Data Extraction Framework Research Articles

Tools and strategies for monitoring hospital environmental hygiene services.

The hospital environment is a significant source of healthcare-associated infections, necessitating effective cleaning practices to mitigate risks. Various tools, including fluorescent markers, adenosine triphosphate assays, microbiological methods, and direct observation, are used for monitoring, but their comparative effectiveness remains unclear. This scoping review followed Joanna Briggs Institute guidelines and PRISMA-ScR standards. Literature from 2000-2024 was sourced from PubMed, Web of Science, and Google Scholar, focusing on tools for monitoring hospital cleaning services. Twenty studies were included and analyzed using a standardized data extraction framework. Fluorescent markers and adenosine triphosphate assays were practical for routine monitoring and training but had limitations in detecting microbial contamination. Microbiological methods provided accurate contamination data but were resource intensive. Direct observation identified gaps in staff compliance but was subject to observer bias. Studies reported variability in standards, cleaning durations, and resource allocation, with limited data on full-time equivalents. Digital technologies, such as Artificial Intelligence and Internet of Things, offer potential for real-time monitoring and optimization. A combination of visual inspection, ATP assays, and microbiological methods is recommended for effective monitoring. Collaborative efforts are needed to establish global standards, incorporating emerging technologies to address resource disparities and improve environmental hygiene practices, ultimately enhancing patient safety.

What is the current state of precision rehabilitation? Protocol for a scoping study with a consultation phase

IntroductionPrecision health can be described as the right intervention, at the right time, for the right person, with a focus on monitoring and maintaining health in a longitudinal approach. Despite...

Congenital colour vision deficiency in healthcare professionals: a scoping review protocol of the impact on clinical practice and patient safety

IntroductionCongenital colour vision deficiency (CVD), known as colour blindness, is a common visual problem affecting around 1 in 12 men and 1 in 200 women. It is known that people...

Protocol for a systematic review and individual participant data meta-analysis for risk factors for lung cancer in individuals with lung nodules identified by low-dose CT screening

BackgroundWorldwide, lung cancer (LC) is the second most frequent cancer and the leading cause of cancer related mortality. Low-dose CT (LDCT) screening reduced LC mortality by 20–24% in randomised trials...

Virtual reality-based fine motor skills training in paediatric rehabilitation: a protocol for a scoping review

IntroductionFine motor skill (FMS) development during childhood is essential to many learning processes, especially in school. FMS impairment can have a major impact on children’s quality of life. Developing effective...

Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights

Computer Vision-Based Framework for Data Extraction From Heterogeneous Financial Tables: A Comprehensive Approach to Unlocking Financial Insights

Exploring Molecular Genetics Research on Obesity in Malaysia: Protocol for a Scoping Review.

Obesity presents a growing challenge to public health, and its intricate association with genetics continues to be a compelling field of study. In countries such as Malaysia, where diverse genetic backgrounds converge, exploring the molecular genetics of obesity is even more imperative. This scoping review aimed to explore the literature on molecular genetics of obesity in Malaysia. Specifically, we sought to characterize existing studies, identify the genetic determinants of obesity, and assess their association with obesity predisposition in the population. This scoping review followed the methodology of the Joanna Briggs Institute and used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist as its guiding framework. Searches were conducted using electronic databases such as PubMed, ScienceDirect, and Scopus, filtering for human studies published until March 2024. Eligible studies included peer-reviewed articles on the Malaysian population irrespective of age or sex. This review excluded review articles, book chapters, non-peer-reviewed conference proceedings, gray literature, and preclinical studies, and the reference lists of the retrieved studies were manually examined to ensure thorough inclusion. The articles were subjected to a 2-stage screening process (title/abstract and full text) conducted by 2 reviewers to assess eligibility. Eligible articles were then extracted following a data extraction framework and organized into a charting table. Only studies investigating the genetics of obesity in Malaysian populations were included. As of March 2024, our extensive search strategy has yielded 572 records. After removing 153 duplicates, 419 records were screened by title and abstract, resulting in 47 selected for full-text review. Of these, 34 were chosen for data extraction and detailed analysis. These studies predominantly involved participants from major ethnic groups (Malay, Chinese, and Indian) recruited from local health centers and university communities. The articles primarily explored the relationship between specific gene variants and obesity or obesity-related health parameters. This ongoing research is expected to be completed with a comprehensive scoping review by April 2025. This review provides valuable insights into the genetic determinants of obesity in Malaysia, despite limitations such as no quality appraisal being conducted for the included studies and the search strategy being restricted to selected databases, potentially omitting relevant studies. However, this review ensured reliability and reproducibility by adhering to the Joanna Briggs Institute and PRISMA-ScR guidelines. Ultimately, this study advances the understanding of local research and sets the foundation for future molecular genetic studies to improve obesity risk prediction and management in Malaysia's multiethnic population. DERR1-10.2196/60838.

