Healthcare Research Research Articles

Global, regional, and national burden of thalassemia, 1990–2021: a systematic analysis for the global burden of disease study 2021

Anemia is a significant contributor to the global disease burden, of which thalassemia is the most common hereditary anaemic disease. Previous estimates were based on data that were geographically limited and lacked comprehensive global analysis. This study provides the prevalence, incidence, mortality and disability-adjusted life years (DALYs) of thalassemia in 204 countries and regions of thalassemia between 1990 and 2021, focusing on the age structure and time trends of the disease burden. To provide effective information for health policy, allocation of medical resources and optimization of patient management programs. Using the standardised Global Burden of Disease (GBD) methodologies, we aimed to derive a more precise representation of the health burden posed by thalassemia by considering four distinct types of epidemiological data, namely the incidence at birth, prevalence, mortality and DALYs. The presented data were meticulously estimated and displayed both as numerical counts and as age-standardised rates per 100,000 persons of the population, accompanied by uncertainty interval (UI) to highlight potential statistical variability. The temporal trends spanning the years 1990-2021 were subjected to a rigorous examination utilizing Joinpoint regression analysis. This methodological approach facilitated the computation of the annual percentage change (APC) and the average annual percentage change (AAPC), along with their corresponding 95% confidence intervals (CIs). Globally, the age-standardized prevalence rates (ASPR), age-standardized incidence rates (ASIR), age-standardized mortality rates (ASMR), and age-standardized DALYs rates for thalassemia in 2021 were 18.28 per 100,000 persons (95% UI 15.29-22.02), 1.93 per 100,000 persons (95% UI 1.51-2.49), 0.15 per 100,000 persons(95% UI 0.11-0.20), and 11.65 per 100,000 persons (95% UI 8.24-14.94), respectively. Compared to 1990, these rates have decreased by 0.18 (95% UI-0.22 to-0.14), 0.25 (95% UI-0.30 to-0.19), 0.48 (95% UI-0.60 to-0.28), and 0.49 (95% UI-0.62 to-0.29) respectively. In 2021, the ASIR of thalassemia was highest in East Asia at 7.35 per 100,000 persons (95% UI 5.37-10.04), and ASMR was highest in Southeast Asia at 0.37 per 100,000 persons (95% UI 0.29-0.45).Gender comparisons showed negligible differences in disease burden, with the highest prevalence noted in children under five, decreasing with age. The global ASPR and ASMR declined from 1990 to 2021 overall, though an increasing trend in prevalence was found among the elderly. Joinpoint analysis revealed that the global ASPR increased between 2018 and 2021 (APC=9.2%, 95% CI: 4.8%-13.8%, P<0.001), ASIR decreased (APC=-7.68%, 95% CI:-10.88% to-4.36%, P<0.001), and there was a significant rise in ASMR from 2019 to 2021 (APC=4.8%, 95% CI: 0.1%-9.6%, P<0.05). Trends in ASPR and ASMR varied across regions, with notable changes in South Asia. The global burden of thalassemia, reflected in its prevalence, incidence, mortality, and DALYs, exhibits significant disparities. Geographic and demographic shifts in disease distribution have been observed from 1990 to 2021, with an overall decrease in burden, yet an increase in cases among the elderly population. Analysis of epidemiological trends over time highlights the influence of health policies and significant public health interventions on thalassemia outcomes. There data are crucial for healthcare professionals, policymakers, and researchers to refine and enhance management strategies, aiming to further mitigate thalassemia's global impact. National Natural Science Foundation of China; Guizhou Province Science and Technology Project; Guizhou Province Science and Technology Foundation of Health Commission.

