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.

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%.

Assessment of Knowledge, Attitude and Practices among Healthcare Workers in a Tertiary Care Hospital on Needle Stick Injury

Purpose: Needle-stick injury (NSI) is one of the most potential occupational hazards for healthcare workers because of the transmission of blood-borne pathogens. As per recent data, around 30 lakh healthcare workers sustain Needle stick injuries each year. This study was conducted to assess healthcare workers’ knowledge, attitude and practices regarding needle stick injury. Materials & Methods: A cross-sectional study was conducted in a Tertiary Care Hospital over the period of 3 months. The study population consisted of Intern Doctors, Post Graduate resident Doctors, Staff Nurses, laboratory technicians of Government Medical College and New Civil Hospital, Surat (n = 300). The data were collected using a self-administered questionnaire via the means of Google Forms. Questionnaire was made with prior review literature. The data obtained were entered and analysed in Microsoft Excel. Results: The prevalence of NSI in our study was 46%, with a higher prevalence among the PG residents (72%). Overall scores regarding knowledge and attitude were better in PG residents (knowledge score > 7 in 71% and Attitude Score > 7 in 68% of PG Residents). Even though the PG residents scored highest in the knowledge category, the majority of them suffered needle stick injuries as a result of poor practice scores. Among those who had NSI (n = 139/300), 70% of study participants had superficial injuries, only 9% reported the incident, 18% got medical attention within 2 hours of the incident, and 7% followed up to recheck their viral markers status. Most incidents of NSI were due to hypodermic needles while recapping needles. Conclusion: Exposure to needle stick injuries and their underreporting remains a common problem. It is imperative that healthcare workers receive regular training on the proper handling of sharp objects. We can also draw the conclusion that preventing NSIs requires putting knowledge into practice.

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.

Levels of Polycyclic Aromatic Hydrocarbons (PAHs) in Healthcare Waste Incinerators’ Bottom Ash from Five County Hospitals in Kenya

Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care waste requires safe handling, treatment and disposal procedures. While incineration reduces the volume and quantity of waste for final disposal, it leads to the production of fly and bottom ashes laden with toxic incomplete combustion products such as Polycyclic Aromatic Hydrocarbons (PAHs), dioxins, furans and heavy metals. This exposes workers who handle and dispose the bottom ashes, hospital patients, the general public and environment. The goal of this study was to determine the total and individual levels of 16 most prevalent and toxic PAHs. Bottom ash samples were collected from incinerators in five county hospitals in Kenya, namely;Moi-Voi, Narok, Kitale, Makindu and Isiolo. Bottom ash samples were collected over a period of six months from the five hospitals. The samples were then sieved, homogenised and stored at 4°C in amber coloured glass containers. The PAHs were extracted using 30 ml of a hexane-acetone solvent (1:1) mixture by ultrasonication at room temperature (23°C) for 45 minutes. The PAHs were then analyzed with a GC-MS spectrophotometer model (Shimadzu GCMS-QP2010 SE) connected to a computer work station was used for the PAHs analysis. The GC-MS was equipped with an SGE BPX5 GC capillary column (30 m × 0.25 mm × 0.25 μm) for the separation of compounds. Helium was used as the carrier gas at a flow rate of 15.5 ml/minute and 14.5 psi. 1 μl of the sample was injected at 280°C, split mode (10:1). The oven programming was set for a total runtime of 40 minutes, which included: 100°C (2-minute hold);10°C /min rise to 200°C;7°C /min rise to 249°C;3°C /min rise to 300°C (2-minute hold). The interface temperature was set at 290°C. Analysis was done in Selected Ion Monitoring (SIM) mode and the peak areas of each of the PAHs were collected from the chromatograph and used for quantification of the 16 PAHs listed by the U.S. Environmental Protection Agency (EPA) which included, BaA (benz[a]anthracene: 4 rings), BaP (benzo[a]pyrene: 5 rings), BbF (benzo [b]fluoranthene: 5 rings), BkF (benzo[k]fluoranthene: 5 rings), Chr (chrysene: 4 rings), DbA (dibenz[a,h]anthracene: 5 rings), InP (indeno[1,2,3 - cd] pyrene: 6 rings) and Acp (acenaphthene: 3 rings), Acpy (acenaphthylene: 3 rings), Ant (anthracene: 3 rings), BghiP (benzo[g,h,i]perylene: 6 rings), Flu (fluorene: 3 rings), FluA (fluoranthene: 4 rings), Nap (naphthalene: 2 rings), PhA (phenanthrene: 3 rings) and Pyr (pyrene: 4 rings). Ion source-interface temperature was set at 200°C - 250°C. Internal standards from Sigma Aldrich were used in the analysis and the acquired mass spectra data were then matched against the NIST 2014 library [1] [2]. The mean PAHs concentration in the bottom ashes of each hospital varied broadly from 0.001 mg/kg to 0.4845 mg/kg, and the mean total concentration levels of individual PAHs ranged from 0.0072 mg/kg to 1.171 mg/kg. Low molecular weight PAHs (Phenanthrene, Naphthalene and Fluorene) were predominant in all the hospital wastes whereas Kitale and Narok presented the lowest PAHs concentrations and the lowest number of individual PAHs. Moi/Voi recorded the highest total PAHs concentration at 1.3129 ± 0.0023 mg/kg from a total of 11 PAHs being detected from the bottom ash samples. Narok had only three PAHs being detected at very low concentrations of 0.0041 ± 0.00 mg/kg, 0.0076 ± 0.00 mg/kg and 0.012 ± 0.00 mg/kg for phenanthrene, anthracene and chrysene respectively. This study presents hospital incinerator bottom ash as containing detectable levels of both carcinogenic and non-carcinogenic PAHs. Continued unprotected exposure of hospital workers (waste handlers) to the bottom ash PAHs could be hazardous to their health because of their cumulative effect. Preventive measures e.g. the use of Personal protective equipment (PPE) should be prioritised to minimise direct contact with the bottom ash. The study recommends an upgrade on incinerator technology for efficient combustion processes thus for better pollution control.

