A systematic review of portable electronic technology for health education in resource-limited settings.

Digital clinical decision support (CDS) tools are of growing importance in supporting healthcare professionals in understanding complex clinical problems and arriving at decisions that improve patient outcomes. CDS tools are also increasingly used to improve antimicrobial stewardship (AMS) practices in healthcare settings. However, far fewer CDS tools are available in lowerand middle-income countries (LMICs) and in animal health settings, where their use in improving diagnostic and treatment decision-making is likely to have the greatest impact. The aim of this study was to evaluate digital CDS tools designed as a direct aid to support diagnosis and/or treatment decisionmaking, by reviewing their scope, functions, methodologies, and quality. Recommendations for the development of veterinary CDS tools in LMICs are then provided. The review considered studies and reports published between January 2017 and October 2023 in the English language in peer-reviewed and gray literature. A total of 41 studies and reports detailing CDS tools were included in the final review, with 35 CDS tools designed for human healthcare settings and six tools for animal healthcare settings. Of the tools reviewed, the majority were deployed in high-income countries (80.5%). Support for AMS programs was a feature in 12 (29.3%) of the tools, with 10 tools in human healthcare settings. The capabilities of the CDS tools varied when reviewed against the GUIDES checklist. We recommend a methodological approach for the development of veterinary CDS tools in LMICs predicated on securing sufficient and sustainable funding. Employing a multidisciplinary development team is an important first step. Developing standalone CDS tools using Bayesian algorithms based on local expert knowledge will provide users with rapid and reliable access to quality guidance on diagnoses and treatments. Such tools are likely to contribute to improved disease management on farms and reduce inappropriate antimicrobial use, thus supporting AMS practices in areas of high need.

To assess the impact of an intervention consisting of a computer-assisted clinical decision support system and performance-based incentives, aiming at improving quality of antenatal and childbirth care. Intervention study in rural primary healthcare (PHC) facilities in Burkina Faso, Ghana and Tanzania. In each country, six intervention and six non-intervention PHC facilities, located in one intervention and one non-intervention rural districts, were selected. Quality was assessed in each facility by health facility surveys, direct observation of antenatal and childbirth care, exit interviews, and reviews of patient records and maternal and child health registers. Findings of pre- and post-intervention and of intervention and non-intervention health facility quality assessments were analysed and assessed for significant (P < 0.05) quality of care differences. Post-intervention quality scores do not show a clear difference to pre-intervention scores and scores at non-intervention facilities. Only a few variables had a statistically significant better post-intervention quality score and when this is the case this is mostly observed in only one study-arm, being pre-/post-intervention or intervention/non-intervention. Post-intervention care shows similar deficiencies in quality of antenatal and childbirth care and in detection, prevention, and management of obstetric complications as at baseline and non-intervention study facilities. Our intervention study did not show a significant improvement in quality of care during the study period. However, the use of new technology seems acceptable and feasible in rural PHC facilities in resource-constrained settings, creating the opportunity to use this technology to improve quality of care.

