Health Care System in Portugal for People with Motor Problems

Purpose: From a social and labor inclusion perspective, the article presents a digital prototype conceptualized to provide a “Diagnostic Page”, which delivers various prescribers and suppliers of support products that mitigate the problems of the respective patients. It also provides a “Patient Card Page”, where all the information about financing the respective products is placed, as well as all the documents likely to be needed for the commercial transactions to be carried out by all the parties involved. It also aims to provide a digital medium to grow a community in this niche market. In the action research methodology approach, the prototype was taken to funding competitions and conferences, where interviews and surveys were carried out, and a number of suggestions were collected on the type of platform to consider in order to respond to the concerns and needs of end users, such as patients, prescribers, suppliers and associations. Methods: The digital platform where the system is hosted uses algorithms that, on the diagnostic page, consider keywords used by patients and return a series of prescribers and suppliers of support products, in which the corresponding percentage of attenuation is taken into account and the best solution found to overcome the level of difficulty presented by the respective patients is delivered. Results and Conclusions: It is hoped that, with this platform, people with motor problems will be able to obtain their diagnosis instantly, through the algorithm implemented, and that they will immediately be provided with a series of prescribers, suppliers and support products best suited to their needs, as well as all the information or conditions necessary to purchase or finance them. On the other hand, prescribers, suppliers and associations have an online platform where they can offer their consultations, products and other support as freelancers who are part of a community.

When a 17-Year-Old Girl Is Diagnosed with Myalgic Encephalomyelitis: A Case Study from the Swedish Health Care System—A Parent Perspective

This case study presents different strategies that were explored by the patient’s mother (who is a researcher in music and medicine) when her 17-year-old daughter was diagnosed with ME (Myalgic Encephalomyelitis), also known as Chronic Fatigue Syndrome (CFS). ME is not widely recognized in the Global as well as the Swedish population at large, and within healthcare, there are no standardized recommended treatments, partly due to the lack of published evidence-based studies. This case study aims to provide insights into how the Swedish healthcare system works, how different clinics and hospitals within it operate and interconnect;and how these contribute to health outcomes after 15 months of treatment.

Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.

Surgical treatment of liver cancer and pancreatic cancer under the China Healthcare Security Diagnosis Related Groups payment system

BACKGROUND Data from the World Health Organization’s International Agency for Research on Cancer reported that China had the highest prevalence of cancer and cancer deaths in 2022.Liver and pancreatic cancers accounted for the highest number of new cases.Real-world data(RWD)is now widely preferred to traditional clinical trials in various fields of medicine and healthcare,as the traditional research approach often involves highly selected populations and interventions and controls that are strictly regulated.Additionally,research results from the RWD match global reality better than those from traditional clinical trials.AIM To analyze the cost disparity between surgical treatments for liver and pancreatic cancer under various factors.METHODS This study analyzed RWD 1137 cases within the HB1 group(patients who underwent pancreatectomy,hepatectomy,and/or shunt surgery)in 2023.It distinguished different expenditure categories,including medical,nursing,technical,management,drug,and consumable costs.Additionally,it assessed the contribution of each expenditure category to total hospital costs and performed cross-group comparisons using the non-parametric Kruskal–Wallis test.This study used the Steel–Dwass test for post-hoc multiple comparisons and the Spearman correlation coefficient to examine the relationships between variables.RESULTS The study found that in HB11 and HB13,the total hospitalization costs were significantly higher for pancreaticoduodenectomy than for pancreatectomy and hepatectomy.Although no significant difference was observed in the length of hospital stay between patients who underwent pancreaticoduodenectomy and pancreatectomy,both were significantly longer than those who underwent liver resection.In HB15,no significant difference was observed in the total cost of hospitalization between pancreaticoduodenectomy and pancreatectomy;however,both were significantly higher than those in hepatectomy.Additionally,the length of hospital stay was significantly longer for patients who underwent pancreaticoduodenectomy than for those who underwent pancreatectomy or liver resection.CONCLUSION China Healthcare Security Diagnosis Related Groups payment system positively impacts liver and pancreatic cancer surgeries by improving medical quality and controlling costs.Further research could refine this grouping system and ensure continuous effectiveness and sustainability.