Med-Tech-AI: Exploring Artificial Intelligence for Enhanced Healthcare Research

Abstract: This paper explores the potential of Artificial Intelligence (AI) to revolutionize healthcare research through the MedTech-AI project. The project investigates the development and evaluation of AI models for medical image analysis, aiming for improved disease detection and classification. It examines the potential of AI-powered clinical decision support systems (CDSS) to assist healthcare professionals. Additionally, the project explores Natural Language Processing (NLP) techniques for extracting valuable insights from unstructured healthcare data like electronic health records and medical literature. Finally, the paper investigates the use of AI for predictive analytics in disease prevention, identifying risk factors and informing preventative measures. By examining these areas, the Med-Tech-AI project establishes a foundation for understanding how AI can enhance healthcare research, potentially leading to improved healthcare efficiency, accuracy, and ultimately, better patient outcomes

Prophylactic efficacy of aqueous extract of unripe pawpaw (Carica papaya) fruit on hematological and biochemical parameters in induced hyperbilirubinemia Wistar rats

This research offers a comprehensive examination of hyperbilirubinaemia, characterized by elevated levels of bilirubin in the bloodstream leading to jaundice. It presents a prevalent challenge during the neonatal phase, as bilirubin can accumulate in various body parts resulting in potential neurotoxicity. The discussion encompasses factors influencing bilirubin production, conjugation, and elimination in the organism, along with an overview of the causes and symptoms of hyperbilirubinaemia. The study delves into the potential advantages of utilizing Carica papaya, a tropical fruit, for the prevention of hyperbilirubinaemia. Traditional medical approaches for managing this condition are linked to numerous adverse effects, hence the necessity to develop more affordable, easily accessible, and efficacious drugs with minimal or no side effects. Blood samples were procured and subjected to full blood cell count analysis via an automated hematologic analyzer, while serum samples were utilized for quantifying liver enzymes and bilirubin employing a spectrophotometric technique. Additionally, the weight of visceral organs was determined using a Mettler weighing balance. The outcomes of an experimental study conducted on rodents revealed that pretreatment with Carica papaya extract notably decreased bilirubin and liver enzyme levels in the subjects. This highlights the potential role of Carica papaya in the preventive management of hyperbilirubinaemia, underscoring the imperative for further investigation in this domain. In essence, this research serves as a valuable repository of knowledge for healthcare practitioners and researchers keen on addressing hyperbilirubinaemia.

The nexus of minds unveiling the significance of AI in mental health and viable remedie

The world is evolving at a cutthroat rate, and the integration of mental well-being and technology can yield promising results. For this reason, I have created The Nexus of Minds, which focuses on exploring the role of AI in changing the dynamics and thought processes of how we address and approach mental health issues. I am focusing on the transformative potential of Artificial Intelligence as a force for accessible, personalized, and effective mental healthcare. Mental healthcare has a huge spectrum, and I am exploring the multifaceted applications of AI, which have the potential to change the landscape. Machine Learning, Data Analytics, and Natural Language Processing have shown immense advancements, which is empowering AI to redefine how mental health issues are diagnosed and treated. Ranging from earlier intervention through continuous monitoring to all-time support, this book focuses on different ways in which AI solutions are improving the quality of mental healthcare and clinical results. It focuses on how AI is empowering people on their mental health journey. In addition to the technological innovation, I will navigate through the ethical considerations that need to be focused on, including but not limited to privacy, algorithmic bias, data privacy and security, and the role of human connection and empathy in mental healthcare. By understanding these essentials, I am offering insights into the responsible, ethical, and balanced integration of AI into mental health support. Moreover, this book focuses on apps and chatbots that are already providing help to people struggling with mental health illnesses and how they are benefiting from AI-centric mental health services. As a result, the readers will be able to gain an understanding of how AI will improve the quality of mental healthcare and destigmatize mental health conditions. With "The Nexus of Minds," I envision a future where AI won't be merely a tool but an absolute force in mental healthcare by breaking down the stigma and societal barriers to give hope to struggling people. This book will become a guiding beacon, providing information about the seamless coexistence of ethical and compassionate mental healthcare and tech advancement. I have taken an interdisciplinary approach, making it a promising resource for healthcare professionals, policymakers, researchers, and common people who want to see a bright future in the mental health industry.

Wake Up Safe in the USA & International Patient Safety.