Clinical Decision Support Tools

The healthcare industry increasingly values high-quality and personalized care. Patients with heart failure (HF) receiving home health care (HHC) often experience hospitalizations due to worsening symptoms and comorbidities. Therefore, close symptom monitoring and timely intervention based on risk prediction could help HHC clinicians prevent emergency department (ED) visits and hospitalizations. This study aims to (1) describe important variables associated with a higher risk of ED visits and hospitalizations in HF patients receiving HHC; (2) map data requirements of a clinical decision support (CDS) tool to the exchangeable data standard for integrating a CDS tool into the care of patients with HF; (3) outline a pipeline for developing a real-time artificial intelligence (AI)-based CDS tool. We used patient data from a large HHC organization in the Northeastern US to determine the factors that can predict ED visits and hospitalizations among patients with HF in HHC (9362 patients in 12,223 care episodes). We examined vital signs, HHC visit details (e.g., the purpose of the visit), and clinical note-derived variables. The study identified critical factors that can predict ED visits and hospitalizations and used these findings to suggest a practical CDS tool for nurses. The tool's proposed design includes a system that can analyze data quickly to offer timely advice to healthcare clinicians. Our research showed that the length of time since a patient was admitted to HHC and how recently they have shown symptoms of HF were significant factors predicting an adverse event. Additionally, we found this information from the last few HHC visits before the occurrence of an ED visit or hospitalization were particularly important in the prediction. One hundred percent of clinical demographic profiles from the Outcome and Assessment Information Set variables were mapped to the exchangeable data standard, while natural language processing-driven variables couldn't be mapped due to their nature, as they are generated from unstructured data. The suggested CDS tool alerts nurses about newly emerging or rising risks, helping them make informed decisions. This study discusses the creation of a time-series risk prediction model and its potential CDS applications within HHC, aiming to enhance patient outcomes, streamline resource utilization, and improve the quality of care for individuals with HF. This study provides a detailed plan for a CDS tool that uses the latest AI technology designed to aid nurses in their day-to-day HHC service. Our proposed CDS tool includes an alert system that serves as a guard rail to prevent ED visits and hospitalizations. This tool can potentially improve how nurses make decisions and improve patient outcomes by providing early warnings about ED visits and hospitalizations.

BACKGROUND Sepsis, a life-threatening condition resulting from a dysregulated immune response to infection, disproportionately affects populations in low and middle-income countries (LMICs). Septic children in LMICs face high mortality rates, with early detection and clinical monitoring posing a significant challenge to effective management. There is great potential for digital technologies, such as wearable devices and mobile clinical decision support (CDS) tools, to enable closer monitoring and more prompt recognition of children at risk of advanced sepsis and death. However, little is known about healthcare providers’ (HCPs) perceptions of introducing new digital health tools for pediatric sepsis care in LMICs. OBJECTIVE The objective of this study was to assess HCPs’ understanding, perceptions, and recommendations regarding the design and implementation of digital CDS tools for pediatric sepsis care in Bangladesh. METHODS Between February and May 2024, 18 individual semi-structured in-depth interviews were conducted with HCPs (nurses and physicians) at three urban Bangladeshi hospitals. The data was transcribed, translated from Bangla to English, and analyzed using a Framework Matrix Analysis approach. Participants were asked about familiarity with digital health tools, feedback on tool design, perceptions of the tool’s utility, and barriers and facilitators to use of similar tools in clinical settings in Bangladesh. RESULTS Participants reported overall positive perceptions toward the potential implementation of wearable and digital tools for pediatric sepsis care in Bangladesh. Some key priorities for the design of a digital, wearable CDS tool were durability, reusability, cost considerations, and reliability and accuracy. Clinicians desired the tool to also have customizable alarm parameters and include additional functions such as glucose monitoring. Many favored audio (ringtone) or visual (light) alarms to alert changes in captured vital signs. HCPs believed these tools could enhance patient care by allowing greater staff capacity to monitor patients, reducing management time, and aiding in faster clinical decision-making, with some suggesting it could lower mortality rates. Concerns regarding implementation included internet availability, affordability of the wearable devices, and trust in the CDS tools’ outputs compared to expert clinician judgement. CONCLUSIONS These findings highlight HCP’s perceptions towards the potential of CDS tools to improve pediatric sepsis outcomes in LMICs, while highlighting the need to address implementation challenges to ensure their effective integration into healthcare systems.

