Personal Health Care Research Articles

DEVELOPMENT OF PATIENT-CENTRED HEALTH CARE APPROACH IN THE NURSES’ AND DOCTOR ASSISSTANTS’ EDUCATION

Nowadays the development of health care system focuses on high quality, effective and safe health services which satisfy patients and clients’ needs. Pacient-centred health care approach and strategies are important factors to improve medical care process and results. Nurses and doctor assistants’ education practises should be based on personalised health care approach and principles. The aim of the article is to identify pacient-centred health care components and ways, how to implement them in nurses and doctor’s assisstants education in order to improve knowledge, skills, competences and attitudes for future work in multi-disciplinary team and multi-cultural environment. The study is based on theoretical and methodological analyses of the concepts and education documents. The results: the deeper understanding of the pacient-centred approach and principles, more detailed complex of skills, competences and attitudes appropriate to urgent health care problems and more competent medical worker in the individualized health care system.

Personal Healthcare Data Records Analysis and Monitoring using The Internet of Things and Cloud Computing

This paper presents a proposed system for cloud-based and Internet o Things (IoT)-based electronic health records (EHRs) that utilize wearable body biosensors to collect biometric data in real time and analyze it to provide personalized therapy recommendations. The study aims to ensure the confidentiality and integrity of patient data through security authorizations, device authentication, and encrypted communication channels. The research method involves describing the proposed system’s communication environment and trust boundary and illustrating the communication mechanisms, including the “starting” and “authentication” procedures. The proposed communication protocols are also explained in detail, and a complete illustration of the symbols and abbreviations used throughout the work is provided. The initialization process involves contacting a body sensor network (BSN) server to register, generating a secret key, and assigning a track sequence number. The proposed systematic verification involves a dependable authentication solution to maintain secure communication between the biosensors, local processing unit (LPU), and BSN server. The verification process involves figuring out the values and generating a random integer, establishing communication with the receiver, and validating the data by searching for a corresponding tuple in the required database. The study emphasizes the importance of a robust IoT communication architecture to ensure secure data transfer between devices, networks, and individuals.

CRISPR gene technology—the next best thing in medicine

CRISPR gene technology—the next best thing in medicine

Practitioners' ability to remotely develop understanding for personalised care and support planning: a thematic analysis of multiple data sources from the feasibility phase of the Dementia Personalised Care Team (D-PACT) intervention.

Practitioner understanding of patients' preferences, wishes and needs is essential for personalised health care i.e., focusing on 'what matters' to people based on their individual life situation. To develop such an understanding, dementia practitioners need to use communication practices that help people share their experiences, preferences, and priorities. Following the COVID-19 pandemic, dementia support is likely to continue to be delivered both remotely and in-person. This study analysed multiple sources of qualitative data to examine the views of practitioners, people living with dementia and carers, and researchers on how an understanding of what matters to people living with dementia can be developed remotely via telephone and video call. Access to environmental stimuli, the remote use of visual tools, peoples' tendency to downplay or omit details about their troubles and carers' ability to disclose privately were interpreted, through thematic analysis, to be factors affecting how practitioners sought to develop understanding remotely. Cumulatively, findings show that while remote support created unique challenges to practitioners' ability to develop understanding for personalised care, practitioners developed adaptive strategies to overcome some of these challenges. Further research should examine how, when and for whom these adapted practices for remote personalised care work, informing the development of evidence-based guidance and training on how practitioners can remotely develop the understanding required for personalised care.

