Healthcare Management Research Articles

Strategic changes and challenges of private dental clinics and practitioners in Israel: adapting to a competitive environment

BackgroundIn the 21st century’s digital age, health organizations face challenges from reduced public healthcare spending, creating a competitive market that impacts healthcare management. The struggle is hardest for small organizations, such as private clinics. Competing under unfavorable conditions, these small businesses must run as independent, profitable units in a government-regulated market where they are subject to numerous restrictions yet receive no financial support. In a world of increasing dependence on digital technologies, these small businesses must adopt competitive business models and be adaptive and flexible in embracing change.MethodsThis qualitative study is based on a thematic qualitative analysis of 20 in-depth, 45-minute-long interviews with dentists and owners of private dental clinics in various specialties. The study employs the strategic change model to examine how dentists who run private dental clinics implement new strategies and technologies to adjust to changes and create a competitive edge.ResultsSix main categories emerged from the analysis of the interviews: changes in the organization’s environment; instituting and assimilating changes; obstacles in embracing change; added value obtained from embracing the change; quality of care and service; and cost-benefit considerations. The categories were map and evaluated in light of the strategic change model. The analysis indicated that digital strategies have been only partially adopted, suggesting an absence of a cohesive, long-term strategic vision for the organizations.ConclusionsThe study explored the actions, perceptions, and challenges of adapting to a competitive digital market in dental private clinics. Based on these insights, recommendations have been provided for global change management, aiming for a sustainable and stable healthcare system that benefits the broader community.

CDSS Adoption and the Role of Artificial Intelligence in Saudi Arabian Primary Healthcare

The integration of advanced Clinical Decision Support Systems (CDSS) is crucial in healthcare, especially in developing countries. However, the challenges to CDSS implementation in these settings are often overlooked, leading to implementation gaps. This study investigates the adoption of CDSS in primary healthcare facilities in Saudi Arabia through in-depth interviews with general practitioners. The advancement of primary healthcare systems in developing nations necessitates appropriate technological solutions to address issues such as equitable access, service quality, and patient satisfaction. The adoption of CDSS can potentially improve patient satisfaction and enhance medical consultations. Artificial intelligence -driven technologies like the Velys robot are transforming the medical field by assisting doctors in surgical procedures. In healthcare, Artificial intelligence (AI) can address issues related to complex diseases, aging populations, and workforce shortages. AI solutions can streamline patient flow, improve care coordination, and support personalized healthcare management. By assessing the impact of AI on patient care, healthcare efficiency, and effectiveness can be enhanced. This research highlights the challenges and opportunities in CDSS adoption in developing countries, with implications for healthcare improvement in Saudi Arabia.

Assessing the Role of Nutrition Knowledge in Shaping Dietary Practices Among Secondary Level School Students: Implications for Healthcare Management

Introduction: This study investigates the nutrition knowledge and dietary practices among secondary-level school students, a demographic prone to unhealthy habits such as junk food consumption, meal skipping for weight loss, and non-compliance with balanced diets. These behaviors increase the risk of non-communicable diseases like obesity and diabetes in adolescents. This study has aims to examine the nutrition knowledge and dietary practices of secondary-level school students, a demographic that is increasingly susceptible to unhealthy eating habits. Specifically, it focuses on behaviors such as junk food consumption, meal skipping for weight loss, and the neglect of balanced diets. These specific practices contribute to a heightened risk of non-communicable diseases, including obesity and diabetes, among adolescents. Methodology: The quantitative research approach was employed an exploratory design, analyzing data from 121 students in Kathmandu valley using a close-ended questionnaire during 7th April to 28th May 2024. The systematic sampling technique was used, and respondents were selected by using a lottery system. As a result, Grade 11 was picked up randomly. Descriptive analyses were made to describe the practices of the students. Result (s): Study indicates that out of 121 respondents, about near to half (48.8%) were male and little more than half (51.2%) were female. Similarly, there were no significant relationship exists between nutrition knowledge and dietary practices among the students. Likewise,, independent variables such as age, gender, and religion do not significantly influence either nutrition knowledge or dietary practices. Despite the students' understanding of nutrition, this knowledge does not translate into healthier eating behaviors. Conclusion and recommendation: The study reveals that there are notable gaps in their knowledge, particularly regarding the energy content of fat-free foods, the role of dietary fiber, and the impact of empty-calorie foods and meal-skipping on health. Likewise, findings highlight the disconnect between what students know about nutrition and how they apply it in their daily lives, suggesting the need for more effective interventions to encourage healthier dietary practices. Moreover, targeted nutritional education programs at school could address these gaps in knowledge and encourage healthier dietary practices.

