Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

The healthcare data requires accurate disease detection analysis,real-timemonitoring,and advancements to ensure proper treatment for patients.Consequently,Machine Learning methods are widely utilized in Smart Healthcare Systems(SHS)to extract valuable features fromheterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities.These methods are employed across different domains that are susceptible to adversarial attacks,necessitating careful consideration.Hence,this paper proposes a crossover-based Multilayer Perceptron(CMLP)model.The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on themedical records of patients.Once an attack is detected,healthcare professionals are promptly alerted to prevent data leakage.The paper utilizes two datasets,namely the synthetic dataset and the University of Queensland Vital Signs(UQVS)dataset,from which numerous samples are collected.Experimental results are conducted to evaluate the performance of the proposed CMLP model,utilizing various performancemeasures such as Recall,Precision,Accuracy,and F1-score to predict patient activities.Comparing the proposed method with existing approaches,it achieves the highest accuracy,precision,recall,and F1-score.Specifically,the proposedmethod achieves a precision of 93%,an accuracy of 97%,an F1-score of 92%,and a recall of 92%.

Enhancing Private Healthcare Effectiveness in Lagos State, Nigeria: An Overview of the Effect of Quality Improvement Initiatives and Implications for Sustainable Healthcare Delivery

Background: Nigeria, a nation grappling with rapid population growth, economic intricacies, and complex healthcare challenges, particularly in Lagos State, the economic hub and most populous state, faces the challenge of ensuring quality healthcare access. The overview of the effect of quality improvement initiatives in this paper focuses on private healthcare providers in Lagos State, Nigeria. The study assesses the impact of donor-funded quality improvement projects on these private healthcare facilities. It explores the level of participation, perceived support, and tangible effects of the initiatives on healthcare delivery within private healthcare facilities. It also examines how these initiatives influence patient inflow and facility ratings, and bring about additional benefits and improvements, provides insights into the challenges faced by private healthcare providers in implementing quality improvement projects and elicits recommendations for improving the effectiveness of such initiatives. Methods: Qualitative research design was employed for in-depth exploration, utilizing semi-structured interviews. Private healthcare providers in Lagos involved in the SP4FP Quality Improvement Project were purposively sampled for diversity. Face-to-face interviews elicited insights into participation, perceived support, and project effects. Questions covered participation levels, support perception, changes observed, challenges faced, and recommendations. Thematic analysis identified recurring themes from interview transcripts. Adherence to ethical guidelines ensured participant confidentiality and informed consent. Results: Respondents affirmed active involvement in the SP4FP Quality Improvement Project, echoing literature emphasizing private-sector collaboration with the public sector. While acknowledging positive influences on facility ratings, respondents highlighted challenges within the broader Nigerian healthcare landscape affecting patient numbers. Respondents cited tangible improvements, particularly in staff management and patient care processes, validating the positive influence of quality improvement projects. Financial constraints emerged as a significant challenge, aligning with existing literature emphasizing the pragmatic difficulties faced by private healthcare providers. Conclusions: This study illuminates the complex landscape of private healthcare provision in Lagos State, emphasizing the positive impact of donor-funded quality improvement projects. The findings provide nuanced insights, guiding policymakers, healthcare managers, and practitioners toward collaborative, sustainable improvements. As Nigeria progresses, these lessons will be crucial in shaping healthcare policies prioritizing population well-being.

Digital Disparities:How Artificial Intelligence Can Facilitate Anti-Black Racism in the U.S.Healthcare Sector

This paper delves into the intricate interplay between artificial intelligence(AI)systems and the perpetuation of Anti-Black racism within the United States medical industry.Despite the promising potential of AI to enhance healthcare outcomes and reduce disparities,there is a growing concern that these technologies may inadvertently/advertently exacerbate existing racial inequalities.Focusing specifically on the experiences of Black patients,this research investigates how the following AI components:medical algorithms,machine learning,and natural learning processes are contributing to the unequal distribution of medical resources,diagnosis,and health care treatment of those classified as Black.Furthermore,this review employs a multidisciplinary approach,combining insights from computer science,medical ethics,and social justice theory to analyze the mechanisms through which AI systems may encode and reinforce racial biases.By dissecting the three primary components of AI,this paper aims to present a clear understanding of how these technologies work,how they intersect,and how they may inherently perpetuate harmful stereotypes resulting in negligent outcomes for Black patients.Furthermore,this paper explores the ethical implications of deploying AI in healthcare settings and calls for increased transparency,accountability,and diversity in the development and implementation of these technologies.Finally,it is important that I prefer the following paper with a clear and concise definition of what I refer to as Anti-Black racism throughout the text.Therefore,I assert the following:Anti-Black racism refers to prejudice,discrimination,or antagonism directed against individuals or communities of African descent based on their race.It involves the belief in the inherent superiority of one race over another and the systemic and institutional practices that perpetuate inequality and disadvantage for Black people.Furthermore,I proclaim that this form of racism can be manifested in various ways,such as unequal access to opportunities,resources,education,employment,and fair treatment within social,economic,and political systems.It is also pertinent to acknowledge that Anti-Black racism is deeply rooted in historical and societal structures throughout the U.S.borders and beyond,leading to systemic disadvantages and disparities that impact the well-being and life chances of Black individuals and communities.Addressing Anti-Black racism involves recognizing and challenging both individual attitudes and systemic structures that contribute to discrimination and inequality.Efforts to combat Anti-Black racism include promoting awareness,education,advocacy for policy changes,and fostering a culture of inclusivity and equality.

