Digital Technologies In Healthcare Research Articles

A stochastic optimisation model to support cybersecurity within the UK national health service

Over the past decade, the adoption of new digital technologies in healthcare has surged, significantly enhancing care delivery and accessibility. However, this digital transformation has been accompanied by a sharp increase in cyber-attacks, posing severe risks to hospital functionality and patient safety. To address the challenge of planning for uncertain future cyber incidents, we propose a two-stage stochastic model designed to bolster the cyber resilience of healthcare providers by selecting optimal countermeasures in preparation for upcoming cyber incidents. Numerical tests demonstrate the model’s effectiveness, with the Value of the Stochastic Solution showing a 21% improvement over a deterministic approach. To be optimally equipped even for low-probability high-impact attacks we incorporate the risk measure Conditional Value-at-Risk. The corresponding countermeasure solution led to 44% fewer rejected patients in a worst-case scenario. The robustness of the proposed solution is underscored by its consistent performance across various scenarios, budget levels, and risk preferences, making it a reliable tool for enhancing cybersecurity in healthcare. These results highlight the importance of tailored, robust cybersecurity strategies in healthcare, ensuring preparedness for a wide range of potential threats.

Development of an evidence- and consensus-based Digital Healthcare Equity Framework.

To develop an evidence- and consensus-based Digital Healthcare Equity Framework (the Framework) that guides users in intentionally considering equity in healthcare solutions that involve digital technologies. We conducted an environmental scan including a scoping review of the literature and key informant interviews with health equity and digital healthcare technology thought leaders and convened a technical expert panel (TEP). We grouped similar concepts from the scoping review and key informant interviews, synthesized them into several primary domains and subdomains, and presented the composite list of domains and subdomains to the TEP for their input. From those, we derived the following domains for the Framework: (1) Patient and Community Characteristics, (2) Health System Characteristics, and (3) Health Information Technology Characteristics. We structured the Framework around the following 5 phases of the digital healthcare lifecycle: planning, development, acquisition, implementation/maintenance, and monitoring/improvement/equity assessment. The proposed Framework is designed to specify the aspects that need to be considered in a systematic and intentional approach to ensure digital healthcare solutions improve, and not exacerbate, healthcare inequities. The proposed Framework serves as a tool to help users and other stakeholders assess whether their healthcare solutions that involve digital technologies are equitable at every phase of the digital healthcare lifecycle.

Advanced Machine Learning Approaches for Multi-Class Lung Disease Classification Using Respiratory Acoustics

This study introduces a novel method for the multi-class classification of lung diseases using respiratory sounds. Lung diseases represent a major global health issue, and early detection is essential for effective treatment and management. Traditional diagnostic techniques often involve specialized equipment and expertise, which can limit accessibility and increase costs. In this research, we propose a machine learning-based approach that utilizes respiratory sounds, which can be easily collected with affordable, non-invasive devices like digital stethoscopes or smartphone apps. Our method involves extracting features from respiratory sound signals and training machine learning models to classify various lung conditions, including pneumonia, asthma, chronic obstructive pulmonary disease (COPD), and bronchiectasis. We validate our approach using realworld respiratory sound datasets and evaluate its performance based on accuracy, sensitivity, and specificity. The results highlight the potential of using respiratory sounds for multi-class lung disease classification, providing an effective solution for early diagnosis and remote monitoring, especially in resource-constrained environments. This research contributes to the evolution of digital healthcare technologies, offering significant potential for enhancing respiratory disease management and improving patient outcomes

PATIENT DATA PROTECTION IN THE DIGITAL ERA (CHALLENGES AND LEGAL SOLUTIONS)

Artificial intelligence (AI) has emerged as a promising technology in the field of healthcare, enabling the development of innovative solutions that enhance the quality and efficiency of medical services. The application of AI in the management of patient data has facilitated the acceleration, optimization, and precision of this process, which can subsequently improve the quality of healthcare services. This study aims to analyse the legal challenges that arise with the use of digital technology and artificial intelligence in patient data management, and to identify relevant legal solutions to overcome these problems. The study employs library research techniques, analysing statutory provisions and drawing on both written and unwritten positive law to examine legal issues. The results show that the integration of AI and digital technology in healthcare has yielded significant advantages, including enhanced efficiency, precision in diagnosis, and the harnessing of big data for patient care. However, a number of significant challenges remain, particularly with regard to the safeguarding of personal data. The formulation of an ethical framework for the utilisation of AI, the establishment of rigorous safety standards and the continuation of research in this domain are vital to enhance the advantages and mitigate the risks associated with the deployment of AI. It is imperative that health institutions and AI developers implement robust cybersecurity standards and periodic assessments for potential system vulnerabilities.