Wearable and Flexible Nanoporous Surface-Enhanced Raman Scattering Substrates for Sweat Enrichment and Analysis

Surface-enhanced Raman spectroscopy (SERS), with high sensitivity to a broad range of molecules, can detect molecular “fingerprints” in a complex substance and thereby offers a promising solution for noninvasive medical diagnostics and personal healthcare monitoring. The eventual realization of such applications relies on SERS substrates with dense and uniform hot spots, good chemical stability, and high mechanical durability. With these criteria in mind, we developed a flexible nanoporous SERS substrate via the in situ synthesis of gold nanostars (AuNSs) on an ion-track-etched polycarbonate membrane. The nanoporous SERS substrate can realize analyte enrichment and exhibit excellent Raman performance by taking the advantage of hot spots on AuNSs. The SERS substrate yields highly repeatable and uniform signals for analytes (e.g., methylene blue) over a wide concentration range from 10–4 to 10–13 M. The flexible SERS substrate even can work well after 2000 times bending and exhibit excellent stability for long-term use. It can be prepared on a large scale with a low-cost and simple fabrication process and can be used repeatedly after cleaning to reduce the use-cost further. On-body experiments prove that the sweat SERS substrate allows effective identification of sweat contents, such as lactic acid and uric acid (UA), and the monitoring of diet-induced variation in sweat UA. The potential of the wearable nanoporous SERS substrates in sweat analysis thus has been demonstrated, opening possibilities for autonomous and noninvasive medical health monitoring.

Factors Predicting Older People's Acceptance of a Personalized Health Care Service App and the Effect of Chronic Disease: Cross-Sectional Questionnaire Study.

Mobile health (mHealth) services enable real-time measurement of information on individuals' biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. The study aims to identify the predictors of older people's intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=-2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

Self‐powered wireless body area network for multi‐joint movements monitoring based on contact‐separation direct current triboelectric nanogenerators

AbstractBody area network has attracted extensive attention for its applications in athletics, medical, diagnosis, and rehabilitation training in the next generation personalized health care solutions. Here, a contact‐separation direct current triboelectric nanogenerators (CSDC‐TENGs) based self‐powered wireless body area network (SWBAN) is reported that enables multi‐joint movements monitoring for human motion. The CSDC‐TENG is designed as a flexible active sensor with an internal contact switch, and the flexible substrate makes the TENG‐sensor stick onto skin easily. Due to the internal switch, the CSDC‐TENG could generate a DC current, a large instantaneous output voltage exceeds 700 V, and an instantaneous power can reach 1.076 W, which is more than 23 000 times higher than that of the traditional contact‐separation mode TENG in same size and materials without the switch. By coupling with flexible coil, the fixed high‐frequency radio signals can be modulated and emitted clearly ranging from 6 to 16 MHz, which can be wirelessly received and demodulated through a reader. Moreover, the SWBAN is demonstrated in a real time monitoring system for joints motion. This work has realized the wearable TENG for self‐powered wireless real‐time monitoring of body movements driven by low‐frequency human daily activities, which may promote a tremendous development of intelligent healthcare, wireless sensing system and body area network.image

Disparities in Health Care Utilization Among Deaf and Hard of Hearing Adults

Purpose: A substantial literature exists, documenting differences in access to quality health care among individuals of different racial/ethnic, residence, income, and disability groups. However, little attention has been given to disparate utilization among those who are deaf or hard of hearing (DHH). The purpose of this study was to evaluate differences between individuals with and without hearing-related issues using a nationally representative survey of health care access outcomes to quantify the magnitude of these differences. Method: Using data from the 2021 Centers for Disease Control Behavioral Risk Factor Surveillance System survey, we compared health care utilization between individuals who reported being DHH and those without hearing-related issues. Logistic regression models were used to explore differences in access to care between DHH and non-DHH individuals controlling for sociodemographic characteristics. Results: The total Behavioral Risk Factor Surveillance System survey sample consisted of 412,427 respondents. Among those were 36,532 (6.85%) who identified as DHH. Logistic models indicated that DHH adults were less likely to have a personal health care provider (odds ratio [ OR ] = 0.91, 95% CI [0.83–2.03]) and less likely to have received a routine checkup within the last 2 years ( OR = 0.91, 95% CI [0.82–1.02]) but more likely to have forgone health care due to difficulty paying ( OR = 1.90, 95% CI [1.71–2.06]) compared to those without hearing difficulties. Differences were robust to race, sex, and other demographic factors. Conclusions: Significant differences in health care utilization were observed between DHH and non-DHH individuals. This is concerning given that DHH adults have a higher prevalence of many chronic conditions that require clinical management. These results indicate that health care providers and systems must offer greater communication options to facilitate better health-related outcomes.

