Personalized Healthcare Services Research Articles

Responsible coordination of municipal health and care services for individuals with serious mental illness: a participatory qualitative study with service users and professionals

BackgroundCare coordination is crucial to ensure the health of individuals with serious mental illness. The aim of this study was to describe and analyze an inclusive innovation process for coordinating municipal health and care services for individuals with serious mental illness.MethodsWe conducted café dialogues with professionals and service users with serious mental illness. The café dialogues engaged participants in conversation and knowledge exchange about care coordination, adressing topics of efficiency, challenges, and improvement. We used a responsible innovation framework to analyze the innovation process.ResultsResponsible coordination requires promoting service users’ health and ensuring communication and mutual awareness between professionals. Individual-level factors supporting responsible coordination included service users knowing their assigned professionals, personalized healthcare services, and access to meaningful activities. Provider-level factors included effective coordination routines, communication, information exchange, and professional familiarity. Results reflect professionals’ and service users’ perspectives on efficient care coordination, existing challenges, and measures to improve care coordination.ConclusionCafé dialogues are an inclusive, participatory method that can produce insights into the responsible coordination of municipal health and care services for individuals with serious mental illness. The responsible innovation framework is helpful in identifying care coordination challenges and measures for responsible coordination.

Optimized models and deep learning methods for drug response prediction in cancer treatments: a review.

Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.

AI and Liability in Medicine: The Case of Assistive-Diagnostic AI

Abstract As the prevalence of assistive-diagnostic artificial intelligence (AI) grows, so too will the legal controversies surrounding its use continue to grow. Consequently, determining liability in cases where patients experience harm due to the use of assistive-diagnostic AI in personal healthcare services requires a re-evaluation of existing civil liability regulations. This article proposes a framework for addressing liability in these situations by exploring medical malpractice, organisational negligence by healthcare institutions, and producer liability.

An interpretable approach using hybrid graph networks and explainable AI for intelligent diagnosis recommendations in chronic disease care

With the rapid advancement of modern medical technology and the increasing demand for a higher quality of life there is an emergent requirement for personalized healthcare services. This is particularly pertinent in the sphere of pharmacological recommendations, where the necessity to provide patients with optimal and efficacious medication regimens is paramount. Traditional methodologies in this domain are increasingly seen as insufficient for the needs of contemporary medicine, prompting a shift towards more sophisticated technologies and algorithms. In this study, we addressed this pressing need by developing GCF++ i.e., two graph-based collaborative filtering methods, GCFYA (with attention) and GCFNA (without attention). These methods hold significant promise in revolutionizing how drug recommendations are made, ensuring that patients receive precise and trustworthy medication suggestions tailored to their unique needs and scenarios. To evaluate and compare these algorithms, we introduced three robust metrics: Precision, RMSE (Root Mean Square Error), and Recall. Precision value for GCF-YA is 88 % for hospital dataset, while 85 % for public dataset, similarly, GCF-NA is 77 % for hospital dataset while 78 % for public dataset which is much higher than other traditional methods. Furthermore, as algorithm models become increasingly intricate, transparency and interpretability have gained paramount importance. In response, we incorporated two model interpretation tools, SHAP and LIME, to demystify the decision-making processes behind these algorithms. These tools not only provide clear insights into the basis of recommendation results for both users and developers but also enhance patients' trust and satisfaction with the recommendation system. This study represents a significant step forward in the pursuit of personalized, transparent, and effective healthcare solutions.

Synergy between blockchain technology and internet of medical things in healthcare: A way to sustainable society

In this study, we investigate the synergy between the Internet of Medical Things (IoMT) and blockchain technology in healthcare for sustainable societies. To address the research goal, a state-of-the-art review on the IoMT and blockchain in healthcare is conducted. The study’s novelty lies in a new approach toward “medical data,” as a potential economic asset for value creation with the help of disruptive technologies. IoMT technology gathers real-time medical data and extracts knowledge in digital healthcare systems. Blockchain technology ensures transparency, privacy, and security in a distributed healthcare ecosystem. Indeed, these two technologies bring greater data connectivity and interoperability, where data is not isolated and underutilized anymore. IoMT-blockchain solutions offer a new paradigm of predictive, integrated, and personalized healthcare services based on medical data for individuals’ vitality and enhanced life expectancy. On a broader scale, healthy people make society more sustainable and resilient, where citizens actively involve in development planning and solving community issues by sharing and monetizing their personal health data. The current research broadens the horizons of researchers and policy-makers through current trends and future research directions.

Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring.

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.

Security Risk Assessment Framework for the Healthcare Industry 5.0

The relevance of Industry 5.0 confirms the collaborative relationship between humans and machines through an inclusive automation process. The healthcare industry at present is facilitated by the use of these emerging technologies, which promise a more personalized, patient-centric approach, enabling more prompt, cost-effective, and efficacious medical care to the affected. However, managing enormous data volumes, lack of standards, risks to data security, and regulatory obstacles, such as regulatory compliance, are critical issues that must be addressed to ensure that Industry 5.0 can be effectively integrated into the healthcare industry. This research assumes significance in the stated context as it seeks to reveal the gaps between security risks and threats assessments for personalized healthcare services based on Industry 5.0. The study’s investigations cite that the identification of security risks and various threats is an imperative need and must be prioritized so as to ensure optimal security for the healthcare system. Furthermore, the study peruses various security threats and security risk assessments for enhancing and safeguarding the healthcare industry. Moreover, the study also proposes a framework for security risk assessment based on Industry 5.0 (SRVFHI5.0) for the healthcare security system. A step-wise procedure is applied to validate the proposed framework and provide support for designing feasible security evaluation criteria and tools for future research. Statistical analysis was performed to evaluate the measure of the applicability of multiple criteria, the tool’s reliability, and factor analysis. This offers an adequate basis for accepting the suggested risk assessment methodology based on Healthcare Industry 5.0 for implementation as well as further research and analysis.

SMART HEALTHCARE EMPOWERMENT: INTEGRATING IOT, CLOUD, AND HEALTHCARE STANDARDS WITH RASPBERRY PI FOR ENHANCED PATIENTDATA COLLECTION

The research paper introduces an innovative framework that harnesses the capabilities of In- ternet of Things (IoT) and cloud computing technologies to revolutionize patient data collection within smart healthcare systems. This framework bears similarities to the Electronic Health Records System (EHRS) in the USA and aligns with the development of a health stack in India. In this context, the Raspberry Pi functions as a central gateway device, facilitating seamless connections between various sensors and medical devices for data acquisition. The collected data is then transmitted to a cloud-based platform for secure storage, comprehensive analysis, and seam- less accessibility by healthcare providers. This system, in line with the development of the health stack in India, aims to create a robust and standardized digital infrastructure for healthcare data management. By doing so, it paves the way for a nationwide health information exchange system, similar to the EHRS [Rahman et al.2019]in the USA. The framework also empowers remote mon- itoring, real-time data analysis, and the provision of personalized healthcare services to patients, thus significantly enhancing the efficiency and effectiveness of healthcare systems in India, while promoting interoperability and data exchange, akin to the EHRS model in the USA.

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.

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.

Personal Health Data Tracking by Blind and Low-Vision People: Survey Study.

Personal health technologies, including wearable tracking devices and mobile apps, have great potential to equip the general population with the ability to monitor and manage their health. However, being designed for sighted people, much of their functionality is largely inaccessible to the blind and low-vision (BLV) population, threatening the equitable access to personal health data (PHD) and health care services. This study aims to understand why and how BLV people collect and use their PHD and the obstacles they face in doing so. Such knowledge can inform accessibility researchers and technology companies of the unique self-tracking needs and accessibility challenges that BLV people experience. We conducted a web-based and phone survey with 156 BLV people. We reported on quantitative and qualitative findings regarding their PHD tracking practices, needs, accessibility barriers, and work-arounds. BLV respondents had strong desires and needs to track PHD, and many of them were already tracking their data despite many hurdles. Popular tracking items (ie, exercise, weight, sleep, and food) and the reasons for tracking were similar to those of sighted people. BLV people, however, face many accessibility challenges throughout all phases of self-tracking, from identifying tracking tools to reviewing data. The main barriers our respondents experienced included suboptimal tracking experiences and insufficient benefits against the extended burden for BLV people. We reported the findings that contribute to an in-depth understanding of BLV people's motivations for PHD tracking, tracking practices, challenges, and work-arounds. Our findings suggest that various accessibility challenges hinder BLV individuals from effectively gaining the benefits of self-tracking technologies. On the basis of the findings, we discussed design opportunities and research areas to focus on making PHD tracking technologies accessible for all, including BLV people.

