Efficient Health Care System Research Articles

Transforming Patient Outcomes: Cutting-Edge Applications of AI and ML in Predictive Healthcare

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has emerged as a transformative force, offering unprecedented potential to enhance patient outcomes. This paper explores the cutting-edge applications of AI and ML in predictive healthcare, focusing on how these technologies are revolutionizing early detection, personalized treatment, and patient management. Through advanced algorithms and data-driven insights, AI and ML are enabling healthcare providers to predict disease progression, optimize therapeutic interventions, and improve patient monitoring in real-time. Key applications discussed include predictive modeling for chronic disease management, AI-powered diagnostic tools, and the use of machine learning in precision medicine. The paper also addresses the challenges associated with these technologies, including data privacy concerns, algorithmic bias, and the need for robust validation. By examining current case studies and future trends, this work aims to highlight the transformative impact of AI and ML on patient care, ultimately driving a shift toward more proactive, tailored, and efficient healthcare systems.

Harnessing the Butterfly Effect: Building an Efficient Healthcare System in Sri Lanka through Research and Clinical Audits

No abstract available

The impact of health insurance integration on patient choice of providers in China

ABSTRACT This paper utilizes a nationwide integration reform of two public health insurance programmes in China to examine the impact of health insurance integration on patient choice of medical providers. Using a dataset that combines longitudinal survey data and government regulations data, we find that the integration leads to an increasing probability of outpatients in nearby primary care facilities and decreased outpatient visits to public healthcare providers. Moreover, the effects of integration vary with patient demographics, the type of healthcare, and the model of insurance management. No significant evidence suggests that the integration affects health behaviours or health status in the short term. These findings provide empirical evidence on the effects of insurance integration and policy implications for building an efficient hierarchical healthcare system.

Home Healthcare Staffing, Routing, and Scheduling Problem With Multiple Shifts and Emergency Considerations

ABSTRACTEffective planning of human resources is critical in designing an efficient home healthcare system. In this study, we present a novel home healthcare staffing, routing, and scheduling problem inspired by a real‐world application. The proposed problem addresses a set of patients, with varying daily visit requirements, being served by a set of caregivers with different qualification levels over a multi‐day multi‐shift planning horizon. The problem aims to minimize the number of extra shifts for caregivers, maximize the allocation of caregivers to emergencies, and minimize the sum of route durations over the planning horizon. These objectives are optimized hierarchically while considering a set of restrictions, including time windows, skill matching, synchronization, care continuity, and labor regulations. To tackle the problem, we introduce a mixed‐integer linear programming model. The model is then extended and two sets of valid inequalities are incorporated to enhance its tightness. Computational experiments are conducted on a set of 20 instances. The results highlight the efficiency of the proposed extension in increasing both the number of instances that can be solved to optimality and the number of instances for which a feasible solution is found.

Hospital Medicare Spending per Beneficiary: A Longitudinal Study

SUMMARY Goal While studies have examined quality and health outcomes related to the Centers for Medicare & Medicaid Services’ (CMS’s) Hospital Value-Based Purchasing (HVBP) Program, a significant gap exists in the literature regarding the relationship between pay-for-performance initiatives and hospital financial performance in the program’s Efficiency and Cost Reduction domain. This study examined the association between hospitals’ cost inefficiency and participation in the HVBP Program by estimating the probability and magnitude of improvement or achievement in the program’s Efficiency and Cost Reduction domain. Methods The 2014–2019 Efficiency and Cost Reduction domain data were obtained from CMS and merged with the American Hospital Association’s Annual Survey Database. We conducted a zero-inflated negative binomial regression to account for the excessive number of zeros in the data. Principal Findings The negative binomial component of the model assessed the magnitude of the impact on the Efficiency and Cost Reduction improvement from each covariate, while the zero-inflated component assessed the odds of being in the “certain-zero” group, meaning no chance to improve or achieve. Hospital ownership, location, size, safety-net status, percentage of Medicare patients, and the number of registered nurses per bed were statistically significant. Additionally, the Herfindahl-Hirschman Index and teaching status significantly influenced efficiency performance. Practical Applications Changes in hospital performance in this domain exist and have evolved. Hospitals might be at a disadvantage with this performance measure because of their inherent organizational structure. The HVBP Program may not provide clear enough direction or actionable incentive to address the needs of stakeholders influenced primarily by measures of Medicare spending per beneficiary. This study’s findings hold practical value for policymakers, healthcare administrators, and researchers. Policymakers can use this information to tailor future pay-for-performance programs and effectively allocate resources. Healthcare administrators can identify areas for improvement and benchmark their performance against similar institutions. Researchers can explore the program’s long-term sustainability and investigate cost drivers within different hospital groups. By understanding the link between hospital characteristics and cost reduction, all stakeholders can contribute to a more efficient healthcare system.