Machine learning integrated patient flow simulation: why and how?

ABSTRACT Stochastic distribution methods were used to construct patient flow simulation sub-models such as patient inflow, length of stay (LoS), cost of treatment (CoT) and clinical pathways (CPs). However, the patient inflow rate demonstrates seasonality, trend, and variation due to natural and human-made factors. LoS, CoT and CPs are determined by social-demographics factors, clinical and laboratory test results, resource availability and healthcare structure. For this reason, patient flow simulation models developed using stochastic methods have limitations including uncertainty, not recognising patient heterogeneity, and not representing personalised and value-based healthcare. This, in turn, results in a low acceptance level and implementation of solutions suggested by patient flow simulation models. On the other hand, machine learning becomes effective in predicting patient inflow, LoS, CoT, and CPs. This paper, therefore, describes why coupling machine learning with patient flow simulation is important, proposes a conceptual architecture for machine learning integrated patient flow simulation and demonstrates its implementation with examples.

Thermally Drawn Multi-material Fibers Based on Polymer Nanocomposite for Continuous Temperature Sensing

With increasing personalized healthcare, fiber-based wearable temperature sensors that can be incorporated into textiles have attracted more attention in the field of wearable electronics. Here, we present a flexible, well-passivated, polymer–nanocomposite–based fiber temperature sensor fabricated by a thermal drawing process of multiple materials. We engineered a preform to optimize material processability and sensor performance by considering the rheological and functional properties of the preform materials. The fiber temperature sensor consisted of a temperature-sensing core made from a conductive polymer composite of thermoplastic polylactic acid, a conductive carbon filler, reduced graphene oxide, and a highly flexible linear low-density polyethylene passivation layer. Our fiber temperature sensor exhibited adequate sensitivity (− 0.285%/°C) within a temperature range of 25–45 °C with rapid response and recovery times of 11.6 and 14.8 s, respectively. In addition, it demonstrated a consistent and reliable temperature response under repeated mechanical and chemical stresses, which satisfied the requirements for the long-term application of wearable fiber sensors. Furthermore, the fiber temperature sensor sewn onto a daily cloth and hand glove exhibited a highly stable performance in response to body temperature changes and temperature detection by touch. These results indicate the great potential of this sensor for applications in wearable, electronic skin, and other biomedical devices.Graphical

Epigenetics of Early Cardiometabolic Disease: Mechanisms and Precision Medicine.

Epigenetics has transformed our understanding of the molecular basis of complex diseases, including cardiovascular and metabolic disorders. This review offers a comprehensive overview of the current state of knowledge on epigenetic processes implicated in cardiovascular and metabolic diseases, highlighting the potential of DNA methylation as a precision medicine biomarker and examining the impact of social determinants of health, gut bacterial epigenomics, noncoding RNA, and epitranscriptomics on disease development and progression. We discuss challenges and barriers to advancing cardiometabolic epigenetics research, along with the opportunities for novel preventive strategies, targeted therapies, and personalized medicine approaches that may arise from a better understanding of epigenetic processes. Emerging technologies, such as single-cell sequencing and epigenetic editing, hold the potential to further enhance our ability to dissect the complex interplay between genetic, environmental, and lifestyle factors. To translate research findings into clinical practice, interdisciplinary collaborations, technical and ethical considerations, and accessibility of resources and knowledge are crucial. Ultimately, the field of epigenetics has the potential to revolutionize the way we approach cardiovascular and metabolic diseases, paving the way for precision medicine and personalized health care, and improving the lives of millions of individuals worldwide affected by these conditions.