BackgroundThe incidence of chronic diseases in low- and middle-income countries is rapidly increasing both in urban and rural regions. A major challenge for health systems globally is to develop innovative solutions for the prevention and control of these diseases. This paper discusses the development and pilot testing of SMARTHealth, a mobile-based, point-of-care Clinical Decision Support (CDS) tool to assess and manage cardiovascular disease (CVD) risk in resource-constrained settings. Through pilot testing, the preliminary acceptability, utility, and efficiency of the CDS tool was obtained.MethodsThe CDS tool was part of an mHealth system comprising a mobile application that consisted of an evidence-based risk prediction and management algorithm, and a server-side electronic medical record system. Through an agile development process and user-centred design approach, key features of the mobile application that fitted the requirements of the end users and environment were obtained. A comprehensive analytics framework facilitated a data-driven approach to investigate four areas, namely, system efficiency, end-user variability, manual data entry errors, and usefulness of point-of-care management recommendations to the healthcare worker. A four-point Likert scale was used at the end of every risk assessment to gauge ease-of-use of the system.ResultsThe system was field-tested with eleven village healthcare workers and three Primary Health Centre doctors, who screened a total of 292 adults aged 40 years and above. 34% of participants screened by health workers were identified by the CDS tool to be high CVD risk and referred to a doctor. In-depth analysis of user interactions found the CDS tool feasible for use and easily integrable into the workflow of healthcare workers. Following completion of the pilot, further technical enhancements were implemented to improve uptake of the mHealth platform. It will then be evaluated for effectiveness and cost-effectiveness in a cluster randomized controlled trial involving 54 southern Indian villages and over 16000 individuals at high CVD risk.ConclusionsAn evidence-based CVD risk prediction and management tool was used to develop an mHealth platform in rural India for CVD screening and management with proper engagement of health care providers and local communities. With over a third of screened participants being high risk, there is a need to demonstrate the clinical impact of the mHealth platform so that it could contribute to improved CVD detection in high risk low resource settings.

Practice-level evidence is needed to clarify the value of population-based clinical decision support (CDS) tools in reducing racial and sex disparities in cardiovascular care. To evaluate the association between CDS tools and racial and sex disparities in the aspirin use, blood pressure control, cholesterol management, and smoking cessation (ABCS) care quality metrics among smaller primary care practices. This cross-sectional study used practice-level data from the Agency for Healthcare Research and Quality-funded EvidenceNOW initiative. The national initiative from May 1, 2015, to April 30, 2021, spanned 12 US states and focused on improving cardiovascular preventive care by providing quality improvement support to smaller primary care practices. A total of 576 primary care practices in EvidenceNOW submitted both survey data and electronic health record (EHR)-derived ABCS data stratified by race and sex. Practice-level estimates of disparities between Black and White patients and between male and female patients were calculated as the difference in proportions of eligible patients within each practice meeting ABCS care quality metrics. The association between CDS tools (EHR prompts, standing orders, and clinical registries) and disparities was evaluated by multiply imputed multivariable models for each CDS tool, adjusted for practice rurality, ownership, and size. Across the 576 practices included in the analysis, 219 (38.0%) had patient panels that were more than half White and 327 (56.8%) had panels that were more than half women. The proportion of White compared with Black patients meeting metrics for blood pressure (difference, 5.16% [95% CI, 4.29%-6.02%]; P < .001) and cholesterol management (difference, 1.49% [95% CI, 0.04%-2.93%] P = .04) was higher; the proportion of men meeting metrics for aspirin use (difference, 4.36% [95% CI, 3.34%-5.38%]; P < .001) and cholesterol management (difference, 3.88% [95% CI, 3.14%-4.63%]; P < .001) was higher compared with women. Conversely, the proportion of women meeting practice blood pressure control (difference, -1.80% [95% CI, -2.32% to -1.28%]; P < .001) and smoking cessation counseling (difference, -1.67% [95% CI, -2.38% to -0.95%]; P < .001) metrics was higher compared with men. Use of CDS tools was not associated with differences in race or sex disparities except for the smoking metric. Practices using CDS tools showed a higher proportion of men meeting the smoking counseling metric than women (coefficient, 3.82 [95% CI, 0.95-6.68]; P = .009). The findings of this cross-sectional study suggest that practices using CDS tools had small disparities that were not statistically significant, but CDS tools were not associated with reductions in disparities. More research is needed on effective practice-level interventions to mitigate disparities.