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.

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.

ERAD: Enhanced Ransomware Attack Defense System for Healthcare Organizations

Digital integration within healthcare systems exacerbates their vulnerability to sophisticated ransomware threats, leading to severe operational disruptions and data breaches. Current defenses are typically categorized into active and passive measures that struggle to achieve comprehensive threat mitigation and often lack real-time response effectiveness. This paper presents an innovative ransomware defense system, ERAD, designed for healthcare environments that apply the MITRE ATT&CK Matrix to coordinate dynamic, stage-specific countermeasures throughout the ransomware attack lifecycle. By systematically identifying and addressing threats based on indicators of compromise (IOCs), the proposed system proactively disrupts the attack chain before serious damage occurs. Validation is provided through a detailed analysis of a system deployment against LockBit 3.0 ransomware, illustrating significant enhancements in mitigating the impact of the attack, reducing the cost of recovery, and strengthening the cybersecurity framework of healthcare organizations, but also applicable to other non-health sectors of the business world.

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.

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.

The Use of Smart Textiles in the Healthcare Space: Towards an Improvement of the User-Patient Experience

This article explores the role of smart textiles in transforming healthcare environments into spaces that prioritize patient well-being. We will examine the advantages of smart textiles in healthcare settings, such as the real-time monitoring of vital signs through connected clothing. Additionally, we will introduce metadesign as a design approach that considers the interactions between users, healthcare environments, and technologies to create fulfilling experiences. By combining the advanced features of smart textiles with a patient-centered metadesign approach, it becomes possible to create care spaces that cater to patient needs. The objective of this article is to present the integration of metadesign in the design of smart textiles as a process aimed at enhancing the quality of the patient user experience. In this process, we will emphasize the collaborative approach and embrace technological innovation to harness the potential for ongoing improvement and provide users with high-quality experiences. Lastly, we will underscore the significance of adopting a multidimensional approach to evaluate the impact of smart textiles on the patient user experience.

Violence study of healthcare workers and systems in the Caribbean:ViSHWaS-Caribbean study

