Improve Health Care Outcomes Research Articles

Exploring the dielectric properties of herbal medicine and modern pharmaceuticals: an integrative review

The integration of herbal medicine with modern pharmaceuticals offers a novel approach to addressing complex healthcare challenges. This study investigates the role of dielectric spectroscopy in analysing key physicochemical properties such as solubility, stability, and molecular interactions. The findings reveal that combining herbal extracts with pharmaceutical agents enhances solubility and stability. It also reduces adverse effects, improving therapeutic efficacy. Dielectric spectroscopy is highlighted as a powerful analytical tool in this process. The study demonstrates how traditional herbal knowledge can be effectively linked with modern scientific methods. This approach enables the development of innovative therapeutic solutions that address safety and efficacy challenges. The results underline the potential of combining advanced analytical techniques with ethnopharmacological practices. This integration paves the way for the creation of safe, effective, and scientifically validated formulations for improved healthcare outcomes.

Development of Predictive Model of Surgical Case Durations Using Machine Learning Approach

Optimizing operating room (OR) utilization is critical for enhancing hospital management and operational efficiency. Accurate surgical case duration predictions are essential for achieving this optimization. Our study aimed to refine the accuracy of these predictions beyond traditional estimation methods by developing Random Forest models tailored to specific surgical departments. Utilizing a comprehensive dataset, we applied several machine learning algorithms, including RandomForest, XGBoost, Linear Regression, LightGBM, and CatBoost, and assessed their performance using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-Squared (R2) metrics. Our findings highlighted that Random Forest models excelled in department-specific applications, achieving an MAE of 16.32, an RMSE of 31.19, and an R2 of 0.92, significantly outperforming general models and conventional estimates. This improvement emphasizes the advantage of customizing models to fit the distinct characteristics and data patterns of each department. Additionally, our SHAP-based feature importance analysis identified morning operation timing, ICU ward assignments, operation codes, and surgeon IDs as key factors influencing surgical duration. This suggests that a detailed and nuanced approach to model development can substantially increase prediction accuracy. By providing a more accurate, reliable tool for predicting surgical case durations, our department-specific Random Forest models promise to enhance surgical scheduling, leading to more effective OR management. This approach underscores the importance of leveraging tailored, data-driven models to improve healthcare outcomes and operational efficiency.

Enhancing organizational performance through fintech innovation: a multi-dimensional analysis of healthcare projects in Africa

This communication explores the factors driving the continuous adoption of digital technologies and fintech innovations in healthcare services in Africa, with a particular emphasis on task-technology fit. By analyzing task-technology alignment within healthcare projects, the study seeks to provide insights into how fintech can enhance organizational performance, supported by effective training programs and government involvement. To assess the scope of fintech in healthcare services, particularly in World Bank-financed projects in Africa, the paper employs a multi-dimensional approach that examines key indicators across four critical dimensions: technology, economy, and environment. Using Principal Component Analysis, the research evaluates fintech development at two key stages of digital transformation investment and development phases allowing for a nuanced examination of the interactions and synergies that shape fintech evolution. Data analysis is conducted using R software to ensure robust and accurate insights. The findings reveal that fintech enhances organizational performance through cost savings, improved transparency, innovative business model creation, and optimized service supply chains. Moreover, fintech facilitates operational adaptation, boosts connectivity, and increases agility in a competitive and complex environment, enabling organizations to operate more efficiently and maintain a competitive edge. This study contributes to the existing literature by providing a comprehensive assessment of fintech's impact on healthcare services within the context of World Bank-financed projects in Africa, highlighting the significance of task-technology fit in enhancing organizational performance and offering valuable insights for practitioners and policymakers looking to leverage fintech for improved healthcare outcomes.

Body Positions and Physical Activity Levels Modulate the Ratio of Abdominal to Thoracic Breathing and Respiratory Rate in Young Individuals.