Challenges in Teledermoscopy Diagnostic Outcome Studies: Scoping Review of Heterogeneous Study Characteristics.

Teledermoscopy has demonstrated benefits such as decreased costs and enhanced access to dermatology care for skin cancer detection. However, the heterogeneity among teledermoscopy studies hinders the systematic reviews' synopsis of diagnostic outcomes, impeding trust and adoption in general practice and limiting overall health care benefits. This study aims to improve understanding and standardization of teledermoscopy diagnostic studies, by identifying and categorizing study characteristics contributing to heterogeneity. Subsequently, the variability and consistency of these characteristics were assessed. A review of systematic reviews regarding the diagnostic outcomes of teledermoscopy was performed to discern reported study characteristics contributing to heterogeneity. These characteristics were thematically grouped into 3 domains (population, index test, and reference standard), forming a data extraction framework. A scoping review on teledermoscopy diagnostic outcomes studies was performed, guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Data pertaining to study characteristics from included studies were extracted and analyzed through descriptive content analysis. Systematic reviews' reference lists validated the scoping review query. The literature search yielded 4 systematic reviews, revealing 15 heterogeneous studies across the population, index test, and reference standard domains. The scoping review identified 49 studies, with 27 overlapping with the systematic reviews. Population characteristics varied, with one-third (16/49, 33%) of studies reporting fewer than 100 samples; most studies (41/49, 84%) reported on the type of lesion, and most (20/49, 41%) teledermoscopy consultations took place in secondary care. One-fifth (11/49, 22%) did not describe inclusion or exclusion criteria, or the criteria varied highly. Index test characteristics showed differences in clinical expertise, profession, and training in dermatoscopic photography, and 59% (29/49) did not report on 1 or more index test characteristics. Image quality and clinical information reporting likewise varied. Reference standard characteristics involved teledermatologists' assessment, but 16 studies did not report teledermatologists' experience levels. Most studies (26/49, 53%) used histopathology as a gold standard. The heterogeneity in the population, index tests, and reference standard domains across teledermoscopy diagnostic outcome studies underscores the need for standardized reporting. This hinders the synopsis of teledermoscopy diagnostic outcomes in systematic reviews and limits the integration of research results into practice. Adopting a (tailored) STARD (Standards for Reporting Diagnostic Accuracy Studies) checklist for teledermoscopy diagnostic outcome studies is recommended to enhance the consistency and comparability of outcomes. We suggest performing a Delphi study to gather consensus on the tailored STARD guideline.

Weight Bias in the Perinatal Period: An Integrative Review

ABSTRACTBackgroundWeight bias toward individuals with higher body weights in healthcare settings is associated with adverse health behaviors, reduced healthcare utilization, and poor health outcomes. The purpose of this integrative review was to explore: (1) What has been measured and described regarding perinatal care providers' and students' weight bias toward pregnant, birthing, and postpartum individuals with higher body weights? (2) What has been measured and described regarding pregnant, birthing, and postpartum individuals' experiences of weight bias? (3) What is the association of experiences of weight bias with perinatal and mental health outcomes among pregnant, birthing, and postpartum individuals?MethodsWe conducted a systematic search in CINAHL, PubMed, and PsycINFO databases to identify relevant research publications related to the Medical Subject Headings (MeSH) terms weight prejudice (and related terms) and pregnancy (and related terms). The review was guided by the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA), Johns Hopkins Nursing Evidence‐Based Practice model for study quality determination, and the Whittemore and Knafl integrative review framework for data extraction and analyses.ResultsTwenty‐two publications met inclusion criteria, representing six countries and varying study designs. This review found pervasive sources of explicit weight bias in the perinatal period, including care providers and close relationships. Experiences of weight bias among pregnant and postpartum individuals are associated with adverse perinatal and mental health outcomes.DiscussionThe findings address a knowledge gap regarding a summary of literature on weight bias in the perinatal period and elucidate its prevalence as well as its negative influence on perinatal and mental health outcomes. Future research efforts on this topic must examine the nature and extent of perinatal care providers' weight bias by demographic factors and explore its association with clinical decision‐making and perinatal and mental health outcomes.

