Systems In Healthcare Research Articles

Healthcare workers’ perceptions and user experiences of biometric technology in the selected healthcare facilities in Zambia

BackgroundBiometric recognition, using traits like fingerprints, faces, and irises, has advanced significantly, enabling accurate identification. Despite progress, implementation in Zambian healthcare is limited. This study explored healthcare workers’ and patients' perceptions and experiences regarding biometric technology in Zambian healthcare settings, offering insights for informed decision-making and product design.MethodsThis phenomenological study explored the perceptions and experiences of healthcare workers and end-users (patients) concerning biometric technology in four healthcare facilities in Lusaka Province: Chilenje, Chawama, Kanyama (first-level hospitals), and Railways Urban Clinic. These facilities were purposefully selected for their active use of biometric services and involvement with the National Health Insurance Management Authority (NHIMA). The study included 20 healthcare workers and 16 patients. Face-to-face interviews with healthcare workers and patients, and focus group discussions with users, lasting 35 to 65 min, were conducted. Interviews were audio-recorded, transcribed verbatim, coded with Nvivo version 12, and analyzed using inductive thematic analysis.ResultsFacilitators included efficient system integration, investment in biometric equipment, workflow transformation, and patient recognition of benefits. Barriers included infrastructure limitations, technical challenges, patient resistance and misconceptions, and accessibility challenges for individuals with disabilities.ConclusionThis study highlights the transformative potential of biometric identification systems in healthcare, emphasizing benefits in efficiency and data integrity. Successful integration requires addressing facilitators like education and infrastructure and mitigating barriers such as cultural resistance and technological limitations. Stakeholders, including policymakers and healthcare providers, can use these insights to align efforts with Sustainable Development Goals for improved healthcare access and reduced inequalities. Considering user perspectives is crucial for effective implementation, and continuous improvement can enhance healthcare delivery and patient outcomes.

Innovations in Deep Learning and Intelligent Systems for Healthcare and Engineering Applications

Innovations in Deep Learning and Intelligent Systems for Healthcare and Engineering Applications

Challenges and Opportunities for AI in Healthcare

The integration of artificial intelligence (AI) in healthcare presents a dual challenge: maximizing the efficiency of medical processes while safeguarding patient privacy. This comprehensive review examines the delicate balance between leveraging AI's potential in healthcare and preserving individual data privacy. Through analysis of recent literature, case studies, and regulatory frameworks, we explore the current landscape of AI applications in healthcare, associated privacy risks, and emerging solutions. Findings reveal that while AI significantly enhances diagnostic accuracy and treatment planning, it also raises concerns about data security and patient confidentiality. Key challenges include ensuring GDPR and HIPAA compliance, managing large-scale health data, and maintaining transparency in AI decision-making processes. Promising approaches such as federated learning and differential privacy emerge as potential solutions. This review underscores the need for a multidisciplinary approach involving healthcare providers, AI developers, ethicists, and policymakers to create robust, privacy-preserving AI systems in healthcare.

Human-AI Teaming in Critical Care: A Comparative Analysis of Data Scientists' and Clinicians' Perspectives on AI Augmentation and Automation.

Artificial intelligence (AI) holds immense potential for enhancing clinical and administrative health care tasks. However, slow adoption and implementation challenges highlight the need to consider how humans can effectively collaborate with AI within broader socio-technical systems in health care. In the example of intensive care units (ICUs), we compare data scientists' and clinicians' assessments of the optimal utilization of human and AI capabilities by determining suitable levels of human-AI teaming for safely and meaningfully augmenting or automating 6 core tasks. The goal is to provide actionable recommendations for policy makers and health care practitioners regarding AI design and implementation. In this multimethod study, we combine a systematic task analysis across 6 ICUs with an international Delphi survey involving 19 health data scientists from the industry and academia and 61 ICU clinicians (25 physicians and 36 nurses) to define and assess optimal levels of human-AI teaming (level 1=no performance benefits; level 2=AI augments human performance; level 3=humans augment AI performance; level 4=AI performs without human input). Stakeholder groups also considered ethical and social implications. Both stakeholder groups chose level 2 and 3 human-AI teaming for 4 out of 6 core tasks in the ICU. For one task (monitoring), level 4 was the preferred design choice. For the task of patient interactions, both data scientists and clinicians agreed that AI should not be used regardless of technological feasibility due to the importance of the physician-patient and nurse-patient relationship and ethical concerns. Human-AI design choices rely on interpretability, predictability, and control over AI systems. If these conditions are not met and AI performs below human-level reliability, a reduction to level 1 or shifting accountability away from human end users is advised. If AI performs at or beyond human-level reliability and these conditions are not met, shifting to level 4 automation should be considered to ensure safe and efficient human-AI teaming. By considering the sociotechnical system and determining appropriate levels of human-AI teaming, our study showcases the potential for improving the safety and effectiveness of AI usage in ICUs and broader health care settings. Regulatory measures should prioritize interpretability, predictability, and control if clinicians hold full accountability. Ethical and social implications must be carefully evaluated to ensure effective collaboration between humans and AI, particularly considering the most recent advancements in generative AI.

