Health System Research Articles

The intersection of business and artificial intelligence in healthcare: Opportunities and ethical challenges

This dissertation seeks to examine the connection between business and artificial intelligence (AI) in the health sector, major possibilities, and the current ethical issues. It is evident that adoption of AI has revolutionized healthcare by improving clients’ satisfaction, productivity and cost by using technological tools such as analytical tools, tele surgery, and disease diagnosis. This is important because the global AI healthcare market has been estimated to grow to $45.2 Billion by 2026. The study of two different countries – the United States and the United Kingdom – offers an understanding of AI implementation, legislation, and ethical standards as the result of different health systems and legislation. This work reveal potential benefits are enhanced diagnostic accuracy, cost savings whereas the potential harms include violations of patient privacy, bias in algorithms, and accountable issues. The target field adopts theoretical concepts such as The Technology Acceptance Model and The Diffusion of Innovation Theory to review factors affecting AI uptake. This also looks into ethical considerations as a way of making sure that properly applied. The implications of the chosen regulatory frameworks as tools for the analysis of business and patient care strategies are demonstrated in the studied examples of GDPR and HIPAA. The studies provide practical recommendations for both healthcare organizations and policy-makers as well as companies that are importing AI into their services. In conclusion, this research provides to the improvement of healthcare while exploring the signals for confronting the moral issues of AI application on global healthcare settings.

Health System, Community-Based, or Usual Dementia Care for Persons With Dementia and Caregivers: The D-CARE Randomized Clinical Trial.

The effectiveness of different approaches to dementia care is unknown. To determine the effectiveness of health system-based, community-based dementia care, and usual care for persons with dementia and for caregiver outcomes. Randomized clinical trial of community-dwelling persons living with dementia and their caregivers conducted at 4 sites in the US (enrollment June 2019-January 2023; final follow-up, August 2023). Participants were randomized 7:7:1 to health system-based care provided by an advanced practice dementia care specialist (n = 1016); community-based care provided by a social worker, nurse, or licensed therapist care consultant (n = 1016); or usual care (n = 144). Primary outcomes were caregiver-reported Neuropsychiatric Inventory Questionnaire (NPI-Q) severity score for persons living with dementia (range, 0-36; higher scores, greater behavioral symptoms severity; minimal clinically important difference [MCID], 2.8-3.2) and Modified Caregiver Strain Index for caregivers (range, 0-26; higher scores, greater strain; MCID, 1.5-2.3). Three secondary outcomes included caregiver self-efficacy (range, 4-20; higher scores, more self-efficacy). Among 2176 dyads (individuals with dementia, mean age, 80.6 years; 58.4%, female; and 20.6%, Black or Hispanic; caregivers, mean age, 65.2 years; 75.8%, female; and 20.8% Black or Hispanic), primary outcomes were assessed for more than 99% of participants, and 1343 participants (62% of those enrolled and 91% still alive and had not withdrawn) completed the study through 18 months. No significant differences existed between the 2 treatments or between treatments vs usual care for the primary outcomes. Overall, the least squares means (LSMs) for NPI-Q scores were 9.8 for health system, 9.5 for community-based, and 10.1 for usual care. The difference between health system vs community-based care was 0.30 (97.5% CI, -0.18 to 0.78); health system vs usual care, -0.33 (97.5% CI, -1.32 to 0.67); and community-based vs usual care, -0.62 (97.5% CI, -1.61 to 0.37). The LSMs for the Modified Caregiver Strain Index were 10.7 for health system, 10.5 for community-based, and 10.6 for usual care. The difference between health system vs community-based care was 0.25 (97.5% CI, -0.16 to 0.66); health system vs usual care, 0.14 (97.5% CI, -0.70 to 0.99); and community-based vs usual care, -0.10 (97.5% CI, -0.94 to 0.74). Only the secondary outcome of caregiver self-efficacy was significantly higher for both treatments vs usual care but not between treatments: LSMs were 15.1 for health system, 15.2 for community-based, and 14.4 for usual care. The difference between health system vs community-based care was -0.16 (95% CI, -0.37 to 0.06); health system vs usual care, 0.70 (95% CI, 0.26-1.14); and community-based vs usual care, 0.85 (95% CI, 0.42 to 1.29). In this randomized trial of dementia care programs, no significant differences existed between health system-based and community-based care interventions nor between either active intervention or usual care regarding patient behavioral symptoms and caregiver strain. ClinicalTrials.gov Identifier: NCT03786471.