Revealing the Trends in the Academic Landscape of the Health Care System Using Contextual Topic Modeling

The health care system encompasses the participation of individuals,groups,agencies,and resources that offer services to address the requirements of the person,community,and population in terms of health.Parallel to the rising debates on the healthcare systems in relation to diseases,treatments,interventions,medication,and clinical practice guidelines,the world is currently discussing the healthcare industry,technology perspectives,and healthcare costs.To gain a comprehensive understanding of the healthcare systems research paradigm,we offered a novel contextual topic modeling approach that links up the CombinedTM model with our healthcare Bert to discover the contextual topics in the domain of healthcare.This research work discovered 60 contextual topics among them fteen topics are the hottest which include smart medical monitoring systems,causes,and effects of stress and anxiety,and healthcare cost estimation and twelve topics are the coldest.Moreover,thirty-three topics are showing in-significant trends.We further investigated various clusters and correlations among the topics exploring inter-topic distance maps which add depth to the understanding of the research structure of this scientific domain.The current study enhances the prior topic modeling methodologies that examine the healthcare literature from a particular disciplinary perspective.It further extends the existing topic modeling approaches that do not incorporate contextual information in the topic discovery process adding contextual information by creating sentence embedding vectors through transformers-based models.We also utilized corpus tuning,the mean pooling technique,and the hugging face tool.Our method gives a higher coherence score as compared to the state-of-the-art models(LSA,LDA,and Ber Topic).

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

Assessment of Breast Cancer Prevention Practices among Women Attending Primary Health Care in Abha City, Aseer Region, Saudi Arabia

Cancer is a leading cause of death worldwide, with breast cancer being the most common (2.26 million new cases and 685,000 deaths). In Saudi Arabia, breast cancer ranked the first among females in 2014, accounting for 15.9% of all cancers reported among Saudi nationals and 28.7% of all cancers reported among females of all ages. Early detection of breast cancer could decrease the risks, have a better prognosis, and have better outcomes/more successful treatments. Prevalence of breast cancer reached more than 25% of all diagnosed cancer in the kingdom among women. Aim: This study aims to assess the knowledge and performance of women attending primary care centers about breast self-examination and mammogram screening for prevention and early detection of breast cancer in Abha city primary healthcare centers, Kingdom of Saudi Arabia. Research Method: cross sectional design was conducted by using questionnaire, which was distributed to primary care center nurses. The collected data was statistically analyzed using the Statistical Package for Social Sciences, version 25. Results: The study found that participants had poor awareness and knowledge about breast self-examination, risk factors for breast cancer, and trends and practices in early diagnosis of breast cancer. Conclusion and Recommendations: It recommends increasing awareness campaigns and providing educational programs to improve knowledge and practices.

Water, Sanitation and Hygiene in Lower-Level Health Care Facilities of Dar es Salaam Region in Tanzania: Status towards Achieving the Sustainable Development Goals and Way Forward

Background: Improving Water, Sanitation and Hygiene in health care settings is a critical prerequisite for achieving national health goals and Sustainable Development Goals (SDGs). The World Health Organization (WHO) has set a target for each United Nations member state to reach by 2030. Each member state is required to reach by 2022, 2025 and 2030 at least 60%, 80% and 100%, respectively of basic level of service of the five elements which are water, sanitation, hygiene, waste management and environmental cleaning. Methods: This study aimed to evaluate and document the current state of basic water, sanitation, and hygiene services in all lower-level health care facilities in the Dar es Salaam region of Tanzania as of July 2022. A cross-sectional study was conducted in 99 public dispensaries in the Dar es Salaam region’s five councils: Ubungo, Kigamboni, Kinondoni and Temeke Municipalities, and Ilala City. The interviewee form and observational checklists were both digitalized using the Kobo tool software. The respondents were health care facility in-charges or nurse in-charges. Data were downloaded, validated, and imported to Stata version 15 for analysis. Results: The basic WASH level per JMP is far below the target in 2022. Each member state by 2022 is required to reach at least 60% of the basic level of service of each element. We found a low coverage of basic WASH in the 99 dispensaries included in this study. The basic WASH coverage was met in only 10 (10.1%) of the dispensaries, while the remaining 89 (89.9%) dispensaries fall on limited WASH services. Conclusion: This study revealed lower coverage of basic WASH services in dispensaries. An urgent need is required to improve the status of WASH in all the dispensaries and facilitate the provision of quality health care services, patient safety and reduce health care associated infections.

