Patient and provider perceptions of the relationship between alcohol use and TB and readiness for treatment: a qualitative study in South Africa.

BACKGROUND: Unhealthy alcohol use is widespread in South Africa and has been linked to tuberculosis (TB) disease and poor treatment outcomes. This study used qualitative methods to explore the relationship between TB and alcohol use during TB treatment. METHODS: Focus group discussions (FGDs) were conducted with 34 participants who had previous or current drug-susceptible TB and self-reported current alcohol use. Eight interviews were conducted with healthcare workers who provide TB services in Worcester, South Africa. RESULTS: In this rural setting, heavy episodic drinking is normalized and perceived to be related to TB transmission and decreased adherence to TB medication. Both healthcare workers and FGD participants recommended the introduction of universal screening, brief interventions, and referral to specialized care for unhealthy alcohol use. However, participants also discussed barriers to the provision of these services, such as limited awareness of the link between alcohol and TB. Healthcare workers also specified resource constraints, while FGD participants or patients mentioned widespread stigma towards people with alcohol concerns. Both FGD participants and health providers would benefit from education on the relationship between TB and unhealthy alcohol use and had specific recommendations about interventions for alcohol use reduction. Healthcare workers also suggested that community health worker-delivered interventions could support access to and engagement in both TB and alcohol-related services. CONCLUSION: Findings support strengthening accessible, specialized services for the identification and provision of interventions and psychosocial services for unhealthy alcohol use among those with TB.

How Primary Healthcare Sector is Organized at the Territorial Level in France? A Typology of Territorial Structuring.

BACKGROUND: Most the Organization for Economic Co-operation and Development (OECD) countries are currently facing the challenges of the health transition, the aging of their populations and the increase in chronic diseases. Effective and comprehensive primary healthcare (PHC) services are considered essential for establishing an equitable, and cost-effective healthcare system. Developing care coordination and, on a broader scale, care integration, is a guarantee of quality healthcare delivery. The development of healthcare systems at the meso-level supports this ambition and results in a process of territorial structuring of PHC. In France, the Health Territorial and Professional Communities (HTPC) constitute meso-level organizations in which healthcare professionals (HCPs) from the same territory gather. We conducted a study to determine, in a qualitative step, the key elements of the territorial structuring of PHC in France and, then, to develop, in a quantitative step, a typology of this structuring. METHODS: A sequential-exploratory mixed-method study with a qualitative step using a multiple case approach and a quantitative step as a hierarchical clustering on principal components (HCPC) from a multiple correspondence analysis (MCA). RESULTS: A total of 7 territories were qualitatively explored. Territorial structuring appears to depend on: past collaborations at the micro-level, meso-level coordination among HCPs and multiprofessional structures, diversity of independent professionals, demographic dynamics attracting young professionals, and public health investment through local health contracts (LHCs). The typology identifies 4 clusters of mainland French territories based on their level of structuring: under or unstructured (38.6%), with potential for structuring (34.7%), in the way for structuring (25.3%) and already structured territories (1.4%). CONCLUSION: Interest in territorial structuring aligns with challenges in meso-level healthcare organization and the need for integrated care. Typologies of territorial structuring should be used to understand its impact on access, care quality, and medical resources.

Effects of Medicaid Accountable Care Organizations on children's access to and utilization of health services.

OBJECTIVE: To evaluate the effects of Medicaid Accountable Care Organizations (ACOs) on children's access to and utilization of health services. STUDY SETTING AND DESIGN: This study employs difference-in-differences models comparing ACO and non-ACO states from 2018 through 2021. Access measures are indicators for preventive and sick care sources, unmet healthcare needs, and having a personal doctor or nurse. Utilization measures are preventive and dental care, mental healthcare, specialist visits, emergency department visits, and hospital admissions. DATA SOURCES AND ANALYTIC SAMPLE: Secondary, de-identified data come from the 2016-2021 National Survey of Children's Health. The sample includes children with public insurance and ranges between 21,452 and 37,177 depending on the outcome. PRINCIPAL FINDINGS: Medicaid ACO implementation was associated with an increase in children's likelihood of having a personal doctor or nurse by about 4 percentage-points concentrated among states that implemented ACOs in 2018. Medicaid ACOs were also associated with an increase in specialist care use and decline in emergency visits by about 5 percentage-points (the latter being concentrated among states that implemented ACOs in 2020). There were no discernable or robust associations with other pediatric outcomes. CONCLUSIONS: There is mixed evidence on the associations of Medicaid ACOs with pediatric access and utilization outcomes. Examining effects over longer periods post-ACO implementation is important.

