This study proposes a novel architecture for designing digital twins in healthcare units. A systematic research methodology was employed to develop architecture design patterns. In particular, a systematic literature review was conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework to answer specific research questions and provide guidelines for designing the architecture. Subsequently, a case study was designed and analyzed at a chemotherapy treatment center for outpatients. System architecture knowledge was distilled from this real-world case study, supplemented by existing software and systems design patterns. A novel five-layer architecture for digital twins in healthcare units was proposed with a focus on the security and privacy of patients' information. The proposed digital twin architecture for healthcare units offers a comprehensive solution that provides modularity, scalability, security, and interoperability. The architecture provides a robust framework for effectively and efficiently managing healthcare environments.

Children and young people with cancer face barriers when engaging with exercise, such as treatment-related side effects, psychosocial burdens and lack of individualised provisions. Digital health tools, such as smartphone applications, have emerged as a promising driver to support healthcare provisions in exercise prescription among patients. It is vital to explore how such technologies can be developed more effectively in order to strengthen the evidence supporting their use and for more appropriate implementation within healthcare. This study aims to explore user experiences, preferences and suggested improvements from healthy children and young people aged 9-21 years. An augmented reality (AR) application was specifically developed for children and young people aged 9-21 years undergoing cancer treatment and a protocol for a pilot study was designed. The target sample of this pilot study is 90 healthy children and young people aged 9-21 years. Practical 30-min workshops will be conducted encouraging participants to engage with the smartphone app. Focus groups will explore participant experiences, preferences, and suggested improvements. Data will be analysed deductively with apriori themes derived from the semi-structured interviews. Obtaining user experiences, preferences and suggested improvements is especially important for the development of novel apps, such as those prescribing exercise and using algorithms and augmented reality software. Results from this study will directly influence the development of an augmented reality application, which will also be applied within a long-term trial in paediatric oncology.

We analyzed a natural language processing (NLP) toolkit's ability to classify unstructured EHR data by psychiatric diagnosis. Expertise can be a barrier to using NLP. We employed an NLP toolkit (CLARK) created to support studies led by investigators with a range of informatics knowledge. The EHR of 652 patients were manually reviewed to establish Depression and Substance Use Disorder (SUD) labeled datasets, which were split into training and evaluation datasets. We used CLARK to train depression and SUD classification models using training datasets; model performance was analyzed against evaluation datasets. The depression model accurately classified 69% of records (sensitivity = 0.68, specificity = 0.70, F1 = 0.68). The SUD model accurately classified 84% of records (sensitivity = 0.56, specificity = 0.92, F1 = 0.57). The depression model performed a more balanced job, while the SUD model's high specificity was paired with a low sensitivity. NLP applications may be especially helpful when combined with a confidence threshold for manual review.

Poorly controlled chronic obstructive pulmonary disease (COPD) can negatively impact quality of life but mobile applications (apps) are popular digital tools that may mitigate these support needs. However, it is unclear if public mobile COPD apps are acceptable to healthcare professionals and patients, people living with COPD. The primary objective is to determine people with COPD and healthcare professionals' perspectives on the appropriateness of public mobile COPD apps for supporting individuals' needs. The secondary objectives were to identify the ideal features and styles of mobile COPD apps for COPD self-management; and to identify the facilitators, barriers and needs for future COPD app research and development. Public mobile COPD apps were rated by questionnaires administered before and after focus group meetings. Ratings were reported as medians with interquartile ranges and median scores were categorized into three levels of appropriateness: 1-3 for inappropriate; 4-6 for uncertain; and 7-9 for appropriate. A total of 6 people with COPD (mean age 68.2 ± 4.8years) and 22 healthcare professionals (mean age 45 ± 8.3years) completed this study. People with COPD identified one and healthcare professionals identified three public mobile COPD apps to be appropriate. They had different preferences for features and engagement styles but similar preferences for facilitators and barriers to use. Stakeholders mutually rated one public mobile COPD app as appropriate for self-management and emphasized the need for apps to be supplementary and customizable, rather than replacements for clinical management.

Pilot 5 utilizes AI and robotics to develop a robotic nurse assisting hospital staff in response to workforce shortages and rising care demands due to an aging population. This project aims to optimize resources, reduce errors, and improve patient satisfaction through personalized care. The Living Lab approach was implemented to split the study into sprints. The first split involves working with project partners and stakeholders to define the problem, brainstorm functionalities, and identify limitations (24 participants). The second split focuses on further requirement gathering, exploring real-world use cases, and considering ethical and privacy concerns (51 participants). The project used iterative development cycles (5-8 months) to continuously improve the solution. Surveys revealed high satisfaction rates, with average scores of 4.0 and 3.6 for Sprints 1 and 2, respectively. Similarly, a team morale survey indicated a positive trend, with average scores of 7.6 and 8.18 for Sprints 1 and 2, respectively. Pilot 5 offers a promising solution to the evolving needs of modern hospitals. This study explores the integration of a social robotic system into nursing care to enhance quality and emphasizes stakeholder engagement, participatory design, and user-centered approaches in AI healthcare solutions.