Starfield’s 4Cs of NCD management in primary healthcare: a conceptual framework development from a case study of 19 countries

IntroductionFaced with a backdrop of an increasing chronic disease burden from an ageing global population compounded with rising healthcare costs, health systems are required to implement cost-effective, safe and equitable...

Understanding the impact of racism on surgical outcomes in settler nation-states USA and Canada: a protocol for a systematic review and meta-analysis

IntroductionTo improve surgical quality and safety, health systems must prioritise equitable care for surgical patients. Racialised patients experience worse postoperative outcomes when compared with non-racialised surgical patients in settler colonial...

Default Bulk Ordering and Text Messaging to Enhance Outreach for Lipid Screening: A Randomized Clinical Trial.

A comprehensive lipid panel is recommended by guidelines to evaluate atherosclerotic cardiovascular disease risk, but uptake is low. To evaluate whether direct outreach including bulk orders with and without text messaging increases lipid screening rates. Pragmatic randomized clinical trial conducted from June 6, 2023, to September 6, 2023, at 2 primary care practices at an academic health system among patients aged 20 to 75 years with at least 1 primary care visit in the past 3 years who were overdue for lipid screening. Data analysis was performed from September 2023 to May 2024. Eligible patients were randomized in a 1:2:2 ratio to usual care (group 1), direct outreach and bulk orders (group 2), and bulk order outreach with additional text message reminders for scheduling assistance (group 3). In group 3, participants received an initial, follow-up, and reminder text message. Patients with electronic portal accounts were encouraged to schedule through them, while others received laboratory contact information. Any participant inquiries were answered either with automated responses for common questions or with study team support. Proportion of patients who completed a lipid panel within 3 months. Among the 1000 participants, the median (IQR) age was 38 (28-55) years; 470 (47.0%) were female; and 22 (2.3%) were Asian, 38 (3.9%) were Black, 32 (3.2%) were Hispanic or Latino, and 862 (88.6%) were White (race and ethnicity were based on self-reported data). At 3 months, a lipid panel was completed by 12 of 202 patients (5.9%; 95% CI, 3.4% to 10.1%) receiving usual care (group 1) vs 62 of 394 patients (15.7%; 95% CI, 12.5% to 19.7%) receiving direct outreach and bulk order (group 2), a difference of 9.8 percentage points (95% CI, 4.6 to 15.0; P = .001). The panel was completed by 73 of 404 patients (18.1%; 95% CI, 14.6% to 22.1%) receiving outreach, bulk order, and text message reminders (group 3), for a difference of 2.4 percentage points (95% CI, -3.1 to 7.8; P = .43) vs outreach with bulk order alone (group 2). At 6 months, there were no significant differences in lipid screening between either group 1 vs group 2 or group 2 vs group 3. Lipid screening among participants receiving bulk orders and outreach letters increased significantly compared with usual care at 3 months. However, there was no difference at 6 months. More than 80% of patients did not follow through with lipid screening despite the intervention, and there was no additional increase in lipid testing at 3 months among participants receiving bulk ordering and supplemental text messaging. ClinicalTrials.gov Identifier: NCT05724615.