Health awareness and lifestyle toward healthcare-seeking behavior among students in Universitas Advent Indonesia

Objective:Healthcare-seeking behavior(HSB)would affect the prevalence of morbidity and mortality.There are various factors that affect one's HSB.This study aimed to determine if health awareness and lifestyle might relate to HSB.Methods:A cross-sectional study was applied by using three questionnaires to determine par ticipants'health awareness,lifestyle,and HSB.This study took place in Universitas Advent Indonesia and the students were recruited to be par ticipants.Results:There were 39 par ticipants joined in this study.Most of the par ticipants were females,third-year students,and from Accounting major.Almost all participants were aware of their low risk of health issues,had a fine lifestyle,and had moderate HSB.Conclusions:One's urge to seek health care facilities was not related to their health awareness and lifestyle.There was no fur ther study to contradict with this finding at this moment.

An Intelligent SDN-IoT Enabled Intrusion Detection System for Healthcare Systems Using a Hybrid Deep Learning and Machine Learning Approach

The advent of pandemics such as COVID-19 significantly impacts human behaviour and lives every day.Therefore,it is essential to make medical services connected to internet,available in every remote location during these situations.Also,the security issues in the Internet of Medical Things(IoMT)used in these service,make the situation even more critical because cyberattacks on the medical devices might cause treatment delays or clinical failures.Hence,services in the healthcare ecosystem need rapid,uninterrupted,and secure facilities.The solution provided in this research addresses security concerns and services availability for patients with critical health in remote areas.This research aims to develop an intelligent Software Defined Networks(SDNs)enabled secure framework for IoT healthcare ecosystem.We propose a hybrid of machine learning and deep learning techniques(DNN+SVM)to identify network intrusions in the sensor-based healthcare data.In addition,this system can efficiently monitor connected devices and suspicious behaviours.Finally,we evaluate the performance of our proposed framework using various performance metrics based on the healthcare application scenarios.the experimental results show that the proposed approach effectively detects and mitigates attacks in the SDN-enabled IoT networks and performs better that other state-of-art-approaches.

Evolutionary Neural Architecture Search and Its Applications in Healthcare

Most of the neural network architectures are based on human experience,which requires a long and tedious trial-and-error process.Neural architecture search(NAS)attempts to detect effective architectures without human intervention.Evolutionary algorithms(EAs)for NAS can find better solutions than human-designed architectures by exploring a large search space for possible architectures.Using multiobjective EAs for NAS,optimal neural architectures that meet various performance criteria can be explored and discovered efficiently.Furthermore,hardware-accelerated NAS methods can improve the efficiency of the NAS.While existing reviews have mainly focused on different strategies to complete NAS,a few studies have explored the use of EAs for NAS.In this paper,we summarize and explore the use of EAs for NAS,as well as large-scale multiobjective optimization strategies and hardware-accelerated NAS methods.NAS performs well in healthcare applications,such as medical image analysis,classification of disease diagnosis,and health monitoring.EAs for NAS can automate the search process and optimize multiple objectives simultaneously in a given healthcare task.Deep neural network has been successfully used in healthcare,but it lacks interpretability.Medical data is highly sensitive,and privacy leaks are frequently reported in the healthcare industry.To solve these problems,in healthcare,we propose an interpretable neuroevolution framework based on federated learning to address search efficiency and privacy protection.Moreover,we also point out future research directions for evolutionary NAS.Overall,for researchers who want to use EAs to optimize NNs in healthcare,we analyze the advantages and disadvantages of doing so to provide detailed guidance,and propose an interpretable privacy-preserving framework for healthcare applications.