Evaluation of technical approaches for real-time data transfer from electronic health record systems

Background and ObjectiveReal-time data (RTD) are data that are delivered immediately after creation. The key feature of RTD is low delivery latency. Information systems in health care are extremely time-sensitive and their building block is the electronic health record (EHR). Real-time data from EHRs play an important role to support decision-making, analytics and coordination of care. This is well mentioned in the literature, but the process has not yet been described, providing reference implementations and testing. Real-time data delivery can technically be achieved using several methods. The objective of this work is to evaluate the performance of different transfer methods of RTD from EHRs by measuring delivery latency. MethodsIn our work we used four approaches to transfer RTD from EHRs: REST hooks, WebSocket notifications, reverse proxy and database triggers. We deployed a Fast Health Interoperability Resources (FHIR) server as it is one of the most widely used EHR standard. For the reference implementations we used Python and Golang. Delivery latency was selected as performance metric, derived by subtracting the timestamp of the EHR resource creation from the timestamp of the EHR resource receipt in millisecond. The data was analyzed using descriptive statistics, cumulative distribution function (CDF), Kruskal-Wallis and post-hoc tests. ResultsThe database trigger approach had the best mean delivery latency 13.52±5.56 ms, followed by the reverse proxy 14.43±4.58 ms, REST hooks 19.26±5.76 ms and WebSocket 27.32±9.44 ms. The reverse proxy showed a tighter range of the values and lower variability. There were significant differences in the latencies between all pairs of approaches, except for reverse proxy and database trigger. ConclusionReal-time data transfer is vital for the development of robust and innovative healthcare applications. Properties of current EHR systems as a data source predefine the approaches for transfer. In our work for the first time the performance of RTD transfer from the EHRs with reference implementations is measured and evaluated. We found that database triggers achieve lowest delivery latency. Reverse proxy performed slightly slower, but offered more stability, followed by REST hooks and WebSocket notifications.

Implementation of Innovative Healthcare Infrastructure Projects: the Experience of Spain

The article focuses on the implementation of innovative healthcare infrastructure projects, with a pilot case in Spain. It emphasizes the critical role of innovation and advance capabilities systems for healthcare for enhancing service quality and creating productive environments aligned with sustainable development principles. The article demonstrates the complexity of implementing social projects and the problems of their dissemination. It explores the key elements of implementing innovative healthcare infrastructure projects (using the example of health-oriented advanced lighting in medical facilities). The methodology incorporates a comprehensive analysis of current lighting technologies in medical institutions and their impact on both staff and patients. The study explores the adoption of biodynamic lighting systems, which adapt to human circadian rhythms, thereby contributing to health and well-being. The methodology incorporates a comprehensive analysis of current lighting technologies in medical institutions and their impact on both staff and patients. The article discusses the project CHRONOlight, implemented in a central Hospital, aiming to develop and optimize a biodynamic lighting system. The project serves as a model for integrating innovative lighting solutions in healthcare facilities, showcasing the potential for significant improvements in patient care and worker productivity. The findings suggest that innovative lighting technologies are essential for advancing healthcare infrastructure towards more sustainable and effective practices. The emphasis is on the necessity of engaging stakeholders in the implementation process of innovations, and their characterization is described using the CHRONOlight project as an example. The need for using a project communication management model is highlighted, and a model for managing project communications for CHRONOlight is proposed. Статтю розміщено в рамках грантової програми ГО «НАУКОВО-ОСВІТНІЙ ІННОВА-ЦІЙНИЙ ЦЕНТР СУСПІЛЬНИХ ТРАНСФОРМАЦІЙ» за підсумками Конкурсу індивідуальнихгрантів «Ініціатива підтримки науковців» https://reicst.sgv.in.ua/index.php/naukovi-osvitniproekti-b/grantovi-programi/79-konkurs-individualnikh-grantiv-initsiativa-pidtrimki-naukovtsiv

