World Of Health Care Research Articles

The Impact Of Oxytocin Massage On Breast Milk Production In Post Partum Mother In The Clinic Hj. Dermawati Medan

As of 2018, just 44% of babies worldwide were breastfed within an hour of their delivery, based on the World Healthcare Organization (WHO). By the time they are six months old, under forty percent of children worldwide are exclusively breastfed. The purpose of this study is to find out how oxytocin massage impacts postpartum moms' ability to produce breast milk at the Hj Clinic. Dermawati Medan. This investigation applies an experimental approach without a control group, using a single group pretest and posttest. All postpartum women who visited the clinic between the third and seventh days were used as the study population. Dermawati. Five people were used in the purposive sampling technique. Data analysis was carried out by a paired t test, namely comparing the data from the pre-test (before therapy) with the post-test (after treatment). It emerged that the routine findings from the test were crucial. The Shapiro-Wilk test results for before and after the oxytocin treatment demonstrated that the distribution of the data was normal (p value = 0.119 > 0.05) and that the allocation was taken out (p value = 0.006 > 0.05). if so, it can be said that the data is distributed normally.At the Hj Dermawati Clinic, a p value (0.00 < 0.05) was achieved for the Paired Sample T Test results using the mother's milk volume variable both prior to and following the oxytocin massage test. The results of this research illustrate how postpartum mothers at the Hj Dermawati Clinic produce greater amounts of breast milk as an effect of oxytocin massage. It is recommended that mothers and families take oxytocin therapy education so they can carry out the therapy themselves at home at least once a week

Herbal Healing Traditions: A Study of Folk Medicines Used by Traditional Healers of Sonamukhi Block, Bankura District, West Bengal, India

This ethnobotanical study aims to dive into the traditional medicinal practices used by tribals in Sonamukhi Block, Bankura district, West Bengal, India. Focusing on the use of medicinal herbs, the study carefully investigates the intergenerational wisdom kept by traditional healers, who play an important role in healthcare administration. Through conversations with traditional healers and patients, a thorough list of medicinal plants used to treat various diseases has been developed. The study demonstrates that traditional healers successfully use these medicinal plants to produce herbal medications, offering insights into the painstaking process of herbal medicine preparation that was explored in depth during the investigation. These findings highlight the significance of joint efforts to preserve indigenous knowledge and encourage the incorporation of traditional healing techniques into contemporary healthcare. The research study focused on the complicated junction of scientific methodologies and traditional beliefs, indicating the possibility for effective collaboration between scientific institutions and indigenous populations to improve healthcare practices. The studies' thorough examination of medicinal plant usage and herbal medication manufacturing emphasizes the importance of preserving, recording, and using this unique traditional knowledge for the benefit of world healthcare.

“Walking Along a Difficult Path Towards Reward”: Home Healthcare Nurses’ Work Experiences and Satisfaction

The growing demand for skilled personnel and the challenges faced by home healthcare (HHC) underscore the significance of these issues for nurses’ job satisfaction and daily experience of working in this field. The aim of the study was to explore the lived experience and job satisfaction of HHC nurses working in Greece. A qualitative study with semi-structured interviews was conducted. The data were collected from November to December 2021. The analysis was based on thematic analysis by Braun and Clarke. Data were organized with Atlas.ti v8. The sample consisted of 9 nurses (8 female and 1 male), with mean value of 10.22 years of working experience in the field of HHC. Four main themes emerged during the thematic analysis: “Nurse as a guest” “Understanding the world of home healthcare,” “Walking along a difficult path towards reward,” and “Looking forward to a better tomorrow.” Nurses’ descriptions gave the framework of working conditions in HHC and the uniqueness of this working environment, highlighting at the same time the factors that positively or negatively affect their job satisfaction. This valuable knowledge could act as a basis for the development of new targeted strategies, with the goal of creating a healthy, productive environment that would strengthen the desire of nurses to work in HHC.

