Health Management Research Articles

Using machine learning algorithms for predicting bridge vertical deflection

Machine learning (ML) is being increasingly used to ease structural health monitoring and management of bridges. Therefore, the main objective of this study is to apply two state-of-the-art ML algorithms, namely locally weighted learning (LWL) and K-Star for the prediction of vertical deflection of composite bridges. To accomplish the objective, 83 track loading tests were carried out at various bridges located in Vietnam and deflection data was collected for models’ development. Model's validation and comparison were carried out using different popular methods, namely mean absolute error, root mean squared error and R on both training (70%) and validation (30%) datasets. The results of this study indicate that the K-Star algorithm outperforms LWL in predicting the vertical deflection of composite bridges. Consequently, K-Star can serve as an effective tool for rapidly predicting vertical deflection, thereby facilitating more efficient bridge health monitoring and management. This efficiency can lead to significant time and cost savings in maintaining the structural integrity of composite bridges.

Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Artificial Intelligence in Occupational Health and Safety Risk Management of Construction, Mining, and Oil and Gas Sectors: Advances and Prospects

Big data analytics for image processing and computer vision technologies in sports health management.

Visualization of sports has a lot of potential for future development in data sports because of how quickly things are changing and how much sports depend on data. Presently, conventional systems fail to accurately address sports persons' dynamic health data change with less error rate. Further, those systems are unable to distinguish players' health data and their visualization in a precise manner. An excellent starting point for building fitness solutions based on computer vision technology is the data visualization technology that arose in the age of big data analytics. This research presents a Big Data Analytic assisted Computer Vision Model (BD-CVM) for effective sports persons healthcare data management with improved accuracy and precision. The fitness and health of professional athletes are analyzed using information from a publiclyavailable sports visualization dataset. Machine learning-assisted computer vision dynamic algorithm has been used for an effective image featuring and classification by categorizing sports videos through temporal and geographical data. The significance of big data's great potential in screening data during a sporting event can be reasonably analyzed and processed effectively with less error rate. The proposed BD-CVM utilized an error analysis module which can be embedded in the design further to ensure the accuracy requirements in the data processing from sports videos. The research findings of this paper demonstrate that the strategy presented here can potentially improve accuracy and precision and optimize mean square error in sports data classification and visualization.

Exploration of understanding of impactibility analysis and application through workshops to inform model design for population health management policy: a qualitative assessment

ObjectivesAssess understanding of impactibility modelling definitions, benefits, challenges and approaches.DesignQualitative assessment.SettingTwo workshops were developed. Workshop 1 was to consider impactibility definitions and terminology through moderated open discussion, what the potential...

Multi-Emission Carbon Dots Combining Turn-On Sensing and Fluorescence Quenching Exhibit Ultrahigh Selectivity for Mercury in Real Water Samples.

Mercury is a ubiquitous heavy-metal pollutant and poses serious ecological and human-health risks. There is an ever-growing demand for rapid, sensitive, and selective detection of mercury in natural waters, particularly for regions lacking infrastructure specialized for mercury analysis. Here, we show that a sensor based on multi-emission carbon dots (M-CDs) exhibits ultrahigh sensing selectivity toward Hg(II) in complex environmental matrices, tested in the presence of a range of environmentally relevant metal/metalloid ions as well as natural and artificial ligands, using various real water samples. By incorporating structural features of calcein and folic acid that enable tunable emissions, the M-CDs couple an emission enhancement at 432 nm and a simultaneous reduction at 521 nm, with the intensity ratio linearly related to the Hg(II) concentration up to 1200 μg/L, independent of matrix compositions. The M-CDs have a detection limit of 5.6 μg/L, a response time of 1 min, and a spike recovery of 94 ± 3.7%. The intensified emission is attributed to proton transfer and aggregation-induced emission enhancement, whereas the quenching is due to proton and electron transfer. These findings also have important implications for mercury identification in other complex matrices for routine, screening-level food safety and health management practices.