Technology And Innovation In HR

In India, the high cost of medical education, particularly for an MBBS degree, poses significant challenges for new doctors, who often find that their qualifications do not justify the Expenses incurred. This financial strain is exacerbated when these highly trained professionals are employed for routine tasks such as insurance treatment and billing verification, roles that are not commercially viable given their level of education and potential. As a cost-effective measure, many insurance companies and third-party administrators (TPAs) opt to employ doctors with BHMS or BMS degrees for these tasks. However, these professionals are often underqualified for the complexities of medical verification, leading to inefficiencies and errors in treatment validation and anomaly detection. The situation also adversely affects programs like the Ex-Servicemen Contributory Health Scheme (ECHS), where the need for accurate and efficient cashless medical services is critical. Delays and inaccuracies in medical verification can complicate the provision of timely healthcare to veterans and their families, underscoring the need for improvement in these processes. This paper proposes the implementation of an Artificial Intelligence (AI) ecosystem designed to function as highly qualified medical agents. This AI system will be capable of independently verifying diagnoses, treatment plans, and medical reports for accuracy while also identifying procedural anomalies. The primary objective is to augment the capabilities of the current workforce, enhancing overall efficiency and effectiveness in medical management. By deploying AI medical agents, the project aims to not only address the gap in qualification and task complexity but also improve the accuracy and reliability of medical administrative processes. This initiative promises to revolutionize the way medical verifications are handled, providing a sustainable, scalable solution to a pressing healthcare management issue in India.

Development and validation of a machine learning model integrated with the clinical workflow for inpatient discharge date prediction.

Discharge date prediction plays a crucial role in healthcare management, enabling efficient resource allocation and patient care planning. Accurate estimation of the discharge date can optimize hospital operations and facilitate better patient outcomes. In this study, we employed a systematic approach to develop a discharge date prediction model. We collaborated closely with clinical experts to identify relevant data elements that contribute to the prediction accuracy. Feature engineering was used to extract predictive features from both structured and unstructured data sources. XGBoost, a powerful machine learning algorithm, was employed for the prediction task. Furthermore, the developed model was seamlessly integrated into a widely used Electronic Medical Record (EMR) system, ensuring practical usability. The model achieved a performance surpassing baseline estimates by up to 35.68% in the F1-score. Post-deployment, the model demonstrated operational value by aligning with MS GMLOS and contributing to an 18.96% reduction in excess hospital days. Our findings highlight the effectiveness and potential value of the developed discharge date prediction model in clinical practice. By improving the accuracy of discharge date estimations, the model has the potential to enhance healthcare resource management and patient care planning. Additional research endeavors should prioritize the evaluation of the model's long-term applicability across diverse scenarios and the comprehensive analysis of its influence on patient outcomes.

Optimizing data management in healthcare: Lessons from clinical trials and beyond

This study comprehensively explores the optimization of data management in healthcare by drawing on lessons learned from clinical trials and extending these insights to broader healthcare contexts. The study's purpose is to examine the challenges, strategies, and technological innovations that are essential for effective data management in modern healthcare systems. Utilizing a detailed literature review, the study identifies key methodologies from clinical trials that have successfully enhanced data integrity, accuracy, and security, such as the implementation of electronic data capture (EDC) systems and rigorous data governance frameworks. These methods are crucial in managing the increasing volume and complexity of healthcare data. The study's findings reveal that big data analytics and artificial intelligence (AI) are pivotal in transforming healthcare data management, enabling personalized and precise patient care. However, these technologies also introduce challenges related to data security, interoperability, and ethical considerations, particularly concerning patient privacy and the transparency of AI-driven decision-making processes. In conclusion, the study emphasizes the need for continuous innovation and improvement in healthcare data management practices. Recommendations include the integration of blockchain technology, cloud computing, and real-time data from wearable devices to address existing challenges and enhance data management capabilities. The study advocates for a balanced approach that prioritizes patient privacy, ethical governance, and regulatory compliance to ensure that technological advancements contribute positively to patient outcomes and healthcare efficiency.

