacy become major issues of IoMT because of the restricted processing abilities,storage,and energy constraints of the sensors.Therefore,it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors.In order to ensure security on sensitive medical data,effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers.In this view,this study designs an effective metaheuristic optimization based encryption with user authentication(EMOE-UA)technique for IoMT environment.This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity.Moreover,the EMOE-UA technique employs optimal multikey homomorphic encryption(OMKHE)technique to encrypt the IoMT data.Furthermore,the improved social spider optimization algorithm(ISSOA)was employed for the optimal multikey generation of the MKHE technique.The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects.The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Energy and Latency Optimization in Edge-Fog-Cloud Computing for the Internet of Medical Things

In this paper,the Internet ofMedical Things(IoMT)is identified as a promising solution,which integrates with the cloud computing environment to provide remote health monitoring solutions and improve the quality of service(QoS)in the healthcare sector.However,problems with the present architectural models such as those related to energy consumption,service latency,execution cost,and resource usage,remain a major concern for adopting IoMT applications.To address these problems,this work presents a four-tier IoMT-edge-fog-cloud architecture along with an optimization model formulated using Mixed Integer Linear Programming(MILP),with the objective of efficiently processing and placing IoMT applications in the edge-fog-cloud computing environment,while maintaining certain quality standards(e.g.,energy consumption,service latency,network utilization).A modeling environment is used to assess and validate the proposed model by considering different traffic loads and processing requirements.In comparison to the other existing models,the performance analysis of the proposed approach shows a maximum saving of 38%in energy consumption and a 73%reduction in service latency.The results also highlight that offloading the IoMT application to the edge and fog nodes compared to the cloud is highly dependent on the tradeoff between the network journey time saved vs.the extra power consumed by edge or fog resources.

On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Exploration and Research on the Integrated Development of“Internet Plus Medical Treatment”

Objective To analyze the development of“internet plus medical treatment”and to explore advantages.Methods The literature of“internet plus medical treatment”was systematically combed and analyzed.Results and Conclusion After exploring the status quo of“internet plus hospitals”,smart pharmacy and web-assisted health management in China,we find that there are some problems in the medical service at present,such as the imperfect laws and regulations,the hidden dangers of information security and the obstacles of medical insurance payment.Therefore,we propose that the development of web-assisted medical service should be led by the government and relevant policies must be improved.Then,self-regulation should be strengthened,and industry standards should be enhanced.Three suggestions are made to improve medical insurance payment and benefit both hospitals and patients,which can provide reference for promoting the development of“internet plus medical treatment”in China.

Analysis of inpatient payments of breast cancer patients with different medical insurance coverages in China(mainland) in 2011–2015

Objective： An understanding of the levels and trends of medical cost is made for breast cancer patients with different medical insurance coverages in China（mainland）, in an attempt to offer a clue to further control the costs.Methods： The inpatient payments of 9,716,180 breast cancer patients spent in medical institutions of different types and grades during 2011–2015 were collected from the inpatient medical record home page（IMRHP） dataset.The data were then processed with SAS（Version 9.3; SAS Institute, Cary, NC, USA）. Indicators like means,increase（decrease） percentages were used to descriptively analyze the average hospitalization expense of each time（AHEET） and its trends of breast cancer patients with different medical insurance coverages treated in medical institutions of different types and grades.Results：In 2011–2015,the AHEET borne by breast cancer patients in China had been constantly increasing.Specifically,the self-pay inpatients had the largest increase,inpatients covered by Urban Employee Basic Medical Insurance（UEBMI）and Urban Resident Basic Medical Insurance（URBMI）were the next,and those covered by New Rural Cooperative Medical System（NRCMS）had the least increase.Breast cancer inpatient treated in public hospitals had quite greater increase and higher expenditure level than those in private hospitals.The AHEET borne by the inpatients in Grade 3 hospitals had greater increase and higher cost than those in Grade 2 hospitals.Conclusions：The inpatient payments of breast cancer patients will be wisely controlled by reducing the number of self-pay inpatients,taking advantage of restriction mechanism of the medical insurances,and promoting healthy competition between private hospitals and public hospitals.The economic burden imposed on the society by breast cancer can be relieved through further control of inpatient payments of UEBMI-and URBMI-covered breast cancer patients and of Grade 3 hospitals.

