Smart cities that make broad use of digital technologies have been touted as possible solutions for the population pressures faced by many cities in developing countries and may help meet the rising demand for services and infrastructure. Nevertheless, the high financial cost involved in infrastructure maintenance, the substantial size of the informal economies, and various governance challenges are curtailing government idealism regarding smart cities. This review examines the state of smart city development in developing countries, which includes understanding the conceptualisations, motivations, and unique drivers behind (and barriers to) smarty city development. A total of 56 studies were identified from a systematic literature review from an initial pool of 3928 social sciences literature identified from two academic databases. Data were analysed using thematic synthesis and thematic analysis. The review found that technology-enabled smart cities in developing countries can only be realised when concurrent socioeconomic, human, legal, and regulatory reforms are instituted. Governments need to step up their efforts to fulfil the basic infrastructure needs of citizens, raise more revenue, construct clear regulatory frameworks to mitigate the technological risks involved, develop human capital, ensure digital inclusivity, and promote environmental sustainability. A supportive ecosystem that encourages citizen participation, nurtures start-ups, and promotes public–private partnerships needs to be created to realise their smart city vision.

Due to rapid technological advancements, remote patient monitoring (RPM) technology has gained traction in recent years. While the effects of specific RPM interventions are known, few published reviews examine RPM in the context of care transitions from an inpatient hospital setting to a home environment. In this systematic review, we addressed this gap by examining the impacts of RPM interventions on patient safety, adherence, clinical and quality of life outcomes and cost-related outcomes during care transition from inpatient care to a home setting. We searched five academic databases (PubMed, CINAHL, PsycINFO, Embase and SCOPUS), screened 2606 articles, and included 29 studies from 16 countries. These studies examined seven types of RPM interventions (communication tools, computer-based systems, smartphone applications, web portals, augmented clinical devices with monitoring capabilities, wearables and standard clinical tools for intermittent monitoring). RPM interventions demonstrated positive outcomes in patient safety and adherence. RPM interventions also improved patients’ mobility and functional statuses, but the impact on other clinical and quality-of-life measures, such as physical and mental health symptoms, remains inconclusive. In terms of cost-related outcomes, there was a clear downward trend in the risks of hospital admission/readmission, length of stay, number of outpatient visits and non-hospitalisation costs. Future research should explore whether incorporating intervention components with a strong human element alongside the deployment of technology enhances the effectiveness of RPM. The review highlights the need for more economic evaluations and implementation studies that shed light on the facilitators and barriers to adopting RPM interventions in different care settings.

Most governments have enacted physical or social distancing measures to control COVID-19 transmission. Yet little is known about the socio-economic trade-offs of these measures, especially for vulnerable populations, who are exposed to increased risks and are susceptible to adverse health outcomes. To examine the impacts of physical distancing measures on the most vulnerable in society, this scoping review screened 39,816 records and synthesised results from 265 studies worldwide documenting the negative impacts of physical distancing on older people, children/students, low-income populations, migrant workers, people in prison, people with disabilities, sex workers, victims of domestic violence, refugees, ethnic minorities, and people from sexual and gender minorities. We show that prolonged loneliness, mental distress, unemployment, income loss, food insecurity, widened inequality and disruption of access to social support and health services were unintended consequences of physical distancing that impacted these vulnerable groups and highlight that physical distancing measures exacerbated the vulnerabilities of different vulnerable populations.

The COVID-19 pandemic had an inequitable and disproportionate impact on vulnerable populations, reversing decades of progress toward healthy populations and poverty alleviation. This study examines various programmatic tools and policy measures used by governments to support vulnerable populations during the pandemic. A comparative case study of 15 countries representing all World Health Organization's regions offers a comprehensive picture of countries with varying income statuses, health system arrangements and COVID-19 public health measures. Through a systematic desk review and key informant interviews, we report a spectrum of mitigation strategies deployed in these countries to address five major types of vulnerabilities (health, economic, social, institutional and communicative). We found a multitude of strategies that supported vulnerable populations such as migrant workers, sex workers, prisoners, older persons and school-going children. Prioritising vulnerable populations during the early phase of COVID-19 vaccination campaigns, direct financial subsidies and food assistance programmes were the most common measures reported. Additionally, framing public health information and implementing culturally sensitive health promotion interventions helped bridge the communication barriers in certain instances. However, these measures remain insufficient to protect vulnerable populations comprehensively. Our findings point to the need to expand fiscal space for health, enlarge healthcare coverage, incorporate equity principles in all policies, leverage technology, multi-stakeholder co-production of policies and tailored community engagement mechanisms.

Background: Health care systems in low- and middle-income countries (LMICs) can greatly benefit from artificial intelligence (AI) interventions in various use cases such as diagnostics, treatment, and public health monitoring but face significant challenges in sharing data for developing and deploying AI in health care. Objective: This study aimed to identify barriers and enablers to data sharing for AI in health care in LMICs and to test the relevance of these in a local context. Methods: First, we conducted a systematic literature search using PubMed, SCOPUS, Embase, Web of Science, and ACM using controlled vocabulary. Primary research studies, perspectives, policy landscape analyses, and commentaries performed in or involving an LMIC context were included. Studies that lacked a clear connection to health information exchange systems or were not reported in English were excluded from the review. Two reviewers independently screened titles and abstracts of the included articles and critically appraised each study. All identified barriers and enablers were classified according to 7 categories as per the predefined framework—technical, motivational, economic, political, legal and policy, ethical, social, organisational, and managerial. Second, we tested the local relevance of barriers and enablers in Thailand through stakeholder interviews with 15 academic experts, technology developers, regulators, policy makers, and health care providers. The interviewers took notes and analyzed data using framework analysis. Coding procedures were standardized to enhance the reliability of our approach. Coded data were reverified and themes were readjusted where necessary to avoid researcher bias. Results: We identified 22 studies, the majority of which were conducted across Africa (n=12, 55%) and Asia (n=6, 27%). The most important data-sharing challenges were unreliable internet connectivity, lack of equipment, poor staff and management motivation, uneven resource distribution, and ethical concerns. Possible solutions included improving IT infrastructure, enhancing funding, introducing user-friendly software, and incentivizing health care organizations and personnel to share data for AI-related tools. In Thailand, inconsistent data systems, limited staff time, low health data literacy, complex and unclear policies, and cybersecurity issues were important data-sharing challenges. Key solutions included building a conducive digital ecosystem—having shared data input platforms for health facilities to ensure data uniformity and to develop easy-to-understand consent forms, having standardized guidelines for data sharing, and having compensation policies for data breach victims. Conclusions: Although AI in LMICs has the potential to overcome health inequalities, these countries face technical, political, legal, policy, and organizational barriers to sharing data, which impede effective AI development and deployment. When tested in a local context, most of these barriers were relevant. Although our findings might not be generalizable to other contexts, this study can be used by LMICs as a framework to identify barriers and strengths within their health care systems and devise localized solutions for enhanced data sharing. Trial Registration: PROSPERO CRD42022360644; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=360644