Digital maternal health education interventions have gained increasing attention as scalable solutions to address the persistent disparities in maternal and child health outcomes, particularly in low-infrastructure environments. This systematic review synthesizes current evidence on the design, implementation, and effectiveness of digital health education programs targeting maternal health in resource-limited settings. The review explores various digital modalities, including mobile health (mHealth), SMS-based communication, interactive voice response (IVR), and mobile applications, and evaluates their role in improving maternal knowledge, antenatal care (ANC) attendance, skilled birth attendance, and postnatal care adherence. A comprehensive literature search was conducted across five electronic databases—PubMed, Scopus, Web of Science, EMBASE, and CINAHL—covering studies published between 2010 and 2021. Inclusion criteria encompassed peer-reviewed articles that assessed digital maternal health education interventions implemented in low- and middle-income countries (LMICs), with clearly defined outcomes related to maternal health behaviors and service uptake. A total of 43 studies met the inclusion criteria, and quality appraisal was performed using the Mixed Methods Appraisal Tool (MMAT). Findings reveal that mobile phone-based interventions were the most commonly used, with SMS and voice calls serving as effective channels for delivering timely maternal health information. Interventions demonstrated improvements in ANC attendance rates, increased health literacy, and enhanced engagement with skilled healthcare providers. However, challenges such as low digital literacy, gendered access to mobile technology, intermittent network coverage, and language barriers were recurrent themes impacting effectiveness and scalability. Notably, community health workers often played a critical role in facilitating digital interventions, bridging technological gaps and ensuring cultural appropriateness. The review underscores the potential of digital maternal health education interventions to transform maternal health outcomes in underserved settings. However, it emphasizes the need for context-sensitive designs, inclusive user-centered approaches, and sustainable implementation strategies that account for infrastructural, socio-cultural, and technological barriers. Future research should explore integrative frameworks that combine digital tools with community-based support systems and assess long-term impacts on maternal and neonatal health.

Effect of genetics clinical decision support tools on health-care providers’ decision making: a mixed-methods systematic review

Reflections of a Cardiac Surgeon Turned Global Health Educator

As the importance of quality in health care provision is increasingly recognised, it is opportune to consider quality care as a key link between clinical and public health approaches to human immunodeficiency virus (HIV) infection in developing countries, especially in sub-Saharan Africa. This region has the lion's share of the global epidemic and the least resources to respond. Looking at health problems using a 'quality lens' may help bridge the gaps between clinical care and public health, the current and desired standard of care, and prevention and treatment. Quality care, with prompt diagnosis and effective treatment, of people with HIV infection is crucial for good individual health outcomes, public health outcomes (in terms of decreased HIV transmission) and societal outcomes (increased productivity and decreased costs of health provision for HIV-related care). A spotlight on quality care can bring clinicians and public health practitioners together in working towards universal access to quality HIV care and prevention - one of the greatest health challenges faced in developing countries in Africa today.

Quality of care has been systematically monitored in hospitals in high-income countries to ensure adequate care. However, in low- and middle-income countries, quality indicators are not readily measured. The primary aim of this study was to assess to what extent it was feasible to monitor the quality of intensive care in an ongoing health emergency, and the secondary aim was to assess a quality of care intervention (twinning project) focused on Intensive Care Unit (ICU) quality of care in public hospitals in Lebanon. We conducted a retrospective cohort study nested within an intervention implemented by the World Health Organization (WHO) together with partners. To assess the quality of care throughout the project, a monitoring system framed in the Donabedian model and included structure, process, and outcome indicators was developed and implemented. Data collection consisted of a checklist performed by external healthcare workers (HCWs) as well as collection of data from all admitted patients performed by each unit. The association between the number of activities within the interventional project and ICU mortality was evaluated. A total of 1679 patients were admitted to five COVID-19 ICUs during the study period. The project was conducted fully across four out of five hospitals. In these hospitals, a significant reduction in ICU mortality was found (OR: 0.83, P < 0.05, CI: 0.72-0.96). We present a feasible way to assess quality of care in ICUs and how it can be used in assessing a quality improvement project during ongoing crises in resource-limited settings. By implementing a quality of care intervention in Lebanon's public hospitals, we have shown that such initiatives might contribute to improvement of ICU care. The observed association between increased numbers of project activities and reduced ICU mortality underscores the potential of quality assurance interventions to improve outcomes for critically ill patients in resource-limited settings. Future research is needed to expand this model to be applicable in similar settings.