Регуляторный эксперимент по внедрению системы искусственного интеллекта в сферу разрешения споров

At present, it is predicted that there will be a demand for the use of experimental legal regimes for such products and solutions as individual artificial intelligence systems in healthcare, artificial intelligence and robotics systems for special and dual-use purposes (in the field of medicine, transport and logistics, construction and the fuel and energy complex); Artificial Intelligence Systems in Public Administration. Methods and forms of dispute resolution (removal of legal uncertainty, settlement of conflicts) in the abovementioned areas cannot be dissonant with the efficiency of production and management processes in these areas. Otherwise, alternative forms of conflict resolution to justice will be implemented.

The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back.

Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.

Payment Systems, Insurance, and Agency Problems in Healthcare: A Medically Framed Real-Effort Experiment

Payment Systems, Insurance, and Agency Problems in Healthcare: A Medically Framed Real-Effort Experiment

Research on intelligent tooth segmentation method combining multiple seed region growth and boundary extension

The segmentation of dental models is a crucial step in computer-aided diagnosis and treatment systems for oral healthcare. To address the issues of poor universality and under-segmentation in tooth segmentation techniques, an intelligent tooth segmentation method combining multiple seed region growth and boundary extension is proposed. This method utilized the distribution characteristics of negative curvature meshes in teeth to obtain new seed points and effectively adapted to the structural differences between the top and sides of teeth through differential region growth. Additionally, the boundaries of the initial segmentation were extended based on geometric features, which was effectively compensated for under-segmentation issues in region growth. Ablation experiments and comparative experiments with current state-of-the-art algorithms demonstrated that the proposed method achieved better segmentation of crowded dental models and exhibited strong algorithm universality, thus possessing the capability to meet the practical segmentation needs in oral healthcare.

Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study

Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.

Challenges of ambient WiFi backscatter systems in healthcare applications

In healthcare, patients commonly use medical devices that require batteries and wired connections, and do not have timely access to monitoring data. Ambient WiFi backscatter systems are attracting attention in healthcare owing to their ultra-low power consumption, wireless communication method, battery-free mode, and the ability for continuous monitoring. However, there is a lack of investigations on it. In this paper, we focus on discovering ambient WiFi backscatter systems’ applications in healthcare. Therefore, in this paper, we provide a comprehensive survey of ambient WiFi backscatter in healthcare. Firstly, we present some existing ambient WiFi backscatter systems to help us better understand the operating principle, and then classify them based on the use of commercial WiFi receivers. After that, we survey their latest applications and discuss the challenges in healthcare, starting from two general directions: smart hospitals and medical devices. Finally, we foresee the development of ambient WiFi backscatter technology and expand it in other fields. The research aims to receive a comprehensive understanding of the applications and challenges of ambient WiFi backscatter systems in healthcare, and thus promoting their further development.