Towards performance governance in healthcare: An analysis of Italian local health units.

PURPOSE: Bouckaert and Halligan (2008) proposed four ideal types of performance management systems, ranging from a disconnected and input-led approach (performance administration) to a model in which performance management is fully integrated with both the internal and external context of an organisation (performance governance). This article empirically analyzes performance plans issued by Italian Local Health Units (LHUs) to provide a first nationwide snapshot of the different ideal-types of performance management that each (LHUs) have reached, in a 'performance governance'-oriented perspective. DESIGN: This paper employs a qualitative methodology based on document analysis. The model orienting the analysis features six dimensions capturing the ideal-types characteristics and what the Italian performance regulations prescribe. Data was derived from the performance plans adopted by the Italian LHUs on the condition that they made the documentation necessary for the analysis public on their institutional website. FINDINGS: For a 'performance governance' oriented approach, two elements are considered relevant: familiarity with the analysis of stakeholders and context. In our sample, consisting of 63 performance plans out of 99 Italian LHUs, it was challenging to identify specific territorial clusters, due to significant heterogeneity. The role of strategic objectives, integration between cycles, and context analysis seems to positively influence the orientation towards a 'performance governance' approach. ORIGINALITY/VALUE: The application of Bouckaert and Halligan's ideal-types has only been episodically investigated in the healthcare sector, mainly at the individual health unit or Region level. This article's innovative contribution consists of conducting a qualitative analysis based on a replicable taxonomy that enables further national comparisons. Furthermore, it highlights the need for public healthcare systems to engage more with external stakeholders to improve the quality of their performance governance.

The State of Telepathology in Africa in the Age of Digital Pathology Advancements: A Bibliometric Analysis and Literature Review.

Telepathology emerges as a vital tool, offering significant promise for enhancing pathology services in Africa, a region historically challenged by healthcare access and resource limitations. This review explores the development, adoption, and impacts of telepathology in Africa through a comprehensive bibliometric analysis and literature review. A methodical search in PubMed for publications up to 2024 revealed 119 pertinent studies, out of which 47 met the inclusion criteria for a focused review on telepathology's role in African healthcare settings. This research has charted a clear trajectory of growing interest in telepathology, as evidenced by the annual increase in related publications and robust international collaboration. It underscores the expanding utility of telepathology in diagnostics, education, and research within Africa, particularly in domains like dermatopathology, neuropathology, and, notably, oncology. The integration of artificial intelligence into telepathology presents new frontiers for enhancing diagnostic accuracy and efficiency. However, the review also identifies persistent challenges such as infrastructural inadequacies, a shortage of skilled professionals, and regulatory hurdles. The study highlights the indispensable role of international partnerships in advancing telepathology in the region. This review proposes a strategic pivot toward "leapfrogging," an approach that allows Africa to skip traditional developmental hurdles by directly adopting cutting-edge technologies and practices.

Technical efficiency and its determinants in healthcare systems of underdeveloped countries.

This study aims to evaluate the technical efficiency levels of healthcare systems in underdeveloped countries and to reveal the impact of COVID-19 along with some other factors on the efficiency levels via a two-stage data envelopment analysis. The study covers panel data from 2013 to 2020. The results show that technical efficiency scores decreased during the pandemic period. It is also understood from the results that immunization and the ratio of population ages 65 and above have significant and negative effects on inefficiency scores while carbon dioxide emissions have significant and positive effects. It is thought that the results of this study can guide decision-makers regarding efficiency gains in healthcare systems.

Exploring Hospital Overcrowding with an Explainable Time-to-Event Machine Learning Approach.