7.A. Skills building seminar: Is Behavioural Science Relevant for Public Health? A Quintuple Helix Lens

Abstract Background On 29 May 2023, the World Health Assembly adopted the first-ever global Behavioural Sciences for Better Health resolution (WHA76.7). This resolution requests the World Health Organization (WHO) to mainstream and support behavioural sciences in public health across the WHO and within Member States; including enhancing awareness, knowledge, and evidence and providing technical assistance, normative guidance, and capacity-building. But is behavioural science really relevant for public health? Why and how it can help us to improve people’s health? Rationale This workshop seeks to delve into the application of behavioural science in public health within the context of the quintuple helix framework - academia, industry, government, civil society, and the broader environment. Through collaborating across sectors, stakeholders can leverage behavioural science to design more effective strategies for promoting positive health behaviour change, preventing diseases, and addressing public health challenges. This workshop will feature discussions and case studies in various public health domains. Aim The main aim of this skills-building seminar is to explore the synergies between behavioural science and the quintuple helix, examining how these collaborations can drive innovation, enhance policy implementation, and foster sustainable health outcomes. By critically examining these intersections, we seek to stimulate dialogue, foster collaborations, and inspire innovative approaches for addressing complex health challenges in our society. Workshop structure This workshop will consist of three parts: - It will start with a short scene-setting presentation by Iveta Nagyova, EUPHA president, highlighting the innovation potential of behavioural science to improve people’s health (10 mins). - In the second part (30 mins), the panellists will discuss concrete examples of interconnections of behavioural science and public health within the quintuple helix framework. 1. Susan Michie will share her experience of interconnections between academia and government as a member of the UK SAGE - Scientific Advisory Group for Emergencies. 2. Simon Bacon will discuss the behavioural lens applied to the commercial determinant of health. 3. Tiina Likki will share WHO/Europe’s experience with developing and implementing the European regional action framework for behavioural and cultural insights for health, 2022-2027, 4. Kim Lavoie will speak about community involvement and social innovation within the context of more efficient healthcare systems. 5. Stefano Capalongo will show us examples of salutogenic design and architecture, applying neurobiobehavioural mechanisms. - The third part (20 mins) will be dedicated to a facilitated discussion with the audience to explore attendees’ opinions regarding the challenges and opportunities of applying behavioural sciences to improve people’s health and well-being, using the quintuple helix framework. Key messages • Integrating behavioural science within the quintuple helix framework cultivates partnerships advancing effective, inclusive and lasting public health initiatives. • Fostering cross-sector collaboration establishes an ecosystem driving innovative, equitable and sustainable solutions in public health. Speakers/Panelists Susan Michie University College London, London, UK Simon Bacon Concordia University, Montréal, Canada Kim Lavoie UQAM, Department of Psychology, Montreal, Canada Tiina Likki WHO/Europe, Hellerup, Denmark Stefano Capolongo Politecnico di Milano, Milan, Italy

Increased access to pediatric specialist healthcare using eConsult: A retrospective observational cohort and case–control study

ABSTRACT Objectives Primary care practitioners (PCPs) report that using the Champlain BASE™ eConsult service (eConsult) averts one-third of face-to-face (FTF) specialist referrals, however, there are concerns about duplication of services and adverse patient outcomes. Following an eConsult, we evaluated patient healthcare utilization and associated treatment costs. Methods Retrospective cohort study (2014 to 2018) of patients (<18 years old) for whom an eConsult visit averted a FTF specialist referral. Patients were linked to provincial health administrative databases and hospital electronic medical records for healthcare use for the same diagnosis and specialty for the 18 months following the eConsult. Concurrently, a retrospective case–control study compared utilization and costs between an eConsult versus a matched FTF visit for the same diagnosis. We also assessed PCP satisfaction. Results In follow-up, <5% of the study cohort of eConsult patients (n = 242) later accessed the healthcare system for the identical diagnosis and specialty type. FTF visits generate more frequent outpatient visits (12.6 times more [95% CI: 2.28 to 69.66, P = 0.002]) and higher costs compared to eConsult visits. There were no hospital admissions or deaths in patients with eConsult. PCPs (98%) described eConsult as an excellent service. Conclusions Using eConsult is associated with <5% of patients subsequently having a FTF visit for the same reason. Matched FTF visits generated more healthcare utilization and higher costs compared with eConsult. eConsult in pediatrics is safe and can minimize FTF specialist visits in elective cases and increase capacity, towards a more efficient and cost-effective healthcare system.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Roles of different organizations in implementing patient-reported measures in routine maternity care in Finland