Knowledge translation initiatives at the Transitional Pain Service: insights from healthcare provider outreach and patient education

Evidence-based treatment of chronic pain requires a multidisciplinary approach grounded in the biopsychosocial model. Implementing this approach within health systems relies on its acceptance by both healthcare providers and patients. While pioneering multidisciplinary pain clinics can serve as a model for implementation, a systematic effort is needed to share knowledge effectively and broadly. In the current paper we provide an overview of the knowledge translation initiatives undertaken at our Transitional Pain Service (TPS) at Toronto General Hospital, a state-of-the-art multidisciplinary pain program established in 2014 for patients at risk of developing chronic pain after surgery. The TPS team strives to enhance acceptance of this model of care among patients and providers, facilitate the establishment of similar clinics, and promote patient understanding of the integrated multidisciplinary pain care approach. Guided by the Knowledge to Action (KTA) framework, knowledge translation activities undertaken by our TPS team include clinician training, resources and outreach activities for providers, and patient education. Resource development was preceded by consultation and needs assessment among patients and providers and feedback from both groups was incorporated as part of the development process. The tailored resources were disseminated via the TPS clinic website and monitoring of online usage enables continuous evaluation of engagement. Barriers to engagement with the resources were examined through patient surveys and staff interviews. Based on these activities, we offer insights gained by our team throughout the knowledge translation process and provide recommendations for other clinical teams who wish to undertake similar initiatives.

Machine-Learning Predictions of Cochlear Implant Functional Outcomes: A Systematic Review.

Cochlear implant (CI) user functional outcomes are challenging to predict because of the variability in individual anatomy, neural health, CI device characteristics, and linguistic and listening experience. Machine learning (ML) techniques are uniquely poised for this predictive challenge because they can analyze nonlinear interactions using large amounts of multidimensional data. The objective of this article is to systematically review the literature regarding ML models that predict functional CI outcomes, defined as sound perception and production. We analyze the potential strengths and weaknesses of various ML models, identify important features for favorable outcomes, and suggest potential future directions of ML applications for CI-related clinical and research purposes. We conducted a systematic literature search with Web of Science, Scopus, MEDLINE, EMBASE, CENTRAL, and CINAHL from the date of inception through September 2024. We included studies with ML models predicting a CI functional outcome, defined as those pertaining to sound perception and production, and excluded simulation studies and those involving patients without CIs. Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we extracted participant population, CI characteristics, ML model, and performance data. Sixteen studies examining 5058 pediatric and adult CI users (range: 4 to 2489) were included from an initial 1442 publications. Studies predicted heterogeneous outcome measures pertaining to sound production (5 studies), sound perception (12 studies), and language (2 studies). ML models use a variety of prediction features, including demographic, audiological, imaging, and subjective measures. Some studies highlighted predictors beyond traditional CI audiometric outcomes, such as anatomical and imaging characteristics (e.g., vestibulocochlear nerve area, brain regions unaffected by auditory deprivation), health system factors (e.g., wait time to referral), and patient-reported measures (e.g., dizziness and tinnitus questionnaires). Used ML models were tree-based, kernel-based, instance-based, probabilistic, or neural networks, with validation and test methods most commonly being k-fold cross-validation and train-test split. Various statistical measures were used to evaluate model performance, however, for studies reporting accuracy, the best-performing models for each study ranged from 71.0% to 98.83%. ML models demonstrate high predictive performance and illuminate factors that contribute to CI user functional outcomes. While many models showed favorable evaluation statistics, the majority were not adequately reported with regard to dataset characteristics, model creation, and validation. Furthermore, the extent of overfitting in these models is unclear and will likely result in poor generalization to new data. This suggests the need for more robust validation procedures and standardization in reporting, with the ultimate hope that the iterative improvement of these models will allow for their adoption as a future clinical tool.

Optimizing health system volume flexibility: key determinants and strategies during the COVID-19 crisis.