Ecosystem of Aviation Maintenance: Transition from Aircraft Health Monitoring to Health Management Based on IoT and AI Synergy

This paper presents an in-depth exploration of the transformative impact of integrating the Internet of Things (IoT), cloud computing, and artificial intelligence (AI) within the domain of aviation maintenance. It articulates the transition from conventional health monitoring practices to a more advanced, comprehensive health management approach, leveraging these modern technologies. This paper emphasizes the pivotal shift from reactive maintenance strategies to proactive and predictive maintenance paradigms, facilitated by the real-time data collection capabilities of IoT devices and the analytical prowess of AI. This transition not only enhances the safety and reliability of flight operations but also optimizes maintenance procedures, thereby reducing operational costs and improving efficiency. This paper meticulously outlines the implementation challenges, including technological integration, regulatory compliance, and security concerns, while proposing a future research agenda to address these issues and further harness the potential of these technologies in revolutionizing aviation maintenance.

Mental health management in healthcare organizations: Challenges and strategies - a review

This research delves into the nuanced realm of mental health management within healthcare organizations, addressing challenges and unveiling transformative strategies. The study underscores the critical importance of integrating mental health into broader healthcare policies by examining the multifaceted role of training, education, technology, and interdisciplinary collaboration. Successful training initiatives empower healthcare professionals, reduce stigma, and enhance collaboration, contributing to a mental health-literate workforce. Technology, including telehealth and innovative solutions, acts as a catalyst, overcoming barriers and ushering in a new era of accessibility. Interdisciplinary collaboration fosters holistic care, while the future direction involves comprehensive research, policy integration, and technological innovation. In conclusion, a proactive and inclusive approach is crucial for shaping a healthcare ecosystem where mental health thrives and is central to holistic patient care. Keywords: Mental Health Management, Healthcare Organizations, Training and Education, Policy Integration.

Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening

BackgroundDiabetes is a significant health threat, with its prevalence and burden increasing worldwide indicating its challenge for global healthcare management. To decrease the disease severity, the diabetic patients are recommended to regularly check their blood glucose levels. The conventional finger-pricking test possesses some drawbacks, including painfulness and infection risk. Nowadays, smartphone has become a part of our lives offering an important benefit in self-health monitoring. Thus, non-invasive wearable sweat glucose sensor connected with a smartphone readout is of interest for real-time glucose detection. ResultsWearable sweat glucose sensing device is fabricated for self-monitoring of diabetes. This device is designed as a body strap consisting of a sensing strip and a portable potentiostat connected with a smartphone readout via Bluetooth. The sensing strip is modified by carbon nanotubes (CNTs)-cellulose nanofibers (CNFs), followed by electrodeposition of Prussian blue. To preserve the activity of glucose oxidase (GOx) immobilized on the modified sensing strip, chitosan is coated on the top layer of the electrode strip. Herein, machine learning is implemented to correlate between the electrochemical results and the nanomaterial content along with deposition cycle of prussian blue, which provide the highest current response signal. The optimized regression models provide an insight, establishing a robust framework for design of high-performance glucose sensor. SignificanceThis wearable glucose sensing device connected with a smartphone readout offers a user-friendly platform for real-time sweat glucose monitoring. This device provides a linear range of 0.1–1.5 mM with a detection limit of 0.1 mM that is sufficient enough for distinguishing between normal and diabetes patient with a cut-off level of 0.3 mM. This platform might be an alternative tool for improving health management for diabetes patients.

Research gaps and priorities for quantitative microbial risk assessment (QMRA).

The coronavirus disease 2019 pandemic highlighted the need for more rapid and routine application of modeling approaches such as quantitative microbial risk assessment (QMRA) for protecting public health. QMRA is a transdisciplinary science dedicated to understanding, predicting, and mitigating infectious disease risks. To better equip QMRA researchers to inform policy and public health management, an Advances in Research for QMRA workshop was held to synthesize a path forward for QMRA research. We summarize insights from 41 QMRA researchers and experts to clarify the role of QMRA in risk analysis by (1) identifying key research needs, (2) highlighting emerging applications of QMRA; and (3) describing data needs and key scientific efforts to improve the science of QMRA. Key identified research priorities included using molecular tools in QMRA, advancing dose-response methodology, addressing needed exposure assessments, harmonizing environmental monitoring for QMRA, unifying a divide between disease transmission and QMRA models, calibrating and/or validating QMRA models, modeling co-exposures and mixtures, and standardizing practices for incorporating variability and uncertainty throughout the source-to-outcome continuum. Cross-cutting needs identified were to: develop a community of research and practice, integrate QMRA with other scientific approaches, increase QMRA translation and impacts, build communication strategies, and encourage sustainable funding mechanisms. Ultimately, a vision for advancing the science of QMRA is outlined for informing national to global health assessments, controls, and policies.