Emergency department (ED) overcrowding is a complex problem that is intricately linked with the operations of other hospital departments. Leveraging ED real-world production data provides a unique opportunity to comprehend this multifaceted problem holistically. This paper introduces a novel approach to analyse healthcare production data, treating the length of stay of patients, and the follow up decision regarding discharge or admission to the hospital as a time-to-event analysis problem. Our methodology employs traditional survival estimators and machine learning models, and Shapley additive explanations values to interpret the model outcomes. The most relevant features influencing length of stay were whether the patient received a scan at the ED, emergency room urgent visit, age, triage level, and the medical alarm unit category. The clinical insights derived from the explanation of the models holds promise for increase understanding of the overcrowding from the data. Our work demonstrates that a time-to-event approach to the over- crowding serves as a valuable initial to uncover crucial insights for further investigation and policy design.

Digital Maturity as a Predictor of Quality and Safety Outcomes in US Hospitals: Cross-Sectional Observational Study.

BACKGROUND: This study demonstrates that digital maturity contributes to strengthened quality and safety performance outcomes in US hospitals. Advanced digital maturity is associated with more digitally enabled work environments with automated flow of data across information systems to enable clinicians and leaders to track quality and safety outcomes. This research illustrates that an advanced digitally enabled workforce is associated with strong safety leadership and culture and better patient health and safety outcomes. OBJECTIVE: This study aimed to examine the relationship between digital maturity and quality and safety outcomes in US hospitals. METHODS: The data sources were hospital safety letter grades as well as quality and safety scores on a continuous scale published by The Leapfrog Group. We used the digital maturity level (measured using the Electronic Medical Record Assessment Model [EMRAM]) of 1026 US hospitals. This was a cross-sectional, observational study. Logistic, linear, and Tweedie regression analyses were used to explore the relationships among The Leapfrog Group's Hospital Safety Grades, individual Leapfrog safety scores, and digital maturity levels classified as advanced or fully developed digital maturity (EMRAM levels 6 and 7) or underdeveloped maturity (EMRAM level 0). Digital maturity was a predictor while controlling for hospital characteristics including teaching status, urban or rural location, hospital size measured by number of beds, whether the hospital was a referral center, and type of hospital ownership as confounding variables. Hospitals were divided into the following 2 groups to compare safety and quality outcomes: hospitals that were digitally advanced and hospitals with underdeveloped digital maturity. Data from The Leapfrog Group's Hospital Safety Grades report published in spring 2019 were matched to the hospitals with completed EMRAM assessments in 2019. Hospital characteristics such as number of hospital beds were obtained from the CMS database. RESULTS: The results revealed that the odds of achieving a higher Leapfrog Group Hospital Safety Grade was statistically significantly higher, by 3.25 times, for hospitals with advanced digital maturity (EMRAM maturity of 6 or 7; odds ratio 3.25, 95% CI 2.33-4.55). CONCLUSIONS: Hospitals with advanced digital maturity had statistically significantly reduced infection rates, reduced adverse events, and improved surgical safety outcomes. The study findings suggest a significant difference in quality and safety outcomes among hospitals with advanced digital maturity compared with hospitals with underdeveloped digital maturity.

A unified component-based data-driven framework to support interoperability in the healthcare systems.

Healthcare organizations must urgently prioritize interoperability to enhance the quality of care they provide. However, achieving this collaboration comes with numerous challenges, including differing approaches, data formats, and standards, as well as concerns about privacy, security, technical complexity, and legal and regulatory issues. To tackle these challenges, we determined a set of interoperability solutions. We also developed a comprehensive, component-based, data-driven framework for healthcare systems. Our study's approach involved three main steps: first, conducting a literature review to gather interoperability requirements and solutions from online databases and grey literature; second, carrying out a qualitative study to develop a framework based on the review results and focus group discussions; and third, using the Delphi method to validate the framework with experts. We extracted information from 36 articles during the screening and assessment process. Based on the proposed framework, we organized the identified themes into various categories, including architecture, architecture components, standards, platforms, policies, data sources, consumers, applications, level of interoperability, healthcare facilities, and considerations. Experts believe that establishing a comprehensive architecture for launching interoperability between health information systems can greatly facilitate this process. All framework components (totaling 197) received unanimous approval. The landscape of healthcare delivery is shifting from a focus on diseases to a patient-centered, data-driven approach. There is a growing demand for personalized healthcare systems, which necessitates increased interoperability among all healthcare stakeholders, particularly when dealing with diverse types of data. Our framework is designed to facilitate the implementation of various types of interoperability in healthcare systems.