Reconfigurable Framework for Data Extraction Using Interoperable Brokers in Manufacturing

Technology is an integral part of society and has undergone significant evolution across various domains, such as production and recreation, leading to the emergence of heterogeneous systems. These diverse systems often need to communicate and interact with one another to fully leverage their capabilities and resources, aiming for maximum efficiency. One effective method to achieve this is through the use of a message broker, a tool that facilitates communication between multiple heterogeneous systems. However, setting up message brokers can be complex, requiring access to multiple systems for configuration and lacking automation tools to streamline the process. The proposed solution involves creating a tool that can be instantiated on different machines to control the deployment, configuration, and usage of any message broker. Early results are promising, demonstrating enhanced data collection from industrial robots and improved connectivity between different message brokers.

Unveiling the hidden dangers: Security risks and forensic analysis of smart bulbs

People often dispose of their useless smart digital gadgets without realizing the potential presence of useful information inside these devices. This is also true for faulty smart bulbs, which cybercriminals might exploit to gain unauthorized access to a smart home and manipulate or steal private information. This research delves into the potential security risks associated with smart bulbs and provides recommendations for mitigating such risks. Through a comprehensive analysis of the functionality of smart bulbs, this study introduced the data extraction framework DEF-IoTF for collecting both hardware and application-level digital artifacts from smart bulbs. This paper presents the FIvM-IoT model for collecting and analyzing evidence from companion app data on mobile phones and Wifi modules at the hardware level. We conduct examinations on the smart bulb's Wifi module and extract its firmware using the developed Wifi_Cred tool. These include evidence related to user credentials, log time stamps, Wifi details, potential forensic information, and investigation procedures for IoT devices. Finally, this study provides prominent IoT forensic use cases along with the key requirements for hardware-level forensic investigation of Wifi modules.

Automated digital oncologic data review for breast cancer research: AI-enabled mCODE and field-of-interest extraction framework.

e12647 Background: Enhancing outcomes in cancer care and research can be optimally achieved by accelerating data extraction from unstructured electronic health resources. Understanding the association between treatment response markers and long-term outcomes is crucial for refining cancer therapeutic approaches and improving patient prognosis. In this study, we explore the association of oncologic outcomes with pathologic complete response (pCR) in high-risk early-stage breast cancer treated with neoadjuvant chemotherapy (NACT) using automated oncologic data extraction framework. Methods: Patients with primary stage I-III breast cancer who received NACT followed by mastectomy, with or without immediate breast reconstruction from January 2010 to August 2023 were retrospectively analyzed. We extracted pCR results and data on clinical and pathological stage, tumor subtype, grade, and treatment regimens and factors affecting postoperative complications from electronic medical records based on the AI enabled-mCODE (Minimal Common Oncology Data Elements) and fields of interest extraction framework. Employing mixed-effects Cox models, we explored the association between pCR and event-free survival (EFS), distant recurrence free Survival (DRFS) and overall survival (OS). Survival curves of patients were estimated using Kaplan-Meier method. Multivariate analysis was performed to evaluate the predictive factors for pCR. Results: Overall, 2725 patients were analyzed. The median age was 50.6 years. The median follow-up was 67 months. pCR was achieved in 31.0%. Hazard ratio for pCR vs. non-pCR were 0.34 for EFS (95% CI, 0.25-0.45), 0.32 for DRFS (95% CI, 0.23-0.43), 0.21 for OS (95% CI, 0.13-0.34), varying from 0.13 to 0.56 for EFS, 0.12 to 0.48 for DRFS and 0.07 to 0.40 for OS. The presence of pCR remained prognostic for event-free survival in multivariate models adjusted for age, tumor size, and nodal status at baseline. Conclusions: This study confirms that patients achieving pCR after NACT have better long-term survival outcomes that patients who do not. Oncologic data extraction from unstructured electronic medical records using AI-enabled extraction framework can be expected to enhance robust cancer research and generate valuable insights from real-world practice settings for tailored therapeutic decision-making. [Table: see text]