Patient safety is the most important aspect of anesthetic care. For both healthcare professionals and patients, the ideal would be no significant morbidity or mortality under anesthesia. Lessons from harm during healthcare can be shared to reduce harm and to increase safety. Many nations and individual institutions have developed robust safety systems to improve the quality and safety of patient care. Large registries that collect rare events, analyze them, and share findings have been developed. The approach, the funding, the included population, support from institutions and government and the methods of each vary. Wake Up Safe (WUS) is a patient safety organization accredited by Agency for Healthcare Research and Quality. Wake Up Safe was established in the United States in 2008 by the Society for Pediatric Anesthesia. The initiative aims to gather data on adverse events, analyze these incidents to gain insights, and apply this knowledge to ultimately reduce their occurrence. The purpose of this review is to describe the patient safety approaches in the USA. Through a national patient safety database WUS. Similar approaches either through WUS international or independent safety approaches have been described in Australia-New Zealand, India, and Singapore. We examine the patient safety processes across the four countries, evaluating their incident review process and the distribution of acquired knowledge. Our focus is on assessing the potential benefits of a WUS collaboration, identifying existing barriers, and determining how such a collaboration would integrate with current incident review databases or systems.

A Systematic Review of Research Gaps in the Built Environment of Inpatient Healthcare Settings.

This study utilized the evidence-gap map method and critically examined the scope, methodologies, and focus of the studies that investigated the influence of the built environment on inpatient healthcare settings over a decade (2010-2021). We conducted a systematic review per the preferred reporting items for systematic reviews and meta-analyses guidelines and surveyed 406 articles, primarily from North America and Europe. Our findings revealed a dominant focus on architectural features (73%), such as room design and ward layout. Comparatively, there was less emphasis on interior-, ambient-, social-, and nature-related features. Most previous studies explored multiple environmental features, which indicated the intricacy of this field. Research outcomes were diverse, with person-centered care (PCC) being the most frequently investigated, followed by safe care, emotional well-being, activity, and behavior. Furthermore, research methods varied considerably based on the study's outcomes and features. Clinical outcomes and safe care favored quantitative methods, activity and behavior favored mixed methods, and PCC favored qualitative research. This review provides an in-depth overview of the existing studies on healthcare design research and sheds light on the current trends and methodological choices. The insights garnered can guide future research, policy-making, and the development of healthcare facilities.

Who are the patients who use non-pharmacological home remedies? Cross-sectional study in Switzerland and France.

Many patients may be tempted to use non-pharmacological home remedies (NPHRs) to relieve various complaints. To the best of our knowledge, there is little data on the characteristics of patients using NPHRs. In this cross-sectional study carried out between March 2020 and July 2021, we examined the socio-demographic factors underlying their use in patient populations in Switzerland and France. Using official registries, we randomly selected 50 primary care physicians (PCPs) in Geneva (Switzerland) and Lyon/Grenoble (France). Seven research assistants consecutively recruited patients from PCP waiting rooms (20-25 patients per practice). Patients completed a paper-based questionnaire assessing the use [yes/no] of 304 NPHRs for 79 medical conditions. The NPHR list was developed by our team with input from 97 patients. We used univariable and multivariable logistic regressions, adjusting for intra-cluster correlations, to examine associations between NPHR use and patient characteristics (gender, age, practice location, nationality, education level, and self-rated health). Of the 1198 eligible patients, 1012 agreed to participate (85%). Overall, 635 patients (63%) reported using at least one of the remedies tested in the study. In multivariable analysis, women (OR = 1.7 [95%CI = 1.3-2.3], P-value < 0.001), younger patients (< 40 years: OR = 2.1 [95%CI = 1.6-2.9], P-value < 0.001), and French patients (OR = 1.6 [95%CI = 1.1-2.3], P-value < 0.001) tended to use NPHRs more often than other patients. Many patients, particularly women, young people, and French patients, reported using NPHRs. This survey's findings hold the potential to inform healthcare providers, policymakers, and researchers about the diverse preferences that shape patients' healthcare choices.

Core outcome Set for Cardio-oncology (COS-CO): development of a set of outcomes for the cardiovascular assessment and monitoring of cancer patients and survivors.