Investigating efficacy of colchicine plus phenolic monoterpenes fraction as a potential treatment for patients diagnosed with COVID-19: A randomized controlled parallel clinical trial

BackgroundCOVID-19 now is a serious concern for the world healthcare system. This study aimed to investigate possible therapeutic effect of colchicine and phenolic monoterpenes accompanied by standard care of treatment (SCT) in patients diagnosed with COVID-19. MethodsIn this randomized controlled parallel clinical trial, a total number of 179 (of 200) patients with confirmed COVID-19 were enrolled according to the inclusion and exclusion criteria. The patients were allocated by simple randomization method into two groups control (receiving SCT with 71 patients) and intervention (receiving SCT plus colchicine and phenolic monoterpenes with 107 patients). The mortality ratio during hospitalization as well as a 2-week follow-up, ICU admission rate, and hospitalization duration were assessed as main outcomes. ResultsThe mortality ratio was 0.9% (1/108) and 8.45% (6/71) in the intervention and the control groups (p-value = 0.035) respectively, these ratios after a 14-day follow-up were 1.85% (2/108), and 9.85 (7/71) respectively (p-value = 0.031). Also, the ICU admission was significantly lower (p-value = 0.006) in the intervention group 2/108 (1.85%) compared with controls 10/71 (14.08%). Moreover, the duration of hospitalization followed a similar pattern to ICU admission with 4.17 ± 1.34 vs. 6.39 ± 2.59 days in the intervention and control groups respectively (p-value< 0.001). Furthermore, no significant side effect was found between the groups. ConclusionAccording to the results, the combination of colchicine plus phenolic monoterpenes could be an additive treatment for the SCT. The authors strongly recommend further trials on this combination with other SCTs.

Identification of complex III, NQR, and SDH as primary bioenergetic enzymes during the stationary phase of Pseudomonas aeruginosa cultured in urine-like conditions.

Pseudomonas aeruginosa is a common cause of urinary tract infections by strains that are often multidrug resistant, representing a major challenge to the world's health care system. This microorganism has a highly adaptable metabolism that allows it to colonize many environments, including the urinary tract. In this work, we have characterized the metabolic strategies used by stationary phase P. aeruginosa cells cultivated in urine-like media to understand the adaptations used by this microorganism to survive and produce disease. Our proteomics results show that cells rely on the Entner-Duodoroff pathway, pentose phosphate pathway, the Krebs cycle/ glyoxylate shunt and the aerobic oxidative phosphorylation to survive in urine-like media and other conditions. A deep characterization of the oxidative phosphorylation showed that the respiratory rate of stationary phase cells is increased 3-4 times compared to cells in the logarithmic phase of growth, indicating that the aerobic metabolism plays critical roles in the stationary phase of cells grown in urine like media. Moreover, the data show that respiratory complex III, succinate dehydrogenase and the NADH dehydrogenase NQR have important functions and could be used as targets to develop new antibiotics against this bacterium.

Significance of saline nasalirrigation for COVID-19 infection: observations and reflections from nursing care of nasopharyngeal carcinoma.

Coronavirus disease 2019 (COVID-19) has placed a tremendous burden on the world's healthcare systems, prompting medical professionals worldwide to diligently research and experiment with treatment methods to prevent infection and alleviate symptoms. Previous studies have shown the potential of nasal irrigation in reducing viral clearance time and alleviating local symptoms of COVID-19. However, views differ regarding its efficacy in improving systemic symptoms. Thus, we sought to examine whether saline nasal irrigation might play a role in treatment and self-care after COVID-19 infection, but further validation is still necessary. We conducted a retrospective analysis of 468 patients and 51 healthcare personnel concurrently. The participants were grouped based on whether they received saline nasal irrigation. We used χ2 tests and Fisher's exact tests to assess the differences in the rates of COVID-19 infection and the rates of developing a fever after COVID-19 infection among different groups. Additionally, we used independent samples t-tests and Mann-Whitney U tests to evaluate differences in the maximum fever temperature and fever duration among participants with fever in different groups. The rate of developing a fever after COVID-19 infection was lower (37.7%) in the patients who underwent saline nasal irrigation. Among all febrile patients, there was no difference in the highest fever temperature, but patients who underwent saline nasal irrigation had a shorter fever duration (1.72±1.05 days). Additionally, the rate of COVID-19 infection and the rate of developing a fever were higher, and fever symptoms were more severe in the healthcare worker group than in the patient group. Saline nasal irrigation can alleviate symptoms caused by COVID-19 infection.