Configuring Desired Payment Systems in Healthcare with the “Iranian Tar Model”

Background: Payment systems in healthcare organizations are critical, behavioral, and dynamic. Given the existing challenges in payment systems, characterized by imbalances in payment amounts and inequitable ratios within and across job categories, there is a pressing need for a novel model to configure desired payment systems which proposed in this paper. Methods: A narrative review was conducted using the PubMed, and ScienceDirect databases with the keywords "payment methods" or "payment system" and "characteristics" or "components" and their Persian equivalents. A total of 16 articles were selected. Additionally, protocols for physician compensation methods and nursing service tariffs from the past five years were obtained and reviewed from the Ministry of Health website. Finally, a novel model was developed based on the research team's expertise and inspired by the Iranian Tar instrument. Findings: The Iranian Tar Model is introduced as a guide for configuring desired payment systems. This model incorporates six tuning pegs (sufficiency, savings, desired behavior, ease of calculation, distinction, and equity) paired in twos within three axes for configuring payment systems. The positioning of common payment systems (including salary, capitation, Fee-for-Service, and Pay for Performance methods) within the three axes of the model is examined. Conclusion: Previous studies have addressed payment system parameters in isolation, with limited attention to their interconnectedness. The Iranian Tar Model's six interrelated tuning pegs empower organizations to effectively configure their payment system at individual, intra-group, and inter-group levels.

Design and Analysis of a High-Gain, Low-Noise, and Low-Power Analog Front End for Electrocardiogram Acquisition in 45 nm Technology Using gm/ID Method

In this work, an analog front-end (AFE) circuit for an electrocardiogram (ECG) detection system has been designed, implemented, and investigated in an industry-standard Cadence simulation framework using an advanced technology node of 45 nm. The AFE consists of an instrumentation amplifier, a Butterworth band-pass filter (with fifth-order low-pass and second-order high-pass sections), and a second-order notch filter—all are based on two-stage, Miller-compensated operational transconductance amplifiers (OTA). The OTAs have been designed employing the gm/ID methodology. Both the pre-layout and post-layout simulation are carried out. The layout consumes an area of 0.00628 mm2 without the resistors and capacitors. Analysis of various simulation results are carried out for the proposed AFE. The circuit demonstrates a post-layout bandwidth of 239 Hz, with a variable gain between 44 and 58 dB, a notch depth of −56.4 dB at 50.1 Hz, a total harmonic distortion (THD) of −59.65 dB (less than 1%), an input-referred noise spectral density of <34 μVrms/Hz at the pass-band, a dynamic range of 52.71 dB, and a total power consumption of 10.88 μW with a supply of ±0.6 V. Hence, the AFE exhibits the promise of high-quality signal acquisition capability required for portable ECG detection systems in modern healthcare.

Segmentation and Feature Extraction for Malaria Detection in Blood Smears

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.

A cross-sectional analysis of antibiotic use: Attitudes and practices in the Albanian community

BackgroundAntibiotic resistance (AR) represents a global health challenge. Within the coordinated efforts to tackle AR, community education is a key strategy to enhance awareness and improve antibiotic use. The current study intends to investigate the Albanian community's attitude and practices regarding antibiotic use, including recent health related developments, like COVID-19. MethodsA cross sectional survey was conducted among Albanian residents between December 2022 and March 2023, using a standardized questionnaire which featured three sections covering demographic data, attitudes, and practices regarding antibiotic use. Statistical analysis included both descriptive statistics and the Chi-square test to assess relationships between categorical variables. ResultsThe questionnaire was effectively completed by 519 participants. The majority were females (70.1 %) within the 18–30 age range (55.5 %) and had a college/university degree (93.3 %). 53.4 % reported antibiotic use without a medical prescription, with amoxicillin and azithromycin being the most used antibiotics. Commonly cited reasons for antibiotic use were self-reported symptoms such as flu/common cold, sore throat, cough, and fever (82.3 %). 69.0 % reported following prescribed antibiotic courses, with higher adherence among females and those with higher education. Awareness of AR was considerable (78.0 %), with females evidencing more awareness compared to males. Only 34.3 % of participants reported antibiotic use for COVID-19, mostly after testing positive, with azithromycin being the most frequently used antibiotic. ConclusionsOur study highlights concerning trends in antibiotic self-medication and awareness gaps in prudent antibiotic use emphasizing the need for tailored educational programs, regulatory oversight, and robust surveillance systems in Albania's healthcare.