Acceptance of Digital Health Care Technology and the Role of Nursing Education.

As digital health care technology develops, the use of technology in the nursing field has become an essential part of nursing education. However, few studies have linked nurses' awareness of digital health care with nursing education. This study examines the direct effects and mediating effects of factors in technology acceptance on nurses' intention to accept digital health care technology. To empirically investigate these relationships, a survey was conducted among nurses in South Korea. This study emphasizes the importance of a multifaceted approach that considers personal, organizational, and innovation-related factors in understanding nurses' intentions toward acceptance of digital health care technologies. The findings confirm that performance expectancy and facilitating conditions play crucial roles in nurses' acceptance of digital health care technologies. The mediating effects of performance expectancy and facilitating conditions on intentions to accept technologies suggest that these factors can also play vital indirect roles. [J Contin Educ Nurs. 202x;5x(x):xx-xx.].

Influence of Blood Sampling Service Process Reengineering on Medical Services Supply: Quasi-Experimental Study.

Tertiary hospitals in China are confronted with significant challenges due to limited spatial capacity and workforce constraints, leading to saturated allocation of medical resources and restricted growth in medical service provision. The incorporation of digital health into medical service process reengineering (MSPR) marks a pivotal transformation and restructuring of conventional health service delivery models. Specifically, the application of MSPR to blood sampling services processes reengineering (BSSPR) holds promise for substantially enhancing the efficiency and quality of medical services through streamlining and optimizing these procedures. However, the comprehensive impact of BSSPR has been infrequently quantified in existing research. This study aims to investigate the influence of BSSPR on the efficiency and quality of medical services and to elucidate the key informative technological support points underpinning BSSPR. Data were collected from both the new and old laboratory information systems from August 1, 2019, to December 31, 2021. A combination of statistical description, chi-square test, and t test was used to compare check-in time and waiting time of outpatients before and after the implementation of BSSPR. An interrupted time-series design was used to analyze the impact of BSSPR on medical service efficiency and quality, enabling the control of confounding variables, including changes in medical human resources and both long- and short-term temporal trends. BSSPR had an impact on the efficiency and quality of medical services. Notably, there was a significant increase in the number of patients receiving blood sampling services, with a daily service volume increase of ~150 individuals (P=.04). The average waiting time for patients decreased substantially from 29 (SD 36) to 11 (SD 11) minutes, indicating a marked improvement in patient experience. During the peak period, the number of patients receiving blood sampling services per working hour statistically increased from 9.56 to 16.77 (P<.001). The interrupted time-series model results demonstrated a reduction in patients' waiting time by an average of 26.1 (SD 3.8; 95% CI -33.64 to -18.57) minutes. Although there was an initial decline in the number of outpatients admitted following BSSPR implementation, an upward trend was observed over time (β=1.13, 95% CI 0.91-1.36). BSSPR implementation for outpatients not only reduced waiting time and improved patients' experience but also augmented the hospital's capacity to provide medical services. This study's findings offer valuable insights into the potential advantages of BSSPR and underscore the significance of harnessing digital technologies to optimize medical service processes. This research serves as a foundational basis and provides scientific support for the promotion and application of BSSPR in other health care contexts. By continuing to explore and refine the integration of digital technologies in health care, we can further enhance patient outcomes and elevate the overall quality of medical services.