Continuity of Care in Primary Health Care Affecting Control of Diabetes

Background: Continuity of care is a distinguishing feature of primary care. Better continuity of care program showed a significant effect in controlling diabetes and it is complications. This study explores the effect of continuity of care on control of diabetes mellites in primary health care centres. Objectives: 1) To assess the effect of Continuity of care on controlling haemoglobin (Hb A1C) and fasting blood Sugar (FBS);2) To compare the control of Diabetes by using (Hb A1C and FBS) indices on same patient before and after application of chronic illness clinic;3) To identify the relation between age and gender affecting continuity of care in diabetic patient. Methods: It is a Prospective cohort study design. Included both gender and diabetic patient age above 24 years old. The data extracted from health care specialty center (HCSC) clinics in National guard hospital at Riyadh, through HCSC data base on three phases: 1) Phase 1: the data extracted of diabetic patients from October to November 2022 including MRN, diabetic patient, age, Hb A1C, Fasting blood glucose. 2) Phase 2: the same MRN extracted from phase 1 was extracted again retrospectively for six months from April to September 2022, to compare the indices before implanting the chronic illness clinic including Hb A1C, Fasting blood glucose. 3) Phase 3: prospectively from December 2022 to September 2023. Results: Among diabetic patients aged 60 years old and above showed better control of HbA1C and FBS comparing to these patients below age of 60 years old, with significant improvement of HbA1C after implanting chronic illness clinic. Conclusion: The significant improvement in the control of diabetic patients followed in primary health care centers reinforce the evidence of the importance of continuity of care.

Ozone Sterilizer for Treatment and Health Care

Ozone is a green broad-spectrum bactericidal disinfectant, and a trace amount of ozone in the atmosphere makes many viruses and bacteria lose their biochemical activity and infectivity. Nature produces trace amounts of ozone in the air through lightning to purify the ecological environment. The product of ozone decomposition is oxygen, without secondary pollution. Ozone sterilizer is widely used in the epidemic prevention and control of intensive breeding farms and achieved remarkable results. If the concentration and action time of ozone can be accurately controlled, then ozone can quickly eliminate pathogens, without harming the normal cells in the human body. How to use medical ozone for epidemic prevention, treatment and health care is a subject worthy of serious study, which should arouse the attention of the experts in the field.

Affordability, Accessibility, and Quality of Maternal Health Care Services and Level of Satisfaction of Mothers in Rural Areas in China

Objective:This study aimed to understand the affordability,accessibility,and quality of maternal and child health services for postpartum mothers,and their impact on satisfaction.Methods:The study utilized the Anderson model and revised the Maternal Pregnancy and Perinatal Health Service Questionnaire.A survey was conducted among 289 mothers aged 20–49 in Feicheng City.Results:Regarding accessibility,most respondents(133)reported that travel time to healthcare services exceeded 60 minutes,while 99 respondents indicated a travel time of 16–30 minutes.The issue of affordability was highlighted,with 86.85%of participants perceiving maternal healthcare services as costly,indicating a significant financial burden.More than 50%of respondents were satisfied with two specific dimensions(P<0.05)regarding the quality of maternal healthcare services.Conclusion:The study found that accessibility,affordability,and quality significantly affect mothers’satisfaction with maternal health services.Future research should focus on developing more suitable service pathways for rural mothers.

The effect of high-fidelity simulation in medical nursing based on the healthcare simulation standards of best practice

Background:High-fidelity simulation has been demonstrated to make great progress in learning.However,there is still ongoing exploration on how to fully harness the advantages of this teaching method and enhance its effectiveness.This study conducted high-fidelity simulation in medical nursing based on the Healthcare Simulation Standards of Best Practice and evaluated its effect.Methods:The study was conducted from September 2019 to June 2020.A total of 82 undergraduate nursing students from a university in Shanghai participated in the high-fidelity simulation in medical nursing.The simulation design scale,educational practices in simulation scale,and students’satisfaction and self-confidence were used to evaluate the effect.Results:The mean score of simulation design scale was 4.06±0.63 with the mean scores of all dimensions being over 3.0.The mean score of educational practices in simulation scale was 4.14±0.56 with the mean scores of all dimensions being over 4.0.The mean scores of students’satisfaction and self-confidence were 4.07±0.72 and 3.89±0.58,respectively.Conclusion:Students reported high levels of simulation design and educational practices in simulation.They were also satisfied with learning and reported high levels of self-confidence.Some key points need to be considered so that the learning effects might be optimized.