Background/Objectives: The COVID-19 pandemic highlighted that body positions substantially affected the mortality rate. We hypothesized that body position modulates the contribution of abdominal (AB) and thoracic breathing (TB) to the breathing cycle (BC), as well as respiratory rate (RR). In addition, we hypothesized that physical activity level can increase the contribution of abdominal breathing. Methods: Thus, we used plethysmography respiratory belts to measure changes in abdominal (AB) and thoracic (TB) circumferences, their ratio (AB/TB), and respiratory rate (RR) under resting conditions. Measurements were taken in four body positions-standing (St), sitting (Si), supine (Su), and prone (Pr)-for two groups of young adults (aged 21 ± 2 years) with different physical activity levels (low and high PA). Results: The AB/TB ratios significantly differed between the body positions (Si: 45.5/54.5%, St: 40.5/59.5%, Su: 56.8/43.2%, Pr: 52.2/47.8% (p < 0.001)). AB was significantly the highest in Su and lowest in Si and St (p < 0.001). There was a significant difference in respiratory rate (RR) between the four body positions (p = 0.005). RR in the four body positions was the following: Si: 15.1, St: 15.0, Su: 13.7, and Pr: 14.4. RR was the lowest in Su (13.7), where AB was the highest (56.8%), and RR was the highest in Si (15.1) and St (15.0), where TB was higher compared to Su (p < 0.001). PA significantly affected the various body positions' AB/TB ratio and RR. The high PA group showed a significant difference in the AB/TB ratio between the body positions (p < 0.001). The low PA group showed a significant difference in RR between the body positions (p = 0.025). Conclusions: In young, healthy adults, we found that body position significantly influenced the abdominal/thoracic breathing ratio during the breathing cycle. The supine position showed the highest contribution of abdominal breathing, which correlated with the lowest respiratory rate. Additionally, a higher level of physical activity increased the contribution of abdominal breathing in the Si, Su, and St positions, suggesting a more energy-efficient breathing pattern. These findings suggest the potentials for breathing pattern monitoring and position-based interventions to improve healthcare outcomes and enhance sports performance and recovery.

The Relationship Between Happiness and Caring Behaviors in Nurses: A Descriptive-Analytical Study

Background: Caring behaviors encompass the supportive actions undertaken by nurses to assist patients, with the primary aim of reducing the duration of illness. Enhanced happiness may positively influence these behaviors, which are essential for nurses in maintaining and promoting patient health. Objectives: This study investigates the factors affecting the happiness of Iranian nurses and examines whether increased happiness correlates with improved caring behaviors. Methods: This descriptive-correlational study employed random sampling to survey 220 nurses in Isfahan, Iran, in 2024. Data were collected through questionnaires evaluating demographics, happiness (measured by the Oxford Happiness Questionnaire-Short Form), and caring behaviors. Data analysis was performed using descriptive statistics, independent t-tests, ANOVA, and Pearson correlation coefficients in SPSS V.23. Results: The Iranian nurses in this study demonstrated above-average happiness (mean score: 34.48 ± 5.82). Happiness showed a significant correlation with caring behaviors (P &lt; 0.05) and was also significantly associated with gender, work experience, and income level. No significant associations were found between happiness and age, marital status, education, employment type, or department (P &gt; 0.05). Conclusions: Higher levels of nurse happiness are associated with improved caring behaviors, suggesting that initiatives aimed at enhancing nurse happiness can lead to better quality of patient care. It is imperative for government and healthcare organizations to prioritize programs and policies that focus on increasing nurse satisfaction to improve healthcare outcomes.

Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.