Digital health intervention for children with ADHD to improve mental health intervention, patient experiences, and outcomes: a study protocol

BackgroundAttention Deficit Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition with profound public health, personal, and family consequences. ADHD requires comprehensive treatment; however, lack of communication and integration across multiple points of care is a substantial barrier to progress. Given the chronic and pervasive challenges associated with ADHD, innovative approaches are crucial. We developed the digital health intervention (DHI)—CoolTaCo [Cool Technology Assisting Co-regulation] to address these critical barriers. CoolTaCo uses Patient-Centered Digital Healthcare Technologies (PC-DHT) to promote co-regulation (child/parent), capture patient data, support efficient healthcare delivery, enhance patient engagement, and facilitate shared decision-making, thereby improving access to timely and targeted mental health intervention for children at significant risk for poor outcomes. The present paper will describe our planned protocol to evaluate the efficacy of CoolTaCo via randomized control trial (RCT).Methods/designWe will recruit 60 children (ages 8–12) with ADHD who will be randomized to either immediate (n = 30) or delayed (n = 30) treatment (i.e., a waitlist control group). Among those randomized to immediate treatment, half will be assigned to DHI (delivered via a smartwatch and smartphone application), the other half to an active control treatment as usual (TAU). Unlike the DHI group, the TAU group will receive the smartwatch with no assigned activities, applications, or interventions on the devices. The intervention period will last 16 weeks; after a participant has been in the delayed treatment group for 16 weeks and has completed the post-waiting period assessment, they will be randomly assigned to either the intervention or active control group. Thus, 30 participants will complete the intervention, and 30 will complete the active control, with half of the total sample completing a waitlist period.DiscussionIndividuals with ADHD have complex needs. Despite improvement in outcomes following cognitive behavioral therapies (CBT) and pharmaceutical treatment, long-term maintenance is a challenge often not addressed by traditional medical approaches, and, as we described, ineffective approaches to information sharing across points of care create further barriers to progress. Our research will fill a significant gap in translating early treatment investments and gains into long-term, sustainable outcomes.This study was registered as a clinic trial at ClinicalTrials.gov (Digital Health Intervention for Children With ADHD, ID# NCT06456372) on 06/13/2024.

Digital communication and techno-stress among health professionals: a cross-sectional study in Italy

Abstract Background Digital technologies are increasingly indispensable in physicians’ clinical practice. Despite their advantages, they are also linked to information overload and constant availability, which might affect healthcare professionals’ quality of life. The study aims to investigate the utilisation of digital technologies among healthcare providers and to identify possible predictors of techno-stress associated with these technologies. Methods A cross-sectional study was conducted through an anonymous questionnaire distributed to Medical Doctors (MDs) via social media. The main outcomes were “Impact of digital technologies” and “E-mail overload” (EO), and “Whatsapp overload” (WO) as indicators of techno-stress. Univariable and multivariable regression models analyzed the association between the outcome variables and selected independent variables. Results Preliminary results were conducted on 246 MDs. Most respondents declared that using digital technologies positively impacted their relationship with patients and colleagues (67.1% and 56.7%, respectively). Multivariable linear regression analyses highlighted that variables associated with a higher or lower EO and/or WO were age (WO: adjusted coefficient (adjCoeff)=0.24, p = 0.004), being a specialist compared to a General Practitioner (GP) (EO: adjCoeff -8.42, p = 0.000; WO: adjCoeff=-5.2, p = 0.02), the number of patients visited each day (EO: adjCoef=16.10, p = 0.000; WO: adjCoeff=10.43, p = 0.001), working in a town with a larger population (EO: adjCoeff=-8.22, p = 0.001). Conclusions In agreement with the literature, GPs were the medical category most affected by techno-stress. Doctors with a higher volume of patients and who work in smaller towns have a higher likelihood of techno-stress. These findings underscore the necessity of interventions to address the issue, specifically through increased MD education and enhanced EU-wide regulations on digital technology use in healthcare. Key messages • Digital technologies positively impact physicians’ relationships with patients and colleagues, but also contribute to techno-stress, especially among General Practitioners and those with more patients. • Interventions like improved Medical Doctors’ education and stronger European regulations are needed to manage techno-stress and optimize the use of digital technologies in healthcare.

Transitioning Perspectives in Digital Health Through Phenomenology Integration.