There is an increasing awareness of the evidence-based selection of outcomes to be measured in clinical trials and clinical practice. Currently, there is no core outcome set (COS) for cardio-oncology, which may hinder the (inter)national comparison of the effectiveness of research and the quality of cardio-oncology care. The aim of this study is to develop a standard and pragmatic patient-centred outcome set to assess and monitor cancer patients and survivors at risk of or with cardiovascular diseases. A list of outcome domains was generated through a review of registries and guidelines, and six patient interviews. The project team reviewed and refined the outcome domains prior to starting a two-round Delphi procedure conducted between January-June 2022. The panellists, including healthcare providers and researchers, were invited to rate the importance of the outcomes. 26 experts from 11 countries rated a list of 93 outcomes (round 1) and 63 outcomes (round 2) to gain consensus on a list of outcome measures, and of demographic factors, health status and treatment variables. The final COS includes 15 outcome measures, reflecting four core areas: life impact (n=2), pathophysiological manifestations (n=9), resource use/economic impact (n=1), and mortality/survival (n=3). Next, six demographic factors, 21 health status, three cardiovascular and nine cancer variables were included. This is the first international development of a COS for cardio-oncology. This set aims to facilitate (inter)national comparison in cardio-oncology care, using standardised parameters and meaningful patient-centred outcomes for research and quality of care assessments.

COVID-19 Admission Rates and Changes in Care Quality in US Hospitals

Unprecedented increases in hospital occupancy rates during COVID-19 surges in 2020 caused concern over hospital care quality for patients without COVID-19. To examine changes in hospital nonsurgical care quality for patients without COVID-19 during periods of high and low COVID-19 admissions. This cross-sectional study used data from the 2019 and 2020 Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project State Inpatient Databases. Data were obtained for all nonfederal, acute care hospitals in 36 states with admissions in 2019 and 2020, and patients without a diagnosis of COVID-19 or pneumonia who were at risk for selected quality indicators were included. The data analysis was performed between January 1, 2023, and March 15, 2024. Each hospital and week in 2020 was categorized based on the number of COVID-19 admissions per 100 beds: less than 1.0, 1.0 to 4.9, 5.0 to 9.9, 10.0 to 14.9, and 15.0 or greater. The main outcomes were rates of adverse outcomes for selected quality indicators, including pressure ulcers and in-hospital mortality for acute myocardial infarction, heart failure, acute stroke, gastrointestinal hemorrhage, hip fracture, and percutaneous coronary intervention. Changes in 2020 compared with 2019 were calculated for each level of the weekly COVID-19 admission rate, adjusting for case-mix and hospital-month fixed effects. Changes during weeks with high COVID-19 admissions (≥15 per 100 beds) were compared with changes during weeks with low COVID-19 admissions (<1 per 100 beds). The analysis included 19 111 629 discharges (50.3% female; mean [SD] age, 63.0 [18.0] years) from 3283 hospitals in 36 states. In weeks 18 to 48 of 2020, 35 851 hospital-weeks (36.7%) had low COVID-19 admission rates, and 8094 (8.3%) had high rates. Quality indicators for patients without COVID-19 significantly worsened in 2020 during weeks with high vs low COVID-19 admissions. Pressure ulcer rates increased by 0.09 per 1000 admissions (95% CI, 0.01-0.17 per 1000 admissions; relative change, 24.3%), heart failure mortality increased by 0.40 per 100 admissions (95% CI, 0.18-0.63 per 100 admissions; relative change, 21.1%), hip fracture mortality increased by 0.40 per 100 admissions (95% CI, 0.04-0.77 per 100 admissions; relative change, 29.4%), and a weighted mean of mortality for the selected indicators increased by 0.30 per 100 admissions (95% CI, 0.14-0.45 per 100 admissions; relative change, 10.6%). In this cross-sectional study, COVID-19 surges were associated with declines in hospital quality, highlighting the importance of identifying and implementing strategies to maintain care quality during periods of high hospital use.

Readability Analysis of Spanish-Language Patient-Reported Outcome Measures in Orthopaedic Surgery.

