Efficiency Of Health Care System Research Articles

A blockchain-based solution for enhancing the efficiency and security of healthcare knowledge management systems in the era of industry 4.0

A blockchain-based solution for enhancing the efficiency and security of healthcare knowledge management systems in the era of industry 4.0

Exploring the administrative burden faced by hematologists: a comprehensive study in Italy.

Administrative burdens have been identified as a major issue impacting patient care, professional practice, and the overall efficiency of healthcare systems. The aim of this study is to assess the administrative burden faced by Italian hematologists. A cross-sectional survey that included both closed-ended quantitative questions and open-ended free text answer options was administered to 1,570 hematologists working with malignancies and members of Italian GIMEMA Foundation - Franco Mandelli ONLUS and the Italian Linfomi Foundation (FIL). The survey was conducted online from May 24 to June 30, 2023. Descriptive statistics were computed for the quantitative data to clearly summarize the responses and descriptive analysis of free text responses was carried out. Surveyed hematologists spend an average of 47.07% of their time on administrative tasks, with 63.22% (n=110) of respondents reporting spending at least half of their time on these activities. More than half (57.47%, n=100) reported that "Patient care" is the medical task most affected by a lack of time. Additionally, 55.17% (n=96) reported experiencing burnout in the past 6 months, with filling out "Forms" being identified as the top contributing administrative task by 27.59% (n=48) of respondents, followed by "Scheduling" (24.71%, n=43) and "Managing IT system failures" (21.84%, n=38). Nearly half of the surveyed hematologists (45.40%, n==79) identified patient care as the top priority requiring more time. The study confirms that the administrative workload of hematologists has a significant impact on patient care, communication, and burnout risk, reducing the time available for patient care, leading to exhaustion and concern about clinical errors.

The global, regional, and national patterns of change in the burden of non-malignant upper gastrointestinal diseases from 1990 to 2019 and the forecast for the next decade.

Non-malignant upper gastrointestinal diseases, including peptic ulcer disease (PUD), gastritis and duodenitis (GD), and gastroesophageal reflux disease (GERD), significantly challenge global healthcare. These conditions not only impact patient health but also highlight socio-economic development issues and healthcare system accessibility and efficiency. Utilizing the Global Burden of Disease (GBD) database, this study aims to assess the global burden of PUD, GD, and GERD comprehensively, examining their association with the sociodemographic index (SDI). Employing data from the GBD 2019 database, this study analyzed the disability-adjusted life years (DALYs) for PUD, GD, and GERD. We integrated the SDI with the inequality slope index and concentration index for an international health inequality analysis, assessing disparities in the burden of these non-malignant upper gastrointestinal diseases. Decomposition analysis was conducted to determine the effects of population growth, aging, and epidemiological shifts on disease burden. Frontier analysis was performed to identify potential improvement areas and disparities among countries by development status. Disease time trends were depicted using a Joinpoint regression model, and the Bayesian age-period-cohort (BAPC) model projected the disease burden up to 2030. Between 1990 and 2019, the age-standardized DALYs rates (ASDR) for non-malignant upper gastrointestinal diseases declined. However, global geographic heterogeneity remained evident, closely linked to the SDI. Notably, low-SDI countries experienced higher disease burdens. Population growth and aging emerged as principal contributors to the increasing disease burden. Despite development levels, many countries have considerable potential for reducing the burden of these diseases. Furthermore, significant variations in the time trends of non-malignant upper gastrointestinal diseases were observed among countries and regions with different SDI levels, a pattern expected to continue through 2030. Non-malignant upper gastrointestinal diseases demonstrate notable heterogeneity across age, gender, and geography, with the disparities most marked in underdeveloped regions or countries. Consequently, it is imperative to focus research on policy development and to enact prevention and treatment strategies tailored to high-risk groups. This targeted approach is essential for mitigating the disease burden effectively.