The evolution of digital health, from its early days as eHealth to its current expansive scope, reflects a significant transformation in health care delivery and management. This transition underscores the integration of digital technologies across the health continuum from prevention and diagnosis to treatment and rehabilitation. The emergence of digital health has introduced innovative solutions but also posed challenges, particularly in aligning technological advancements with health needs, human experiences, and ethical considerations. This position paper aims to explore the integration of phenomenology in digital health, advocating for a paradigm that emphasizes the centrality of human experience in the design and implementation of digital health solutions. It specifically seeks to address challenges related to relevance for individuals who "speak" different languages, ensuring long-term use, addressing digital and health literacy, coordinating various sources, and navigating ethical issues in the rapidly evolving digital health landscape. Drawing upon years of research and practical experience in communication technologies and health, this paper uses a reflective approach to examine the intersection of digital health and phenomenology. It reviews the historical development of digital health, identifies the challenges faced during its evolution, and discusses the potential of phenomenological methods to enhance user-centered design and ethical practices in digital health. The integration of phenomenology into digital health facilitates a deeper understanding of user experiences, enabling the development of more responsive and ethical digital health solutions. Participatory design models, informed by phenomenological perspectives, offer a pathway to bridge the gap between technological innovation and human-centric health care. The paper highlights successful practices in digital health development, including mobile apps for vaccination decision-making and platforms for managing chronic conditions, illustrating the benefits of a phenomenological approach. Transitioning perspectives in digital health through phenomenology integration represents a critical step toward realizing the full potential of digital technologies in health care.

Policies, Progress, and Prospects for Internet Telemedicine in China

ABSTRACT Around the world, the adoption of digital technologies in health care has accelerated considerably in the wake of the COVID-19 pandemic. Prior to the emergence of the pandemic, China had already embarked on a private sector, technology enterprise-led creation of an innovative internet health care ecosystem, which has dramatically transformed China’s health care landscape. In this article, we describe the evolution of China’s internet health market, focusing on internet telemedicine. We trace its early origins with the establishment of information networks in the 1980s, to the emergence of a fast-growing internet health market in the 2010s that leveraged the capabilities of technology enterprises in e-commerce, logistics, and payment systems. Private health care platforms have played a central role throughout this transformative process. The supplyside of the market has a unique public-private mix structure, with the coexistence of public and private internet hospitals, and with the majority of telemedicine services provided by public hospital doctors working on private platforms in a dual practice capacity. We conclude with a discussion of the prospects of internet telemedicine, including how it should be optimized and harnessed to improve China’s health system. Finally, we set out areas where more research is needed.

Remote asthma monitoring: How and why?

Background: The coronavirus disease 2019 pandemic contributed to substantial advancement in digital technologies in health care. Health-care professionals involved in respiratory care can now tailor their therapy for their patients with asthma, who still account for &gt;1 million emergency department visits annually, with remote monitoring services. Remote patient monitoring (RPM) and remote therapeutic monitoring (RTM) are both ways to help patients with chronic asthma and other patients who need respiratory monitoring in pulmonary and in allergy and immunology clinics. RPM and RTM allow clinicians to gain further understanding as to what is going on in the patient’s home on a regular basis. Objectives: To ensure that the populations to monitor are correctly identified, privacy and security measures are in place, staffing resources are sufficient, and billing practices are understood before initiating RPM and RTM. Methods: A review of the current literature (2019‐2023) for RPM and RTM from PubMed, National Center of Biotechnical Information, American College of Allergy, Asthma and Immunology and the U.S. Centers for Disease Control and Prevention as well as practical experiences are outlined to provide an easy-to-follow guide for implementation in practice. Results: RPM and RTM services help identify and address gaps in care for patients such as patients with uncontrolled asthma and to assist with populations of need such as the underserved, the elderly, and rural populations. Conclusion: As artificial intelligence and other technologies continue to grow in the healthcare space, remote monitoring services are expected to transform health-care delivery to new heights yet to be seen.