Salivary and serum haptoglobin, adenosine deaminase, and immunoglobulin G in growing pigs

BackgroundIdentification of animals in need of medical treatment is important in porcine health management, where analytical samples applicable at farm level could be utilized. Several biomarkers are measurable in saliva, which is less stressful to collect than blood. Saliva sampling is easy to learn and repeatable, making it suitable for monitoring purposes. Previous research suggests that porcine health biomarkers are dependent on production stage and gender, and that combining biomarkers improves diagnostic sensitivity. However, proper monitoring of biomarkers during the complete production cycle has not been studied. We aimed to describe the dynamics of salivary and serum haptoglobin (Hp), adenosine deaminase (ADA), and immunoglobulin G (IgG) in four production stages (suckling, early growing, late growing, finishing), on commercial Finnish pig farms using a total of 117 piglets. The relationship between gender and biomarker dynamics was investigated, as well as the relationships between these biomarkers in saliva and serum.ResultsThe highest salivary concentrations of Hp, ADA and IgG were measured in suckling piglets. The differences between production stages were generally larger in saliva than for the corresponding serum biomarkers. All correlation coefficients between salivary biomarkers were positive in each production stage and the strength of the correlation varied from 0.245 to 0.762. No similar trend was observed regarding correlation coefficients either between serum biomarkers or between salivary and serum biomarkers. Gender was associated with some biomarker concentrations.ConclusionsThe biomarker dynamics supported previous findings that collection of analytical samples should be conducted in age-matched populations. Positive and even strong relationships between salivary biomarkers indicate the potential to use especially saliva for health monitoring. Our results also suggest the importance of considering gender effects when assessing some salivary or serum biomarkers.

Enhanced clinical decisions for management of benign prostatic hyperplasia using patient-reported outcomes: protocol for a prospective observational study

BackgroundLower urinary tract symptoms (LUTS) due to benign prostatic hyperplasia (BPH) significantly impact quality of life among older men. Despite the prevalent use of the American Urological Association Symptom Index (AUA-SI) for BPH, this measure overlooks key symptoms such as pain and incontinence, underscoring the need for more comprehensive patient-reported outcome (PRO) tools. This study aims to integrate enhanced PROs into routine clinical practice to better capture the spectrum of LUTS, thereby improving clinical outcomes and patient care.MethodsThis prospective observational study will recruit men with LUTS secondary to BPH aged ≥ 50 years from urology clinics. Participants will be stratified into medical and surgical management groups, with PRO assessments scheduled at regular intervals to monitor LUTS and other health outcomes. The study will employ the LURN Symptom Index (SI)-29 alongside the traditional AUA-SI and other non-urologic PROs to evaluate a broad range of symptoms. Data on comorbidities, symptom severity, and treatment efficacy will be collected through a combination of electronic health records and PROs. Analyses will focus on the predictive power of these tools in relation to symptom trajectories and treatment responses. Aims are to: (1) integrate routine clinical tests with PRO assessment to enhance screening, diagnosis, and management of patients with BPH; (2) examine psychometric properties of the LURN SIs, including test-retest reliability and establishment of clinically meaningful differences; and (3) create care-coordination recommendations to facilitate management of persistent symptoms and common comorbidities measured by PROs.DiscussionBy employing comprehensive PRO measures, this study expects to refine symptom assessment and enhance treatment monitoring, potentially leading to improved personalized care strategies. The integration of these tools into clinical settings could revolutionize the management of LUTS/BPH by providing more nuanced insights into patient experiences and outcomes. The findings could have significant implications for clinical practices, potentially leading to updates in clinical guidelines and better health management strategies for men with LUTS/BPH.Trial registrationThis study is registered in ClinicalTrials.gov (NCT05898932).