Human Digital Twin for Personalized Healthcare: Vision, Architecture and Future Directions

Human digital twin (HDT) is an emerging technology that can revolutionize the existing healthcare system with the ability to enable personalized healthcare services (PHS) using various tools such as artificial intelligence and blockchain. Its implementation is expected to be similar to digital twins proposed in other application areas such as manufacturing and aviation by consisting of three key dimensions: the physical entity, virtual model, and connection that characterizes the physical-virtual interactions. The known complexity of human body structure because of the constant molecular and physiological changes, however, means extracting precise medical data as well as modeling of HDT is very difficult. Hence, HDT is much more complex than its counterparts and its implementation methods remain unclear, which deserves further investigation. This paper thus presents the architectural framework and key design requirements of HDT and then discusses the key technologies and technical challenges to suggest future directions. We believe that this paper will open up new research opportunities and motivate new research efforts towards the development of HDT for PHS.

Assessing Lesotho's Financial Burden of Household Payments and Access to Healthcare, 2022

Background: Health care financing is primarily about paying for health care. It motivates health care providers to increase the supply of health care goods and services to ensure that all individuals have access to effective public health and personal health care services and goods. The ultimate intent is to improve the health of individuals and the general population in line with the principles of Universal Health Coverage. When patients’ OOP reaches a certain level, some people forgo health care due to the price, and others who access services face financial difficulties. Objective: The specific objective of the present study is to determine access to essential health services by households and the financial burden posed as a result of their intent to utilize theses services. Methods: The paper uses data from the national household income and expenditure survey (2018/19). It provides an insight into the health care expenditure and evidence on whose health service needs the health system meets and the household financial burden by health payments. Results: Out-of-pocket health expenditure (OOP) as a share of total health expenditure on average stands at 11.1% in 2018/19. About 3.3 % of households or 4,295 households that corresponds to about 17,180 individuals spend 52.1 % of their net of food income (non-subsistence income) on healthcare – a catastrophic level of household spending. Conclusion: Despite the low rate of out-of-pocket investing by families, and moderately expanding domestic investing in health care, Lesotho health care system requires a combination of policies to ensure fair financing of health care.

Providing A Personalized Healthcare Service To The Patients Using AIOPs Monitoring

We see the critical role technology plays in healthcare in the midst of a continuous worldwide health crisis. To combat the epidemic, South Korea, for example, used smart city' technology and government-developed applications that follow individuals in quarantine. A biosensor that can identify the virus in saliva samples has just been created by India's National Institute of Animal Biotechnology, Hyderabad. These developments are made feasible by the usage of certain biosensors that have printed circuit boards with metal core components capable of withstanding large variations in moisture and temperature. A very sophisticated breakthrough, artificial intelligence for IT operations, is altering the healthcare sector in addition to the technology that aid nations in combating the epidemic. This general phrase, also known as AIOps, describes the automated detection and repair of typical IT problems using big data, machine learning, and other AI technologies. AIOps may be used in the healthcare industry to teach computers to analyse CT scan pictures, follow the progression of various illnesses, assess the effectiveness of patient therapy, and much moren.

Providing A Personalized Healthcare Service To The Patients Using AIOPs Monitoring

We see the critical role technology plays in healthcare in the midst of a continuous worldwide health crisis. To combat the epidemic, South Korea, for example, used smart city' technology and government-developed applications that follow individuals in quarantine. A biosensor that can identify the virus in saliva samples has just been created by India's National Institute of Animal Biotechnology, Hyderabad. These developments are made feasible by the usage of certain biosensors that have printed circuit boards with metal core components capable of withstanding large variations in moisture and temperature. A very sophisticated breakthrough, artificial intelligence for IT operations, is altering the healthcare sector in addition to the technology that aid nations in combating the epidemic. This general phrase, also known as AIOps, describes the automated detection and repair of typical IT problems using big data, machine learning, and other AI technologies. AIOps may be used in the healthcare industry to teach computers to analyse CT scan pictures, follow the progression of various illnesses, assess the effectiveness of patient therapy, and much moren.