A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Federated learning is an innovative machine learning technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing machine learning models using datasets spread across several data centers,including medical facilities,clinical research facilities,Internet of Things devices,and even mobile devices.The main goal of federated learning is to improve robust models that benefit from the collective knowledge of these disparate datasets without centralizing sensitive information,reducing the risk of data loss,privacy breaches,or data exposure.The application of federated learning in the healthcare industry holds significant promise due to the wealth of data generated from various sources,such as patient records,medical imaging,wearable devices,and clinical research surveys.This research conducts a systematic evaluation and highlights essential issues for the selection and implementation of federated learning approaches in healthcare.It evaluates the effectiveness of federated learning strategies in the field of healthcare.It offers a systematic analysis of federated learning in the healthcare domain,encompassing the evaluation metrics employed.In addition,this study highlights the increasing interest in federated learning applications in healthcare among scholars and provides foundations for further studies.

Adaptation of Federated Explainable Artificial Intelligence for Efficient and Secure E-Healthcare Systems

Explainable Artificial Intelligence(XAI)has an advanced feature to enhance the decision-making feature and improve the rule-based technique by using more advanced Machine Learning(ML)and Deep Learning(DL)based algorithms.In this paper,we chose e-healthcare systems for efficient decision-making and data classification,especially in data security,data handling,diagnostics,laboratories,and decision-making.Federated Machine Learning(FML)is a new and advanced technology that helps to maintain privacy for Personal Health Records(PHR)and handle a large amount of medical data effectively.In this context,XAI,along with FML,increases efficiency and improves the security of e-healthcare systems.The experiments show efficient system performance by implementing a federated averaging algorithm on an open-source Federated Learning(FL)platform.The experimental evaluation demonstrates the accuracy rate by taking epochs size 5,batch size 16,and the number of clients 5,which shows a higher accuracy rate(19,104).We conclude the paper by discussing the existing gaps and future work in an e-healthcare system.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration

Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care,which is essential for independent living,especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common.Recent advances in the Internet of Things(IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition,gaining significant attention in personalized healthcare.This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring.Relevant papers were extracted and analyzed using a systematic numerical review method,covering various aspects such as sports monitoring,disease detection,patient monitoring,and medical diagnosis.The review highlights the transformative impact of IoTenabled wearable devices in healthcare,facilitating real-time monitoring of vital signs,including blood pressure,temperature,oxygen levels,and heart rate.Results from the reviewed papers demonstrate high accuracy and efficiency in predicting health conditions,improving sports performance,enhancing patient care,and diagnosing diseases.The integration of IoT in wearable healthcare devices enables remote patient monitoring,personalized care,and efficient data transmission,ultimately transcending traditional boundaries of healthcare and leading to better patient outcomes.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Managerial perspectives of scaling up robotic-assisted surgery in healthcare systems:A systematic literature review

Objectives Robotic-assisted surgery(RAS)is a minimally invasive technique practiced in multiple specialties.Standard training is essential for the acquisition of RAS skills.The cost of RAS is considered to be high,which makes it a burden for institutes and unaffordable for patients.This systematic literature review(SLR)focused on the various RAS training methods applied in different surgical specialties,as well as the cost elements of RAS,and was to summarize the opportunities and challenges associated with scaling up RAS.Methods An SLR was carried out based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses reporting guidelines.The PubMed,EBSCO,and Scopus databases were searched for reports from January 2018 through January 2024.Full-text reviews and research articles in the English language from Asia-Pacific countries were included.Articles that outlined training and costs associated with RAS were chosen.Results The most common training system is the da Vinci system.The simulation technique,which includes dry-lab,wet-lab,and virtual reality training,was found to be a common and important practice.The cost of RAS encompasses the installation and maintenance costs of the robotic system,the operation theatre rent,personnel cost,surgical instrument and material cost,and other miscellaneous charges.The synthesis of SLR revealed the challenges and opportunities regarding RAS training and cost.Conclusions The results of this SLR will help stakeholders such as decision-makers,influencers,and end users of RAS to understand the significance of training and cost in scaling up RAS from a managerial perspective.For any healthcare innovation to reach a vast population,cost-effectiveness and standard training are crucial.

Exploring the intersection of the medical metaverse and healthcare ethics:future considerations and caveats

The medical metaverse and digital twin are set to revolutionise healthcare.Like all emerging technologies their benefits must be weighed against their ethical and social,impacts.If we consider the advances of medical technology as an expression of our values,such as the pursuit of knowledge,cures and healing,an ethical study allows us to align our values and steer the technology towards an agreed goal.However,to appreciate the long-term consequents of a technology,those consequences must be considered in the context of a society already shaped by that technology.This paper identifies the technologies currently shaping society and considers the ethical,and social consequences of the medical metaverse and digital twin in that future society.