Willingness to use clinical scales for menopause management among general practitioners

Objectives The use of digital healthcare technologies to enhance healthcare delivery has seen significant growth. However, a notable a notable research gap exists in the application of clinical scales for menopause management by general practitioners (GPs). This study aims to investigate willingness of GPs to use specific menopausal scale tools in the care of females for menopause management. Method An anonymous online survey was developed, which received responses from 348 French GPs in 2023. Multiple backward logistic regression was performed to identify the factors influencing the willingness to use a practical menopause management scale. Results In total, 87.93% of GPs are not familiar with the Greene Climacteric Scale and 90.52% are not familiar with the Menopause Quick 6 scale. In contrast, 90.52% would be interested in having access to such scales. The willingness to use a menopause management scale is associated with caring for menopausal females (odds ratio [OR] = 6.13, 95% confidence interval [CI] [2.08–18.08], p = 0.001), less experience (OR = 7.10, 95% CI [2.05–25.22], p = 0.002), the importance of health prevention in daily practice (comparing ‘very important’ to ‘not’, OR = 12.98, 95% CI [1.68–97.60], p = 0.004) and the use of a digital scale in daily practice for menopausal management (OR = 2.13, 95% CI [1.04–5.83], p = 0.014). Conclusion Future research is essential in representative population to confirm these findings in menopause management.

Emotional and Psychological Safety in Healthcare Digitalization: A Design Ethnographic Study.

Emotional and psychological safety is important during the use of digital technologies in healthcare. We aimed to gain comprehensive insight into needs, influencing factors and outcomes in the context of perceived safety and digital technologies in healthcare. We employed a participatory, design ethnographic research approach with 16 participants in 10 use cases. The methods included in an iterative process were, think-aloud, guideline-based interviews, process mapping, storyboard creation, and photo documentation. A qualitative, primarily inductive data analysis and synthesis was performed. Perceived safety is influenced by various factors and unmet needs. Increased perceived safety can positively support the use of digital technologies, whereas low perceived safety can limit or even hinder its use. The needs of the different target groups should be considered throughout the entire process of digital technology development and healthcare provision to support their implementation. These findings support further research by providing specific aspects of emotional and psychological safety regarding target groups, settings, and ages and those with different levels of affinity for digital technologies.

Digital Technology in Healthcare—An Ethical Overview

This overview of ethical aspects of artificial intelligence (AI) in healthcare is organized according to major types of medical activities. It discusses important ethical problems in (1) diagnostic uses of AI such as image interpretation and text interpretation, (2) the use of AI as decision support or even as decision-maker, (3) its use for treatment, for instance in robotic surgery, exoskeletons, virtual psychotherapy and therapeutic robots (care robots), and (4) the use of AI for healthcare documentation and communication. Summing up, we emphasize two major challenges in the introduction of AI that should concern not only healthcare professionals but also higher level decision-makers such as politicians and hospital directors. The first of these is the need to retain human contacts between patients and healthcare, in particular contacts that are conducive to patient health and well-being. The second is the need to ensure that the introduction of AI in healthcare does not create or exacerbate injustices in healthcare. This can happen if prevalent human biases are encoded in AI tools. It can also occur if people with less money have to use inexpensive AI tools such as virtual psychotherapists whereas the well-to-do have access to a human psychotherapist. On the other hand, AI tools can be used to provide better healthcare to all who need it, and they can also be programmed to avoid biases and other mistakes that humans are prone to. The article concludes with brief summaries of the articles in the special issue on AI in healthcare that it introduces.

The Frontier Exploration of the Development Trend of Key Technologies in Digital Healthcare in Medical Big Data Analysis

With the rapid development of medical big data, digital healthcare as an interdisciplinary field is gradually transforming the traditional healthcare model, continuously advancing the realization of personalized medical services and remote health monitoring. This paper systematically reviews 138 research articles related to the development trends of key technologies in digital healthcare, exploring various critical areas including digital health and privacy security, the application of artificial intelligence and machine learning in health, remote monitoring and mobile health, medical data analysis and electronic health records, and the ethical and policy challenges in digital health. Studies indicate that the improvement of privacy security measures and efficient medical data processing technologies are the foundation for the safe promotion of digital healthcare; Artificial intelligence and machine learning technologies play an important role in enhancing the accuracy of diagnosis and disease prediction. Meanwhile, mobile health and remote monitoring technologies show great potential in improving the accessibility and continuity of medical services. In addition, this paper discusses the challenges of ethics and policies in digital health practice, such as data sharing, conflicts of interest, and respect for patient privacy. Future research must focus on refining technological solutions to address these challenges and fully integrate digital healthcare technologies into clinical practice.

Patients' Expectations of Doctors' Clinical Competencies in the Digital Health Care Era: Qualitative Semistructured Interview Study Among Patients.