Determining direct, indirect healthcare and social costs for diabetic retinopathy management: a systematic review

IntroductionDiabetic retinopathy (DR) is a rapidly growing global public health threat; it affects 1 in 3 people with diabetes and is still the leading cause of blindness among the working-age population. The management of diabetic retinopathy is becoming more advanced and effective but is highly expensive compared to other ocular diseases.AimTo report direct medical, indirect medical, and nonmedical costs of diabetic retinopathy in developed and developing countries through a systematic review.MethodsRelated articles published in the PubMed, Google Scholar, and EMBASE electronic databases from 1985 to 2022 were identified using the keywords direct medical and indirect medical and social costs of diabetic retinopathy. However, previous systematic reviews, abstracts, and case reports were excluded.ResultsThirteen articles were eligible for assessing the economic burden of diabetes management and its complications. Our analysis revealed that increasing prevalence and severity of diabetic retinopathy (DR) are associated with higher direct and indirect healthcare expenditures. The impact of DR on working-age adults, leading to irreversible blindness in advanced stages, underscores the urgent need for cost-effective prevention and management strategies.DiscussionThis study systematically reviewed the direct medical, indirect medical, and nonmedical costs of DR in developed and developing countries. Our findings highlight the significant economic burden of DR, emphasizing the importance of implementing effective prevention and management measures to alleviate costs and enhance patient outcomes.ConclusionThe substantial financial burden of DR necessitates a re-evaluation of current screening and management programs. Revision of these programs is crucial to improve quality of care, reduce costs, and ultimately achieve Sustainable Development Goal 3, which aims to ensure good health and well-being for all.

Utilization of Smart Healthcare for Gestational Diabetes Mellitus Management

Gestational diabetes mellitus (GDM) poses significant health risks to both mothers and newborns, requiring rigorous self-management and frequent medical consultations. Advances in information and communications technology (ICT) have shown promising results in reducing the number of in-person visits for GDM management. ICT enhances patient self-care engagement, with some studies reporting reductions in average blood glucose and HbA1c levels. ICT for GDM management has demonstrated benefits such as fewer in-person visits, improved adherence to self-monitoring of blood glucose, increased global user satisfaction, and maintenance of blood glucose control and perinatal outcomes. Common barriers to ICT for GDM include technological literacy, inadequate education, limited technical support, the additional burden of non-customized applications, and restricted interoperability. Further research is needed on the impact of technology on GDM management to optimize digital health solutions.

Adaptive Mobility and Reliability-based Routing Protocol for Smart Healthcare Management Systems in Vehicular Ad‐hoc Networks

Ambulances enable digital and widespread remote care for older people in the healthcare sector. The Vehicular Ad Hoc Network (VANET) is critical for allowing the digital and Intelligent Transportation System (ITS) for the smart healthcare world. Enhancing the dependability and flexibility of an adaptive routing algorithm improves vehicular communications' Quality of Service (QoS) effectiveness. An essential concern in vehicular technology is enabling drivers to make reliable judgments. Developing an effective routing system that ensures a suitable QoS takes time and effort. The vehicular network ecosystem is characterized by limited mobility, fast vehicle speeds, and constantly changing configurations. This study makes four distinct contributions. Initially, it presents adaptive routing protocols, capable of recognizing changes in the network environment, aware of the mobility of nodes, and dependable in delivering data packets. The experimental configuration is implemented inside a discrete Network Simulator (NS-3) scenario to enhance the QoS. Mobility-aware routing algorithms can provide ITS with satisfactory mobility, vital dependability, high Packet Delivery Ratio (PDR), high throughput, and low End-to-End (E2E) Delay. The proposed method has an average throughput of 664.3 kbps, a delay of 151.3 ms, and a packet delivery ratio (PDR) of 94.23%.

Infants Sucking Pattern Identification Using Machine-Learned Computational Modeling

Abstract Breastfeeding involves a complex coordination of swallowing, breathing, and sucking, with the infant's sucking proficiency being crucial for adequate nutrient intake. However, real-time assessment of milk intake is difficult, and issues with sucking often become apparent after the infant shows signs of nutrient deficiency. Traditional assessments by clinicians rely on the expertise and subjective judgment of healthcare professionals, enabling personalized evaluations. In this research, we introduce a novel approach to identifying sucking patterns by leveraging data collected from infants during breastfeeding sessions. This method utilizes artificial nipple-based sensors to capture the tongue forces exerted by infants, generating valuable clinical data. In the analysis of the collected time-series data, we applied machine-learned computational modeling (MLCM) algorithms to extract pertinent features and identify distinctive sucking patterns. The best-performing model demonstrated an accuracy of 90%, an 80% recall score, a perfect 100% precision score, a 0.90 f1-score, and an area under the curve (AUC) of 0.80. The proposed classification system has the potential to serve as a reliable decision-support tool for clinicians, offering valuable insights into infants' sucking behaviors. By integrating machine learning (ML)-based computational modeling into clinical practice, we aim to enhance the early identification of unhealthy sucking patterns, allowing for timely interventions and pro-active healthcare management.