Privacy Protection for Blockchain-Based Healthcare IoT Systems: A Survey

To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising technique.With the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public concerns.On the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT systems.In this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy protection.In addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is presented.Finally,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.

Differences between the healthcare systems of Quebec and France for the treatment of pain due to spinal disorders

In Quebec,Canada,the public healthcare system offers free medical services.However,patients with spinal pain often encounter long waiting times for specialist appointments and limited physiotherapy coverage.In contrast,private clinics provide expedited care but are relatively scarce and entail out-of-pocket expenses.Once a patient with pain caused by a spinal disorder meets a pain medicine specialist,spinal intervention is quickly performed when indicated,and patients are provided lifestyle advice.Transforaminal epidural steroid injections are frequently administered to patients with radicular pain,and steroid injections are administered on a facet joint to control low back or neck pain.Additionally,medial branch blocks are performed prior to thermocoagulation.France’s universal healthcare system ensures accessibility at controlled costs.It emphasizes physical activity and provides free physical therapy services.However,certain interventions,such as transforaminal and interlaminar epidural injections,are not routinely used in France owing to limited therapeutic efficacy and safety concerns.This underutilization may be a potential cause of chronic pain for many patients.By examining the differences,strengths,and weaknesses of these two systems,valuable insights can be gained for the enhancement of global spinal pain management strategies,ultimately leading to improved patient outcomes and satisfaction.

Malpractice or masterful practice?Navigating vicarious liability in healthcare

In the intricate landscape of healthcare,vicarious liability looms large,shaping the responsibilities and actions of healthcare practitioners and administrators alike.Illustrated by a poignant scenario of a medication error,this article navigates the complexities of vicarious liability in healthcare.It explains the legal basis and ramifications of this theory,emphasizing its importance in fostering responsibility,protecting patient welfare,and easing access to justice.The paper explores the practical effects of vicarious responsibility on day-to-day operations,leadership practices,and decision-making processes via the eyes of senior consultants,junior doctors,and hospital administrators.Through comprehensive insights and real-world examples,it underscores the imperative of fostering a culture of accountability,communication,and quality care to navigate the intricate web of liabilities inherent in modern healthcare.

Assessing healthcare workers’knowledge and confidence in the diagnosis,management and prevention of Monkeypox

BACKGROUND Monkeypox(Mpox),is a disease of global public health concern,as it does not affect only countries in western and central Africa.AIM To assess Burundi healthcare workers(HCWs)s’level of knowledge and confidence in the diagnosis and management of Mpox.METHODS We conducted a cross-sectional study via an online survey designed mainly from the World Health Organization course distributed among Burundi HCWs from June-July 2023.The questionnaire comprises 8 socioprofessional-related questions,22 questions about Mpox disease knowledge,and 3 questions to assess confidence in Mpox diagnosis and management.The data were analyzed via SPSS software version 25.0.A P value<0.05 was considered to indicate statistical significance.RESULTS The study sample comprised 471 HCWs who were mainly medical doctors(63.9%)and nurses(30.1%).None of the 22 questions concerning Mpox knowledge had at least 50%correct responses.A very low number of HCWs(17.4%)knew that Mpox has a vaccine.The confidence level to diagnose(21.20%),treat(18.00%)or prevent(23.30%)Mpox was low among HCWs.The confidence level in the diagnosis of Mpox was associated with the HCWs’age(P value=0.009),sex(P value<0.001),work experience(P value=0.002),and residence(P value<0.001).The confidence level to treat Mpox was significantly associated with the HCWs’age(P value=0.050),sex(P value<0.001),education(P value=0.033)and occupation(P value=0.005).The confidence level to prevent Mpox was associated with the HCWs’education(P value<0.001),work experience(P value=0.002),residence(P value<0.001)and type of work institution(P value=0.003).CONCLUSION This study revealed that HCWs have the lowest level of knowledge regarding Mpox and a lack of confidence in the ability to diagnose,treat or prevent it.There is an urgent need to organize continuing medical education programs on Mpox epidemiology and preparedness for Burundi HCWs.We encourage future researchers to assess potential hesitancy toward Mpox vaccination and its associated factors.