A Secure Biometric-Based User Authentication Scheme for Cyber-Physical Systems in Healthcare

A Secure Biometric-Based User Authentication Scheme for Cyber-Physical Systems in Healthcare

A Classification of Cloud Services and Their Security Assessment in the Healthcare Industry

The role of cloud-based services in healthcare settings is crucial in the 21st century. This paper discusses their advantages and tools in hospitals, clinics, and diagnostic centers, categorizing existing applications and services into data storage, computing power, networking, PaaS, SaaS, data analytics, business intelligence, and project management. It also provides an in-depth analysis of security and risk assessment issues in cloud-based services, supported by case studies. Additionally, it includes a comparative risk analysis diagram of secured versus non-secured cloud systems in healthcare. Finally, the paper concludes with recommendations for future work. Key Words: Healthcare facilities, diagnostic centers, cloud services, information technology systems, security, risk analysis.

Wearable sensors in patient acuity assessment in critical care.

Acuity assessments are vital for timely interventions and fair resource allocation in critical care settings. Conventional acuity scoring systems heavily depend on subjective patient assessments, leaving room for implicit bias and errors. These assessments are often manual, time-consuming, intermittent, and challenging to interpret accurately, especially for healthcare providers. This risk of bias and error is likely most pronounced in time-constrained and high-stakes environments, such as critical care settings. Furthermore, such scores do not incorporate other information, such as patients' mobility level, which can indicate recovery or deterioration in the intensive care unit (ICU), especially at a granular level. We hypothesized that wearable sensor data could assist in assessing patient acuity granularly, especially in conjunction with clinical data from electronic health records (EHR). In this prospective study, we evaluated the impact of integrating mobility data collected from wrist-worn accelerometers with clinical data obtained from EHR for estimating acuity. Accelerometry data were collected from 87 patients wearing accelerometers on their wrists in an academic hospital setting. The data was evaluated using five deep neural network models: VGG, ResNet, MobileNet, SqueezeNet, and a custom Transformer network. These models outperformed a rule-based clinical score (Sequential Organ Failure Assessment, SOFA) used as a baseline when predicting acuity state (for ground truth we labeled as unstable patients if they needed life-supporting therapies, and as stable otherwise), particularly regarding the precision, sensitivity, and F1 score. The results demonstrate that integrating accelerometer data with demographics and clinical variables improves predictive performance compared to traditional scoring systems in healthcare. Deep learning models consistently outperformed the SOFA score baseline across various scenarios, showing notable enhancements in metrics such as the area under the receiver operating characteristic (ROC) Curve (AUC), precision, sensitivity, specificity, and F1 score. The most comprehensive scenario, leveraging accelerometer, demographics, and clinical data, achieved the highest AUC of 0.73, compared to 0.53 when using SOFA score as the baseline, with significant improvements in precision (0.80 vs. 0.23), specificity (0.79 vs. 0.73), and F1 score (0.77 vs. 0.66). This study demonstrates a novel approach beyond the simplistic differentiation between stable and unstable conditions. By incorporating mobility and comprehensive patient information, we distinguish between these states in critically ill patients and capture essential nuances in physiology and functional status. Unlike rudimentary definitions, such as equating low blood pressure with instability, our methodology delves deeper, offering a more holistic understanding and potentially valuable insights for acuity assessment.

Wearable Self‐Powered Smart Sensors Enabled by 2D Flower‐Like Ni–Co Layered Double Hydroxides for Copper Monitoring in Sweat

AbstractThe growing power demands of wearable electronic devices have stimulated the development of self‐powered systems in noninvasive healthcare. Herein, ultrathin homogeneous 2D flower‐like nickel–cobalt layered double hydroxide (Ni–Co LDH) nanocomposites are prepared by a one‐step hydrothermal method to integrate a self‐powered system for wearable sweat copper ions (Cu2+) monitoring. The Ni–Co LDH nanocomposites with a lamellar structure and numerous active sites, exhibit sensitive selectivity and low detection limits for Cu2+. In addition, the optimized Ni–Co LDH provides favorable capacitive characteristics as supercapacitors for self‐powering sensors, ensuring continuous sensing energy sources. By connecting wirelessly to a smart device, a self‐powered system is constructed to achieve wearable sensing of Cu2+ in the practical application of sweat. This simple yet effective strategy provides new opportunities for the development of self‐powering health monitoring sensors, as well as smartsensing more heavy metals.