Accreditation Process and Employee Well-Being in Healthcare Organizations

Organizations may choose means for their aim of accomplishing an extensive spectrum of activities. One of the possible choices is performance accreditation—a voluntary undertaking evaluating organizational performance compliance with the determined standards. Accreditation is considered a tool not only for improving the quality of services but also for developing coherence in the organization; such a process contributes to the achievement of the organization members’ well-being. Yet personal healthcare organizations choose accreditation as an alternative to the execution of their quality management system, and accreditation is considered a tool for service quality improvement by these organizations, even though several studies provide data on the impact on employees. This study evaluates the attitudes of employees of personal healthcare organizations toward the effect of accreditation on organizational cohesion and employee well-being. Five hundred sixty-nine employees of healthcare organizations participated in the study. This study revealed that healthcare professionals statistically significantly better evaluate the impact of accreditation on service quality improvement than on organizational performance, which is related to the influence on the employees’ well-being.

A Study on Role of Health Care Management Streams

Health care is the maintenance or improvement of health care via the prevention, diagnosis, treatment, amelioration, or cure of disease, illness, injury, and other physical and mental impairments in people. Health care is delivered by health care professionals and allied health care fields. Medicine, dentistry, pharmacy, midwifery, nursing, optometry, audiology, psychology, occupational therapy, physical therapy, athletic training, and other health professions are all part of health care. It includes work done in providing primary care, secondary care, and tertiary care, as well as in public healthcare. Access to health care may vary across countries, communities, and individuals, influenced by social and economic conditions as well as health care policies. Providing health care services means “the timely use of personal health care services to achieve the best possible health care outcomes. A limitation to health care services affects negatively the use of medical services, the efficacy of treatments, and overall outcome (well-being, mortality rates). An efficient health care system can contribute to a significant part of a country’s economy, development, and industrialization. Health care is conventionally regarded as an important determinant in promoting the general physical and mental health care and well-being of people around the world, An example of this was the worldwide eradication of smallpox in 1980, declared by the WHO as the first disease in human history to be eliminated by deliberate health care interventions.

Inclusive healthcare for persons with disabilities (PWDs) in advocacy and partnership in breast and cervical cancer screening through the EMPOWER Program.

e22510 Background: With the growing burden of Women cancers in Kenya, integrating cancer screening and prevention to optimize horizontal care delivery models is a paramount approach to strengthening women-centered and cost-effective primary healthcare. EMPOWER is a unique public-private-partnership with the County governments of Kenya, County referral hospitals, County First Ladies Association, International Cancer Institute, Patient organizations (Women4Cancer &amp; Africa cancer Foundation), AMREF and Roche Kenya to demonstrate and deliver integrated cancer prevention, treatment and capacity building including Persons living with Disability (PWDs). Objectives: 1. Generate buy in from county leadership and key stakeholders for PWDs inclusivity in human resources for health. 2. Tangibly demonstrate women PWDs can contribute to community health systems community health volunteers and Promote the Integrated approach screening. 3. Improve access to breast and cervical cancer screening for and through PWDs. Methods: The Project is a multi-faceted advocacy approach, including political leadership engagement and stakeholder alignment. The political leadership engaged with local government officials to raise awareness of the issue and secure support for the initiative. The stakeholder alignment involved working with healthcare providers, community organizations, and PWDs to ensure that the initiative was inclusive and accessible. Qualitative and quantitative methods were used to evaluate the impact of the advocacy efforts. Results: The project was successful in health management teams co-creating selecting and training 50 Women PWDs as community health volunteers, the first in Africa. The advocacy efforts led to an increase in screening rates for PWDs, with 30% more PWDs accessing screening services compared to the previous year. The PWDs involved in the initiative reported high levels of satisfaction with the program and appreciated the opportunity to play an active role in their own healthcare. Conclusions: The project demonstrated that it is possible to effectively implement inclusive healthcare for persons with disabilities (PWDs) through advocacy and partnerships. The successful adoption of advocacy to address disparities in breast and cervical cancer screening among PWDs highlights the potential for scaling up this work to reach a larger population. The alignment of political leadership and stakeholders, highlights the crucial role of creating a supportive environment to ensure the rights of PWDs to the highest attainable standard of health are upheld. Future efforts to address health disparities among PWDs should build on this approach, leveraging advocacy, partnerships, and cocreation of implementation to drive change at the community level.