BackgroundThe integration of patient-centered care (PCC) and value-based healthcare (VBHC) principles, emphasizing personalized, responsive care and cost efficiency, is crucial in modern healthcare. Despite advocation from the International Consortium for Health Outcomes Measurement (ICHOM) for the global adoption of these principles through patient-reported measures (PRMs), their implementation, especially the pregnancy and childbirth (PCB) set, remains limited in maternity care. This study focuses on understanding the optimal organizational entity for integrating standard ICHOM-PCB-PRMs into routine maternity care in Finland. It aims to clarify the distribution of tasks among stakeholders and gather Finnish maternity healthcare professionals’ perspectives on organizational responsibility in PRM collection. The emphasis was on identifying the optimal organizational framework for managing PRMs in maternity care.ResultsA total of 66 maternity healthcare professionals participated in the study, reaching a consensus that public maternity care centers in Finland should be the primary entity responsible for managing PRMs in the maternity sector. Key aspects such as confidence with the role as a mother, maternal confidence with breastfeeding, and satisfaction with the result of care were identified as crucial and should be inquired about in both public maternity care centers and hospital maternity wards. The findings highlight the importance of comprehensive and consistent attention to these PRMs across public maternity care centers and hospital maternity settings to ensure holistic and effective maternal care.ConclusionsThe study highlights the central role of public maternity care centers in the collection and management of PRMs within Finnish maternity care, as agreed upon by the professional consensus. It underscores the importance of a consistent and holistic approach to PRM inquiry across different care settings to enhance the quality and effectiveness of maternity care. This finding is crucial for policymakers and healthcare practitioners, suggesting that reinforcing the collaborative efforts between public maternity care centers and hospital maternity wards is vital for a patient-centric, efficient healthcare system. Aligning with PCC and VBHC principles, this approach aims to improve healthcare outcomes for pregnant and postpartum women in Finland, emphasizing the need for a unified strategy in managing maternity care.

Analysis of registered clinical trials in Italy regarding dermatology (2003-2022): a cross-sectional study on historical trend and prospects for industry investments.

This cross-sectional analysis intends to evaluate trend and outlook of industry clinical research in Italy regarding dermatology and its prospects for the next few years. A computerized search of ClinicalTrial.gov database was carried out considering the 20-year period 2003-2022 using the following string: "Skin Diseases OR Skin cancer OR Skin Neoplasms OR Skin Infection OR Skin Lesion OR Skin Ulcer OR Skin Laxity OR Skin toxicity OR Dermatologic Complication OR Skin abnormalities". During the last 20 years 690 Industry Clinical Trials (IndCTs) were conducted in Italy regarding dermatological investigations, almost entirely funded by non-Italian pharmaceutical companies, involving 4497 centers, and recruiting an average of 13.0 subjects per site. The number of controlled and randomized studies has grown quite regularly and in the last 5 years they represented 79.6% of IndCTs initiated. Countries most frequently sharing with Italy international protocols were Spain (75.0%), Germany (71.8%), France (68.5%), the USA (67.4%), and the UK (54.8%). These data point to a historical involvement of Italy in dermatology IndCTs lower than in the major European countries. However, the war in Ukraine has deprived IndCTs of the contribution of Russia, Ukraine, and Belarus, meaning a mean of about 40 new IndCTs per year in a population of 200 million inhabitants. In contrast, many indicators point to Italy as a country with potential in dermatology wider than used in the past: an efficient health care system accessible to any citizen or resident, 4.6 million dermatological interventions per year, low density of IndCTs concerning dermatology, good production of scientific papers in indexed medical journals, relatively low cost of services and labour and geopolitical stability. The European Clinical Trials Information System's (CTIS), mandatory for the EU member states since early 2023 allows common, simplified, and harmonized regulatory procedures over the life cycle of clinical trials in Europe (EMA, 2023) and plays in favor of Italy's greater competitiveness in clinical research.