Design thinking in medical education to tackle real world healthcare problems: The MasterMinds Challenge

Educational challenge Medical education must equip future professionals with the necessary skills to navigate the complex healthcare landscape. Clinical knowledge is essential, and critical and creative thinking skills are vital to meet the challenges of the system. Design thinking offers a structured approach that integrates creativity and innovation, yet its application in medical education is absent. Solution and implementation The compulsory MasterMinds Challenge course at Leiden University Medical Center utilizes design thinking principles to address real world healthcare challenges. Final-year medical students participated in a two-day program. The course encompassed empathizing with stakeholders, problem definition, ideation, prototyping, and refining solutions. Presentation skills were emphasized, culminating in a symposium where teams showcase their outcomes. Implementation of the MasterMinds Challenge course was successful with 33 sessions delivered to 1217 medical students. Challenges covered various healthcare topics, yielding creative yet practical outcomes. Students appreciate the real world healthcare challenge, team-based approach, and the applicability of design thinking principles. Challenge owners expressed satisfaction with students’ commitment, creativity, and empathizing abilities. Lessons learned and next steps To further enhance the MasterMinds Challenge course, a more longitudinal format is being designed, enabling greater autonomy and emphasizing the refining and implementation phases. The course can be extended to medical postgraduate professionals and interdisciplinary collaborations, fostering innovative ideas beyond current practices. By developing problem-solving skills, the MasterMinds Challenge course contributes to a future-proof medical education program and prepares students to meet the evolving needs of healthcare.

Real world outcomes, healthcare utilisation and costs of Alzheimer's disease in England

BackgroundEvidence on health and social care resource utilisation and associated costs, and how this varies across people with Alzheimer's disease (AD) dementia is limited. MethodsRetrospective cohort, using the Discover dataset, which includes linked health and social care records for approximately 2.5 million people in North-West London. Individuals were followed up from the latter of 2010 or from diagnosis of AD dementia (index) up to 2019. Outcomes were overall survival, care home admission, health and social care utilisation and associated costs, and cardiovascular outcomes. Variation was explored in subpopulations and by stratifying by cost quintile. Generalised linear modelling was used to estimate the association between clinical and demographic characteristics and healthcare costs. ResultsThe cohort included 18,116 people diagnosed with AD dementia whose mean age at index was 81 years, 62 % were female and 65 % were of white ethnicity. Median survival from index was 4.9 years (95 %CI: 4.8–5.0). Mean healthcare costs were £4,548(£4,491–£4,604) per person year(ppy). Healthcare costs for the 48 % who used social care were £5,433ppy(£5,353–£5,514) and social care costs were £24,374ppy(£24,372–£24,376). In the overall cohort costs in the highest cost quintile were £13,665ppy(£13,420–£13,911), of which 70 % was from inpatient hospitalisation. Subpopulations admitted to care home (£7,535 £7,362–£7,709), with cardiovascular disease (£6,106;£5,990–£6,222) and with type 2 diabetes (£6,049;£5,901–£6,198) accrued the highest healthcare costs. Factors most strongly associated with cost were dying during follow up (+£2,224;£2,010–£2,493), being frail (+£1,246;£1,051–£1,440) and prior stroke (+£1,207;£908–£1,507). ConclusionCharacteristics of individuals with high healthcare costs include requirement for social care and cardiometabolic comorbidities. Identifying individuals early in their disease course may improve health outcomes and reduce the cost of AD dementia in later life.

Using Artificial Intelligence (AI) Technology in the Health Sector has Several Goals

In a broad sense, artificial intelligence (AI) refers to any computer or system behavior that resembles human behavior. A subfield of artificial intelligence called "machine learning" enables computers to learn from data without explicit human programming. One of the most significant contemporary trends in global healthcare is the use of artificial intelligence (AI) technologies in medicine. Technologies based on artificial intelligence are profoundly transforming the world's healthcare system, enabling a dramatic reconstruction of the medical diagnostics system while simultaneously lowering healthcare expenditures. Identifying the class of diseases to which a disease belongs is crucial before treating it. It is feasible to categorize the type of disease based on the feature space of the condition. Algorithms for machine learning can address this issue.