Patient-reported outcome measures (PROMs) are increasingly used to quantify patient symptomatology when assessing intervention effectiveness as well as to guide patient-centered health-care management and research. Previous studies have analyzed the readability of PROMs used in orthopaedics in the English language; however, the readability of PROMs used in orthopaedics in the Spanish language has not been evaluated. Given the increasing number of Spanish-speaking individuals seeking orthopaedic care in the United States, it is imperative that PROMs are at an adequate reading level for patients to answer appropriately in their native language. This study aimed to assess the readability of publicly available Spanish-language PROMs used in orthopaedics. Searches of the PubMed, Google Scholar, and Embase databases were conducted to identify publicly available Spanish-language versions of PROMs used in orthopaedics. Additionally, the PROMIS Health Organization was contacted to obtain Spanish-language versions of the PROMIS (Patient-Reported Outcomes Measurement Information System) questionnaires. A total of 42 Spanish-language PROMs were identified and included in this study. The text of each PROM was inserted into multilingual readability software (legible.es), which analyzed the readability of each PROM using the Fernández Huerta and Índice de Legibilidad de Flesch-Szigriszt (INFLESZ) readability formulas. The mean and standard deviation (SD) of the raw readability scores were calculated for the PROMs. The percentage of PROMs at or below the 6th-grade reading level was also calculated. The mean readability of PROMs using the INFLESZ formula was at a 7th to 10th-grade reading level (mean = 63, SD = 16), and the mean readability using the Fernández Huerta formula was 68 (SD = 15). On average, 57% of the PROMs used in orthopaedics were at or below the INFLESZ 6th-grade readability level. Approximately half of Spanish-language PROMs used in orthopaedics are written at a reading level that does not meet the National Institutes of Health and the American Medical Association recommendations at or below the 6th-grade level. PROM developers and translators should consider the importance of readability when translating PROMs, to maximize their use and efficacy among orthopaedic patients speaking that language.

Measuring Signs of Atopic Dermatitis in Clinical Practice: A HOME-CP Consensus Statement.

Outcome measurement is an essential component of value-based health care and can aid patient care, quality improvement, and clinical effectiveness evidence generation. The Harmonising Outcome Measures for Eczema Clinical Practice initiative aims to identify a list of validated, feasible, outcome measurement instruments recommended to measure atopic dermatitis (AD) in the clinical practice setting. The clinical practice set is a list of instruments that clinicians can pick and choose from to suit their needs in the context of clinical care. To recommend instruments to measure clinical signs of AD in clinical practice. Following the predefined roadmap, a mixed methods design was implemented and incorporated systematic reviews and qualitative consensus methods. Previous systematic reviews identified few clinical signs instruments with sufficient validation for recommendation. An updated systematic review evaluating the validity of clinical signs instruments informed an international meeting to reach consensus on recommended instruments to measure AD clinical signs in clinical practice. Consensus was defined as less than 30% disagreement. An in-person consensus exercise was held in Montreal, Canada, on October 16, 2022. The 34 attendees included patient and patient advocate research partners, health care professionals, researchers, methodologists, and industry representatives. The updated systematic review found that the Eczema Area and Severity Index (EASI), Scoring Atopic Dermatitis, and objective Scoring Atopic Dermatitis were the only instruments that demonstrated sufficient performance in all assessed measurement properties. The modified EASI and Signs Global Assessment × Body Surface Area instruments were also recommended. The EASI, Validated Investigator Global Assessment, and Investigator's Global Assessment multiplied by or measured concurrently with a body surface area measure achieved consensus in criteria and were adopted. This consensus statement by the Harmonising Outcome Measures for Eczema initiative suggests that when assessing and documenting clinical signs of AD, there are several valid and feasible instruments that can best fit a clinician's specific practice needs. These instruments should improve and standardize the documentation of signs severity, help determine the effect of treatment, facilitate the generation of clinical effectiveness evidence, and enhance the implementation of value-based health care.