Numerous studies have explored image processing techniques aimed at enhancing ultrasound images to narrow the performance gap between low-quality portable devices and high-end ultrasound equipment. These investigations often use registered image pairs created by modifying the same image through methods like down sampling or adding noise, rather than using separate images from different machines. Additionally, they rely on organ-specific features, limiting the models' generalizability across various imaging conditions and devices. The challenge remains to develop a universal framework capable of improving image quality across different devices and conditions, independent of registration or specific organ characteristics. This study aims to develop a robust framework that enhances the quality of ultrasound images, particularly those captured with compact, portable devices, which are often constrained by low quality due to hardware limitations. The framework is designed to effectively process nonregistered ultrasound image pairs, a common challenge in medical imaging, across various clinical settings and device types. By addressing these challenges, the research seeks to provide a more generalized and adaptable solution that can be widely applied across diverse medical scenarios, improving the accessibility and quality of diagnostic imaging. A retrospective analysis was conducted by using a cycle-consistent generative adversarial network (CycleGAN) framework enhanced with perceptual loss to improve the quality of ultrasound images, focusing on nonregistered image pairs from various organ systems. The perceptual loss was integrated to preserve anatomical integrity by comparing deep features extracted from pretrained neural networks. The model's performance was evaluated against corresponding high-resolution images, ensuring that the enhanced outputs closely mimic those from high-end ultrasound devices. The model was trained and validated using a publicly available, diverse dataset to ensure robustness and generalizability across different imaging scenarios. The advanced CycleGAN framework, enhanced with perceptual loss, significantly outperformed the previous state-of-the-art, stable CycleGAN, in multiple evaluation metrics. Specifically, our method achieved a structural similarity index of 0.2889 versus 0.2502 (P<.001), a peak signal-to-noise ratio of 15.8935 versus 14.9430 (P<.001), and a learned perceptual image patch similarity score of 0.4490 versus 0.5005 (P<.001). These results demonstrate the model's superior ability to enhance image quality while preserving critical anatomical details, thereby improving diagnostic usefulness. This study presents a significant advancement in ultrasound imaging by leveraging a CycleGAN model enhanced with perceptual loss to bridge the quality gap between images from different devices. By processing nonregistered image pairs, the model not only enhances visual quality but also ensures the preservation of essential anatomical structures, crucial for accurate diagnosis. This approach holds the potential to democratize high-quality ultrasound imaging, making it accessible through low-cost portable devices, thereby improving health care outcomes, particularly in resource-limited settings. Future research will focus on further validation and optimization for clinical use.

Recommendations on Rapid Diagnostic Point-of-care Molecular Tests for Respiratory Infections in the United Arab Emirates

Traditional testing methods in the Middle East Region, including the United Arab Emirates (UAE), particularly the testing of Respiratory Syncytial Virus (RSV), influenza, group A streptococcus (GAS), and COVID-19 have the potential to be upgraded to new and advanced diagnostics methods that improve lead time to diagnosis, consumption of healthcare resources and patient experience. In addition, based on the research, it was reported that there is an underreporting of respiratory cases, overuse of antibiotics, and prolonged hospitalizations which is posing pressure on UAE healthcare stakeholders. A literature review was done exploring UAE's current diagnostic practices, recommended guidelines, diagnostic gaps, and challenges in RSV, GAS, Influenza, and COVID-19. This was followed by stakeholder discussions focusing on assessing current diagnostic practices, usage of rapid molecular point-of-care (POC) diagnostic tests, current gaps in diagnosis, targeted profiles for POC testing, and potential impact on patient management for targeted respiratory infections. A round table discussion with healthcare experts, insurance experts, key opinion leaders, and pulmonologists discussed challenges and opportunities in treating respiratory diseases. UAE healthcare stakeholders suggest that introducing alternative and up-to-date diagnostic methods such as POC molecular testing is expected to improve healthcare outcomes, optimize resources, and develop a robust case management of respiratory tract infections. It is essential to emphasize that by introducing POC testing, precision medicine is reinforced, efficiency is achieved, and the overall management of population health is enhanced.

The rise of artificial intelligence in teledentistry: A comprehensive review

The integration of Artificial Intelligence (AI) into teledentistry represents an unprecedented step towards increased accessibility in dental care, given India's diversity and vast population. This comprehensive review attempts to outline the practical applications of AI in teledentistry, from patient education, remote diagnosis, treatment planning, to follow-up care. It critically analyses works relating to AI's effectiveness on the detection of caries, monitoring orthodontic, and the preliminary screening of OPMDs in relation to improving health care outcomes, especially in underserved regions. It also highlights challenges such as data privacy, accuracy, acceptance, and need for regulatory clarity among dental professionals and patients. This review primarily proposes the recommendations that we should be attempting to fuse AI based solutions with a human touch as a fixture of provider of dental rendering, based on peer-reviewed study evidence. The practice will change as collaboration is required with AI to pursue a teledental practice that is ethical, effective and accessible to everyone.

Voices of Care: Nurses' Perspectives on End-of-Life Care for Black/African American Patients.