Enhancing Clinical Documentation with Synthetic Data: Leveraging Generative Models for Improved Accuracy

Accurate and comprehensive clinical documentation is crucial for delivering high-quality healthcare, facilitating effective communication among providers, and ensuring compliance with regulatory requirements. However, manual transcription and data entry processes can be time-consuming, error-prone, and susceptible to inconsistencies, leading to incomplete or inaccurate medical records. This paper proposes a novel approach to augment clinical documentation by leveraging synthetic data generation techniques to generate realistic and diverse clinical transcripts. We present a methodology that combines state-of- the-art generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), with real-world clinical transcript and other forms of clinical data to generate synthetic transcripts. These synthetic transcripts can then be used to supplement existing documentation workflows, providing additional training data for natural language processing models and enabling more accurate and efficient transcription processes. Through extensive experiments on a large dataset of anonymized clinical transcripts, we demonstrate the effectiveness of our approach in generating high- quality synthetic transcripts that closely resemble real- world data. Quantitative evaluation metrics, including perplexity scores and BLEU scores, as well as qualitative assessments by domain experts, validate the fidelity and utility of the generated synthetic transcripts. Our findings highlight synthetic data generation's potential to address clinical documentation challenges, improving patient care, reducing administrative burdens, and enhancing healthcare system efficiency.

CNN and XGBoost for Automatic Segmentation of Stroke Lesions using CT Data

The negative impact of stroke on society has led to a concerted effort to improve stroke management and diagnosis. Technological advances have led to the development of deep learning techniques that can be used to accurately detect and characterize brain damage caused by stroke. The ubiquitous growth of artificial intelligence and its medical applications has improved the efficiency of healthcare systems for patients requiring rapid care. Today, chronic diseases such as stroke are the leading cause of death worldwide. A stroke is a type of brain injury. Stroke lesions occur when a group of brain cells dies due to a lack of blood supply. Stroke damage can disrupt brain function, causing a wide range of symptoms such as weakness, disturbance of one or more senses and confusion. It is important to detect and characterize the damaged areas of brain tissue caused by stroke accurately, quickly and effectively in order to save the patient. Stroke diagnosis often relies on expensive imaging techniques such as CT and magnetic resonance imaging (MRI), which are expensive nevertheless CT is more available and accessible in some African hospitals in general and in Senegal in particular. We therefore use a CT dataset to automatically segment stroke lesions. We find that CNN combined with XGBoost can effectively detect, classify and characterize stroke-damaged areas.

Situational analysis of hypertension management at primary health care level in São Paulo, Brazil: population, healthcare professional and health system perspectives

BackgroundGovernment-led, population-wide initiatives are crucial for advancing the management of hypertension − a leading cause of cardiovascular disease (CVD) morbidity and mortality. An urban population health initiative was conducted against this backdrop, focussing on hypertension in the primary health system in São Paulo, Brazil. Within the frame of the initiative and under the supervision and leadership of the municipal health authorities, a situational analysis was conducted on the needs in hypertension management, marking the first phase of a Design Thinking process. This article describes the situational analysis process and presents the identified elements to be strengthened considering hypertension diagnosis, treatment and control.MethodsFirst, a mixed-methods approach was used, starting with a literature review of municipal hypertension data followed by meetings (N = 20) with the local public health administration to assess health system level components. To investigate activities on hypertension diagnosis, treatment and control, nine primary healthcare units were selected from two districts of São Paulo city– Itaquera and Penha– which received an online form addressed to managers, participated in conversation circles of staff and patients, and underwent shadowing of community health agents.ResultsData gave rise to two main outputs: (i) a patient care journey map; and (ii) a matrix summarizing the identified needs at patient, healthcare professional and health system level for diagnosis, treatment and control of hypertension. Patient awareness and knowledge of hypertension was found to be insufficient and its management needs to be improved. For health professionals, disease awareness, technical training, more time dedicated to patients, and simplified guidelines and clinical decision-making tools for hypertension management were identified as principal needs. The situational analysis found that the healthcare systems efficiency might be improved by establishing defined treatment and care delivery goals with a focus on outcomes and implemented through action plans.ConclusionsThis situational analysis identified several needs related to hypertension control in São Paulo that are in line with global challenges to improve the control of CVD risk factors. Findings were also confirmed locally in an expansion phase of this situational analysis to additional primary care facilities. As a consequence, solutions were designed, promptly taken up and implemented by the municipal health secretariat.

Global burden and socioeconomic impact of knee osteoarthritis: a comprehensive analysis.