Digital twin-enabled robust production scheduling for equipment in degraded state

Technological advancements are leading to a world where digital twins will become integral to manufacturing operations management. While wide-ranging applications of digital twins are being researched, robust production scheduling remains an enduring challenge, especially considering the numerous sources of uncertainty in a complex manufacturing system that can affect the validity of the obtained solution. Thus, the article proposes a Prognostics and Health Management (PHM)-enabled digital twin framework to perform job scheduling for a flow shop scheduling problem considering real-time equipment health state. The framework combines the adoption of a Genetic Algorithm optimizer with data-driven modelling leveraging algorithms like Principal Component Analysis and models like Discrete Event Simulation with the purpose to solve the engineering task of scheduling at hand. Building on such a technological blend, the framework incorporates the degradation and fault detection and diagnosis of failure modes across multiple components. The effect of degradation and faults on job processing times is learned as a distribution from the field data. The proposed framework is then validated in a laboratory environment where degradation is produced by inducing degradation and faults in the equipment. By means of various experiments, the optimized makespan is compared for the output schedules in different configurations. When equipment is degraded, production scheduling with PHM-enabled field-synchronized digital twin results in better makespan estimation, if compared to the digital twin without PHM. This shows the superiority of the framework in terms of more realistic makespan estimations, which finally corresponds to improved production schedule optimization, sensitive also to degraded states.

Professional education and training of public health and preventive medicine specialists in Canada

Public health and preventive medicine (PHPM) specialist physicians focus on the population rather than individuals as their patients. These specialists assess the health status of a community and subsequently develop, implement and evaluate effective interventions at the population level to improve health. This work brings together their medical expertise, population health approach and management/ leadership skills. The Royal College of Physicians and Surgeons of Canada (RCPSC) states that ‘the Public Health and Preventive Medicine or community medicine (as called previously) specialist uses population health knowledge and skills to play leading and collaborative roles in the maintenance and improvement of the health and well-being of the community’. It is a broad speciality that requires strong generalist skills including in preventive medicine, management, epidemiology, emergency response, health promotion, and health protection.

AI Health Agents: Pathway2vec, ReflectE, Category Theory, and Longevity

Health Agents are introduced as the concept of a personalized AI health advisor overlay for continuous health monitoring (e.g. 1000x/minute) medical-grade smartwatches and wearables for “healthcare by app” instead of “sickcare by appointment.” Individuals can customize the level of detail in the information they view. Health Agents “speak” natural language to humans and formal language to the computational infrastructure, possibly outputting the mathematics of personalized homeostatic health as part of their reinforcement learning agent behavior. As an AI health interface, the agent facilitates the management of precision medicine as a service. Healthy longevity is a high-profile area characterized by the increasing acceptance of medical intervention, longevity biotech venture capital investment, and global priority as 2 billion people will be over 65 in 2050. Aging hallmarks, biomarkers, and clocks provide a quantitative measure for intervention. Some of the leading interventions include metformin, rapamycin, spermidine, NAD+/sirtuins, alpha-ketoglutarate, and taurine. AI-driven digital biology, longevity medicine, and Web3 personalized healthcare come together in the idea of Health Agents. This Web3 genAI tool for automated health management, specifically via digital-biological twins and pathway2vec approaches, demonstrates human-AI intelligence amplification and works towards healthy longevity for global well-being.

Validating the indicator "maternal death review coverage" to improve maternal mortality data: A retrospective analysis of district, facility, and individual medical record data.