Downsizing Health Care in the Community

This study reviewed recent changes in health care utilization in the health care providers of Syracuse, New York. The data indicated the largest decline in the numbers of inpatient volumes involved adult surgery and orthopedics. Numbers of inpatient discharges for this service declined by more than 2900 discharges for the combined Syracuse hospitals. The data also indicated that adult medicine discharges declined by more than 2600 during this time. For Diagnosis Related Groups with discharge differences of 30 or more, adult medicine discharges declined by 451 in neurology, 943 in respiratory medicine, and 625 in circulatory medicine. It was estimated that the value of the inpatient discharges amounted to approximately $1,740,000 in adult surgery and more than $1,560,000 for adult medicine. The savings that were achieved in this process related to staffing, pharmaceuticals, and testing.

Estimating the Future of Health Care at the Community Level

Developments in health care in the United States are changing the delivery of services for providers and payors. This study focused on inpatient hospital discharges in the Syracuse hospitals and other services. It demonstrated that, during the past five years, numbers of inpatient adult medicine discharges had increased while adult surgery discharges had declined. This information suggested that adult medicine discharges could be expected to increase and approach levels of five years ago. It also suggested adult surgery discharges could be expected to remain at previous levels or decline. This information indicated that the combined emergency department visits declined from 238,000 to 202,000 between 2019 and 2020, then increased from 218,000 to 228,000 visits between 2021 and 2023. These developments will probably result in greater efficiency at the community level. With a decline in numbers of inpatient beds, providers will be able to focus on the more efficient management by reducing numbers of staff as well as fewer pharmaceuticals and testing.

Advance IoT Intelligent Healthcare System for Lung Disease Classification Using Ensemble Techniques

In healthcare systems,the Internet of Things(IoT)innovation and development approached new ways to evaluate patient data.A cloud-based platform tends to process data generated by IoT medical devices instead of high storage,and computational hardware.In this paper,an intelligent healthcare system has been proposed for the prediction and severity analysis of lung disease from chest computer tomography(CT)images of patients with pneumonia,Covid-19,tuberculosis(TB),and cancer.Firstly,the CT images are captured and transmitted to the fog node through IoT devices.In the fog node,the image gets modified into a convenient and efficient format for further processing.advanced encryption Standard(AES)algorithm serves a substantial role in IoT and fog nodes for preventing data from being accessed by other operating systems.Finally,the preprocessed image can be classified automatically in the cloud by using various transfer and ensemble learning models.Herein different pre-trained deep learning architectures(Inception-ResNet-v2,VGG-19,ResNet-50)used transfer learning is adopted for feature extraction.The softmax of heterogeneous base classifiers assists to make individual predictions.As a meta-classifier,the ensemble approach is employed to obtain final optimal results.Disease predicted image is consigned to the recurrent neural network with long short-term memory(RNN-LSTM)for severity analysis,and the patient is directed to seek therapy based on the outcome.The proposed method achieved 98.6%accuracy,0.978 precision,0.982 recalls,and 0.974 F1-score on five class classifications.The experimental findings reveal that the proposed framework assists medical experts with lung disease screening and provides a valuable second perspective.

“Three essential elements”of the primary health care system:A comparison between California in the US and Guangdong in China

The primary health care system(PHCS)is the portal and basis for the national health delivery system.There are a number of elements which comprise the PHCS,but the system cannot be established and developed without the support of“three essential elements,”including general practitioners(GPs),health insurance,and government health investment.Through comparative analysis of the“three essential elements”of the PHCS between California in the US and Guangdong in China,this article indicates that there are three important aspects in developing a PHCS in China,as follows:training qualified GPs;establishing a diversified health insurance system to strengthen the policy connections between health insurance and the PHCS thereby increasing government health investment;and broadening health financing channels.