To report the trend changes of the prevalence, incidence, and disability-adjusted life years (DALYs) of knee osteoarthritis (KOA) according to age, sex, sociodemographic index (SDI), and income. This analysis used estimates from the broader Global Burden of Disease (GBD) study 2019, which covered 201 countries from 1990 to 2019. National prevalence, incidence, and DALYs were shown by using ggplot2 and maps packages. Five-year intervals were used for age groupings. The Commonwealth and the World Bank income classifications were used for income grouping. Globally, there were ~364.58 million prevalent cases (females: 225.16 million), 29.51 million incident cases (females: 17.53 million), and 11.53 million DALYs (females: 7.09 million) due to KOA in 2019. The Western Pacific Region had a high endemicity of ~7,319.87 cases per 100,000 people (7.64%). Japan's prevalence rate (12,610.12 cases per 100,000 population) was 10 times that of Somalia (1,178.23) in 2019. In 200 countries (except the United Arab Emirates), the prevalence, incidence, and DALY rates of KOA in 2019 were higher among females than among males and increased with age up to the oldest age group. The prevalence was highest in the high-middle SDI countries, and the incidence and DALYs were highest in the middle SDI countries. There was a large burden of KOA worldwide, with some notable intercountry variation. Some countries had 10 times the burden of other countries. Increasing population awareness regarding the prevalence, incidence, and DALYs of KOA with a focus on the population in the Western Pacific Region is needed, particularly for older females. informs health policy development, and contributes to improving the efficiency, equity, and effectiveness of healthcare systems.

Caring for Older Patients: Quality and Efficiency of Australia's Healthcare System

AbstractWith population ageing and increased longevity, Australia faces pressing policy issues on caring for older people. As Medicare turns 40, it is timely to assess how the system has been performing, and what changes are needed to modernise Medicare. This article examines the quality and efficiency of care using hospital administrative data. We find that the current system provides good quality and efficient care for older patients, except for those living in residential aged care homes. These patients receive lower quality care yet stay longer in hospitals. We suggest alternative approaches designed around bundled payments and a single budget holder to provide incentives for integrated team‐based care.

Machine Learning Based X-RAY Prediction Model

This study aimed to develop and evaluate a convolutional neural network (CNN) model for multi-disease classification using a large dataset of 53,000+ chest X-ray images. The CNN architecture was trained to predict the presence of 14 different diseases based on input chest X-ray images. Key findings indicate the model achieves competitive performance with high accuracy, demonstrating potential for automated disease diagnosis. Leveraging the power of deep learning, particularly CNNs, this study shows promising results in improving diagnostic processes in healthcare. Automating disease diagnosis using deep learning methods can significantly enhance the efficiency of healthcare systems, potentially reducing the burden on medical professionals and improving patient outcomes. The success of this CNN model in multi-disease classification based on chest X-ray images highlights the potential of artificial intelligence in revolutionizing diagnostic processes in healthcare, underscoring the importance and effectiveness of deep learning methods, particularly CNNs, in advancing medical diagnostics and improving patient care.

A multi-stage optimization model for managing epidemic outbreaks and hospital bed planning in Intensive Care Units

Intensive Care Unit (ICU) capacity can be significantly affected by disease outbreaks, epidemics, and pandemics, impeding the operational efficiency of healthcare systems and compromising patient care. This paper presents a multi-stage optimization approach to planning the location and distribution of ICU beds to increase accessibility and reduce mortality caused by a shortage of beds in a geographic region during epidemic events. Using a Brazilian state monthly hospital admissions due to Covid-19 from October 2020 to April 2021, we show the amount and the allocation of extra ICU beds that could reduce mortality, minimize patient travel and transportation, and increase accessibility while considering budget limitations. Our findings show coverage for 21 previously underserved municipalities, providing extra ICU beds for 69 municipalities, ranging from 880 to 1670 beds across seven months. On average, patients are displaced 56% less and access ICUs within 17 ± 2.3 kilometres (CI 95%). The strategy contributes to public health planning and the equitable allocation of hospital resources among the population.