Empowering Nurse Leaders: The Promise of the DNP/MBA Dual Degree

Healthcare in the U.S. is the most expensive in the world, presenting a complex environment that demands nurse leaders possess consummate business skills. This article chronicles the 30-year journey of incorporating business acumen into nursing education, detailing the resistance and gradual acceptance within the nursing profession. Despite significant progress, a notable gap remains in financial literacy and business proficiency among nurse leaders, often limiting their engagement in financial management roles. The discussion extends to the challenges and opportunities the evolving healthcare landscape presents, where business and financial competencies are increasingly essential for effective leadership. This article aims to underscore the critical need for nurse leaders to be equipped with advanced business skills to successfully navigate the complex healthcare environment. It also emphasizes the pivotal role of nursing academia in integrating business education into program curricula. The Doctor of Nursing Practice/Master of Business Administration dual degree is presented as an innovative educational pathway designed to bridge this gap by providing comprehensive business knowledge and skills. With this degree, the next generation of nurse executives will be well-prepared to become 'guardians at the gate' in the complex world of healthcare, driving meaningful change and improving healthcare outcomes.

Reducing Supply Chain Management Risks in the Health Sector of Ghana: An Evaluation of the Central Level Supply Chain Management System of The Ghana Health Service

Supply Chain Management plays an indispensable role in any organization’s overall performance and success. The domain of Supply Chain Management has been of great research interest in recent years. In the ever-growing healthcare world, where most healthcare organizations are confronted with diverse SCM risks and growing customer demand trends, effective and efficient SCM has been identified as a tool to help them overcome this challenge. However, the main hurdle had been how to find innovative approaches to meet customer demands that will minimize operational costs. To address these concerns and deliver effective and efficient healthcare, the Government of Ghana represented by the Ministry of Health (MOH) and Ghana Health Service (GHS) in partnership with the Global Fund, GAVI, and the World Bank have deployed several interventions fundamentally founded on procurement and supply chain management to improve healthcare service delivery across the country. The interventions: Health commodity supply chain master plan (HCSCMP), the Last Mile Distribution (LMD), and the Ghana Integrated Logistics Management Information System (GhiLMIS), focus mainly on procurement, inventory, warehousing, and transportation of medical and non-medical products. The success and sustenance of these collaborative interventions in Supply Chain management within the health sector in Ghana hinges on key elements such as putting in place stringent procurement policies, ensuring availability of qualified personnel, transportation facilities to aid effective and efficient distribution, and paying attention to risk management practices. This study sought to examine the supply chain risk management practices at the central level of GHS and its regional and districts medical stores to identify the challenges of the existing SCM system and propose measures of addressing these challenges. The scope of the study was limited to the Ghana Health Service Supply Chain Practitioners (GHSSCP) at the central level and their collaborators at the regional levels (the Regional Health Directorates). Quantitative method was used in the study and the main research instrument used for the study was questionnaire administration. However, the study used convenience and purposive sampling techniques due to the sensitive nature of the subject matter. The target respondents were mainly Supply Chain Practitioners: supplies managers, procurement officers, general stores and pharmacy stores officers. The study relied on SPSS and used descriptive and inferential statistics to analyze data. The study found that notwithstanding the introduction of key interventions such as health commodity supply chain master plan (HCSCMP), the Last Mile Distribution (LMD) of health commodities from the Central level to Regional medical stores and to health facilities (Hospitals and the District Health Directorates), and the introduction of the Ghana Integrated Logistics Management Information System (GhiLMIS) as a technology tool for health commodity management at all levels across the public health sector SCM system which collective objective is to ensure the effectiveness and efficiency of the system through the minimization of SCM risks, interruptions, and vulnerabilities, there still exist a yawning underperforming gap due to the systems overly centralization and lack of priority for SCM risk reduction measures making the system still not performing at its full potential. The study concluded that there are basic weaknesses in almost all the supply chain dimensions covered: risk identification, risk assessment and mitigation, Supply Chain Risk Management Governance, impact of supply chain risk management, and employee supply chain risk routine activities because the much-needed attention is not given to SCM activities. The study recommended reforms including the establishment of a Supply Chain Risk Management Unit (SCRMU) at all levels (national, regional, and districts) under the direct supervision of Health Administration and Support services (HASS), the introduction of SCM performance contracts, and the implementation of a stronger but user-friendly technology driven SCM system including e-procurement, warehouse management tracking, supported by a robust feedback system to ensure risk free SCM

Integrating social and healthcare in Community Homes: what architectural topos does the social and healthcare world need?