Understanding causes and contributors to maternal mortality is critical from a quality improvement perspective to inform decision making and monitor progress toward ending preventable maternal mortality. The indicator "maternal death review coverage" is defined as the percentage of maternal deaths occurring in a facility that are audited. Both the numerator and denominator of this indicator are subject to misclassification errors, underreporting, and bias. This study assessed the validity of the indicator by examining both its numerator-the number and quality of death reviews-and denominator-the number of facility-based maternal deaths and comparing estimates of the indicator obtained from facility- versus district-level data. We collected data on the number of maternal deaths and content of death reviews from all health facilities serving as birthing sites in 12 districts in three countries: Argentina, Ghana, and India. Additional data were extracted from health management information systems on the number and dates of maternal deaths and maternal death reviews reported from health facilities to the district-level. We tabulated the percentage of facility deaths with evidence of a review, the percentage of reviews that met the World Health Organization defined standard for maternal and perinatal death surveillance and response. Results were stratified by sociodemographic characteristics of women and facility location and type. We compared these estimates to that obtained using district-level data. and looked at evidence of the review at the district/provincial level. Study teams reviewed facility records at 34 facilities in Argentina, 51 facilities in Ghana, and 282 facilities in India. In total, we found 17 deaths in Argentina, 14 deaths in Ghana, and 58 deaths in India evidenced at facilities. Overall, >80% of deaths had evidence of a review at facilities. In India, a much lower percentage of deaths occurring at secondary-level facilities (61.1%) had evidence of a review compared to deaths in tertiary-level facilities (92.1%). In all three countries, only about half of deaths in each country had complete reviews: 58.8% (n = 10) in Argentina, 57.2% (n = 8) in Ghana, and 41.1% (n = 24) in India. Dramatic reductions in indicator value were seen in several subnational geographic areas, including Gonda and Meerut in India and Sunyani in Ghana. For example, in Gonda only three of the 18 reviews conducted at facilities met the definitional standard (16.7%), which caused the value of the indicator to decrease from 81.8% to 13.6%. Stratification by women's sociodemographic factors suggested systematic differences in completeness of reviews by women's age, place of residence, and timing of death. Our study assessed the validity of an important indicator for ending preventable deaths: the coverage of reviews of maternal deaths occurring in facilities in three study settings. We found discrepancies in deaths recorded at facilities and those reported to districts from facilities. Further, few maternal death reviews met global quality standards for completeness. The value of the calculated indicator masked inaccuracies in counts of both deaths and reviews and gave no indication of completeness, thus undermining the ultimate utility of the measure in achieving an accurate measure of coverage.

Multifunctional electronic skins for ultra-sensitive strain, temperature, humidity sensing, and energy harvesting

Though gaining increasing attention in various portable and wireless electronic devices and sensors, flexible gels used in strain sensors and triboelectric nanogenerators (TENGs) still suffer from inferior mechanical properties and lack long-term stability. Herein, multifunctional electronic skins (e-skin) from oriented 2D nanosheets enhanced eutectogel were facilely fabricated via a one-step curing strategy with excellent transparency (96%), superb elasticity (1613%), toughness (17.22 MPa). Moreover, the multifunctional e-skin also exhibits temperature and humidity sensibility. Notably, the as-assembled TENG e-skin exhibits high and stable electrical output performances, including an open-circuit voltage of 224 V, short-circuit current of 7.1 μA, and short-circuit charge of 640 nC. Meanwhile, the TENG can easily light up 130 LEDs, charge capacitors to drive a calculator, and sustain a long service life (120 days). Furthermore, it can serve as a self-powered biomechanical sensor to realize the real-time monitoring of various human motions, showing application potentials in wearable electronics, human health management, and energy harvesting systems.

How to plan and provide general anesthesia for a troop of 98 hamadryas baboons (Papio hamadryas) for contraceptive and preventative health interventions.

Present an approach to the safe and efficient provision of anesthesia and birth control measures to a large group of primates. 98 hamadryas baboons (Papio hamadryas) held in a German zoological institution. A group of 12 veterinarians, 2 zookeepers, and 6 volunteers anesthetized all animals within 2 days. The baboons were orally premedicated with midazolam (0.1 to 0.5 mg/kg) and anesthetized with medetomidine (40 to 60 µg/kg, IM) and ketamine (2 to 4 mg/kg, IM); isoflurane at rates of 1.5% to 2% was used for maintaining anesthesia if necessary. All animals received a physical examination, prophylactic medication, and tuberculin testing. For population management, the animals received a contraceptive implant (adult females), orchiectomy (young males), or vasectomy (breeding males). Young males received intratesticular blocks with lidocaine. All animals received atipamezole (125 to 150 µg/kg) before recovery. Premedication resulted in anxiolysis, which facilitated separating and darting. Median time from darting to access to the animal was 10 minutes. Mean anesthetic times were 25 minutes for females and 55 minutes for males. The depth of anesthesia was appropriate for the procedures. No fatalities were recorded. One animal was injured by other baboons but recovered after treatment. Health management and birth control measures are necessary in baboon troops under human care. Anesthesia and/or contraception of individual animals often leads to intraspecific aggression. This case series describes how to provide anesthesia and contraception to an entire troop as an alternative approach that can be adopted to future similar interventions.