Interhospital transfer dynamics for patients with intracranial hemorrhage in Massachusetts.

Introduction: Intracranial hemorrhages present across a spectrum of clinical phenotypes, with many patients transferred across hospitals to access higher levels of neurocritical care. We sought to characterize patient dispositions following intracranial hemorrhage and examine disparities associated with interhospital transfers. Methods: Using the Healthcare Cost and Utilization Project database, we mapped and identified factors influencing the likelihood of patient transfers and receipt of specialist interventional procedures following intracranial hemorrhage. Results: Of 11,660 patients with intracranial hemorrhage, 59.4% had non-traumatic and 87.5% single compartment bleeds. After presentation, about a quarter of patients were transferred to another facility either directly from the ED (23.0%) or after inpatient admission (1.8%). On unadjusted analysis, patients who were white, in the upper income quartiles, with private insurance, or resided in suburban areas were more frequently transferred. After adjusting for patient-and hospital-level variables, younger and non-white patients had higher odds of transfer. Hospital capabilities, residence location, insurance status, and prior therapeutic relationship remained as transfer predictors. Transferred patients had a similar hospital length of stay compared to admitted patients, with 43.1% having no recorded surgical or specialist interventional procedure after transfer. Discussion: Our analysis reveals opportunities for improvement in risk stratification guiding transfers, as well as structural challenges likely impacting transfer decisions.

Advancing Healthcare Sustainability: Embracing MACH Architecture for Health IT System Transformation.

The emergence of new collaborative systems, coupled with uncontrolled data accumulation in traditional systems, poses sustainability challenges. This poster advocates the adoption of the MACH architecture as a potential framework to enhance sustainability and credibility within healthcare systems by offering robust system designs and management controls.

Experiences of Multiple Myeloma Patients With Treatment in the Palestinian Practice: A Multicenter Qualitative Study in a Resource-Limited Healthcare System.

Background Multiple myeloma is a crippling cancer that puts a significant strain on patients and their families alike. The long and exhausting treatment journey with the disease is challenging not only for patients but also for healthcare systems. This exploratory study was conducted to look into these patients' experiences with their treatment and explore their recommendations and views to improve the Palestinian healthcare system, which can be viewed as an evolving healthcare system within a resource-limited and developing country. Methods The consolidated criteria for reporting qualitative research (COREQ) checklist was used for conducting this multicenter exploratory qualitative study. A total number of eight patients with multiple myeloma who received treatment in the Palestinian healthcare system participated in semi-structured in-depth interviews. The semi-structured in-depth interviews followed a set interview schedule. Thematic analysis of the data was done using the qualitative interpretive description approach. Results A total of 5.48 h (329 min) of total interview time was analyzed. Among the patients, 6 (75%) were males, 5 (63.5%) lived in urban areas, 5 (62.5%) reported satisfaction with their household income, 6 (75%) underwent bone marrow transplantation, and all of them (100%) had governmental insurance. The qualitative data that emerged after analysis were classified into three major themes and multiple sub-themes. The three major themes were: (1) treatment side effects, (2) factors affecting treatment experience, and (3) recommendations to improve healthcare service. Conclusion The results of this qualitative study offer insight into how people with multiple myeloma view the healthcare system in Palestine and shed light on the variable and challenging experiences with their treatment, side effects, and communication with healthcare providers within the context of a resource-limited and developing country. Future research should involve hemato-oncology doctors and benefit from their expertise in the field.

Ruthlessly Subtract to Build Oncology Programs and Add Value.

Building modern healthcare programs and systems caring for populations requires expert skills in strategy, finance, people operations, workflow, evaluation, and more. Build often connotes adding services and people, but it al.

Cervical Cancer in Mexico: From a Renowned Vaccination Program to Unfulfilled Needs in Treatment Access.