Acceptance of the Internet-Based Sharing Nursing Service among caregivers of elderly with chronic diseases

BackgroundIn China, Internet-Based Sharing Nursing Service (IBSNS) is a new mode of nursing service delivery that has been in practice for over five years, which enables nurses to provide care at clients’ home. However, the acceptance and associated factors of IBSNS among caregivers of elderly with chronic diseases who are the major clients of the service were unclear.AimTo explore the acceptance of IBSNS and its associated factors among Chinese caregivers of elderly patient with chronic diseases based on the modified Technology Acceptance Model (mTAM).MethodsA cross-sectional study was conducted from February 2023 to March 2023. Caregivers of hospitalized elderly with chronic diseases were recruited using convenience sampling method from three hospitals in Beijing, China. Data were obtained from self-reports of participants. Structural equation modeling was used to analyze data.ResultsA total of 65.1% of the caregivers had neutral to weakly positive behavioral intention of IBSNS use. The mTAM model was supported with good model fit. Perceived ease of use was positively associated with perceived usefulness. Both perceived usefulness and perceived security were positively associated with attitude, consequently, attitude was associated with behavioral intention.ConclusionsImproving the ease of use, security, and usefulness may be helpful to increase the positive attitude towards IBSNS and behavioral intention of using IBSNS, which provides valuable insights that can help healthcare providers improve the integration of technology in patient care, ultimately leading to better health outcomes and more efficient healthcare systems.

Primary health care in Nigeria: best practices and quality of care in Nigeria

IntroductionThe significance of Primary Healthcare Centers (PHCCs) in fostering health equity and enhancing health outcomes cannot be overstated, especially in low and middle-income countries. This paper’s primary area of interest is to create evidence and innovation for PHCs, with a particular emphasis on underserved groups. Thus, the study aimed to investigate the state of primary health care in Nigeria, with a particular emphasis on best practices, challenges to best practices, and quality of care.MethodologyA cross-sectional quantitative study was conducted to collect secondary data from 29 primary healthcare facilities. The data was collected using staff and facility activity indicators over a year (2022). Descriptive statistics and chi-square tests were used to analyze the collected data.ResultOur study data showed that all of the PHCCs have service rosters that can be utilized for follow-up with patients. About 85 (84.1%) respondents have reported that deliveries were taken by the reporting midwife, and 55 (54.5%) respondents indicated that their facilities provided on-the-job training to their staff. This is considered as the best practice in the facility. However, the majority of the PHCCs (71.3%) lacked assessment teams, which was found to have a negative impact on the quality of healthcare provided at these facilities (p < 0.05).ConclusionOur study underscores the pressing need for primary healthcare services in Nigeria. Government authorities and healthcare providers must take action to overcome the challenges of limited capacity, service delivery, and quality of care. By adopting effective strategies and ensuring access to primary healthcare, citizens’ health outcomes can be greatly enhanced. Thus, it is essential to revamp and optimize healthcare facilities to establish an efficient healthcare system that caters to the needs of all citizens.

Evaluation of AI-Driven LabTest Checker for Diagnostic Accuracy and Safety: Prospective Cohort Study.

In recent years, the implementation of artificial intelligence (AI) in health care is progressively transforming medical fields, with the use of clinical decision support systems (CDSSs) as a notable application. Laboratory tests are vital for accurate diagnoses, but their increasing reliance presents challenges. The need for effective strategies for managing laboratory test interpretation is evident from the millions of monthly searches on test results' significance. As the potential role of CDSSs in laboratory diagnostics gains significance, however, more research is needed to explore this area. The primary objective of our study was to assess the accuracy and safety of LabTest Checker (LTC), a CDSS designed to support medical diagnoses by analyzing both laboratory test results and patients' medical histories. This cohort study embraced a prospective data collection approach. A total of 101 patients aged ≥18 years, in stable condition, and requiring comprehensive diagnosis were enrolled. A panel of blood laboratory tests was conducted for each participant. Participants used LTC for test result interpretation. The accuracy and safety of the tool were assessed by comparing AI-generated suggestions to experienced doctor (consultant) recommendations, which are considered the gold standard. The system achieved a 74.3% accuracy and 100% sensitivity for emergency safety and 92.3% sensitivity for urgent cases. It potentially reduced unnecessary medical visits by 41.6% (42/101) and achieved an 82.9% accuracy in identifying underlying pathologies. This study underscores the transformative potential of AI-based CDSSs in laboratory diagnostics, contributing to enhanced patient care, efficient health care systems, and improved medical outcomes. LTC's performance evaluation highlights the advancements in AI's role in laboratory medicine.