Waitlist management in child and adolescent mental health care: A scoping review

Background: Although many mental health disorders first emerge during early childhood or adolescence, there is a significant gap between demand and availability of mental health resources, leading to long waitlists for services. Objective: The objective of this scoping review was to identify and characterize the research literature related to the range of waitlist management strategies that have been implemented in outpatient child and adolescent mental health care. Methods: Electronic databases reviewed included: Medline (Ovid), Embase (Ovid), PubMed, PsychINFO, SCOPUS, CINAHL, and ISI Web of Science. Grey literature databases included: OpenGrey, Conference Papers Index, and Proquest Digital Dissertations. Articles were screened by two reviewers in two steps: first by title and abstract, then full text level.Data were extracted using an a-priori developed data extraction framework, which was piloted and modified iteratively. Results: A total of 119 papers related to waitlist interventions in child and adolescent mental health were reviewed. Of these 119 papers, 11% were reviews, summary, or theoretical papers; 8% used a randomized control trial design and 2.5% were trial protocols. Most studies used less rigorous designs, such as uncontrolled before-and- after designs. The large majority focused on just one waitlist strategy each. The most commonly used approaches included: prioritization/triage and initial assessment; brief consultation and advice or brief therapy approaches; group-based models; interim services; increasing capacity; and strategies to decrease non-attendance. Most studies were conducted in the United Kingdom or Canada. Discussion: While mental health systems are complex, most studies examining waitlist initiatives explored the implementation of single initiatives. It is unlikely that a single waitlist strategy can be effective in managing wait times for children’s mental health. Rather, consistent and systemic approaches to address wait times that consider the impact of the reduction approach on the patient, the program, and the community are needed.

A protocol for a scoping review of methodologies used to explore patient experience in post-acute rehabilitation settings.

Patient experience is routinely collected in the clinical environment in many different ways throughout various person-provider encounters, but so far limited research focused on understanding the methods of using it to improve the quality of healthcare. This paper presents a protocol for a methodological scoping review examining the methods of obtaining, analysing, reporting, and using patient experience data for quality improvement in rehabilitation settings. The scoping review will be conducted according to the guidelines from the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis and the methodological framework by Arksey & O'Malley. A comprehensive search of the literature will be performed using a three-step search strategy: an initial limited search of two databases was already performed and helped to identified relevant key words and index terms. The developed search string will be adapted and applied across four databases. This will be followed by search of the reference lists of selected sources and hand-search relevant data-hubs. Studies with a clear focus on patient experience or feedback collected or used for healthcare improvement in rehabilitation context, will be included. A data extraction framework will be developed and piloted to guide the literature screening and data charting. Qualitative content analysis will be employed to address research questions and the results will be presented - beside the descriptive format - as a map of data in chart and tabular formats. This scoping review will show the extent and scope of the literature on the applied methods of collecting, communicating, and using patient experience for quality improvement in post-acute rehabilitation settings and will evaluate and map the evidence on these topics. The findings will inform a research project entitled "An exploration into the use of patient experience to inform improvement in a National Rehabilitation Hospital".

The implementation, use and impact of patient reported outcome measures in value-based healthcare programmes: A scoping review.

Value-Based Healthcare (VBHC) focuses on the value of patient outcomes and is achieved by ensuring resources already available are managed to realise the best possible individual and population health outcomes. Patient reported outcome measures (PROMs) measure the impact of illnesses from the patient perspective. We conducted a scoping review to understand how PROMs were implemented and used, and their impact in the context of VBHC. Arksey and O'Malley's overarching framework supplemented by principles from mixed-methods Framework Synthesis were used. CINAHL, Cochrane Library, EMBASE, MEDLINE, PsycINFO, Web of Science, Google Scholar and reference lists were searched. An a priori data extraction framework was created using the review question and objectives as key domains against which to extract data. Mixed-methods data were organised, integrated and preserved in original format and reported for each domain. Forty-three studies were included with 60,200 participants. Few studies reported a well-developed programme theory and we found little robust evidence of effect. PROMs were universally considered to have the potential to increase patient satisfaction with treatment and services, enhance patient awareness of symptoms and self-management, and improve health outcomes such as quality of life and global health status. Evidence is currently limited on how PROMs work and how best to optimally implement PROMs to achieve the target outcome. Implementation challenges commonly prevented the realisation of optimal outcomes and patients generally needed better and clearer communication about why PROMs were being given and how they could optimally be used to support their own self-management. PROMSs have yet to demonstrate their full potential in a VBHC context. Optimal PROMs implementation is poorly understood by clinicians and patients. Future studies should explore different models of PROM implementation and use within VBHC programmes to understand what works best and why for each specific context, condition, and population.