Contemporary Insights into Hepatitis C Virus: A Comprehensive Review

Hepatitis C virus (HCV) remains a significant global health challenge. Approximately 50 million people were living with chronic hepatitis C based on the World Health Organization as of 2024, contributing extensively to global morbidity and mortality. The advent and approval of several direct-acting antiviral (DAA) regimens significantly improved HCV treatment, offering potentially high rates of cure for chronic hepatitis C. However, the promising aim of eventual HCV eradication remains challenging. Key challenges include the variability in DAA access across different regions, slightly variable response rates to DAAs across diverse patient populations and HCV genotypes/subtypes, and the emergence of resistance-associated substitutions (RASs), potentially conferring resistance to DAAs. Therefore, periodic reassessment of current HCV knowledge is needed. An up-to-date review on HCV is also necessitated based on the observed shifts in HCV epidemiological trends, continuous development and approval of therapeutic strategies, and changes in public health policies. Thus, the current comprehensive review aimed to integrate the latest knowledge on the epidemiology, pathophysiology, diagnostic approaches, treatment options and preventive strategies for HCV, with a particular focus on the current challenges associated with RASs and ongoing efforts in vaccine development. This review sought to provide healthcare professionals, researchers, and policymakers with the necessary insights to address the HCV burden more effectively. We aimed to highlight the progress made in managing and preventing HCV infection and to highlight the persistent barriers challenging the prevention of HCV infection. The overarching goal was to align with global health objectives towards reducing the burden of chronic hepatitis, aiming for its eventual elimination as a public health threat by 2030.

Analysing the performance of a health innovation ecosystem in the COVID-19 crisis: complexity and chaos theory perspective

BackgroundThis research delves into the complexity management of collaborative networks and interorganizational systems in the health innovation ecosystem on the basis of a best practice in the coronavirus disease 2019 (COVID-19) crisis. The objective is to offer specific solutions and guidelines to stakeholders in the health innovation ecosystem to control the chaos resulting from unexpected events along the ecosystem development and evolution path.MethodsFor this purpose, the performance of the Health Innovation Ecosystem in Iran (the Every Home is a Health Base plan) has been examined through a detailed and in-depth analysis of events and actions taken using documents, reports and interviews with experts. The practical application of chaos and complex adaptive system features (adaptation, time horizons, edge of chaos, sensitivity to initial conditions, state space and strange attractors) is introduced to identify and manage the transition from a state where the health innovation ecosystem is on the edge of chaos and prone to failure. Data were collected through studying documents, reports and interviews with experts, and then analysed using qualitative content analysis techniques, open and axial coding and metaphors derived from complexity and chaos theories.ResultsThe findings indicate that to understand and embrace the complexity of the health innovation ecosystem throughout its development and evolution and manage and lead it through the edge of chaos towards successful interorganizational systems performance, it is necessary to use gap analysis to achieve consensus, establish a highly interactive governance structure with key stakeholders of the ecosystem, maintain flexibility to control bifurcations (butterfly effect), prevent transforming emergency solutions into standard routines and ensure the sustainability of the ecosystem against future threats by long-term financial security.ConclusionsThis research provides insights into the dynamics of complex health systems and offers strategies for promoting successful innovation through collaborative networks and interorganizational systems in the development and evolution of the health innovation ecosystem. By embracing complexity and chaos, healthcare professionals, policy-makers and researchers can collaboratively address complex challenges and improve outcomes in health network activities. The conclusion section provides guidelines for successfully managing the complexity of the ecosystem and offers suggestions for further research.

Epistemic appropriation and the ethics of engaging with trans community knowledge in the context of mental healthcare research

Mental healthcare research increasingly focuses the needs of trans people and, in doing so, acknowledges knowledge and epistemic resources developed in trans communities. In this article, we aim to raise awareness of an ethical issue described by Emmalon Davis that may arise in the context of engaging with community knowledge and epistemic resources: the risk of epistemic appropriation. It is composed of two harms (1) a detachment of epistemic resources developed in the originating community and (2) a misdirection of these epistemic resources for epistemic goals of a dominant community. In this article, we map and discuss the ethical concerns in using knowledge originating in trans communities in terms of epistemic appropriation in the context of mental healthcare research. We first argue that misgendering, failing to reference non-academic sources and a lack of attribution in community authorship are forms of epistemic detachment. Second, we problematize cases of epistemic misdirection of trans epistemic resources, focusing on the examples of detransition and transition regret. We discuss harms related to epistemic appropriation in relationship to risks to safety. The article aims to raise awareness about the risk of epistemic appropriation both in researchers engaging with trans knowledge as well as in mental healthcare workers who seek information on trans.