Online health communities (OHCs) facilitate patient-physician interaction and the adoption of online health services. However, few studies explored the impact of network effects on patients' continuance intentions in OHCs. This study aims to explore the determinants affecting OHC patients' continuance intentions based on the network effects theory and expectation confirmation model (ECM). An integrated research model and relative hypotheses are proposed. A total of 420 valid responses are collected through an online questionnaire survey to test the research framework using structural equation modeling. The results reveal that direct network effect, cross network effect, and indirect network effect all positively affect perceived ease of use, and the latter two also positively affect perceived usefulness that further affect continuance intention. In addition, other results are consistent with the ECM-based hypotheses and the positive impact of perceived e-health literacy on continuance intention is also explained. Patients' continuance intention to use OHCs can be improved by network effects through direct, cross, and indirect formats. ECM-based determinants, including confirmation, perceived usefulness, and satisfaction, provide valuable insights for OHC patients' continuous use. Enhancing e-health literacy helps maintain patients' intention to continue using OHCs.

Research indicates that recording medical consultations benefits patients by helping them recall information pertinent to their care. Cancer Care Alberta set out to develop a mobile recording app to enable patients to safely and securely record appointments and take notes. Stakeholder engagement was conducted with patients, healthcare providers, and the Alberta Health Services Legal & Privacy team. App testing was completed with patient and family advisors. The app was piloted in a clinic to assess workflow impacts before moving to a public launch. The app launched in late November 2018 and continues to be used by patients in the cancer program and beyond. Earlier in 2024, the app underwent additional testing with advisors and user-friendly improvements were made based on feedback and previous user reviews. This article summarizes the development, implementation, and sustainment of the My Care Conversations app. Implementation challenges and effective strategies are highlighted.

Electronic Health Records (EHRs) are now an integral part of health systems in middle and high-income countries despite recognized deficits in the digital competencies of Healthcare Professionals (HCPs). Therefore, we undertook a scoping review of factors influencing compliance with EHR data privacy policies. Seven databases revealed 27 relevant studies, covering a range of countries, professional groups, and research methods. The diverse nature of these factors meant that 18 separate theoretical frameworks representing technology-acceptance to behavioral psychology were used to interpret these. The predominant factors influencing compliance with EHR data privacy policies included confidence and competence to comply, perceived ease of use, facilitatory environmental factors, perceived usefulness, fear that non-compliance would be detected and/or punished and the expectations of others. Human factors such as attitudes, social pressure, confidence, and perceived usefulness are as important as technical factors and must be addressed to improve compliance.

The Internet of Medical Things (IoMT) is transforming healthcare systems, but concerns about device integrity and sensitive data are growing. The study aims to develop a framework for evaluating and prioritizing integrity schemes in healthcare for IoT-based medical sensor devices, addressing the challenges of selecting the right authentication solution due to its complexity and intricacy. A unified health-hesitant fuzzy expert system for IoMT sensor integrity assessment in Saudi Arabia is described in this paper. Medical sensor integrity literature and professionals are contacted first. Delphi is used to gather attributes of integrity approaches while an Internet of Things medical sensor integrity specialist supervises the operation. After collecting characteristics, good assessment criteria are created and the hesitant fuzzy analytic network procedure is used to assess integrity. Functional integrity and measurement accuracy are the biggest factors in IoMT sensor security and integrity, according to assessment. The framework achieves 93%, 94%, and 95% precision, accuracy, and recall compared to current approaches. The framework helps healthcare integrity security professionals and stakeholders assess and resolve IoT medical sensor authentication issues. This health-hesitant fuzzy expert system will let Saudi Arabian and international healthcare stakeholders safely deploy IoMT sensors in the changing healthcare landscape.

To develop and test an NLP algorithm that accurately detects the presence of information reported from DXA scans containing femoral neck T-scores of the patients scanned. A rule-based NLP algorithm that iteratively built a collection of regular expressions in testing data consisting of 889 snippets of text pulled from DXA reports. This was manually checked by clinical experts to determine the proportion of manually verified annotations that contained T-score information detected by this algorithm called 'BoneScore'. Testing of 30- and 50-word lengths on each side of the key term 'femoral' were pursued until achievement of adequate accuracy. A separate clinical validation regressed the extracted T-score values on five risk factors with established associations. BoneScore built a set of 20 regular expressions that in concert with a width of 50 words on each side of the key term yielded an accuracy of 98% in the testing data. The extracted T-scores, when modeled with multivariable linear regression, consistently exhibited associations supported by the literature. BoneScore uses regular expressions to accurately extract annotations of T-score values of bone mineral density with a width of 50 words on each side of the key term. The extracted T-scores exhibit clinical face validity.