BACKGROUND Violence against healthcare workers(HCWs)in the Caribbean continues to prevail yet remains underreported.Our aim is to determine the cause,traits,and consequences of violence on HCWs in the Caribbean.AIM To determine the cause,traits,and consequences of violence on HCWs in the Caribbean.METHODS This research adopted an online cross-sectional survey approach,spanning over eight weeks(between June 6th and August 9th,2022).The survey was generated using Research Electronic Data Capture forms and followed a snowballing strategy to contact individuals using emails,social media,text messages,etc.Logistic regression analysis was performed to evaluate the variables that influence violence,including gender,age,years of experience,institution type,and night shift frequency.RESULTS The survey was completed by 225 HCWs.Females comprised 61%.Over 51%of respondents belonged to the 21 to 35 age group.Dominica(n=61),Haiti(n=50),and Grenada(n=31)had the most responses.Most HCWs(49%)worked for government academic institutions,followed by community hospitals(23%).Medical students(32%),followed by attending physicians(22%),and others(16%)comprised the most common cadre of respondents.About 39%of the participants reported experiencing violence themselves,and 18%reported violence against colleague(s).Verbal violence(48%),emotional abuse(24%),and physical misconduct(14%)were the most common types of violence.Nearly 63%of respondents identified patients or their relatives as the most frequent aggressors.Univariate logistic regression analyses demonstrated that female gender(OR=2.08;95%CI:1.16-3.76,P=0.014)and higher frequency of night shifts(OR=2.22;95%CI:1.08-4.58,P=0.030)were associated with significantly higher odds of experiencing violence.More than 50%of HCWs felt less motivated and had decreased job satisfaction post-violent conduct.CONCLUSION A large proportion of HCWS in the Caribbean are exposed to violence,yet the phenomenon remains underreported.As a result,HCWs’job satisfaction has diminished.

Ethical framework for artificial intelligence in healthcare research:A path to integrity

The integration of Artificial Intelligence(AI)into healthcare research promises unprecedented advancements in medical diagnostics,treatment personalization,and patient care management.However,these innovations also bring forth significant ethical challenges that must be addressed to maintain public trust,ensure patient safety,and uphold data integrity.This article sets out to introduce a detailed framework designed to steer governance and offer a systematic method for assuring that AI applications in healthcare research are developed and executed with integrity and adherence to medical research ethics.

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.

Method“Monte Carlo”in healthcare

In public health,simulation modeling stands as an invaluable asset,enabling the evaluation of new systems without their physical implementation,experimentation with existing systems without operational adjustments,and testing system limits without real-world repercussions.In simulation modeling,the Monte Carlo method emerges as a powerful yet underutilized tool.Although the Monte Carlo method has not yet gained widespread prominence in healthcare,its technological capabilities hold promise for substantial cost reduction and risk mitigation.In this review article,we aimed to explore the transformative potential of the Monte Carlo method in healthcare contexts.We underscore the significance of experiential insights derived from simulated experimentation,especially in resource-constrained scenarios where time,financial constraints,and limited resources necessitate innovative and efficient approaches.As public health faces increasing challenges,incorporating the Monte Carlo method presents an opportunity for enhanced system construction,analysis,and evaluation.

Revolutionizing Healthcare—The Integration of Virtual Worlds, AR, and Metaverse Technology

This paper explores the transformative impact of virtual worlds, augmented reality (AR), and the metaverse in the healthcare sector. It delves into the ways these technologies are reshaping patient care, medical education, and research, while also addressing the challenges and opportunities they present. The paper highlights the potential benefits of these technologies and emphasizes the need for comprehensive regulatory frameworks and ethical guidelines to ensure responsible integration. Finally it outlines their transformative impact and discusses the challenges and opportunities they present for the future of healthcare provision.

Early versus delayed necrosectomy in pancreatic necrosis:A population-based cohort study on readmission,healthcare utilization,and in-hospital mortality

BACKGROUND Acute necrotizing pancreatitis is a severe and life-threatening condition.It poses a considerable challenge for clinicians due to its complex nature and the high risk of complications.Several minimally invasive and open necrosectomy procedures have been developed.Despite advancements in treatment modalities,the optimal timing to perform necrosectomy lacks consensus.AIM To evaluate the impact of necrosectomy timing on patients with pancreatic necrosis in the United States.METHODS A national retrospective cohort study was conducted using the 2016-2019 Nationwide Readmissions Database.Patients with non-elective admissions for pancreatic necrosis were identified.The participants were divided into two groups based on the necrosectomy timing:The early group received intervention within 48 hours,whereas the delayed group underwent the procedure after 48 hours.The various intervention techniques included endoscopic,percutaneous,or surgical necrosectomy.The major outcomes of interest were 30-day readmission rates,healthcare utilization,and inpatient mortality.RESULTS A total of 1309 patients with pancreatic necrosis were included.After propensity score matching,349 cases treated with early necrosectomy were matched to 375 controls who received delayed intervention.The early cohort had a 30-day readmission rate of 8.6% compared to 4.8%in the delayed cohort(P=0.040).Early necrosectomy had lower rates of mechanical ventilation(2.9%vs 10.9%,P<0.001),septic shock(8%vs 19.5%,P<0.001),and in-hospital mortality(1.1%vs 4.3%,P=0.01).Patients in the early intervention group incurred lower healthcare costs,with median total charges of $52202 compared to$147418 in the delayed group.Participants in the early cohort also had a relatively shorter median length of stay(6 vs 16 days,P<0.001).The timing of necrosectomy did not significantly influence the risk of 30-day readmission,with a hazard ratio of 0.56(95%confidence interval:0.31-1.02,P=0.06).CONCLUSION Our findings show that early necrosectomy is associated with better clinical outcomes and lower healthcare costs.Delayed intervention does not significantly alter the risk of 30-day readmission.