The need to match highly variable demand for healthcare services with existing capacity has been a significant health system challenge experienced by the COVID-19 pandemic. Despite mounting research on various COVID-19 responses in the recent literature, there has not been a structured review examining key determinants of health system volume flexibility. The focus of the present research is to review recent literature based on a conceptual framework developed based on the volume flexibility literature on COVID-19. The review also provides a meaningful way to guide practice and future research in the area. A literature review was performed guided by the theoretical frameworks developed in the previous volume of flexibility literature. Selected research papers were reviewed and analyzed to identify key determinants of health system volume flexibility. Seven determinants of health system volume flexibility under three domains (demand management, capacity management, and performance) included demand management strategies, integration, capacity management strategies, workforce management, technology, quality of disease outcomes, and health system knowledge. The research provides both health systems researchers as well as practitioners with a foundational conceptual framework and knowledge related to health system volume flexibility. In addition, this research identifies future research areas to expand knowledge relevant to health system volume flexibility.

Estimating greenhouse gas emissions from the health sector in Canada: Mind the gap.

Healthcare is a surprisingly large contributor to climate change, responsible for a significant quantity of global Greenhouse Gas (GHG) emissions. Global commitments to achieve "net zero" health systems, including by the federal government in Canada, suggest a growing need to understand and mobilize capacity for GHG emissions estimation across Canada's health sector. Our analysis highlights efforts by public sector healthcare organizations in Canada to estimate an increasingly broad scope of GHG emissions, building on longstanding efforts to report or reduce energy-related emissions from facilities. It also identifies why such efforts will not be sufficient. Developing capacity for routine system-wide greenhouse gas emissions estimation can help Canada's health systems to better understand their progress, including through international comparison. Yet emissions estimation is itself an investment, one that should not displace efforts to reduce the full scope of pollutants from the healthcare enterprise, and to build a truly sustainable health system.

Institutions of Care: A Qualitative Study with Ancestral Black Nova Scotian Nurses in Healthcare.

Ancestral Black Nova Scotian (ABNS) nurses are a culturally distinct group yet, little is known about their experiences. Available literature suggests that ABNS nurses are underrepresented in nursing and that they encounter discrimination throughout the health system. Understanding the experiences of ABNS nurses facilitates addressing antiBlack racism in nursing and healthcare. This study sought to critically examine the leadership experiences of ABNS nurses in healthcare. This qualitative study was guided by Black feminist theory and involved one-on-one semi-structured telephone interviews with eighteen ABNS nurses. Critical Discourse Analysis was applied in the reading of interview transcripts to examine words used by participants in relation to nursing and healthcare. The findings are presented in two conceptual themes. Black Tax in Nursing captures the added physical, mental, and spiritual strain experienced by ABNS nurses navigating nursing and healthcare. Black Tax encompassed everyday microaggressions and systemic processes, including intra-profession tensions. Integrating into nursing was made increasingly difficult by a reinforcing network of gatekeepers, policies, and structural design. Nova Scotia Healthcare as an Archaic Institution depicts an antiquated "broken" paternalistic system that did not empower patients nor promote health. Additionally, nursing education was accused of reinforcing negative stereotypes, competency gaps, and mistrust with patients. Institution of Care show how ABNS nurses challenge institutional standards and norms in their approach to nursing. ABNS nurses navigate nursing and the health system by maintaining a community-oriented approach to health. Addressing anti-Black racism in nursing and healthcare requires attention to multi-level processes within institutions.

ARTIFICIAL INTELLIGENCE IN HEALTHCARE SUPPLY CHAIN MANAGEMENT: ENHANCING RESILIENCE AND EFFICIENCY IN U.S. MEDICAL SUPPLY DISTRIBUTION