Long COVID Definitions and Models of Care : A Scoping Review.

Definitions of long COVID are evolving, and optimal models of care are uncertain. To perform a scoping review on definitions of long COVID and provide an overview of care models, including a proposed framework to describe and distinguish models. English-language articles from Ovid MEDLINE, PsycINFO, the Cochrane Library, SocINDEX, Scopus, Embase, and CINAHL published between January 2021 and November 2023; gray literature; and discussions with 18 key informants. Publications describing long COVID definitions or models of care, supplemented by models described by key informants. Data were extracted by one reviewer and verified for accuracy by another reviewer. Of 1960 screened citations, 38 were included. Five clinical definitions of long COVID varied with regard to timing since symptom onset and the minimum duration required for diagnosis; 1 additional definition was symptom score-based. Forty-nine long COVID care models were informed by 5 key principles: a core "lead" team, multidisciplinary expertise, comprehensive access to diagnostic and therapeutic services, a patient-centered approach, and providing capacity to meet demand. Seven characteristics provided a framework for distinguishing models: home department or clinical setting, clinical lead, collocation of other specialties, primary care role, population managed, use of teleservices, and whether the model was practice- or systems-based. Using this framework, 10 representative practice-based and 3 systems-based models of care were identified. Published literature often lacked key model details, data were insufficient to assess model outcomes, and there was overlap between and variability within models. Definitions of long COVID and care models are evolving. Research is needed to optimize models and evaluate outcomes of different models. Agency for Healthcare Research and Quality. (Protocol posted at https://effectivehealthcare.ahrq.gov/products/long-covid-models-care/protocol.).

Critical Review on the Contribution of Machine Learning to Health Science

Background: The field of machine learning in health science is evolving exponentially, with a focus on accelerating scientific discoveries, improving holistic well-being, and advancing personalized healthcare. Aim: In this same spirit, this critical review article aims to provide a comprehensive understanding of the role, challenges, opportunities, and ethical considerations of integrating machine learning into health science, with an emphasis on healthcare research and practice. Methods: To base its critiques on previous literature, the elucidative survey considered specific criteria, such as the significance and contribution of each source to the field, methodology or approach, and argument, as well as the use of evidence. Results: The study results indicate that machine learning holds great promise to improve evidence-based health science, but significant work is needed to ensure the technology is developed and deployed in a way that is trustworthy and ethical. Conclusion: In conclusion, the literature review presents a balanced assessment of the strengths, weaknesses, and notable features of the current state of machine learning in health science. The key takeaway point is that while machine learning has demonstrated significant potential to improve health science outcomes and strategic management, there are still important challenges, limitations, and research gaps that need to be addressed to facilitate widespread adoption and trust in these technologies.

Global Trends in Kidney Stone Awareness: A Time Series Analysis from 2004-2023.

Despite the prevalence and incidence of kidney stones progressively increasing worldwide, public awareness of this condition remains unclear. Understanding trends of awareness can assist healthcare professionals and policymakers in planning and implementing targeted health interventions. This study investigated online search interest in "kidney stone" by analyzing Google Trends, focusing on stationarity of the trends and predicting future trends. We performed time series analysis on worldwide Google monthly search data from January 2004 to November 2023. The Augmented Dickey-Fuller (ADF) test was used to assess the stationarity of the data, with a p-value below 0.05 indicating stationarity. Time series forecasting was performed using the autoregressive integrated moving average to predict future trends. The highest search interest for "kidney stone" (score 100) was in August 2022, while the lowest was in December 2007 (score 36). As of November 2023, search interest remained high, at 92. The ADF test was significant (p = 0.023), confirming data stationarity. The time series forecasting projected continued high public interest, likely reflecting ongoing concern and awareness. Notably, diverse regions such as Iran, the Philippines, Ecuador, the United States, and Nepal showed significant interest, suggesting widespread awareness of nephrolithiasis. This study highlighted that "kidney stone" is a consistently relevant health issue globally. The increase and stationarity of search trends, the forecasted sustained interest, and diverse regional interest emphasize the need for collaborative research and educational initiatives. This study's analysis serves as a valuable tool for shaping future healthcare policies and research directions in addressing nephrolithiasis related health challenges.