Mexico's national human papillomavirus (HPV) vaccination program was established in 2008, providing free access to HPV vaccines and quickly becoming an immense success story, achieving significant coverage among young Mexican females. However, despite these efforts and notable achievements, cervical cancer caused mainly by HPV remains a challenging issue among Mexican women aged 15 years or older. A critical obstacle faced by women in the country is a lack of early detection and screening resources, coupled with delays in diagnosis and treatment, exacerbated by the poor distribution of already insufficient healthcare resources. This situation creates adverse conditions for the female demographic in the country. Our editorial aims to draw attention to the urgent need to improve access to adequate prevention, screening, and treatment for cervical cancer patients in Mexico, advocating for a collective effort between the Mexican government, public health professionals, and civil society.

Exploring the readiness of the Irish healthcare system to adopt advanced therapies: a scoping review protocol.

Aim: Advanced therapy medicinal products (ATMPs) are medicines for human use that are based on genes, tissues or cells. They offer groundbreaking new opportunities for the treatment of disease and injury. However, ATMP adoption requires adjustments to current clinical practices and frameworks. This study investigates the readiness of the Irish healthcare system to adopt licensed ATMPs. Materials & methods: Scoping review, guided by the preferred reporting items for systematic reviews and meta-analyses - scoping review extension. A systematic search of English articles from 2013 to 2023 (published and grey literature) will be conducted. Results: Findings will be presented via narrative summary, graphical and tabular formats. Discussion: Review findings will be discussed in the context of recommendations that will inform national policy and strategy on the adoption of ATMPs in Ireland.

This study examines whether Ireland's healthcare system is ready to use new kinds of medicines called advanced therapy medicinal products (ATMPs). These medicines are made from genes, tissues or cells and can be effective in treating certain diseases. However, delivering these medicines might mean changing current practices in clinics and hospitals.A scoping review will examine publications from 2013 to 2023 that focus on this topic. Results will be reported via narrative summary, graphs and tables. A discussion of the findings will be completed to inform recommendations for preparing the Irish healthcare system to deliver ATMPs.Ethics and dissemination: Ethical approval is not required for this scoping review. Findings will be disseminated through publication, stakeholder meetings and public engagement.

From theory to practice: Implementing next-generation sequencing and public health genomics in healthcare systems.

If Europe's health systems make a conscious decision to increase their utilization of technology and techniques that can enhance prevention and expedite early-stage diagnosis, they can effectively address the growing challenges of disease. By embracing these advancements, these health systems can significantly improve their response to emerging health issues.However, at present the effective integration and exploitation of these opportunities remains hesitant and suboptimal, and health and health services underperform accordingly, with patients suffering from the continuing variations in diagnosis and access to innovation. This paper presents a comprehensive study that examines the current state of various influential disciplines and factors in European countries. It specifically focuses on the adoption of Next Generation Screening technologies and the development stage of Public Health Genomics. The assessment of these areas is presented in the context of a rapidly changing policy environment, which provides an opportunity for a fundamental reconsideration of how and where new tools can be integrated into healthcare systems and routine practices. Top of Form.

Utilization of machine learning for dengue case screening.

Dengue causes approximately 10.000 deaths and 100 million symptomatic infections annually worldwide, making it a significant public health concern. To address this, artificial intelligence tools like machine learning can play a crucial role in developing more effective strategies for control, diagnosis, and treatment. This study identifies relevant variables for the screening of dengue cases through machine learning models and evaluates the accuracy of the models. Data from reported dengue cases in the states of Rio de Janeiro and Minas Gerais for the years 2016 and 2019 were obtained through the National Notifiable Diseases Surveillance System (SINAN). The mutual information technique was used to assess which variables were most related to laboratory-confirmed dengue cases. Next, a random selection of 10,000 confirmed cases and 10,000 discarded cases was performed, and the dataset was divided into training (70%) and testing (30%). Machine learning models were then tested to classify the cases. It was found that the logistic regression model with 10 variables (gender, age, fever, myalgia, headache, vomiting, nausea, back pain, rash, retro-orbital pain) and the Decision Tree and Multilayer Perceptron (MLP) models achieved the best results in decision metrics, with an accuracy of 98%. Therefore, a tree-based model would be suitable for building an application and implementing it on smartphones. This resource would be available to healthcare professionals such as doctors and nurses.