Artificial intelligence (AI) has become a transformative force in healthcare supply chain management. This paper analyzes the transformative role of artificial intelligence (AI) in enhancing the resilience and efficiency of healthcare supply chain management within the U.S. medical supply distribution system. The study analyzes current implementations and emerging technologies to show how AI-enabled solutions are transforming routine supply chain operations in healthcare settings. Key findings show that healthcare organizations that implement AI-powered supply chain systems observe improvements in forecasting accuracy up to 87% for predictive analytics models that can anticipate supply chain disruption. The research also shows how AI systems enable 40% faster recovery times during crises versus traditional methods, while automated decision support systems reduce response times to supply chain disruptions by almost 65%. The study however points out critical challenges such as data privacy concerns, high implementation costs, and a requirement for strong governance frameworks. Strategically, the paper offers recommendations to healthcare organizations on how to better collaborate with technologists, healthcare providers, and policymakers to enhance innovation while maintaining ethical standards. With the evolution of healthcare supply chains, this research underlines the importance of AI in designing a more resilient and efficient medical supply distribution system that is commensurate with the need to address challenges of implementation and ethical considerations in ensuring equitable healthcare delivery. KEYWORDS: Artificial Intelligence, Healthcare Supply Chain, Medical Supply Distribution, Predictive Analytics, Supply Chain Resilience

A Digital Tool (Technology-Assisted Problem Management Plus) for Lay Health Workers to Address Common Mental Health Disorders: Co-production and Usability Study in Pakistan.

Mental health remains among the top 10 leading causes of disease burden globally, and there is a significant treatment gap due to limited resources, stigma, limited accessibility, and low perceived need for treatment. Problem Management Plus, a World Health Organization-endorsed brief psychological intervention for mental health disorders, has been shown to be effective and cost-effective in various countries globally but faces implementation challenges, such as quality control in training, supervision, and delivery. While digital technologies to foster mental health care have the potential to close treatment gaps and address the issues of quality control, their development requires context-specific, interdisciplinary, and participatory approaches to enhance impact and acceptance. We aimed to co-produce Technology-Assisted Problem Management Plus (TA-PM+) for "lady health workers" (LHWs; this is the terminology used by the Lady Health Worker Programme for lay health workers) to efficiently deliver sessions to women with symptoms of common mental health disorders within the community settings of Pakistan and conducted usability testing in community settings. A 3-stage framework was used for co-producing and prototyping the intervention. Stage 1 (evidence review and stakeholder consultation) included 3 focus group discussions with 32 LHWs and 7 in-depth interviews with key stakeholders working in the health system or at the health policy level. Thematic analyses using the Capability, Opportunity, and Motivation for Behavioral Change (COM-B) model were conducted. Stage 2 included over eight online workshops, and a multidisciplinary intervention development group co-produced TA-PM+. Stage 3 (prototyping) involved 2 usability testing rounds. In round 1 conducted in laboratory settings, 6 LHWs participated in role plays and completed the 15-item mHealth Usability App Questionnaire (MUAQ) (score range 0-7). In round 2 conducted in community settings, trained LHWs delivered the intervention to 6 participants screened for depression and anxiety. Data were collected using the MUAQ completed by LHWs and the Patient Satisfaction Questionnaire (PSQ) (score range 0-46) completed by participants. Qualitative analysis indicated that a lack of digital skills among LHWs, high workload, resource scarcity for digitization (specifically internet bandwidth in the community), and need for comprehensive training were barriers for TA-PM+ implementation in the community through LHWs. Training, professional support, user guidance, an easy and automated interface, offline functionalities, incentives, and strong credibility among communities were perceived to enhance the capability, opportunity, and motivation of LHWs to implement TA-PM+. TA-PM+ was co-produced with features like an automated interface, a personal dashboard, guidance videos, and a connected supervisory panel. The mean MUAQ score was 5.62 in round 1 of usability testing and improved to 5.96 after incorporating LHW feedback in round 2. The mean PSQ score for TA-PM+ was 40 in round 2. Co-production of TA-PM+ for LHWs balanced context and evidence. The 3-stage iterative development approach resulted in high usability and acceptability of TA-PM+ for LHWs and participants.

Individuals' Desire for Social Needs Sharing Among Healthcare Providers: Findings from the 2022 Health Information National Trends Survey.