Le modificazioni epidemiologiche delle ultime decadi con la presenza di un numero sempre maggiore di pazienti fragili e polipatologici e le crescenti problematiche sociali di abbandono, solitudine, disgregazione della comunità richiedono un ripensamento dei percorsi e delle strutture di cura e un rafforzamento delle politiche socio-sanitarie. La Case della Comunità, previste dal DM 77/2022, rappresentano al contempo un’opportunità e una sfida. Il territorio di Parma ha elaborato un progetto interistituzionale per guidare questo processo. In questo articolo si analizzano il contesto socio-sanitario e le esigenze architettonico-funzionali delle nuove Case della Comunità dal punto di vista dei clinici.

CNP blocks mitochondrial depolarization and inhibits SARS-CoV-2 replication in vitro and in vivo.

The COVID-19 pandemic has claimed over 6.5 million lives worldwide and continues to have lasting impacts on the world's healthcare and economic systems. Several approved and emergency authorized therapeutics that inhibit early stages of the virus replication cycle have been developed however, effective late-stage therapeutical targets have yet to be identified. To that end, our lab identified that 2',3' cyclic-nucleotide 3'-phosphodiesterase (CNP) inhibits SARS-CoV-2 virion assembly. We show that CNP inhibits the generation of new SARS-CoV-2 virions, reducing intracellular titers without inhibiting viral structural protein translation. Additionally, we show that targeting of CNP to mitochondria is necessary for inhibition, blocking mitochondrial depolarization and implicating CNP's proposed role as an inhibitor of the mitochondrial permeabilization transition pore (mPTP) as the mechanism of virion assembly inhibition. We also demonstrate that an adenovirus expressing virus expressing both human ACE2 and CNP inhibits SARS-CoV-2 titers to undetectable levels in lungs of mice. Collectively, this work shows the potential of CNP to be a new SARS-CoV-2 antiviral target.

Use of a virtual world to manifest a neurological reality

Artificial intelligence (AI) is rapidly affecting the way we conduct our everyday lives, with great progress being made within the world of medicine and healthcare. AI enables precision and accuracy in surgery, in diagnostic tests and in the treatment of many conditions. It assists staff to practice their profession in a virtual world before taking their skill to the real world. This article will explore how AI and virtual reality are assisting in the care and treatment of neurosurgery patients.

S22: Social Connectedness, Ageing and Mental Health in Doctors

Being a doctor is tough, especially in a post-pandemic world. Never has the field of doctors’ health been so stretched by need. Senior doctors, and medical leaders in particular, have been particularly taxed, as have their families. Unpacking some of the insights gleaned about medical leadership and wisdom in medicine, and conversely when this is lacking, is important for the sake of patients and doctors alike. In this symposium we will address new perspectives in doctors’ health tailored to this new healthcare world. Topics include: (i) Systemic interventions to support senior medical leadership; (ii) Wisdom in medicine; (iii) The dysfunctional or disruptive physician in healthcare; (iv) Senior doctors and their families; (v) Medical retirement in a post-pandemic world. Strong audience participation will be encouraged in this symposium.

Pattern of cephalosporin and carbapenem-resistant Pseudomonas aeruginosa: a retrospective analysis

ObjectivesDespite its financial cost on the world's health care system, Pseudomonas aeruginosa antibiotic resistance has been increasing. Therefore, the goal of this study was to assess the level of antimicrobial resistance to anti-pseudomonas medicines, specifically β-lactam medications such as cephalosporin and carbapenems. In addition, we evaluate the prevalence of multi-drug resistance to P. aeruginosa, particularly during the years of the COVID-19 pandemic. MethodsThis retrospective analysis covered the period from January 2019 to December 2022 and included cephalosporin- and carbapenem-resistant P. aeruginosa isolates. The real-time polymerase chain reaction Genexpert test (CARBA-R kit) was used for the detection of genes responsible for carbapenemase resistance. ResultsDuring the time of the study, 1815 clinical isolates of P. aeruginosa were identified and 160 (9%) were resistant to carbapenems and cephalosporins. The resistance rates were 32.5% (13/597) in 2019, 11.2% (44/393) in 2020, 7% (26/369) in 2021, and 11% (50/456) in 2022. Of those isolates, multidrug-resistant rates were 6.7%, 86.3%, 57.7%, and 56%, per year over the study period. Using Genexpert test, 88 (93.6%) of multidrug-resistant P. aeruginosa were negative for carbapenemase genes. ConclusionThis study emphasizes the alarming patterns of carbapenem and cephalosporin resistance among P. aeruginosa clinical isolates. Furhter surviellance from different centers and different regions is required.