Wearable Sensors for Physiological Condition and Activity Monitoring

Rapid technological advancements have transformed the healthcare sector from traditional diagnosis and treatment to personalized health management. Biofluids such as teardrops, sweat, interstitial fluids, and exhaled breath condensate offer a rich source of metabolites that can be linked to the physiological status of an individual. More importantly, these biofluids contain biomarkers similar to those in the blood. Therefore, developing sensors for the noninvasive determination of biofluid‐based metabolites can overcome traditionally invasive and laborious blood‐test‐based diagnostics. In this context, wearable devices offer real‐time and continuous physiological conditions and activity monitoring. The first‐generation wearables included wristwatches capable of tracking heart rate variations, breathing rate, body temperature, stress responses, and sleeping patterns. However, wearable sensors that can accurately measure the metabolites are needed to achieve real‐time analysis of biomarkers. In this review, recent progresses in wearable sensors utilized to monitor metabolites in teardrops, breath condensate, sweat, and interstitial fluids are thoroughly analyzed. More importantly, how metabolites can be selectively detected, quantified, and monitored in real‐time is discussed. Furthermore, the review includes a discussion on the utility of, multifunctional sensors that combine metabolite sensing, human activity monitoring, and on‐demand drug delivery system for theranostic applications.

Factors influencing telehealth awareness, utilization, and satisfaction in KSA: A national population-based study

BackgroundTelehealth involves delivering healthcare remotely through digital platforms such as telephone and video calls. Use of telehealth surged during the COVID-19 pandemic because of the need for contactless healthcare. The Saudi Ministry of Health established the “937 telephone medical consultation call center” almost 10 years ago. ObjectivesThe aim of this study was to examine public awareness of, utilization of, and satisfaction with the 937 call center service, and to assess associated factors. Materials and methodsThis national community-based, cross-sectional study was conducted through multistage sampling with proportional allocation from 20 health directorates. Participants were selected from the general population in public places and were interviewed with a validated questionnaire. ResultsOf 7951 approached individuals, 7692 agreed to participate (response rate 96.7%). Seventy-eight percent of participants were aware of the 937 service, and 56.4% had previously used the service. Most (86%) users were satisfied with the service. Long waiting times were the most frequent reason (46.6%) for dissatisfaction. In multivariable analysis, service awareness and utilization were both significantly associated with having higher education (aOR 4.03, 95%CI: 3.17–5.12 and aOR 3.42, 95%CI: 2.64–4.42, respectively). Positive impressions of telephone medical consultation services, Saudi nationality, having children, having medical insurance, and having a history of chronic disease were significantly associated with awareness of, utilization of, and satisfaction with the service. ConclusionThe study revealed high awareness of, utilization of, and satisfaction with the 937 telephone medical consultation call center, thus suggesting increased public acceptance of the service. Moreover, the study identified socio-demographic factors influencing public awareness of, utilization of, and satisfaction with telehealth. Further studies are required to increase understanding of the facilitators of, and barriers to, the use of service among various population groups.

Gestational syphilis: a socioeconomic analysis of the increase in rates in Brazil

Syphilis is a sexually transmitted infection (STI) that has been known since the 15th century. This pathology, whose etiological agent is Treponema Pallidum, can be classified as: acquired, gestational or congenital, since transmission can be sexual or vertical, from mother to fetus. In recent years, gestational syphilis has seen a progressive and considerable increase in the number of cases detected in Brazil. Thus, knowing that this disease puts not only the pregnant woman's life at risk, but also that of the fetus, which can acquire the infection via transplacental transmission, it is extremely important to understand the factors involved in the increase in the number of detected cases of gestational syphilis, which is the main objective of this research. From a socio-economic perspective, the article discusses the correlation between the increase in cases and social inequality factors in areas of Brazil, understanding how the particularities of each region are reflected in health management, based on the incidence of the disease. As a methodology, we plan to carry out a bibliographical review based on books and catalogued scientific articles published in online indexed databases, such as Scientific Library Online (SciElo) and Pubmed. In addition, an analysis of data from the DATASUS platform will be carried out, also from the 2010 Census. As expected results, we believe that the increase in the rate of gestational syphilis is closely related to socioeconomic factors and access to adequate prenatal care.