This qualitative study examines the distinct experiences of Black/African American nurses in providing end-of-life care to patients of the same racial background and their families. It fills a significant gap in existing research by focusing on the lived experiences and perceptions of Black/African American nurses, gathered through semistructured interviews. Using phenomenological analysis with Colaizzi's method, the study identified principal themes such as racial and ethnic disparities in treatment, challenges in pain management, the need for cultural sensitivity, and the significant influence of historical Black/African American patients' mistrust in health care interactions. The findings illustrate pervasive health care disparities, which are intensified by socioeconomic variables, cultural incompetence, and systemic bias. The nurses highlighted the urgent need for enhanced cultural competence in health care practices to ensure equitable treatment and patient-centric care. This research underscores the necessity for systemic reforms to improve cultural sensitivity and trust within health care services, advocating for comprehensive cultural competency training, greater diversity in health care representation, and effective mechanisms for reporting and addressing disparities. These measures are critical for improving health care outcomes and trust among Black/African American communities at the end of life.

Strategic Policy Framework for Ensuring the Sustainability of Essential Healthcare in Korea

South Korea’s rapid economic growth over the past 50 years has significantly improved healthcare outcomes, with life expectancy rising from 62.3 years in the 1970s to 82.7 years in 2022. However, the sustainability of its high-performing healthcare system is increasingly uncertain. Healthcare expenditure as a percentage of GDP reached 9.7% in 2022, surpassing the OECD average, while the fertility rate dropped to 0.72, undermining the system’s demographic foundation. Challenges include freezing health insurance premiums despite rising costs, short-term fiscal injections without thorough evaluation, and expanding the physician workforce without accounting for demographic changes. These policies risk overburdening future generations and escalating systemic financial pressures. To ensure sustainability, a shift in healthcare policy is necessary; from expanding services to managing limited resources and controlling demand. South Korea must adopt a strategic, long-term framework that balances current needs with future sustainability, ensuring its healthcare system remains equitable and resilient.

A Quantitative Analysis of Human and Material Resources for Endoscopy Services in Pacific Island Countries.

This study aims to evaluate the current state of endoscopy services in Pacific Island Countries (PICs) by quantifying human and material resources, including the number of trained endoscopists and nurses, the types of procedures performed, and the availability and maintenance of endoscopic equipment. A mixed methods survey was conducted in 2023, targeting doctors and nurses who participated in the WGO-FNU-ANZGITA endoscopy training program as well as non-participants. Survey invitations were sent through email, WhatsApp, and Facebook Messenger. Data were collected from 16 sites across 12 PICs, achieving an 85% response rate. Survey results indicated a total of 58 endoscopists (2.1/100000 population) and 52 nurses (1.9/100000 population), with a majority having received training through international partnerships. Basic endoscopy services, such as gastroscopy and colonoscopy, were widely available, but advanced procedures were limited to a few sites. Most sites reported using donated equipment, with significant challenges in equipment maintenance and repair. The availability of functional endoscopes averaged four per site. Common issues included outdated equipment, lack of qualified personnel, and insufficient funding for new equipment. Despite efforts to enhance endoscopy services in PICs through international collaborations, significant gaps remain, particularly in terms of advanced procedural capacity and equipment maintenance. Recommendations include expanding training programs, improving equipment maintenance infrastructure, securing funding for new equipment, and fostering stronger partnerships to support the sustainability of endoscopy services. Addressing these areas can enhance the quality and availability of endoscopy services, ultimately improving healthcare outcomes for populations in PICs.

Leveraging regulatory pharmacist expertise to develop a framework for drug repurposing in underserved areas of the USA

The United States continues to grapple with healthcare access disparities, particularly in underserved regions. Regulatory pharmacists, with their unique expertise in pharmaceutical laws, drug development, and safety regulations, can play a vital role in drug repurposing efforts. This paper proposes a framework for utilising regulatory pharmacist expertise to develop drug repurposing strategies to meet unmet medical needs in underserved areas. We explore the regulatory hurdles, the necessity for collaborative efforts between healthcare professionals, and the potential for drug repurposing to improve healthcare outcomes in rural and socioeconomically disadvantaged communities.