Future MEDIC: Medical Student&amp;ndash;Led Healthcare Career Exploration Program for Underserved Middle School Students

Future Medical Education and Development Introductory Course (Future MEDIC) is a medical student–led school program designed to inspire primary school students in underserved communities by sharing a passion for medicine and healthcare careers. Through hands-on activities—including physical examination workshops, an on-site airport introduction to flight medicine, and interactive demonstrations with a patient simulator—26 eighth-grade students were empowered to actively explore the world of healthcare. To assess the impact of Future MEDIC on students’ knowledge and interest in healthcare careers, we administered pre- and post-surveys, informal exit interviews, and participation prizes. Survey responses were analyzed with a Mann Whitney U test for significance. There was a significant increase (p = 0.01) from pre- to post-survey about “I enjoy learning about how the body works.” Yet there was also a significant decrease (p = 0.03) from pre- to post-survey about “In the future, I am interested in working in healthcare or medicine.” Despite enjoying the activities and content of the Future MEDIC program, students did not have an increased interest in entering the healthcare field. Although interest in entering the healthcare field was not increased, Future MEDIC was a rewarding and fun experience for students to partake in, and an opportunity to create a multidimensional view of the healthcare field.

World Healthcare System and Role of Pharmacist

World Healthcare System and Role of Pharmacist

Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture

Hair diseases are common and can be caused by a variety of factors, including genetics, stress, nutritional deficiencies, as well as exposure to sunlight and air pollution. Accurate diagnosis of hair diseases is important for proper treatment, but can be challenging due to overlapping symptoms. The development of the healthcare world has widely utilized machine learning and deep learning approaches to assist in the healthcare field. This research aims to develop hair disease classification using Convolutional neural network (CNN). The CNN-based approach is expected to help health professionals diagnose hair diseases accurately and provide targeted treatment. This research involves an experimental design with three main stages: identifying the research problem, conducting a literature review, and collecting data. The research uses a dataset of hair disease images obtained from Kaggle, which are annotated and organized based on different hair disease types. After the image data is collected, the image dataset will go through the image preprocessing stage. Experiments were conducted using hair disease image data with 15 epochs on a CNN Deep Learning model with VGG-16 architecture, and resulted in an accuracy of 94.5% and a loss rate of 18.47%, with a testing epoch time of 9 hours 48 minutes. The results of this study show that CNN with VGG-16 architecture can successfully classify 10 types of hair diseases

Choquet integral based deep learning model for COVID-19 diagnosis using eXplainable AI for NG-IoT models

The COVID-19 outbreak has caused a global threat to the world healthcare system. The virus has mutated into different variants and mutations which spread more rapidly, are more deadly and have the lesser effect of vaccines. The number of cases is way more than the number of cases doctors can handle. Widely used methods of RT-PCR are tedious and time-consuming, Instant methods like antigen are very less effective and give incorrect results. Hence Artificial Intelligence (AI)-based Computer-aided Diagnosis (CAD) methods that help doctors in the correct and efficient diagnosis are the need of the hour. In this study, we propose a Deep Learning based XAI model for aiding clinical interpretation on Chest X-ray (CXR) images. The XAI diagnostic model can be integrated into NG-IoT devices using CAD models by provide accurate explainable diagnostics. We used deep-learning models with integrated Choquet integral as an aggregation function for more precise classification. Integrating it with the XAI model Grad CAM ++ increased its explainability. In our proposed work we used four different deep learning models ResNet-50, Inception V3, Densenet-121 and DCNN with integrated Choquet Integral function which increased the accuracy by 2% for Densenet-121 and DCNN and 1% for ResNet-50 and Inception V3. Using Grad CAM++ on the images increases the acceptance by medical professionals by clearly depicting how the models took the decision. We can depict that our proposed model Densenet 121 integrated with ChI outperformed all the other methods.