Case Study of Multichannel Interaction in Healthcare Services

A multichannel interaction service is a practice whereby organizations communicate and interact with their existing customers and potential new customers through different channels. This article presents a brief case study of multichannel interaction in healthcare services, which studies the viability of continuous multichannel interaction for personalized healthcare services to enable health professionals to follow up and monitor patients in home-based care. Furthermore, this study aims to explore the possibility of the continuity and complementarity of the interactions across different communication channels with the patients. The data used for this study was gathered during the first wave of the COVID-19 pandemic. This study showed that despite this type of interaction being relatively new in healthcare services, it has considerable potential for improving the relationship between patients, health professionals, and care providers. Upon completion of the data analysis, several conclusions were drawn. One such conclusion was the ability to maintain continuity of interaction across multiple channels, as well as the synergy between the different channels of interaction available to patients and the impact this has on the way patients and health professionals interact. Additionally, it was determined that the complementarity of different interaction channels is crucial when implementing multichannel interaction services. Furthermore, the implementation of this solution resulted in improved communication between patients and health professionals. Also, it has decreased health professional’s workload and reduced care providers costs regarding remote patient follow-up.

A Survey on Blockchain for Healthcare: Challenges, Benefits, and Future Directions

Continuously generated volumes of health data make healthcare a data-intensive domain. This data needs to be collected, stored, and shared among different healthcare actors for various purposes, such as reporting, analysis, collaborative research, and personalized healthcare services. However, the existing data storage and exchange solutions in the healthcare domain exhibit several challenges related to, e.g., data security, patient privacy, and interoperability. Recently, the industry and research community turned its focus to the possible use of blockchain technology to solve some of these challenges in the healthcare domain. The blockchain technology along with the support from smart contracts is considered a salient facilitator for secure and efficient health data sharing. This is due to its unique features, such as decentralization, trustlessness, immutability, traceability, and transparency. In this paper, we provide a comprehensive survey of the state-of-the-art efforts that envision the use of blockchain-based solutions in the healthcare domain. To this end, we introduce a systematic framework for classifying and analyzing such systems. The framework consists of classification in several dimensions: interactions between healthcare entities, functional components of healthcare storage systems, challenges in the healthcare domain that can be overcome by using the blockchain technology, and benefits for healthcare storage systems derived from the fundamental features of the technology. When analyzing over 40 systems and solutions proposed in the state-of-the-art, we perform their rigorous placement by identifying the exact scope of each solution and mapping it to the above taxonomies of interactions, functional components, challenges, and benefits. We additionally provide an extensive discussion of compliance with privacy-related regulations of General Data Protection Regulation (GDPR) in EU, and Health Insurance Portability and Accountability Act (HIPAA). Following the results of the analysis, we have outlined a number of important research gaps and future directions yet to be addressed.

SENSIBLE: SEquestered aNd SynergIstic BLockchain Ecosystem

AbstractHealth care interoperability unfolds the way for personalized health care services at a reduced cost. Furthermore, a decentralized system holds the promise to prevent compromises such as cyber‐attacks due to data breaches. Hence, there is a need for a framework that seamlessly integrates and shares data across the system stakeholders. We propose SEquestered aNd SynergIstic BLockchain Ecosystem (SENSIBLE), a blockchain‐powered, knowledge‐driven data‐sharing framework that gives patients complete control of their medical history and can extract rich information hidden in it using knowledge graphs (KGs). By incorporating both blockchain and KGs, we can provide a platform for secure data sharing among stakeholders by maintaining data privacy and integrity through data authentication and robust data integration. We present a Proof‐of‐Concept of the SENSIBLE network with Ethereum to share dynamic knowledge across stakeholders. Dynamic knowledge generation on the blockchain provides a two‐fold advantage of cooperation and communication amongst the stakeholders in the health care ecosystem. This leads to operational ease through sharing relevant portions of complex information while also ensuring the isolation of sensitive medical data.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review.

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

The Digital Twin in Medicine: A Key to the Future of Healthcare?

There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.