ASSESSMENT OF JOB BURNOUT AND JOB SATISFACTION AMONG INTENSIVE CARE UNIT NURSES A TERTIARY HEALTHCARE HOSPITAL

Job satisfaction and burnout are critical concerns for nurses working in intensive care units (ICUs), impacting their well-being and the quality of patient care. Understanding these factors is crucial for developing strategies to improve healthcare outcomes. Objective: To assess job satisfaction and burnout among ICU nurses at a tertiary healthcare hospital in Pakistan. Methods: A descriptive cross-sectional study was conducted among 121 ICU nurses using a structured questionnaire. Data were analyzed using SPSS version 26, with descriptive statistics summarizing key findings. Results: The majority of nurses reported high job satisfaction, with 69.4% agreeing that their work provided significant fulfilment, and 65.3% feeling able to learn on the job. However, moderate to high burnout levels were observed, with 43% reporting feelings of burnout and 38.8% feeling overburdened with professional duties. Despite these challenges, 57% of nurses did not perceive burnout as significantly affecting their performance. Conclusion: While job satisfaction levels were high, the presence of burnout underscores the need for targeted interventions to improve workplace conditions and support nurses’ well-being. Addressing workload challenges and fostering a supportive work environment is essential for sustaining job satisfaction and reducing burnout in ICUs.

Evaluating the Role of ISO Standards in Enhancing the Performance of Medical Laboratories

The role of ISO standards in enhancing the performance of medical laboratories has become increasingly significant in ensuring quality and reliability in healthcare services. This review evaluates the impact of ISO standards, particularly ISO 15189, on the operational efficiency, diagnostic accuracy, and risk management of medical laboratories. It highlights the key components of these standards, including quality management systems, personnel competence, and continuous improvement, and examines their contributions to patient safety and institutional credibility. The article also explores the challenges of implementing ISO standards, such as financial constraints and resistance to change, and proposes strategies to overcome these barriers. The findings underscore the importance of ISO standards in promoting a culture of quality and innovation within medical laboratories, ultimately improving healthcare outcomes.

Development of a Low-Cost Electrochemical Sensor Based on Oxygen Plasma-Modified Plastic Chip Electrode for Detection of Cxcl-10 Biomarker

The development of low-cost electrochemical sensors is crucial for point-of-care diagnostics and biomedical research. In this study, we present a novel approach using an Oxygen Plasma-Modified Plastic Chip Electrode for the detection of the CXCl-10 biomarker. CXCl-10, or interferon-inducible protein-10 (IP-10), is a key chemokine involved in immune responses and inflammation regulation, secreted by various cell types in response to interferon-gamma (IFN-γ) stimulation. Elevated levels of CXCl-10 have been linked to autoimmune disorders, infectious diseases, and cancer. The proposed sensor utilizes oxygen plasma treatment to modify the surface of a plastic chip electrode, enhancing its sensitivity and specificity towards CXCl-10 detection. Scanning Electron Microscopy (SEM) analysis reveals significant surface modifications post-plasma treatment, optimizing receptor attachment and analyte capture. Electrochemical techniques such as Electrochemical Impedance Spectroscopy (EIS) and Differential Pulse Voltammetry (DPV) are employed to confirm receptor attachment and assess sensor performance.This low-cost electrochemical sensor demonstrates promising results for sensitive and selective detection of CXCl-10, offering potential applications in point-of-care diagnostics and biomedical research for various diseases associated with CXCl-10 dysregulation. The development of such sensors contributes to advancing affordable and accessible diagnostic tools for improved healthcare outcomes.

Robust diabetic prediction using ensemble machine learning models with synthetic minority over-sampling technique

This paper addresses the pressing issue of diabetes, which is a widespread condition affecting a huge population worldwide. As cells become less responsive to insulin or fail to produce it adequately, blood sugar levels rise. This has the potential to cause severe health complications including kidney disease, vision impairment and heart conditions. Early diagnosis is paramount in mitigating the risk and severity of diabetes-related complications. To tackle this, we proposed a robust framework for diabetes prediction using Synthetic Minority Over-sampling Technique (SMOTE) with ensemble machine learning techniques. Our approach incorporates strategies such as imputation of missing values, outlier rejection, feature selection using correlation analysis and class distribution balancing using SMOTE. The extensive experimentation shows that the proposed combination of AdaBoost and XGBoost shows exceptional performance, with an impressive AUC of 0.968+/-0.015. This outperforms not only alternative methodologies presented in our study but also surpasses current state-of-the-art results. We anticipate that our model will significantly improve diabetes prediction, offering a promising avenue for improved healthcare outcomes in diabetes management.