Drug Delivery Systems for Personal Healthcare by Smart Wearable Patch System.

Smart wearable patch systems that combine biosensing and therapeutic components have emerged as promising approaches for personalized healthcare and therapeutic platforms that enable self-administered, noninvasive, user-friendly, and long-acting smart drug delivery. Sensing components can continuously monitor physiological and biochemical parameters, and the monitoring signals can be transferred to various stimuli using actuators. In therapeutic components, stimuli-responsive carrier-based drug delivery systems (DDSs) provide on-demand drug delivery in a closed-loop manner. This review provides an overview of the recent advances in smart wearable patch systems, focusing on sensing components, stimuli, and therapeutic components. Additionally, this review highlights the potential of fully integrated smart wearable patch systems for personalized medicine. Furthermore, challenges associated with the clinical applications of this system and future perspectives are discussed, including issues related to drug loading and reloading, biocompatibility, accuracy of sensing and drug delivery, and largescale fabrication.

Smokers with Access to Care are More Likely to Attempt Quitting Smoking

Objective: The Affordable Care Act (ACA) increased healthcare access and smoking cessation services. We examined the association between access to care and quit attempts after the ACA. Methods: We analyzed 2015-2018 Behavioral Risk Factor Surveillance System data (N = 209,213). Using logistic regression, independent variables included having a personal healthcare provider and time since last healthcare provider visit. The dependent variable included smoking cessation attempt in the past 12 months (yes or no). Interactions were examined for demographic variables. Results: Smokers with one (OR = 1.27, 95% CI=1.21, 1.33) or more (OR = 1.28, 95% CI=1.28, 1.48) providers were more likely to have a quit attempt versus no provider. Smokers with a recent check-up were more likely to have a quit attempt versus those who never had/had a check-up greater than 5 years ago (OR2-5 years = 1.19, 95% CI = 1.10, 1.29, OR1-2 years = 1.34, 95% CI = 1.25, 1.44, OR1 year = 1.50, 95% CI = 1.42, 1.58). Age and education modified these associations. Conclusions: Instead of disincentive measures, policies should promote healthcare system interactions to support cessation.

Design and Development of IoT and Deep Ensemble Learning Based Model for Disease Monitoring and Prediction.

With the rapidly increasing reliance on advances in IoT, we persist towards pushing technology to new heights. From ordering food online to gene editing-based personalized healthcare, disruptive technologies like ML and AI continue to grow beyond our wildest dreams. Early detection and treatment through AI-assisted diagnostic models have outperformed human intelligence. In many cases, these tools can act upon the structured data containing probable symptoms, offer medication schedules based on the appropriate code related to diagnosis conventions, and predict adverse drug effects, if any, in accordance with medications. Utilizing AI and IoT in healthcare has facilitated innumerable benefits like minimizing cost, reducing hospital-obtained infections, decreasing mortality and morbidity etc. DL algorithms have opened up several frontiers by contributing towards healthcare opportunities through their ability to understand and learn from different levels of demonstration and generalization, which is significant in data analysis and interpretation. In contrast to ML which relies more on structured, labeled data and domain expertise to facilitate feature extractions, DL employs human-like cognitive abilities to extract hidden relationships and patterns from uncategorized data. Through the efficient application of DL techniques on the medical dataset, precise prediction, and classification of infectious/rare diseases, avoiding surgeries that can be preventable, minimization of over-dosage of harmful contrast agents for scans and biopsies can be reduced to a greater extent in future. Our study is focused on deploying ensemble deep learning algorithms and IoT devices to design and develop a diagnostic model that can effectively analyze medical Big Data and diagnose diseases by identifying abnormalities in early stages through medical images provided as input. This AI-assisted diagnostic model based on Ensemble Deep learning aims to be a valuable tool for healthcare systems and patients through its ability to diagnose diseases in the initial stages and present valuable insights to facilitate personalized treatment by aggregating the prediction of each base model and generating a final prediction.