An Efficient Blockchain-Based Healthcare System Using Artificial Intelligence

Personal health records and electronic health records are considered as the most sensitive information in the healthcare domain.Several solutions have been provided for implementing the digital health system using blockchain,but there are several challenges,such as secure access control and privacy is one of the prominent issues.Hence,we propose a novel framework and implemented an attribute-based access control system using blockchain.Moreover,we have also integrated artificial intelligence(AI)based approach to identify the behavior and activity for security reasons.The current methods only focus on the related clinical records received from a medical diagnosis.Moreover,existing methods are too inflexible to resourcefully sustenance metadata changes.A secure patient data access framework is proposed in this research,integrating blockchain,trust chain,and blockchain methods to overcome these problems in the literature for sharing and accessing digital healthcare data.We have used a neural network and classifier to categorize the user access to our proposed system.Our proposed scheme provides an intelligent and secure blockchain-based access control system in the digital healthcare system.Experimental results surpass the existing solutions by collecting attributes such as the number of transactions,number of nodes,transaction delay,block creation,and signature verification time.

Primary Healthcare System of Pakistan: Challenges to Self-Management of Type 2 Diabetes

This review article is aimed at describing the primary healthcare system of Pakistan and its challenges in the face of epidemic of type 2 diabetes, focusing particularly on the middle-aged population of rural area of Pakistan. The main concern in Pakistan is that its middle-aged population is facing the onslaught of obesity and overweight due to lack of physical activity. In addition unhealthy eating habits making it more difficult for this population to control their weight. All these factors are contributing to a high risk of type 2 diabetes for the population of Pakistan. This article provides insight into the primary health care system of Pakistan and highlights its deficiencies by identifying that its primary healthcare system has a poor utilization of health care services, the poor accessibility to health system and poor management of diabetes by the healthcare system, gender disparity and inequity in the health care system. The primary objective of this study is to provide an overview of self-management of diabetes among the middle-aged population of Pakistan and to identify the overall deficiencies in the primary healthcare system, its delivery and access to the system, barriers to self-management of diabetes and quality of life in that region.

Electrochemical biosensors for point-of-care testing

Point-of-care testing(POCT)is the practice of diagnosing and monitoring diseases where the patient is located,as opposed to traditional treatment conducted solely in a medical laboratory or other clinical setting.POCT has been less common in the recent past due to a lack of portable medical devices capable of facilitating effective medical testing.However,recent growth has occurred in this field due to advances in diagnostic technologies,device miniaturization,and progress in wearable electronics.Among these developments,electrochemical sensors have attracted interest in the POCT field due to their high sensitivity,compact size,and affordability.They are used in various applications,from disease diagnosis to health status monitoring.In this paper we explore recent advancements in electrochemical sensors,the methods of fabricating them,and the various types of sensing mechanisms that can be used.Furthermore,we delve into methods for immobilizing specific biorecognition elements,including enzymes,antibodies,and aptamers,onto electrode surfaces and how these sensors are used in real-world POCT settings.

To scan or not to scan:Use of transient elastography in an integrated health system

BACKGROUND Non-invasive tests,such as Fibrosis-4 index and transient elastography(com-monly FibroScan),are utilized in clinical pathways to risk stratify and diagnose non-alcoholic fatty liver disease(NAFLD).In 2018,a clinical decision support tool(CDST)was implemented to guide primary care providers(PCPs)on use of FibroScan for NAFLD.AIM To analyze how this CDST impacted health care utilization and patient outcomes.METHODS We performed a retrospective review of adults who had FibroScan for NAFLD indication from January 2015 to December 2017(pre-CDST)or January 2018 to December 2020(post-CDST).Outcomes included FibroScan result,laboratory tests,imaging studies,specialty referral,patient morbidity and mortality.RESULTS We identified 958 patients who had FibroScan,115 before and 843 after the CDST was implemented.The percentage of FibroScans ordered by PCPs increased from 33%to 67.1%.The percentage of patients diagnosed with early F1 fibrosis,on a scale from F0 to F4,increased from 7.8%to 14.2%.Those diagnosed with ad-vanced F4 fibrosis decreased from 28.7%to 16.5%.There were fewer laboratory tests,imaging studies and biopsy after the CDST was implemented.Though there were more specialty referrals placed after the CDST was implemented,multivariate analysis revealed that healthcare utilization aligned with fibrosis score,whereby patients with more advanced disease had more referrals.Very few patients were hospitalized or died.CONCLUSION This CDST empowered PCPs to diagnose and manage patients with NAFLD with appropriate allocation of care towards patients with more advanced disease.