Background: Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Objective: The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan. Methods: This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization’s (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy. Results: Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations’ inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects. Conclusions: We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

This paper discusses the findings of research conducted between 2013 and 2016, which concerned the development of technology roadmaps for the Creative Industries. The roadmap presented in this paper was built based on input from communities of creative and Information and Communication Technologies (ICT) experts collected during the consultation and validation phases of the research. It provides a synthesis of challenges and recommendations from the five creative sectors examined by the project – Architecture, Art, Design, Games, Media and e-Publishing – and proposes research directions for the development of desired future technologies, by highlighting innovative future developments in the Creative Industries, while also assessing their technology maturity in the short, medium and longer terms. By rating the desired technologies as ‘present’ (1–2 years), ‘possible’ (2--5 years), or ‘probable’ (5–10 years or beyond), the roadmap gives orientation towards the development of new technologies and related business models and skills and provides guidance for informed policy-making. The paper thus aims at enabling stakeholders – creators, professionals, SMEs, creative groups, creative communities, associations, organisations and institutions, as well as governments and policy makers – to maximise their benefit and the societal value from the new emerging technology landscape in the Creative Industries.

This paper explores the notion of the modular building construction site as an applied instance of redistributed manufacturing; in so doing, this research seeks to reduce the environmental footprint of building sites, treating them as small digitally connected subunits. In seeking to provide a whole lifecycle appreciation of a construction project, it is noted that the presence of a framework to provide guidance on the consideration of Internet of Things (IoT) data streams and connected construction objects is currently lacking. This paper proposes use of embedded IoT enabled sensing technology within all stages of a modular building lifecycle. An expanded four-phase model of intelligent assets use in construction is proposed along with an outline of the required data flows between the stages of a given building’s entire lifecycle that need to be facilitated for a BIM (Buildings Information Modelling) representation to begin to describe a building project as a sustainable asset within the circular economy. This paper also describes the use of concrete as a modular sensing structure; proposing that health monitoring of the material in situ along with the recoding of environmental factors over time could help to extend the longevity of such structures.

Brain stroke is the second leading cause of death worldwide, following ischemic heart disease. Ischemic stroke occurs when blood vessels are obstructed by a thrombus or other blockages. Prompt and accurate diagnosis of ischemic stroke is critical for patient survival. This study proposes a novel approach for ischemic stroke detection from computed tomography (CT) images, utilizing a hybrid feature extraction technique combined with a convolutional block attention module (CBAM). The hybrid feature extraction leverages the strengths of two pre-trained models, DenseNet121 and MobileNetV3, through feature fusion to provide a comprehensive representation of brain CT images. The CBAM module is integrated to enhance the most relevant features by focusing on both channel and spatial attention mechanisms, significantly improving the model’s ability to detect ischemic strokes with high accuracy. The proposed approach was developed and evaluated on a unique first-hand dataset (Dataset 1) collected from the Specialized private Hospital in Palestine. To further demonstrate the robustness and generalizability of the method, it was also tested on a public dataset (Dataset 2). The results show that the proposed model achieved outstanding performance across all metrics: on Dataset 1, it reached an accuracy of 99.21%, precision of 99.17%, recall of 99.32%, and an F1-score of 99.24%. On Dataset 2, it achieved an accuracy of 98.73%, precision of 98.71%, recall of 98.74%, and an F1-score of 98.88%. The exceptional results are attributed to the power of feature fusion, which creates a robust and comprehensive feature representation. In addition to the CBAM, which refines the feature maps by selectively focusing on the most important channels and spatial regions in brain CT images. This has led to substantial improvement in the detection of ischemic stroke areas.

Background: The immunization uptake rates in Pakistan are much lower than desired. Major reasons include lack of awareness, parental forgetfulness regarding schedules, and misinformation regarding vaccines. In light of the COVID-19 pandemic and distancing measures, routine childhood immunization (RCI) coverage has been adversely affected, as caregivers avoid tertiary care hospitals or primary health centers. Innovative and cost-effective measures must be taken to understand and deal with the issue of low immunization rates. However, only a few smartphone-based interventions have been carried out in low- and middle-income countries (LMICs) to improve RCI. Objective: The primary objectives of this study are to evaluate whether a personalized mobile app can improve children’s on-time visits at 10 and 14 weeks of age for RCI as compared with standard care and to determine whether an artificial intelligence model can be incorporated into the app. Secondary objectives are to determine the perceptions and attitudes of caregivers regarding childhood vaccinations and to understand the factors that might influence the effect of a mobile phone–based app on vaccination improvement. Methods: A mixed methods randomized controlled trial was designed with intervention and control arms. The study will be conducted at the Aga Khan University Hospital vaccination center. Caregivers of newborns or infants visiting the center for their children’s 6-week vaccination will be recruited. The intervention arm will have access to a smartphone app with text, voice, video, and pictorial messages regarding RCI. This app will be developed based on the findings of the pretrial qualitative component of the study, in addition to no-show study findings, which will explore caregivers’ perceptions about RCI and a mobile phone–based app in improving RCI coverage. Results: Pretrial qualitative in-depth interviews were conducted in February 2020. Enrollment of study participants for the randomized controlled trial is in process. Study exit interviews will be conducted at the 14-week immunization visits, provided the caregivers visit the immunization facility at that time, or over the phone when the children are 18 weeks of age. Conclusions: This study will generate useful insights into the feasibility, acceptability, and usability of an Android-based smartphone app for improving RCI in Pakistan and in LMICs. Trial Registration: ClinicalTrials.gov NCT04449107; https://clinicaltrials.gov/ct2/show/NCT04449107 International Registered Report Identifier (IRRID): DERR1-10.2196/22996

Healthcare is a service-based system where patients and providers (clinicians and managers) are integral to the system's overall functioning. As a human-centric system at both the demand and supply sides, it is important to consider the behavioral aspects of the entities that make up the healthcare system and their effect on system performance. Modeling and Simulation (M&S) is widely used in healthcare to give stakeholders better insights into real-world problems; incorporating human behavior makes the models more realistic and leads to more informed decision-making. M&S for behavioral analysis (BA) seeks to understand and explore individuals’ behavior and reactions to different interventions within healthcare systems. We reviewed 59 papers published between 1992 and 2023 to synthesize existing literature and ascertain its current development. Our findings show that in most studies, the impact of human behavior is either ignored, studied unsystematically, or treated as a second-order effect. The study contributes to the understanding of M&S methodologies for BA in healthcare.