Detection of Attention Deficit/Hyperactivity Disorder (ADHD)

Abstract: In recent years, Machine Learning has had a huge impact on a range of technological disciplines. As technology expands, machine learning provides an exciting opportunity in health care to help medical professionals care for patients and manage clinical data. It improves the accuracy of diagnoses, personalize health care, and find novel solutions to decades-old problems. Hospitals and health care companies have begun to recognize the ability of machine learning to improve decisionmaking and reduce risk in the medical field, which has led to several new and exciting career opportunities. Attention Deficit/Hyperactivity Disorder (ADHD) is a chronic condition generally characterized by hyperactivity, impulsiveness, and difficulty in paying attention. The early diagnosis of ADHD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. At present, there are two main methods available - using fMRI data and tracking eye movements. The proposed approach offers a method to assist with the ADHD diagnosis with an emphasis on young children who are still in the early stages of development. It tries to discover the eye-tracking patterns of ADHD using machine learning. The fundamental concept is to convert eye-tracking scan-paths into a visual representation, thus the diagnosis may be viewed as an image classification task.

Primary and reproductive healthcare access and use among reproductive aged women and female family planning patients in 3 states.

Public funding plays a key role in reducing cost barriers to sexual and reproductive health (SRH) care in the United States. In this analysis, we examine sociodemographic and healthcare seeking profiles of individuals in three states where public funding for health services has recently changed: Arizona, Iowa, and Wisconsin. In addition, we examine associations between individuals' health insurance status and whether they experienced delays or had trouble in obtaining their preferred contraception. This descriptive study draws on data collected between 2018 to 2021 in two distinct cross-sectional surveys in each state, one among a representative sample of female residents aged 18-44 and the other among a representative sample of female patients ages eighteen and older seeking family planning services at healthcare sites that receive public funding to deliver this care. The majority of reproductive-aged women and female family planning patients across states reported having a personal healthcare provider, had received at least one SRH service in the preceding 12 months, and were using a method of birth control. Between 49-81% across groups reported receiving recent person-centered contraceptive care. At least one-fifth of each group reported wanting healthcare in the past year but not getting it, and between 10-19% reported a delay or trouble getting birth control in the past 12 months. Common reasons for these outcomes involved cost and insurance-related issues, as well as logistical ones. Among all populations except Wisconsin family planning clinic patients, those with no health insurance had greater odds of being delayed or having trouble getting desired birth control in the past 12 months than those with health insurance. These data serve as a baseline to monitor access and use of SRH services in Arizona, Wisconsin, Iowa in the wake of drastic family planning funding shifts that changed the availability and capacity of the family planning service infrastructure across the country. Continuing to monitor these SRH metrics is critical to understand the potential effect of current political shifts.

The Digital Twin: A Potential Solution for the Personalized Diagnosis and Treatment of Musculoskeletal System Diseases.

Due to the high prevalence and rates of disability associated with musculoskeletal system diseases, more thorough research into diagnosis, pathogenesis, and treatments is required. One of the key contributors to the emergence of diseases of the musculoskeletal system is thought to be changes in the biomechanics of the human musculoskeletal system. However, there are some defects concerning personal analysis or dynamic responses in current biomechanical research methodologies. Digital twin (DT) was initially an engineering concept that reflected the mirror image of a physical entity. With the application of medical image analysis and artificial intelligence (AI), it entered our lives and showed its potential to be further applied in the medical field. Consequently, we believe that DT can take a step towards personalized healthcare by guiding the design of industrial personalized healthcare systems. In this perspective article, we discuss the limitations of traditional biomechanical methods and the initial exploration of DT in musculoskeletal system diseases. We provide a new opinion that DT could be an effective solution for musculoskeletal system diseases in the future, which will help us analyze the real-time biomechanical properties of the musculoskeletal system and achieve personalized medicine.