Increasingly, health systems are collecting and using social needs data, yet there is limited information about individuals' preferences for how social needs information is shared among providers for treatment purposes. To explore the connection between experiencing social needs and concerns about healthcare providers sharing social needs information. A nationally representative, cross-sectional study of 6252 US community-dwelling adults (≥ 18 years of age) who responded to the Health Information National Trends Survey (HINTS 6) (response rate 28.1%) from March to November 2022. Core measures include experiencing social needs in the past year (food, transportation, housing issues), and comfort with providers sharing social needs information with each other for treatment purposes. Other key independent variables included patient-centered communication, discrimination, trust, and quality of care. Odds of reporting experiencing a social need varied by age, race, education, and income, yet those 75 years or older (compared to ages 18-34) had higher odds of reporting feeling comfortable with providers sharing information about social needs. Those who reported having experienced discrimination in healthcare had lower odds of reporting comfort with providers sharing information about food (adjusted odds ratio [aOR], 0.63; 95% confidence interval [CI], 0.41-0.98) and transportation (aOR, 0.64; 95%CI, 0.44-0.94) needs. Those who trusted the healthcare system had higher odds of being comfortable with providers sharing information about unmet needs for food (aOR, 1.33; 95%CI, 1.07-1.65). Also, those who report better patient-centered communication and quality of care had higher odds of being comfortable with providers sharing information on unmet social needs. Individuals' preferences regarding social needs information sharing for treatment purposes, as well as experience of discrimination, trust in healthcare, quality of care, and patient-provider communication, should be considered in health system efforts to capture social needs information.

Thyroid Nodules with Indeterminate Cytology and Negative Molecular Profile: Prevalence of Malignancy and Practice Paradigms for Surveillance.

Background: In the era of molecular testing, thyroid nodules with indeterminate cytology are increasingly being managed nonoperatively. The false-negative rates of these molecular tests, and therefore missed malignancies, are not well defined in real-world clinical practice. Methods: This retrospective study of patients undergoing fine needle aspiration (FNA) biopsy at our health system between November 2017 and March 2022 included nodules with The Bethesda System for Reporting Thyroid Cytopathology (TBSRTC) III and IV cytology and negative, currently negative, or negative but limited ThyroSeq version 3 (TSv3) results. Surgical pathology of resected nodules, as well as details of ultrasound (US) surveillance practices, was recorded. A range of prevalence of malignancy (PoM) estimates were calculated based on all nodules (PoM low) and surgically resected nodules (PoM high). Results: The study cohort consisted of 556 nodules. TSv3 results were distributed as 443 (80%) negative, 85 (15%) currently negative, and 28 (5%) negative but limited. Overall, 75 nodules were resected: 54 nodules (9.7%) had immediate surgery, and 21 nodules (3.8%) had delayed surgery after surveillance imaging. Currently negative and negative but limited nodules were more likely to undergo immediate surgical resection compared with negative nodules (20%, 18%, and 7%, respectively, p < 0.001). The PoM in molecularly benign TBSRTC III and IV thyroid nodules ranged between 3% and 23% depending on the inclusion of all versus resected nodules. TBSRTC IV molecularly benign nodules had a higher PoM than TBSRTC III (PoM low 7.3% vs. 1.6%, p < 0.001; PoM high 48% vs. 13%, p = 0.0013). In the 90% of nodules that were managed nonoperatively, 63% had at least one surveillance US. Timing of initial surveillance US ranged from 3 to 60 months (median 13 months, interquartile [IQR] 11-19 months). Median follow-up duration was 25 months (IQR 17-34 months). Nodule growth occurred in 24% of nodules; only a minority (7%) underwent repeat FNA. Conclusions: Negative subtype of TSv3 should be considered in clinical management recommendations. For negative but limited samples, repeat FNA should be performed. Optimal surveillance strategy for nonresected negative and currently negative nodules remains unknown. Until further real-world data are available, surveillance ultrasonography is recommended for TSN and TSCN nodules, similar to the ATA guidelines for TBSRTC II nodules.