Blockchain Targets Integrated IoT for Smart Healthcare Systems - A Bibliometric Analysis

Blockchain technology and the Internet of Things (IoT) for smart healthcare systems has emerged as a promising area of research, as evidenced by a bibliometric analysis. This analysis explores the scholarly literature on the integration of blockchain and IoT in healthcare, identifying key trends, influential publications, and research directions. By leveraging blockchain's decentralized and immutable nature alongside the IoT's ability to collect and transmit real-time health data from various devices, smart healthcare systems aim to enhance data security, interoperability, and patient privacy. Bibliometric analysis provides valuable insights into the evolution of research in this field, highlighting emerging topics, interdisciplinary collaborations, and potential applications that can revolutionize healthcare delivery and management in the future. This paper introduces the Weighted Key Signature for Smart Healthcare Systems (WKSS), a framework designed to optimize healthcare operations within smart environments. With advanced technologies such as blockchain, Internet of Things (IoT), and integration technologies, WKSS aims to improve patient care, resource utilization, and overall healthcare system efficiency. The framework encompasses key components including digital signature generation, computational efficiency, and bibliometric analysis to provide insights into emerging trends and key contributors within smart healthcare systems. Through a comprehensive analysis, including simulations and bibliometric studies, this paper demonstrates the effectiveness of WKSS in reducing patient waiting times, enhancing resource utilization rates, and improving healthcare provider efficiency. Through simulations and bibliometric analysis, significant improvements were observed in key metrics. The average patient waiting time decreased from 45 to 34 minutes, resource utilization rates increased from 70% to 85%, and examination room occupancy rose from 60% to 75%. Additionally, physician and nurse utilization rates improved from 80% to 90% and from 75% to 85%, respectively.

Commentary] Healthspan Horizon — Pioneering Preventive Care as the New Standard of Healthcare

The contemporary healthcare landscape is experiencing a profound transformation, transitioning from a reactive to a proactive model focused on ongoing health maintenance, prediction, and disease prevention. This shift is driven by several factors, including the demographic shift towards an ageing population, increasing prevalence of chronic diseases, and economic challenges posed by the current hospital-centric care model. Proactive prevention, emphasizing early intervention and health promotion, emerges as imperative to mitigate the burden of chronic diseases and improve patient well-being. Innovative approaches leveraging technology, personalized medicine, and comprehensive risk management are at the forefront of this paradigm shift. This evolution requires significant investment and collaboration among stakeholders, however. Startups and investors will play a crucial role in driving meaningful innovation and navigating the regulatory landscape to realize the full potential of preventive medicine. Sustainability and efficiency in healthcare systems are paramount, necessitating smarter distribution of resources and prioritization of preventive care to ensure healthier populations and manageable expenditures. While implementation challenges exist, promising trends in personalized and digital health technologies offer new opportunities to address health proactively. Ultimately, the goal has to be to create a global healthcare ecosystem that prioritizes preventive measures and fosters interdisciplinary collaboration, ensuring access to quality healthcare for all. With this editorial, we want to initiate the discussion and subsequently provide more specific information through subsequent selected chapters on different clinical topics and future-oriented technological approaches.

Impact of AI on Healthcare: A Descriptive Study

Artificial intelligence (AI) has become a multifaceted breakthrough in healthcare with the promise of transforming patient care and clinical practices. The paper presents a comprehensive retrospective mainly concerned with AI and its future scope in health-care services. The paper offers an overview of prevailing trends, illustrating AI’s transformative potential towards diagnostic accuracy, treatment efficiency and operational productivity in healthcare systems. This research paper will delve into the rise of AI in healthcare, from its early stages to technologies that are cutting edge and influencing the future medicine. Furthermore, we will investigate how AI affects patient outcomes, healthcare staff dynamics and the overall healthcare ecosystem through aprism of multidimensionality. The goal of the research as such is to equip all takeholders with historical trends, current advancements and future trajectories so that they can use AI in healthcare effectively but responsibly. The aim is to create a healthcare environment where there are AI driven improvements in patient care which provide equal opportunity for all.