Caries status in 12-year-old children, geographical location and socioeconomic conditions across European countries: A systematic review and meta-analysis.

BACKGROUND: Understanding of socioeconomic context might enable more efficient evidence-based preventive strategies in oral health. AIM: The study assessed the caries-related socioeconomic macro-factors in 12-year-olds across European countries. DESIGN: This systematic review involved epidemiological surveys on the caries status of 12-year-olds from 2011 to 2022. DMFT was analyzed in relation to gross national income (GNI), United Nations Statistical Division geographical categorization of European countries (M49), unemployment rate, Human Development Index (HDI), and per capita expenditure on dental health care. A meta-analysis was performed for countries reporting data on DMFT, stratified by GNI, and geographical location of European countries, using a random-effects model. RESULTS: The study involved 493 360 children from 36 countries in the geographic region of Europe. The analysis confirmed a strong negative correlation between income and caries experience (p < .01). Children living in higher-income countries showed 90% lower odds of poor oral health than in middle-income countries. Children living in West Europe showed 90% lower odds of poor oral health than children living in East Europe. CONCLUSION: The strong effect of macro-level socioeconomic contexts on children's oral health suggests favoring upstream preventive oral health strategies in countries with economic growth difficulties, Eastern and Southern parts of Europe.

Bridging technology and care: integrating web-based PROMs in mental health services for refugees: a study on clinician training and technology adoption.

This study explores the integration of a web-based electronic database technology containing patient-reported outcome measures (PROMs) with electronic health records for refugees with PTSD, emphasizing the systematic inclusion of patient perspectives in clinical decision-making. Our research addresses the notable gap in literature regarding training clinicians for the competent integration of health information technology in healthcare. The training program developed aimed at equipping clinicians, particularly inexperienced with technology, to effectively utilize an electronic PROM system for collecting systematic patient information. Our study is set in the context of the Mental Health Services (MHS) in Denmark, focusing on a specialized clinic for treating trauma-affected refugees. The multidisciplinary team involved in this project reflects a wide range of healthcare professionals. The training program employed a variety of activities over nearly 2 years, adapting to feedback and aiming to engage clinicians in continuous improvement processes. Analyzing qualitative data with thematic analysis we interpreted that the training's extended focus on discussion of the implementation process, with limited hands-on experience, potentially reinforced clinicians' hesitations toward new technology, rather than reducing them. Clinicians prioritized immediate concerns over potential long-term benefits. Despite this, their approach reflects a strong commitment to patient welfare and careful evaluation of new practices. Notably, there were also positive engagements with the technology, highlighting its potential in patient care. This study concludes that the successful integration of technology in clinical settings hinges on its alignment with clinicians' workflows, respect for their professional judgment, and clear benefits to patient care.

Using artificial intelligence and predictive modelling to enable learning healthcare systems (LHS) for pandemic preparedness.

In anticipation of potential future pandemics, we examined the challenges and opportunities presented by the COVID-19 outbreak. This analysis highlights how artificial intelligence (AI) and predictive models can support both patients and clinicians in managing subsequent infectious diseases, and how legislators and policymakers could support these efforts, to bring learning healthcare system (LHS) from guidelines to real-world implementation. This report chronicles the trajectory of the COVID-19 pandemic, emphasizing the diverse data sets generated throughout its course. We propose strategies for harnessing this data via AI and predictive modelling to enhance the functioning of LHS. The challenges faced by patients and healthcare systems around the world during this unprecedented crisis could have been mitigated with an informed and timely adoption of the three pillars of the LHS: Knowledge, Data and Practice. By harnessing AI and predictive analytics, we can develop tools that not only detect potential pandemic-prone diseases early on but also assist in patient management, provide decision support, offer treatment recommendations, deliver patient outcome triage, predict post-recovery long-term disease impacts, monitor viral mutations and variant emergence, and assess vaccine and treatment efficacy in real-time. A patient-centric approach remains paramount, ensuring patients are both informed and actively involved in disease mitigation strategies.