The current study aims to understand how Apple Watch helped users maintain wellness routines during the COVID-19 lockdown period, where access to public gyms and spaces was curtailed. We explore the effectiveness of biofeedback engagement aspects of Apple Watch: goals, alerts and notifications, and sociability aspects of the device or social interaction with other users. We report the results of a 2-week digital diary study based in the United States with 10 adults with 6 months or longer exposure to Apple Watch, followed by online survey responses gathered from 330 additional users. The study findings show how Apple Watch transforms notifications from distractions into positive wellness tools. Data suggests that personal context (custom goals and supported intent) combined with motivational nudges from alerts and notifications as well as contextually triggered nudges contribute to Apple Watch user adoption and satisfaction. This study highlights how Apple Watch transforms notifications from distractions into positive wellness tools; emphasizing the importance of balancing nudging with customization with user control. Sociability and privacy remain crucial, especially with biofeedback-enabled fitness trackers. We conclude that Apple Watch enhances user engagement by triggering context-relevant interactions, nudging users to achieve their goals through small, motivated behaviors.

Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.

Dupuytren disease is a common condition that causes progressive finger contractures resulting in impaired hand function and difficulties in performing daily activities. Patient reported outcome measures (PROMs) are commonly used in research and clinical practice to evaluate treatment outcomes. Both general upper extremity PROMs and Dupuytren-specific PROMs are available, typically developed using conventional methodology based on classical test theory. However, most current PROMs have been shown to have low responsiveness and the relevance of included items have been questioned. In this study we aim to develop a new Dupuytren-specific PROM using modern measurement methodology based on item response theory (IRT). The study will be performed in three phases. In Phase 1, (completed), an expert group developed a questionnaire with a large number of potentially relevant items derived from existing PROMs and patient collaboration. In Phase 2, the questionnaire will be administered to 300 patients with Dupuytren disease, and their responses will be analyzed with IRT methodology to identify the best performing items to be included in the new PROM. In Phase 3, the new PROM will be administered to 300 additional patients for validation. This new Dupuytren-specific patient-reported outcome measure will help advance clinical research on Dupuytren disease.

Integrating artificial intelligence (AI) solutions into clinical practice, particularly in the field of video capsule endoscopy (VCE), necessitates the execution of rigorous clinical studies. This work introduces a novel software platform tailored to facilitate the conduct of multi-reader multi-case clinical studies in VCE. The platform, developed as a web application, prioritizes remote accessibility to accommodate multi-center studies. Notably, considerable attention was devoted to user interface and user experience design elements to ensure a seamless and engaging interface. To evaluate the usability of the platform, a pilot study is conducted. The results indicate a high level of usability and acceptance among users, providing valuable insights into the expectations and preferences of gastroenterologists navigating AI-driven VCE solutions. This research lays a foundation for future advancements in AI integration within clinical VCE practice.

Machine learning-based analytics over uni-modal medical data has shown considerable promise and is now routinely deployed in diagnostic procedures. However, patient data consists of diverse types of data. By exploiting such data, multimodal approaches promise to revolutionize our ability to provide personalized care. Attempts to combine two modalities in a single diagnostic task have utilized the evolving field of multimodal representation learning (MRL), which learns a shared latent space between related modality samples. This new space can be used to improve the performance of machine-learning-based analytics. So far, however, our understanding of how modalities have been applied in MRL-based medical applications and which modalities are best suited for specific medical tasks is still unclear, as previous reviews have not addressed the medical analytics domain and its unique challenges and opportunities. Instead, this work aims to review the landscape of MRL for medical tasks to highlight opportunities for advancing medical applications. This paper presents a framework for positioning MRL techniques and medical modalities. More than 1000 papers related to medical analytics were reviewed, positioned, and classified using the proposed framework in the most extensive review to date. The paper further provides an online tool for researchers and developers of medical analytics to dive into the rapidly changing landscape of MRL for medical applications. The main finding is that work in the domain has been sparse: only a few medical informatics tasks have been the target of much MRL-based work, with the overwhelming majority of tasks being diagnostic rather than prognostic. Similarly, numerous potentially compatible information modality combinations are unexplored or under-explored for most medical tasks. There is much to gain from using MRL in many unexplored combinations of medical tasks and modalities. This work can guide researchers working on a specific medical application to identify under-explored modality combinations and identify novel and emerging MRL techniques that can be adapted to the task at hand.