Understand the perceptions of primary care clinicians on the challenges, barriers, and successful strategies for implementing and disseminating clinical decision support (CDS) tools in primary care. Qualitative research involving in-depth interviews with 32 primary care clinicians practicing in a range of settings across the United States. Semi-structured interviews were conducted between July 2021 and September 2023. All participants reported using CDS tools for patient care, with high variability in the frequency of use and the type of tools used. Fewer clinicians described using machine learning-based systems and risk assessment tools using predictive analytics. Most clinicians were favorable toward enhanced use of CDS tools for patient care if used along with clinical judgment and patient preferences. Clinicians described tremendous barriers to the adoption and implementation of EMR-integrated CDS tools, including clinician resistance, organizational approval, and lack of infrastructure and resources. Clinicians stressed the importance of communicating evidence on the effectiveness of CDS tools, integrating tools with existing EMR systems, and having an easy-to-navigate interface. Strategies for the implementation of CDS tools included an organizational champion, technical assistance, and education and training. CDS tools have the potential to be valuable assets in treating patients in primary care and could improve diagnostic accuracy, enhance personalized treatment plans, and ultimately advance the quality of patient care. There are many concerns with the use of EMR-integrated CDS tools in primary care that should be considered including evidence of the tool's effectiveness, data security and privacy protocols, workflow integration, and clinician burden.

Overall this important area of quality of care will clearly require more research to be fully understood. Better quality monitoring tools also need to be developed with a focus on labour and labour outcome prediction. Meanwhile to complement Kruk and colleagues’ research efforts smaller facilities need to be better equipped with both the requisite and trained human resources for health care as well as with other inputs that are essential to quality provision of EmONC services. (Excerpt) Copyright © The Author(s). Published by Elsevier Ltd. Open Access.

BackgroundThe availability of mobile clinical decision support (CDS) tools has grown substantially with the increased prevalence of smartphone devices and apps. Although health care providers express interest in integrating mobile health (mHealth) technologies into their clinical settings, concerns have been raised, including perceived disagreements between information provided by mobile CDS tools and standard guidelines. Despite their potential to transform health care delivery, there remains limited literature on the provider’s perspective on the clinical utility of mobile CDS tools for improving patient outcomes, especially in low- and middle-income countries.ObjectiveThis study aims to describe providers’ perceptions about the utility of a mobile CDS tool accessed via a smartphone app for diarrhea management in Bangladesh. In addition, feedback was collected on the preliminary components of the mobile CDS tool to address clinicians’ concerns and incorporate their preferences.MethodsFrom November to December 2020, qualitative data were gathered through 8 web-based focus group discussions with physicians and nurses from 3 Bangladeshi hospitals. Each discussion was conducted in the local language—Bangla—and audio recorded for transcription and translation by the local research team. Transcripts and codes were entered into NVivo (version 12; QSR International), and applied thematic analysis was used to identify themes that explore the clinical utility of an mHealth app for assessing dehydration severity in patients with acute diarrhea. Summaries of concepts and themes were generated from reviews of the aggregated coded data; thematic memos were written and used for the final analysis.ResultsOf the 27 focus group participants, 14 (52%) were nurses and 13 (48%) were physicians; 15 (56%) worked at a diarrhea specialty hospital and 12 (44%) worked in government district or subdistrict hospitals. Participants’ experience in their current position ranged from 2 to 14 years, with an average of 10.3 (SD 9.0) years. Key themes from the qualitative data analysis included current experience with CDS, overall perception of the app’s utility and its potential role in clinical care, barriers to and facilitators of app use, considerations of overtreatment and undertreatment, and guidelines for the app’s clinical recommendations. Participants felt that the tool would initially take time to use, but once learned, it could be useful during epidemic cholera. Some felt that clinical experience remains an important part of treatment that can be supplemented, but not replaced, by a CDS tool. In addition, diagnostic information, including mid-upper arm circumference and blood pressure, might not be available to directly inform programming decisions.ConclusionsParticipants were positive about the mHealth app and its potential to inform diarrhea management. They provided detailed feedback, which developers used to revise the mobile CDS tool. These formative qualitative data provided timely and relevant feedback to improve the utility of a CDS tool for diarrhea treatment in Bangladesh.