Digital technologies have impacted health care delivery globally, and are increasingly being deployed in clinical practice. However, there is limited research on patients' expectations of doctors' clinical competencies when using digital health care technologies (DHTs) in medical care. Understanding these expectations can reveal competency gaps, enhance patient confidence, and contribute to digital innovation initiatives. This study explores patients' perceptions of doctors' use of DHTs in clinical care. Using Singapore as a case study, it examines patients' expectations regarding doctors' communication, diagnosis, and treatment skills when using telemedicine, health apps, wearable devices, electronic health records, and artificial intelligence. Findings were drawn from individual semistructured interviews with patients from outpatient clinics. Participants were recruited using purposive sampling. Data were analyzed qualitatively using thematic analysis. Twenty-five participants from different backgrounds and with various chronic conditions participated in the study. They expected doctors to be adept in handling medical data from apps and wearable devices. For telemedicine, participants expected a level of assessment of their medical conditions akin to in-person consultations. In addition, they valued doctors recognizing when a physical examination was necessary. Interestingly, eye contact was appreciated but deemed nonessential by participants across all age bands when electronic health records were used, as they valued the doctor's efficiency more than eye contact. Nonetheless, participants emphasized the need for empathy throughout the clinical encounter regardless of DHT use. Furthermore, younger participants had a greater expectation for DHT use among doctors compared to older ones, who preferred DHTs as a complement rather than a replacement for clinical skills. The former expected doctors to be knowledgeable about the algorithms, principles, and purposes of DHTs such as artificial intelligence technologies to better assist them in diagnosis and treatment. By identifying patients' expectations of doctors amid increasing health care digitalization, this study highlights that while basic clinical skills remain crucial in the digital age, the role of clinicians needs to evolve with the introduction of DHTs. It has also provided insights into how DHTs can be integrated effectively into clinical settings, aligning with patients' expectations and preferences. Overall, the findings offer a framework for high-income countries to harness DHTs in enhancing health care delivery in the digital era.

(Invited) Multi-Modal Solid-State Gas Sensors-Based Breath Analysis System with Convolutional Neural Network (CNN) for Early Detection of Lung Cancer

Currently, digital healthcare technologies based on Internet of Things (IOT) are emerging, and the market for digital healthcare was estimated to be 151.5 billion dollars by 2020. Among the digital healthcare technologies, disease diagnosis is attracting maximum attention. Traditional diagnostic methods, such as radiology and human biological material analysis, have limitations of long study time, high cost, radiation exposure, and pain. Therefore, non-invasive diagnostics methods, for early diagnosis of specific diseases and personal healthcare management, have attracted attention.Breath sensors are one of the promising candidates for the realization of non-invasive diagnosis owing to their ease of use, safety, and cost effectiveness. The exhaled breath analysis technique holds great promise as a non-invasive method for diagnosing lung cancer. In the breath of lung cancer patients, various volatile organic compounds (VOCs) markers are generated during metabolic processes. Analyzing these VOCs markers presents a compelling avenue for early and non-invasive lung cancer diagnosis. Solid state gas sensors capable of measuring specific VOCs are currently being researched and utilized as commercial gas sensors for this purpose. However, the concentration of lung cancer VOCs markers in exhaled breath is commonly very low, and individual commercial gas sensors often lack the necessary sensitivity and selectivity to accurately diagnose lung cancer. In this study, we propose a multimodal solid state electronic biosensor system designed to differentiate lung cancer VOCs characteristics among multiple VOCs, enabling exhalation-based lung cancer diagnosis. Initially, we developed multimodal biosensors using more than 20 gas sensors, consisting of metal oxide semiconductor sensors, electrochemical sensors, photoionized detectors, metalorganic frame sensors and so on. And each exhibiting varying sensitivity profiles for complex gas mixtures. Additionally, we designed a classification algorithm based on convolutional neural networks (CNN) and multi-layered perceptron (MLP) to distinguish between lung cancer patients and healthy individuals using the output from these diverse multimodal gas sensors. To evaluate the system's performance, we collected exhaled air samples from 74 lung cancer patients and 107 normal subjects, conducting clinical trials with our developed system. We did model evaluation with confusion matrix and ROC curve. The results of these tests confirmed that our system achieved a high predictive accuracy of over 95% in classifying lung cancer patients. The simplicity of the sensor configuration in our developed system makes it suitable for large-scale screening tests to identify potential candidates for precise lung cancer diagnosis. This technology has the potential to contribute to reduced national welfare costs and improved patient survival rates through early diagnosis.