Job satisfaction, work engagement and psychological distress among nurses: A mediated moderation model

Nurses play a vital role in the healthcare system. Their job satisfaction and work engagement can significantly impact patient outcomes and the overall effectiveness and efficiency of the system. This research examines the role of work engagement as a mediating factor in the relationship between job satisfaction and psychological distress among nurses. A cross-sectional study was conducted in UAE hospitals. The study was performed between August and December 2022. The Utrecht Work Engagement Scale measured work engagement, while stress, anxiety, and depression were measured using the Depression Anxiety and Stress Scales (DASS-21). The findings show significant (p< 0.05) relationships between psychological distress, work engagement, and job satisfaction. The structural equation modeling shows that work engagement mediated the relationships between psychological distress and job satisfaction (indirect effect 0.000, p< 0.001; direct effect −0.283, p< 0.001; total effect 580, p< 0.001) and between psychological distress and job satisfaction (indirect effect 0.000 p < 0.01; direct effect −0.226, p< 0.001; total effect − 0.509 p< 0.001). Nursing policies and healthcare organizations should promote resilience, work engagement, and job satisfaction among nurses. Doing so can improve nurses’ overall health and well-being, leading to better patient outcomes and a more efficient healthcare system.

Can they or won't they?

Almost all experts agree that the German healthcare system has reached its limits and needs to be comprehensively overhauled. Although insured persons and taxpayers spend a great deal of money on healthcare, they do not always receive top-quality services in return. This is despite the fact that highly competent service providers are active in the healthcare system and efficient facilities and structures are available in many areas. On the other hand, there are brakes on performance that create dysfunctionalities in various areas, sometimes with serious consequences: insufficient ability to compromise between federal and state policy, dysfunctional sectoralisation, discrepancies between supply and demand, competition between quality and economy and, on top of all this, the inadequate participation of patients/insured persons - after all, they finance the healthcare system! This article goes beyond the description of the current situation and attempts to analyse why Germany has not succeeded in translating the very good substantive and structural conditions into a modern, efficient and adaptive healthcare system.

The Effect of Academic Model Supervision on the Workload and Stress of Nurses in Hospitals: A Literature Review

Nurses often face high workloads and significant levels of stress due to the demands of complex work and high pressure. The academic supervision model is important because it can help nurses manage workload, improve skills and knowledge, and reduce stress levels. Effective supervision is expected to improve the quality of health services, job satisfaction, and have a positive impact on patients and the health system as a whole. This study aims to determine the influence of academic model supervision on the workload and stress of nurses in hospitals. This study uses a literature review method from research articles in the Scopus and Google Scholar databases published in 2014-2023 with the keywords "Supervision", "Academic Model", "Nurse Workload", and "Nurse Stress". Based on the analysis of 22 research articles, it was found that the supervision of the academic model had a positive impact on nurse performance, job satisfaction, work motivation, quality of nursing care, reduction of stress levels, and improvement of overall health service quality. Academic model supervision can help nurses improve skills and knowledge, improve services provided, develop professionalism, increase independence, improve nursing care, reduce workload, improve nurse performance, provide good emotional support, and continuous learning, and reduce the level of stress experienced by nurses. The implications of this research suggest that implementing academic model supervision in hospitals can lead to better nurse outcomes, including enhanced job satisfaction, reduced stress, and improved quality of care. Consequently, this can result in higher patient satisfaction and a more efficient and effective health care system overall.

Investigating primary healthcare resource utilization, triage system awareness, and time expectations among patients presenting at emergency departments in Jordan: a cross-sectional study

This study aims to investigate patients' knowledge of the triage system, utilization of primary healthcare resources, and expectations for waiting times in Jordanian Emergency Departments (EDs). A descriptive, cross-sectional design was employed in the EDs in the largest public hospitals in Jordan. Convenient sampling resulted in 726 participants. A self-reported questionnaire included socio-demographic information and instruments assessing primary healthcare use, triage system awareness, and expected waiting times. Most participants (61.3%) lacked awareness of the triage system. The use of primary healthcare was influenced by age, education, marital status, current job, nationality, and location. Having a primary healthcare provider was associated with higher patient satisfaction. Significant variations in expected waiting times for diagnostic test results were noted based on gender, place of residence, education, current job, and marital status. Addressing patient awareness of the triage system is crucial for optimizing healthcare accessibility and quality in Jordanian EDs. Improving patient education, communication, and primary care utilization can enhance patient outcomes, reduce ED burden, and contribute to a more efficient healthcare system.

Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

This study delves into hospital mobility within the Italian regions of Apulia and Emilia-Romagna, interpreting it as an indicator of perceived service quality. Utilizing logistic regression alongside other machine learning techniques, we analyze the impact of structural, operational, and clinical variables on patient perceptions of quality, thus influencing their healthcare choices. The analysis of mobility trends has uncovered significant regional differences, emphasizing how the regional context shapes perceived service quality. To further enhance the analysis, SHAP (SHapley Additive exPlanations) values have been integrated into the logistic regression model. These values quantify the specific contributions of each variable to the perceived quality of service, significantly improving the interpretability and fairness of evaluations. A methodological innovation of this study is the use of these SHAP impact scores as weights in the data envelopment analysis (DEA), facilitating a comparative efficiency analysis of healthcare facilities that is both weighted and normative. The combination of logistic regression and SHAP-weighted DEA provides a deeper understanding of perceived quality dynamics and offers essential insights for optimizing the distribution of healthcare resources. This approach underscores the importance of data-driven strategies to develop more equitable, efficient, and patient-centered healthcare systems. This research significantly contributes to the understanding of perceived quality dynamics within the healthcare context and promotes further investigations to enhance service accessibility and quality, leveraging machine learning as a tool to increase the efficiency of healthcare services across diverse regional settings. These findings are pivotal for policymakers and healthcare system managers aiming to reduce regional disparities and promote a more responsive and personalized healthcare service.

A New Horizon for Healthcare Delivery: A System of Systems Perspective and Governing Proposition

AbstractThe healthcare system is grappling with inherent challenges, functioning akin to a fragmented cottage‐like industry, leading to significant cost implications and compromised care quality. This paper employs a systematic approach to scrutinize the Healthcare Delivery System (HDS) comprehensively, categorizing it as a Collaborative System of Systems (SoS), where multiple independent systems operate collectively, maintaining individual autonomy. Through a detailed examination, we identify the Collaborative SoS nature of the current healthcare system as the primary cause of its fragmentation. We address a gap in the current literature on the characteristics of SoS, focusing on the often‐overlooked aspect of dependence and exploring why constituent systems collaborate to achieve common objectives. Then, we propose a hybrid SoS model where an external governing entity at the national level assumes the authority to determine objectives and drivers for the healthcare SoS. We contend that effective SoS governance is indispensable for addressing systemic issues, providing necessary coordination, allocating resources, establishing policies, fostering sustainable change, and ensuring a well‐organized and efficient healthcare system.

Fostering Tomorrow: Uniting Artificial Intelligence and Social Pediatrics for Comprehensive Child Well-being.

This comprehensive review explores the integration of artificial intelligence (AI) in the field of social pediatrics, emphasizing its potential to revolutionize child healthcare. Social pediatrics, a specialized branch within the discipline, focuses on the significant influence of societal, environmental, and economic factors on children's health and development. This field adopts a holistic approach, integrating medical, psychological, and environmental considerations. This review aims to explore the potential of AI in revolutionizing child healthcare from social pediatrics perspective. To achieve that, we explored AI applications in preventive care, growth monitoring, nutritional guidance, environmental risk factor prediction, and early detection of child abuse. The findings highlight AI's significant contributions in various areas of social pediatrics. Artificial intelligence's proficiency in handling large datasets is shown to enhance diagnostic processes, personalize treatments, and improve overall healthcare management. Notable advancements are observed in preventive care, growth monitoring, nutritional counseling, predicting environmental risks, and early child abuse detection. We find that integrating AI into social pediatric healthcare aims to enhance the effectiveness, accessibility, and equity of pediatric health services. This integration ensures high-quality care for every child, regardless of their social background. The study elucidates AI's multifaceted applications in social pediatrics, including natural language processing, machine learning algorithms for health outcome predictions, and AI-driven tools for health and environmental monitoring, collectively fostering a more efficient, informed, and responsive pediatric healthcare system.