Efficiency of Public Healthcare Systems Across Indian States

This article analyses the technical efficiency of public healthcare systems within various states of India. Slacks-based efficiency model (SBM) to compare the efficiencies of the public healthcare systems of selected Indian states has been used. A comparison of budget documents and government responses during the pandemic revealed the changing stereotypical stand of the Central Government towards the health sector, especially after the advent of the pandemic. Further, the study found that the public healthcare systems of four states (Bihar, Kerala, Maharashtra and NCT of Delhi) are SBM efficient. Healthcare resources are over-utilised in most states, but health outcomes still need to be improved. The study recommends connecting poor and illiterate citizens in remote villages with public health utilities to improve health outcomes. JEL Codes: H51, H52, H75

Same-Day Versus Staged Spinal Fusion: A Meta-analysis of Clinical Outcomes.

Meta-analysis. This meta-analysis aims to compare same-day versus staged spine surgery, assessing their effects on patient care and health care system efficiency. In spinal surgery, the debate between whether same-day and staged surgeries are better for patients continues, as the decision may impact patient-related outcomes, health care resources, and overall costs. While some surgeons advocate for staged surgeries, citing reduced risks of complications, others proclaim same-day surgeries may minimize costs and length of hospital stays. PubMed, Cochrane, and Google Scholar (pages 1-20) were searched up until February 2024. The studied outcomes were operative room (OR) time, estimated blood loss (EBL), length of hospital stay (LOS), overall complications, venous thromboembolism (VTE), death, operations, and nonhome discharge. Sixteen retrospective studies were included in this meta-analysis, representing a total of 2346 patients, of which 644 underwent staged spinal fusion surgeries and 1702 same-day surgeries. No statistically significant difference was observed in EBL between staged and same-day surgery groups. However, the staged group exhibited a statistically significant longer OR time ( P =0.05) and LOS ( P =0.004). A higher rate of overall complications ( P =0.002) and VTE ( P =0.0008) was significantly associated with the staged group. No significant differences were found in the rates of death, reoperations, and nonhome discharge between the 2 groups. Both staged and same-day spinal fusion surgeries showed comparable rates of death, operations, and nonhome discharges for patients undergoing spinal surgeries. However, given the increased OR time, LOS, and complications associated with staged spinal surgeries, this study supports same-day surgeries when possible to minimize the burden on healthcare resources and enhance efficiency.

Socioeconomic inequalities in healthcare system efficiency in Japan during COVID-19 pandemic: an analysis of the moderating role of vaccination.

In the context of the COVID-19 pandemic, limited research has focused on socioeconomic disparities in Local Healthcare System Efficiency (LHSE) among Japanese prefectures. This study seeks to investigate the moderating impact of vaccination on the relationship between LHSE and socioeconomic characteristics and endowments. To explore these relationships, we first utilized the Data Envelopment Analysis with Slack-Based Measure to measure the LHSE, based on data from Japanese prefectures during waves 2 to 5 of the pandemic. Then estimating the impact of socioeconomic variables on LHSE. Finally, we assessed the changes in the way socioeconomic variables affect LHSE before and after vaccine deployment using the Seemingly Unrelated Estimation t-test methodology. The research findings suggest an overall reduction in LHSE disparities across various regions due to the utilization of vaccines. Particularly in areas with relatively nsufficient bed resources, a significant improvement in LHSE was observed in most regions. However, there was no evidence supporting the role of vaccine deployment in mitigating socioeconomic inequalities in LHSE. Conversely, the utilization of vaccines showed a positive correlation between the improvement in LHSE and the proportion of older adult population in regions with sufficient bed resources. In regions facing bed shortages, the enhancement of LHSE became more reliant on reducing the occupancy rate of secured beds for severe cases after the introduction of vaccination. In regions facing bed shortages, the enhancement of LHSE became more reliant on reducing the occupancy rate of secured beds for severe cases. This underscores the importance for policymakers and implementers to prioritize the treatment of severe cases and ensure an effective supply of medical resources, particularly secured beds for severe cases, in their efforts to improve LHSE, in the post-COVID-19 era with rising vaccine coverage.

Advancing Point-of-Care Diagnosis: Digitalizing Combinatorial Biomarker Signals for Lupus Nephritis.