Prevention and Management of Multimorbidity in Southeast Asia: A Narrative Review

ABSTRACTMultimorbidity, the coexistence of two or more chronic conditions, presents a growing global challenge, particularly in low‐ and middle‐income countries such as Southeast Asia. This trend necessitates the development of sustainable integrated care models to prevent and manage multimorbidity effectively. However, progress in this area has been hampered, especially in underdeveloped regions, by various barriers, including the epidemiology of multimorbidity, how to get different specialists and doctors to work together most availably and manage the multiple medication issues and how to develop cost‐effective approaches to reduce the health burden of multimorbidity. Preventive measures in Southeast Asia, which could tackle multiple components which commonly comprise multimorbidity, include enhancing health literacy and health promotion through school‐ and community‐based educational activities, primary healthcare and related policies on employing taxes on tobacco, alcohol and sugary beverages. The social determinants of health‐encompassing poverty and low education may also influence research on multimorbidity. Moreover, stakeholder engagements involving national governments, World Health Organization (WHO) and Association of Southeast Asian Nations (ASEAN) are crucial. Management strategies focus on integrated care models, including patient‐centred primary healthcare, digital healthcare technologies, and medication management to control polypharmacy. Although research on multimorbidity in Southeast Asia is increasing, translating findings into practical measures was limited. Future efforts should prioritize evidence‐based approaches to prevent and manage multimorbidity effectively, addressing challenges like health system focusing on single chronic disease treatment independently, resource limitations, healthcare provider shortages and individual adherence issues. These ways promise to enhance the quality of life and health outcomes in this region.

Understanding Factors Influencing Diabetic Patients' Intention to Use Digital Health Services in Saudi Arabia Using the Technology Acceptance Model.

This study employs the Technology Acceptance Model to investigate the factors influencing Saudi Arabian diabetic patients' intention to use digital health services. There is an urgent need to investigate the possibilities of digital health services in managing diabetes given the startlingly rapidly increasing prevalence rate of diabetes in KSA. The study examines the variables affecting patients' acceptance and desire to use digital health tools to manage their diabetes. The study employs the Technology Acceptance Model to ascertain the crucial factors that impact patients' opinions regarding the usefulness and ease of use of digital healthcare technologies. The proposed model extends the traditional Technology Acceptance Model by adding two new constructs, perceived privacy and trust. These constructs were examined by analyzing the intentions of 600 respondents through online surveys. The study's conclusions showed that attitudes toward using digital health services for KSA diabetic patients are greatly influenced by every component of the extended Technology Acceptance Model. The study's conclusions add to the body of knowledge already in existence and offer insightful information to decision-makers hoping to improve digital health services.

Challenges Experienced by Saudi Patients With Cancer and Their Family Caregivers in Using Digital Healthcare Technology Platforms in the COVID-19 Pandemic.

COVID-19 has provided a unique boost to the use of digital healthcare technology, putting many vulnerable people at risk of digital exclusion. To promote digital healthcare equity, it is important to identify the challenges that may inhibit cancer patients and family caregivers from benefiting from such technology. This study explored the challenges that cancer patients and family caregivers experience in using digital healthcare technology platforms during the COVID-19 pandemic. A qualitative descriptive study using face-to-face semistructured individual interviews was carried out. A purposive sample of 21 participants was recruited from a public cancer hospital in Saudi Arabia. Qualitative content analysis with an inductive approach was utilized. The factors that challenged the ability of participants to benefit from digital healthcare technology were similar. Four themes related to the challenges the two groups experienced emerged: access to platforms, use of platforms for cancer health-related purposes, attitudes toward these platforms, and individual user preferences. This study identified numerous areas for improvement regarding digital healthcare technology platform implementation, which could increase future benefits and equal use. This study's findings also provide useful information to investigators who intend to create digital nursing interventions for both groups amid COVID-19 and other worldwide health crises.