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

This study endeavors to enhance the macro-ergonomics of pharmaceutical supply chains by introducing an innovative hybrid AI methodology, incorporating fuzzy data envelopment analysis (FDEA). A comprehensive case study is conducted to evaluate the efficiency of the pharmaceutical supply chain, focusing on macro-ergonomic work system assessment. This evaluation aims to identify design flaws contributing to communication delays between physicians and patients. The proposed integrated approach utilizes a hybrid AI framework, specifically FDEA, to accurately measure macro-ergonomic influences on the healthcare supply chain under uncertain conditions. The case study involves a prominent urban outpatient medical facility with 20 clinics, selecting 20 Decision-Making Units for a holistic perspective. Results uncover factors causing delays, emphasizing weak or absent feedback structures as critical elements affecting the healthcare supply chain’s effectiveness. In conclusion, the study recommends modifications to optimize the pharmaceutical supply chain, enhancing overall healthcare efficiency. The findings provide valuable insights for healthcare management, underscoring the crucial role of advanced AI methodologies in addressing complex challenges within healthcare supply chains.

Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption

Factors Influencing ICT Usage in the Healthcare Management System at Government Hospitals in Tanzania: A Case of Kilwa Road Police Hospital

This study assesses the factors influencing Information and Communication Technology (ICT) usage in healthcare management systems at government hospitals in Tanzania, with a specific focus on Kilwa Road Police Hospital. The study aims to: examine the extent to which ICT infrastructure availability influences ICT usage in healthcare management systems; determine the influence of staff awareness on ICT usage at Kilwa Road Police Hospital; and investigate the influence of management support on ICT usage in healthcare management systems at the hospital. The study was guided by the Technology Acceptance Model (TAM) and Resource-Based Theory (RBT) and employed a descriptive research design. Systematic sampling was used to select 162 respondents from a target population of 272 staff members. Data was collected through questionnaires and analysed using both descriptive and inferential statistics. Descriptive statistics included frequencies and percentages, while inferential statistics utilized multiple regression analysis. The findings reveal that the availability of necessary hardware and software is significantly and positively related to the effective utilization of ICT in healthcare management systems at Kilwa Road Police Hospital. Additionally, the level of staff awareness was identified as a crucial factor influencing ICT usage. The relationship between hardware and software availability and ICT utilization was statistically significant (p < 0.05), highlighting its importance in enhancing healthcare management systems. Furthermore, management support was found to play a significant role in ICT usage for healthcare management at Kilwa Road Police Hospital. The study concludes that ICT infrastructure, staff awareness, and management support are critical factors influencing ICT usage in healthcare management systems at Kilwa Road Police Hospital. It recommends that the hospital's management ensure the continuous availability of ICT infrastructure to sustain effective healthcare management systems.

Phytochemical Investigations and In-vitro Antioxidant Analysis of Passiflora incarnata L.

The present study aims to investigate the presence of the phytochemical and in-vitro antioxidant study of hydroalcoholic (HA) and ethyl acetate (EA) preparations of Passiflora incarnata L. leaves. The hydroalcoholic (HA) and ethyl acetate (EA) extracts showed strong antioxidant activity when examined in a laboratory setting using DPPH, H2O2, NO, reducing power, total phenol, and TOC assays. Also compounds like saponin, alkaloids, flavonoids, and phenolics were all identified through phytochemical testing. In addition to expanding our understanding of the pharmacological characteristics of P. incarnata L., the results of this study have the potential to change the way medicinal botanicals are viewed forever. Nature’s treasure trove, concealed within the leaves of P. incarnata L., offers a pharmacological narrative that transcends traditional boundaries, suggesting new dimensions for antioxidant-based therapeutic strategies for the development of novel botanical interventions in preventive healthcare and disease management.

Do not dismiss the commitment to the humanisation of medicine

News of the new achievements and successes of cutting-edge medicine are taking a back seat, or being forgotten, to the day-to-day problems of healthcare management outside and inside hospitals.