Investigating Remote Healthcare Accessibility with AI: Deep Learning and NLP-Based Knowledge Graph for Digitalized Diagnostics

Introduction: Recently, vast generational modern AI techniques have facilitated developments for accessing digital healthcare diagnosis with capabilities of detecting illnesses. Problem: There exists a lot of scepticism for e-health couple with high similarities on health symptoms which hinder text data analysis for remote diagnosis limiting remote services and affecting tech development. Objective: This research investigates and substantiates opportunities associated with computational leverage of text data analytics and cognitive extraction of knowledge insights to improve healthcare outcomes. Significance: The study presents an overview of public, an integrated deep learning (DL), and AI knowledge graph (KG) for healthcare accessibility of remote diagnostics with NLP assist. Method: This research applied both qualitative and quantitative analysis. Questionnaires were used to understand the computational analytics and cognitive extraction of AI knowledge graphs on healthcare data. Also, an AI model was built to detect, diagnose based on text data and streamline five (5) related disease symptoms for each given text input. Results: The result of the survey was tested with hypotheses of H1, H2, H3, H4, H5. Results show that deep learning models and knowledge graphs can effectively lead to a well-defined class of data classification. Our model also exhibits a tremendous level of acceptable prediction of health symptoms based on text data. The significant group was accepted as an identified health issue and the non-significant was identified as a non-health issue. Conclusion: The study concludes that a well-defined system based on a rigorous ethical healthcare standard can easily support determining a feasible remote diagnosis.

Surgical Training Pathways in the United Kingdom and the United States: Lessons for Resource-Limited Settings From High-Income Countries.

Background Surgical training is a critical component of healthcare, especially in high-income countries such as the United Kingdom (UK) and the United States (US), which have established distinct, well-organised training frameworks. Comparing these systems provides valuable insights that may enhance global surgical education, particularly in low- and middle-income countries, where training and retaining proficient surgeons are considerable challenges. Methodology This comparative study examines the surgical training systems in the UK and the US, focusing on key aspects, including training structure, competency-based assessments, and work-hour regulations. Data were sourced from regulatory organisations such as the General Medical Council, the Royal College of Surgeons, the Accreditation Council for Graduate Medical Education, and the American Board of Surgery. The analysis explores how elements of these models might be adapted to support sustainable surgical education frameworks in resource-limited environments. Ethical approval was not required due to the use of publicly accessible data and no patient involvement. Results The UK and US surgical training systems differ substantially in their structure, training duration, and specialisation timing. The UK employs a tiered approach, offering generalist experience before specialisation, while the US favours early specialisation directly after medical school. Both systems implement competency-based evaluations, though the US system places a greater emphasis on case volume and procedural exposure. Work-hour regulations also vary, with the UK capping weekly hours at 48 under the European Working Time Directive, compared to an 80-hour maximum in the US, which results in differing levels of trainee satisfaction and burnout rates. Conclusions The competency-based assessments in both the UK and the US offer adaptable frameworks for resource-limited settings. The phased training approach in the UK is well-suited for environments requiring versatile surgeons capable of handling a wide range of cases. By implementing these adaptable elements, along with cost-effective training innovations such as simulation tools, e-learning platforms, and international partnerships, resource-constrained regions can foster a sustainable, skilled surgical workforce. These insights offer pathways to improve healthcare outcomes and equity globally by enhancing surgical capacity in regions with limited resources.

Improving Healthcare Outcomes with Predictive Analytics: Machine Learning for Disease Prediction and Diagnosis

Improving Healthcare Outcomes with Predictive Analytics: Machine Learning for Disease Prediction and Diagnosis

Simulation for Systems Integration: A Win-Win to Achieve Your Education, Quality, and Safety Goals.

Health care education is a cornerstone of clinical excellence, ensuring the highest level of readiness to achieve high-quality and safe care. Integrating simulation into health care systems can provide a modality to address educational, quality, and safety goals. Simulation is a methodology used to immerse individuals, teams, and medical systems into clinical scenarios or environments. Through facilitation and debriefing, the immersive experience can be used to provide vital education, develop optimal health care practices, and identify prospects for improvement. Simulation provides an opportunity to blend the needs of continuing multiprofessional education while addressing quality and safety goals, and ultimately, promoting a positive safety culture. Applied comprehensively, simulation for systems integration can both promote the transformation of the health care system into a learning organization, as well as translate to improved health care outcomes. [Pediatr Ann. 2024;53(11):e408-e413.].