Trends and Challenges in the Application of Artificial Intelligence in the Healthcare Sector

The integration of Artificial Intelligence(AI)in healthcare is setting the stage for a transformative shift in how patient care is delivered,research is conducted,and operations are managed.Propelled by the exponential growth of data,computational advancements,and AI innovations,this integration promises a new era of precision medicine with highly personalized and effective treatment strategies.However,the journey towards seamlessly embedding AI into healthcare systems is complex,marked by challenges such as ensuring data privacy and security,addressing ethical considerations,and overcoming barriers to technology integration and adoption.This paper delves into the current trends driving AI in healthcare,including machine learning,natural language processing,robotics,and the Internet of Medical Things,while also tackling the significant challenges these innovations present.It further explores strategies for navigating these obstacles,aiming to pave the way for the successful adoption of AI technologies that enhance healthcare delivery and patient outcomes.In doing so,this work underscores the critical role of collaborative efforts among stakeholders and the need for robust frameworks to ensure AI's ethical,secure,and effective integration into healthcare.

Analysis of the Application Effect of Structured Healthcare Education in Brittle Diabetic Patients

Objective:To explore the application effect of structured healthcare education in patients with brittle diabetes mellitus.Methods:188 brittle diabetic patients admitted to our hospital from May 2021 to December 2023 were selected as the study subjects,and were divided into the control group(n=94)and the observation group(n=94)according to the random number table method.The control group used conventional nursing intervention and the observation group used structured healthcare education.The general information,glycemic indexes,self-efficacy,compliance,and nursing satisfaction of patients in the two groups were observed.Results:There was no statistical significance in the basic information of the two groups of patients(P>0.05);after the intervention,the fasting plasma glucose,2-hour postprandial blood glucose,and HbA1c of the patients in the observation group were lower than those of the control group(P<0.001);after the intervention,the self-efficacy scores of the patients in the two groups increased,and the scores of the observation group were significantly higher than those of the control group(P<0.001);the total adherence rate of the patients in the observation group(90/95.75%)was significantly higher than that of the control group(80/90.10%)(χ^(2)=6.144,P<0.05);and the total satisfaction rate of patients in the observation group(92/97.87%)was significantly higher than that of the control group(78/82.98%)(χ^(2)=12.042,P<0.05).Conclusion:In patients with brittle diabetes mellitus,structured healthcare education can effectively control patients’blood glucose levels,improve patients’self-efficacy and adherence,and enhance patient satisfaction.

Knowledge and associated factors of healthcare workers on measles vaccine and cold chain management at health institutions in Gondar,Ethiopia

Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar City Administration public health institutions among 165 healthcare workers.Data were collected using a structured questionnaire.In addition,an on-spot observation checklist was used to assess the availability,status and management of the cold chain.A logistic regression model was used to assess the relationship between the outcome and predictor variables.Crude and adjusted odds ratios were calculated with 95%confidence intervals.Results:Overall,87(52.7%;95%CI 44.8%-60.5%)of the healthcare workers had unsatisfactory knowledge regarding the measles vaccine and its cold chain management.One hundred thirty-six(82.4%)healthcare workers correctly mentioned the recommended range of temperature(2-8℃)for measles vaccine storage.Healthcare workers aged 18-29 years(P=0.001)and 30-44 years(P=0.014)were observed as determinants of unsatisfactory knowledge on the measles vaccine and its cold chain management.One hundred and five(63.6%)of the healthcare workers did not correctly mention the type of measles vaccine used in routine immunization.More than one-third(36.4%)of the healthcare workers perceived that the measles vaccine is not safe and could cause measles.Conclusions:More than half of the healthcare workers in the study area had unsatisfactory knowledge on the measles vaccine and its cold chain management.It is necessary to provide technical support and in-service training for healthcare workers to ensure optimal immunization effectiveness.