The role of innovation in healthcare management for long-term progress: a systematic review

Background: The crucial role of innovation in healthcare management today as a driving force for long-term progress is more important than ever. Effective healthcare management is pivotal in achieving optimal health outcomes and operational excellence. Innovation in healthcare management refers to the implementation of contemporary or signif¬icantly improved methods, practices, or technologies that lead to better health outcomes, greater efficiency, and ame¬liorated patient experiences. Aim: The aim of this systematic review was to identify and analyze innovative practices in healthcare management that contribute to long-term progress. Methodology: An electronic search was conducted and databases included PubMed, Scopus, and the Cochrane Library, so as to examine the relevant material between January 2024 and May 2024. Inclusion and exclusion criteria were implemented. “Healthcare, management, innova¬tion, AI, progress, long-term” was the combination of indexing words that was used. Results: Based on the undertaken search, a total of ten studies met the eligibility criteria as for the specific systematic review. Regarding the analysis of these ten studies, two were cross-sectional studies, two were qualitative studies, one was a qualitative interview study, one was a qualitative research through face-to-face survey, one was a quantitative study, one was a descriptive survey design, one was a study based on longitudinal data, and one was a study employing a multimethod qualitative approach. Conclusion: The role of innovation in healthcare management for long-term progress is pretty valuable. By offering solid leadership, a supportive culture, and a patient-centered approach, managers can promote innovation in healthcare organizations effectively and sustainably. Hence, there could be better patient outcomes and more moti¬vated and engaged employees as a result of the effective implementation of innovation in healthcare management.

Evaluation of a training programme on the management of infectious and sharp healthcare waste in Tunisia

IntroductionThe management of healthcare waste is a crucial issue for public health and the environment. To minimise risks, it is essential to ensure that the management of healthcare waste is...

Development of a maturity model for demand and capacity management in healthcare

BackgroundThe aim of this paper is to develop a maturity model (MM) for demand and capacity management (DCM) processes in healthcare settings, which yields opportunities for organisations to diagnose their planning and production processes, identify gaps in their operations and evaluate improvements.MethodsInformed by existing DCM maturity frameworks, qualitative research methods were used to develop the MM, including major adaptations and additions in the healthcare context. The development phases for maturity assessment models proposed by de Bruin et al. were used as a structure for the research procedure: (1) determination of scope, (2) design of a conceptual MM, (3) adjustments and population of the MM to the specific context and (4) test of construct and validity. An embedded single-case study was conducted for the latter two - four units divided into two hospitals with specialised outpatient care introducing a structured DCM work process. Data was collected through interviews, observations, field notes and document studies. Thematic analyses were carried out using a systematic combination of deductive and inductive analyses - an abductive approach - with the MM progressing with incremental modifications.ResultsWe propose a five-stage MM with six categories for assessing healthcare DCM determined in relation to patient flows (vertical alignment) and organisational levels (horizontal alignment). Our application of this model to our specific case indicates its usefulness in evaluating DCM maturity. Specifically, it reveals that transitioning from service activities to a holistic focus on patient flows during the planning process is necessary to progress to more advanced stages.ConclusionIn this paper, a model for assessing healthcare DCM and for creating roadmaps for improvements towards more mature levels has been developed and tested. To refine and finalise the model, we propose further evaluations of its usefulness and validity by including more contextual differences in patient demand and supply prerequisites.

A robust algorithm for authenticated health data access via blockchain and cloud computing.

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting.

AI and Human-Centric Approach in Smart Cities Management: Case Studies from Silesian and Lesser Poland Voivodships

The presented paper examines the integration of Artificial Intelligence (AI) in the management of smart cities, focusing on the Silesian and Lesser Poland Voivodships in Poland. This research addresses a notable gap in the analysis of regional AI strategies within urban management, providing a comparative analysis of AI implementation in these two distinct regions. The Silesian Voivodship, with its emphasis on traditional industries such as manufacturing and energy, contrasts with the broader approach of the Lesser Poland Voivodship, which includes applications in life sciences and ICT. The paper explores how AI technologies enhance urban efficiency, sustainability, and livability through practical applications in traffic management, healthcare, energy efficiency, and environmental management. It highlights the importance of a human-centric approach in smart city development, emphasizing inclusivity, transparency, and ethical considerations. The paper also delves into the socio-technical dynamics of AI deployment, illustrating how these technologies can transform urban environments while ensuring that the benefits are equitably distributed and that urban developments are sustainable and resilient. By analyzing specific case studies, the authors aim to provide empirical evidence and insights that contribute to the academic and practical understanding of AI’s role in smart cities, ultimately advocating for the design of AI applications that prioritize human well-being and environmental health.