To improve the efficiency and patient coverage of the current healthcare system, user-friendly novel homecare devices are urgently needed. In this work, we developed a smartphone-based analyzing and reporting system (SBARS) for biomarker detection in lupus nephritis (LN). This system offers a cost-effective alternative to traditional, expensive large equipment in signal detection and quantification. This innovative approach involves using a portable and affordable microscopic reader to capture biomarker signals. Through smartphone-based image processing techniques, the intensity of each biomarker signal is analyzed. This system exhibited comparable performance to a commercial Genepix scanner in the detection of two potential novel biomarkers of LN, VISG4 and TNFRSF1b. Importantly, this smartphone-based analyzing and reporting system allows for discriminating LN patients with active renal disease from healthy controls with the area-under-the-curve (AUC) value = 0.9 for TNFRSF1b and 1.0 for VSIG4, respectively, indicating high predictive accuracy.

Analysis of Right to Health Using Queueing Theory: A Critical Overview

The Rajasthan Government has passed a “Right to Health Bill 2023” on March,21 and became the first state to enact a Right to Health Act. The bill gives every resident of the state the right to avail free Out Patient Department (OPD) services and in Patient Department (IPD) services at all public health facilities. The bill sparked massive protests from the medical community. In this article, we analysis the requirement of this bill and problems in implementation of RTH. Does this bill play a significant role to improve health care system at local level? We also discuss the practical situation of hospitals system capacity for these types of revolution. Here we use Queueing theory techniques for manpower planning in hospitals to improve efficiency of healthcare system. This is a theoretical article and provide decision support system for Policymakers and Public administration.

Barriers to and facilitators for the creation, dissemination, implementation, monitoring, and evaluation of oral health policies in the WHO Africa region: A scoping review protocol.

Evidence-informed oral health policies (OHP) can be instrumental in ending the neglect of oral health globally. When appropriately developed and implemented, OHP can improve the efficiency of healthcare systems and the quality of health outcomes. However, more than half of the countries in the World Health Organization (WHO) African region do not have an oral health policy or even the existence of a policy in need of additional and more national-specific OHP as part of non-communicable diseases and universal health coverage agendas. The objective of this protocol's study is to determine the barriers to and facilitators for the creation, dissemination, implementation, monitoring, and evaluation of OHP in the WHO Africa region. We will conduct a systematic search in Global Health, Embase, PubMed, PAIS, ABI/Inform, Web of Science, Academic Search Complete, Scopus, databases that index gray literature, and the WHO policy repositories. We will include qualitative, quantitative, or mixed-methods research studies and OHP documents published since January 1, 2002, which address stakeholders' perceptions and experiences regarding barriers to and facilitators for the creation, dissemination, implementation, monitoring, and evaluation of OHP in countries part of the WHO African region. We will produce descriptive statistics (frequencies and proportions) for quantitative data and conduct descriptive content analysis for qualitative data. To effectively establish evidence-based OHP in the WHO African region, it is crucial to recognize existing challenges and opportunities for progress. The findings of this review will be relevant for Chief Dental Officers at ministries of health, administrators of dental schools, or academic institutions in the WHO African region and will inform a stakeholder dialogue meeting in Kenya in November of 2023. Open Science Framework: https://doi.org/10.17605/OSF.IO/9KMWR.

The Wearable Activity Tracker Checklist for Healthcare (WATCH): a 12-point guide for the implementation of wearable activity trackers in healthcare

Increasing physical activity in patients offers dual benefits, fostering improved patient health and recovery, while also bolstering healthcare system efficiency by minimizing costs related to extended hospital stays, complications, and readmissions. Wearable activity trackers offer valuable opportunities to enhance physical activity across various healthcare settings and among different patient groups. However, their integration into healthcare faces multiple implementation challenges related to the devices themselves, patients, clinicians, and systemic factors. This article presents the Wearable Activity Tracker Checklist for Healthcare (WATCH), which was recently developed through an international Delphi study. The WATCH provides a comprehensive framework for implementation and evaluation of wearable activity trackers in healthcare. It covers the purpose and setting for usage; patient, provider, and support personnel roles; selection of relevant metrics; device specifications; procedural steps for issuance and maintenance; data management; timelines; necessary adaptations for specific scenarios; and essential resources (such as education and training) for effective implementation. The WATCH is designed to support the implementation of wearable activity trackers across a wide range of healthcare populations and settings, and in those with varied levels of experience. The overarching goal is to support broader, sustained, and systematic use of wearable activity trackers in healthcare, therefore fostering enhanced physical activity promotion and improved patient outcomes.