Software Process Modeling with UML in Development of Medical Insurance MIS

This paper describes how to use the Unified Modeling Language (UML) to modeling software processes in medical insurance MIS, and compares UML Modeling method with classic PO(Process Oriented) Modeling method. It indicates that the whole performance of application system model described by UML is much better than the one described by PO.

An Ensemble Methods for Medical Insurance Costs Prediction Task

The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs.The open dataset is used for data analysis methods development.The usage of artificial intelligence in the management of financial risks will facilitate economic wear time and money and protect patients’health.Machine learning is associated withmany expectations,but its quality is determined by choosing a good algorithm and the proper steps to plan,develop,and implement the model.The paper aims to develop three new ensembles for individual insurance costs prediction to provide high prediction accuracy.Pierson coefficient and Boruta algorithm are used for feature selection.The boosting,stacking,and bagging ensembles are built.A comparison with existing machine learning algorithms is given.Boosting modes based on regression tree and stochastic gradient descent is built.Bagged CART and Random Forest algorithms are proposed.The boosting and stacking ensembles shown better accuracy than bagging.The tuning parameters for boosting do not allow to decrease the RMSE too.So,bagging shows its weakness in generalizing the prediction.The stacking is developed using K Nearest Neighbors(KNN),Support Vector Machine(SVM),Regression Tree,Linear Regression,Stochastic Gradient Boosting.The random forest(RF)algorithm is used to combine the predictions.One hundred trees are built forRF.RootMean Square Error(RMSE)has lifted the to 3173.213 in comparison with other predictors.The quality of the developed ensemble for RootMean Squared Error metric is 1.47 better than for the best weak predictor(SVR).

Analysis of the effect of medical insurance on cancer inpatients: A 10-year retrospective study on a large hospital in Northeast China

Objective The aim of the study was to analyze hospital costs for cancer inpatients availing different methods of payment and the influencing factors, to provide inputs to improve the medical insurance payment policy. Methods We analyzed the information related to length of hospital stay, hospitalization cost, and self-pay cost, collected from one large-scale, Grade A, Class Three hospital in Shenyang, China, during 2004–2013.Results The number of cancer inpatients with different payment types(medical insurance group and non-medical insurance group) presented a rising trend. Further, the ratio of medical insurance inpatients increased rapidly(from 22.2% to 48.7%); however, this group was still a minority. The length of hospital stay became shorter(21 d vs. 17 d; P = 0.000) while the gap got narrower; the hospitalized expense showed an upward trend and the difference was remarkable($24048.6 ± $4376.28 vs. $20544.36 ± $4057.01; P = 0.000). Conclusion Along with normalization of cancer therapy, the influence of payment on treatment has been getting weak, the policy has impact on controlling hospitalization cost, lightening burden of cancer patient, as well as allocating medical resources in a reasonable way, becoming an important defray pattern of hospitalization cost.

The sustainability study of medical insurance fund in China under the background of aging

the arrival of the aging means being able to direct production value to reduce the proportion of people, the elderly population gradually become pure consumer groups, and the elderly because of its body function gradually blurred, risk is higher than that of other age members of society, medical consumption occupy a large proportion in the consumption structure. As the degree of aging, the country＇s medical expenses will be bigger, that is to say, in an aging society, may because the elderly medical consumption spending huge, and affect the sustainable development of society. And as countries deeper levels of population aging in our country, can very good deal with the impact of the aging of medical insurance fund, guarantee the sustainability of the medical insurance fund, also determines the sustainable and stable development of our country. Therefore, under the background of aging degree deeper, the development of the medical insurance fund is to seek new paths, to cope with increasing aging population is particularly important