Exploring experiences of dementia post-diagnosis support and ideas for improving practice: A co-produced study.

It takes time to adjust to a diagnosis of dementia. Post-diagnosis support has an important part to play in navigating this transition. However, it is often scarce and variable according to location. This co-produced study explored experiences of support across the UK. The project was co-designed and implemented with people living with dementia. Five focus groups were attended by 18 people with dementia and 10 spouses from across the UK. The aim was to find out what support people currently received, what aspects they valued, and what they would like to see done differently. Most participants reported feeling abandoned after diagnosis, with little continuity of care. Many felt the burden of navigating post-diagnosis life was on them and their loved ones, with a perceived scarcity of support, and difficulties navigating the benefits system. Peer/voluntary support was seen as vital in promoting confidence, a source of relational support, and finding out crucial information relating to benefits, healthcare, and management of symptoms. Participants felt dementia was stigmatised, receiving less support than other medical conditions. Participants identified aspects of post-diagnosis support they valued, and collaborated on ideas for support structures and services they would like to see introduced. In concluding this study, we argue that post-diagnosis support services could be improved with the input of people living with dementia. This could reduce the burden on those receiving the diagnosis to find advice and support outside of health and social care systems. For future research, the financial implications of a dementia diagnosis have rarely been discussed and warrants further exploration.

Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System

Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System

Robotic surgery for paediatric neurogenic lower urinary tract dysfunction: a systematic review.

To evaluate in a systematic review the outcomes, benefits, and limitations of robot-assisted surgeries for paediatric neurogenic lower urinary tract dysfunction (LUTD), as robot-assisted techniques have emerged as a potential alternative, offering enhanced precision, dexterity, and visualisation. This review was registered in the International Prospective Register of Systematic Reviews (PROSPERO identifier CRD42023464849) and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We included studies of paediatric patients (aged <18 years) with neurogenic LUTD undergoing robot-assisted continence surgery, assessing safety and efficacy. Literature searches in the Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Scopus were conducted until 12 July 2024. Data extraction included surgical procedures, complications, operative times, lengths of stay, and bladder function outcomes. A total of 42 studies (20 case reports, 10 case series, six cohort studies, six comparative cohort studies) were included. Robotic procedures for continent catherisable channel construction, augmentation cystoplasty, and bladder neck reconstruction showed comparable peri- and postoperative outcomes. Meta-analysis of five studies comparing robotic vs open appendicovesicostomy indicated a significant reduction in length of stay for robotic groups, while operative time, complications, and re-intervention rates were not significantly different. Conversions to open surgery were rare, indicated by adhesions or small appendices during channel constructions. Robot-assisted surgeries for paediatric neurogenic LUTD demonstrate potential benefits, including reduced hospital stays and comparable complication rates to open surgery in certain contexts. However, the available evidence is limited by heterogeneity in study designs, small sample sizes, and single-centre experiences, which constrain generalisability. Standardised reporting of complications and outcomes, alongside multicentre studies, is essential to clarify the long-term efficacy and broader applicability of these techniques.

Editorial: Multilevel medical security systems and big data in healthcare: trends and developments

Editorial: Multilevel medical security systems and big data in healthcare: trends and developments

The Nursing Station Podcast: An Innovative Approach to Connect an Evolving Nursing Workforce.

Podcasting is an innovative methodology for information exchange and storytelling. Podcasts in nursing can promote communication, education, and recognition across an expansive workforce. A nurse-focused podcast was created at an academic health system in efforts to enhance diversity of information exchange and promote consistency in nursing professional practice. Findings of the implementation of podcasts support their use as relevant, convenient, and satisfactory for clinical nurses in attaining new knowledge.

Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center

Advancing environmentally sustainable learning health systems: Perspectives from a Canadian health center