Evaluating nomogram models for predicting survival outcomes in gastric gastrointestinal stromal tumors with SEER database analysis

Gastrointestinal stromal tumors (GISTs) predominantly develop in the stomach. While nomogram offer tremendous therapeutic promise, there is yet no ideal nomogram comparison customized specifically for handling categorical data and model selection related gastric GISTs. (1) We selected 5463 patients with gastric GISTs from the SEER Research Plus database spanning from 2000 to 2020; (2) We proposed an advanced missing data imputation algorithm specifically designed for categorical variables; (3) We constructed five Cox nomogram models, each employing distinct methods for the selection and modeling of categorical variables, including Cox (Two-Stage), Lasso-Cox, Ridge-Cox, Elastic Net-Cox, and Cox With Lasso; (4) We conducted a comprehensive comparison of both overall survival (OS) and cancer-specific survival (CSS) tasks at six different time points; (5) To ensure robustness, we performed 50 randomized splits for each task, maintaining a 7:3 ratio between the training and test cohorts with no discernible statistical differences. Among the five models, the Cox (Two-Stage) nomogram contains the fewest features. Notably, at Near-term, Mid-term, and Long-term intervals, the Cox (Two-Stage) model attains the highest Area Under the Curve (AUC), top-1 ratio, and top-3 ratio in both OS and CSS tasks. For the prediction of survival in patients with gastric GISTs, the Cox (Two-Stage) nomogram stands as a simple, stable, and accurate predictive model with substantial promise for clinical application. To enhance the clinical utility and accessibility of our findings, we have deployed the nomogram model online, allowing healthcare professionals and researchers worldwide to access and utilize this predictive tool.

Salzburg Intensive Care database (SICdb): a detailed exploration and comparative analysis with MIMIC-IV.

The utilization of artificial intelligence (AI) in healthcare is on the rise, demanding increased accessibility to (public) medical data for benchmarking. The digitization of healthcare in recent years has facilitated medical data scientists' access to extensive hospital data, fostering AI-based research. A notable addition to this trend is the Salzburg Intensive Care database (SICdb), made publicly available in early 2023. Covering over 27 thousand intensive care admissions at the University Hospital Salzburg from 2013 to 2021, this dataset presents a valuable resource for AI-driven investigations. This article explores the SICdb and conducts a comparative analysis with the widely recognized Medical Information Mart for Intensive Care - version IV (MIMIC-IV) database. The comparison focuses on key aspects, emphasizing the availability and granularity of data provided by the SICdb, particularly vital signs and laboratory measurements. The analysis demonstrates that the SICdb offers more detailed information with higher data availability and temporal resolution for signal data, especially for vital signs, compared to the MIMIC-IV. This is advantageous for longitudinal studies of patients' health conditions in the intensive care unit. The SICdb provides a valuable resource for medical data scientists and researchers. The database offers comprehensive and diverse healthcare data in a European country, making it well suited for benchmarking and enhancing AI-based healthcare research. The importance of ongoing efforts to expand and make public datasets available for advancing AI applications in the healthcare domain is emphasized by the findings.

Smart Report for Healthcare System

In the ever-evolving landscape of healthcare, the effective interpretation and presentation of pathology patient data play a pivotal role in diagnosis, treatment, and research. Recognizing the transformative power of advanced data visualization techniques, this project endeavors to forge a comprehensive and user-friendly a platform dedicated to illuminating pathology patient data. By leveraging cutting-edge visualization tools, our the initiative aspires to furnish healthcare professionals, researchers, and patients with an intuitive and holistic representation of intricate pathology data. As healthcare continues to evolve, the potential benefits of Smart Healthcare Systems are evident, including enhanced patient outcomes, reduced costs, and improved healthcare delivery. In conclusion, this report underscores the profound impact of Smart Healthcare Systems on the future of medical services. It provides valuable insights for healthcare professionals, policymakers, and stakeholders to navigate the changing landscape and maximizing the benefits of smart technologies in healthcare.