Fortifying Healthcare Data Security in the Cloud:A Comprehensive Examination of the EPM-KEA Encryption Protocol

A new era of data access and management has begun with the use of cloud computing in the healthcare industry.Despite the efficiency and scalability that the cloud provides, the security of private patient data is still a majorconcern. Encryption, network security, and adherence to data protection laws are key to ensuring the confidentialityand integrity of healthcare data in the cloud. The computational overhead of encryption technologies could leadto delays in data access and processing rates. To address these challenges, we introduced the Enhanced ParallelMulti-Key Encryption Algorithm (EPM-KEA), aiming to bolster healthcare data security and facilitate the securestorage of critical patient records in the cloud. The data was gathered from two categories Authorization forHospital Admission (AIH) and Authorization for High Complexity Operations.We use Z-score normalization forpreprocessing. The primary goal of implementing encryption techniques is to secure and store massive amountsof data on the cloud. It is feasible that cloud storage alternatives for protecting healthcare data will become morewidely available if security issues can be successfully fixed. As a result of our analysis using specific parametersincluding Execution time (42%), Encryption time (45%), Decryption time (40%), Security level (97%), and Energyconsumption (53%), the system demonstrated favorable performance when compared to the traditional method.This suggests that by addressing these security concerns, there is the potential for broader accessibility to cloudstorage solutions for safeguarding healthcare data.

Influence of the continuing COVID-19 epidemic on sleep quality and psychological status of healthcare workers in private institutions

BACKGROUND On January 22,2020,Macao reported its first case of coronavirus disease 2019(COVID-19)infection.By August 2021,the situation had escalated into a crisis of community transmission.In response,the government launched a recruitment campaign seeking assistance and services of healthcare workers(HCWs)from the private sector throughout Macao.These participants faced concerns about their own health and that of their families,as well as the responsibility of maintaining public health and wellness.This study aims to determine whether the ongoing epidemic has caused them physical and psychological distress.AIM To examine the influence of COVID-19 on the sleep quality and psychological status of HCWs in private institutions in Macao during the pandemic.METHODS Data were collected from December 2020 to January 2022.Two consecutive surveys were conducted.The Pittsburgh Sleep Quality Index(PSQI)scale,Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were employed as investigation tools.RESULTS In the first-stage survey,32%of HCWs experienced a sleep disorder,compared to 28.45%in the second-stage survey.A total of 31.25%of HCWs in the first-stage survey and 28.03%in the second had varying degrees of anxiety.A total of 50.00%of HCWs in the first-stage survey and 50.63%in the second experienced varying degrees of depression.No difference in PSQI scores,SAS scores,or SDS scores were observed between the two surveys,indicating that the COVID-19 epidemic influenced the sleep quality and psychological status of HCWs.The negative influence persisted over both periods but did not increase remarkably for more than a year.However,a positive correlation was observed between the PSQI,SAS,and SDS scores(r=0.428-0.775,P<0.01),indicating that when one of these states deteriorated,the other two tended to deteriorate as well.CONCLUSION The sleep quality,anxiety,and depression of HCWs in private institution in Macao were affected by the COVID-19 epidemic.While these factors did not deteriorate significantly,the negative effects persisted for a year and remained noteworthy.

Cost analysis of radical resection of malignant breast tumors under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND Breast cancer is one of the most common malignant tumors in women worldwide and poses a severe threat to their health.Therefore,this study examined patients who underwent breast cancer surgery,analyzed hospitalization costs and structure,and explored the impact of China Healthcare Security Diagnosis Related Groups(CHS-DRG)management on patient costs.It aimed to provide medical institutions with ways to reduce costs,optimize cost structures,reduce patient burden,and improve service efficiency.AIM To study the CHS-DRG payment system’s impact on breast cancer surgery costs.METHODS Using the CHS-DRG(version 1.1)grouping criteria,4073 patients,who underwent the radical resection of breast malignant tumors from January to December 2023,were included in the JA29 group;1028 patients were part of the CHS-DRG payment system,unlike the rest.Through an independent sample t-test,the length of hospital stay as well as total hospitalization,medicine and consumables,medical,nursing,medical technology,and management expenses were compared.Pearson’s correlation coefficient was used to test the cost correlation.RESULTS In terms of hospitalization expenses,patients in the CHS-DRG payment group had lower medical,nursing,and management expenses than those in the diagnosis-related group(DRG)non-payment group.For patients in the DRG payment group,the factors affecting the total hospitalization cost,in descending order of relevance,were medicine and consumable costs,consumable costs,medicine costs,medical costs,medical technology costs,management costs,nursing costs,and length of hospital stay.For patients in the DRG nonpayment group,the factors affecting the total hospitalization expenses in descending order of relevance were medicines and consumable expenses,consumable expenses,medical technology expenses,the cost of medicines,medical expenses,nursing expenses,length of hospital stay,and management expenses.CONCLUSION The CHS-DRG system can help control and reduce unnecessary medical expenses by controlling medicine costs,medical consumable costs,and the length of hospital stay while ensuring medical safety.