Rapid epidemiological investigations are fundamental to prevent the spread of infectious diseases such as coronavirus disease 2019. An epidemiological investigation presents significant challenges for both epidemiologists and infected individuals. It requires creating an environment that enables people to independently manage infectious diseases and voluntarily participate in epidemiological investigations. We developed the KODARI application, an epidemiological investigation support system that users can voluntarily use. We developed the questionnaires based on literature reviews. We evaluated the application through an online survey from December 2 to 14, 2022. The application automatically or manually collect epidemiological investigation information. The application improved data accuracy through accurate information collection. It voluntarily can transmit self-management information to epidemiologist terminals or users in real time. We collected 248 users from an online survey. Most users had high ratings and willingness to use. They have willingness to manage infectious patients was substantial. The application was evaluated as helpful for epidemiological investigations and could shorten the time required for epidemiological investigations by more than 30 min. The application proposes a model based on people's voluntary participation. We demonstrated that the application could enhance epidemiological investigations and diminish the duration of existing epidemiological investigation processes.

Smartphone apps can be highly effective in supporting young people experiencing mood disorders, but an appealing visual design is a key predictor of engagement with such apps. However, there has been little research about the interaction between visual design, mood and wellbeing in young people using a mental health app. This study aimed to explore young people's perspectives on colour and visual design in the development of a music-based app for mood management. Workshops were conducted with 24 participants (aged 13-25 years) with data analysis following a general inductive approach. Results indicated that colour could impact wellbeing in both positive and negative ways. Participants favoured a subtle use of colour within sophisticated, dark palettes and were influenced by a complex interplay of common semiotic values, experiences with other apps, and mood. These findings highlight the highly contextual nature of the relationship between colour and mood, emphasising the importance of co-design in app development.

Despite the ongoing efforts to digitalize the healthcare sector in developing countries, the full adoption of big data analytics in healthcare settings is yet to be attained Exploring opportunities and challenges encountered is essential for designing and implementing effective interventional strategies. Exploring opportunities and challenges towards integrating big data analytics technologies in the healthcare industry in developing countries. This was a narrative review study design. A literature search on different databases was conducted including PubMed, ScienceDirect, MEDLINE, Scopus, and Google Scholar. Articles with predetermined keywords and written in English were included. Big data analytics finds its application in population health management and clinical decision-support systems even in developing countries. The major challenges towards the integration of big data analytics in the healthcare sector in developing countries include fragmentation of healthcare data and lack of interoperability, data security, privacy and confidentiality concerns, limited resources and inadequate regulatory and policy frameworks for governing big data analytics technologies and limited reliable power and internet infrastructures. Digitalization of healthcare delivery in developing countries faces several significant challenges. However, the integration of big data analytics can potentially open new avenues for enhancing healthcare delivery with cost-effective benefits.

The aim of this study was to evaluate and compare the impressions of older adults with mild dementia/MCI (mild cognitive impairment) and people with schizophrenia towards the usability of GRADIOR (version 4.5) and their user experience (UX) with this computerized cognitive rehabilitation program.

Organisational interoperability (OIoP) of the Healthcare Information System (HIS) is crucial for the success of HIS, however little is known about the impact of institutional factors. This cross-sectional study aimed to investigate the direct effect of institutional factors on OIoP for HIS in public sector hospitals in Malaysia. A conceptual OIoP framework was developed using the Personal Health Systems Interoperability and Refined eHealth European Interoperability frameworks. A self-administered questionnaire survey was used to solicit data from 300 healthcare professionals. Data were assessed through an Exploratory Factor Analysis followed by a Confirmatory Factor Analysis. Structured equation modelling revealed Security and Privacy Compliance, and Stakeholder Engagement and Awareness to significantly and positively affect OIoP (R = 0.380). Healthcare organisations should prioritise clear and effective policies and regulations and enough budget and resources for the suggested framework.

A large number of people with obstructive sleep apnea (OSA) also suffer from major depressive disorder (MDD), leading to underdiagnosis due to overlapping symptoms. Polysomnography has been considered to identify MDD. However, limited access to sleep clinics makes this challenging. In this study, we propose a model to detect MDD in people with OSA using an electrocardiogram (ECG) during sleep. The single-lead ECG data of 32 people with OSA (OSAD-) and 23 with OSA and MDD (OSAD+) were investigated. The first 60 min of their recordings after sleep were segmented into 30-s segments and 13 parameters were extracted: PR, QT, ST, QRS, PP, and RR; mean heart rate; two time-domain HRV parameters: SDNN, RMSSD; and four frequency heart rate variability parameters: LF_power, HF_power, total power, and the ratio of LF_power/HF_power. The mean and standard deviation of these parameters were the input to a support vector machine which was trained to separate OSAD- and OSAD+. The proposed model distinguished between OSAD+ and OSAD- groups with an accuracy of 78.18%, a sensitivity of 73.91%, a specificity of 81.25%, and a precision of 73.91%. This study shows the potential of using only ECG for detecting depression in OSA patients.