Infodemics surveillance system to detect and analyze health misinformation using big data and AI

Abstract Background Health misinformation disseminated on social media detrimentally affects the public's reactions toward public health measures. This has led to costly and harmful public health crises. Due to a lack of a comprehensive expert system that collects and analyzes large volumes of social media data, public health officials are incapable of mitigating health misinformation trends online. This study aimed to remedy this by designing and developing a big data pipeline and ecosystem (Health Misinformation Analysis System (HMAS)) capable of identifying and analyzing health misinformation online. Methods The HMAS system relies on Python, Elastic Stack, and the Twitter V2 API as the main technologies forming the backbone of the system. It comprises of five main components: (1) Data Extraction Framework (DEF); (2) Latent Dirichlet Allocation Topic Model; (3) Sentiment Analyzer; (4) Misinformation Classifier; (5) Elastic Cloud Deployment. Components 1-4 are hosted on a virtual machine with low computing and memory requirements. Through the DEF, HMAS extracts data from the Twitter V2 API. The HMAS expert system uses pre-trained models to perform automatic health misinformation analysis. The analyzed data are loaded and visualized in Elastic Cloud through dashboards and analytics. Results The HMAS system performance is accurate and efficient. It has been successfully utilized by independent investigators to extract significant insights, including a fluoride-related health misinformation use case, which analyzed data from 2015 to 2021. Moreover, HMAS has been applied to a vaccine hesitancy use case (2007-2022) and a heat-related illnesses use case (2011-2022). Conclusions The novel HMAS expert system can potentially aid public health officials globally to detect and analyze concerning trends in health misinformation online. This system can also grow to integrate social media data from several platforms for multiplatform analysis and support non-Western language content. Key messages • The novel HMAS pipeline can collect, detect, and analyze the rapidly increasing amounts of misinformation proliferating on social media related to a particular topic or set of related topics. • Identifying and monitoring health misinformation trends prevalent on social media through analytics dashboards and anomaly/risk detection alerts can lead to prompt governmental intervention.

Learning healthcare systems and rehabilitation: a scoping review protocol

Background: Healthcare literature has proposed that “learning organisations” (LO) or “learning healthcare systems” (LHS) that continuously generate and apply evidence, innovation, quality, and value can provide better care. This is of value to non-acute healthcare settings such as rehabilitation, which are complex, multi-dimensional and multi-disciplinary in nature. Little is known about how these frameworks have been applied to rehabilitation settings. Objective and inclusion criteria: The aim of this scoping review is to systematically summarise the literature conceptualising and operationalising LHS and LO in rehabilitation settings. Studies will be included which define a LO or LHS, describe an operating LHS/LO or include the translation of research evidence generated from LHS/LO data into healthcare improvement within a rehabilitation context. All study designs will be included. Methods: The guidelines from the Joanna Briggs institute methodology for scoping reviews will be used for this review. The literature search will be performed using a three-step search strategy: an initial limited search of two databases has been performed to identify relevant key words and index terms. The developed search string will be adapted and applied across the following databases: OVID MEDLINE, EMBASE, CINAHL Plus, APA PsycINFO and COCHRANE Database of Systematic Reviews. Reference lists of selected sources and relevant data-hubs will also be searched. A draft data extraction framework will be used and updated iteratively to extract data. Data will be extracted and described to address the research question of how LHS and LO have been conceptualised and operationalised in the context of rehabilitation. Discussion and Implications: There is potential for rehabilitation focused LHSs to advance global rehabilitation services and facilitate best practice. Little is known about how rehabilitation focused LHSs have been constructed and sustained. This will be the first review to explore LHS application in rehabilitation, guiding future development and research.

A Largely Unsupervised Domain-Independent Qualitative Data Extraction Approach for Empirical Agent-Based Model Development

Agent-based model (ABM) development needs information on system components and interactions. Qualitative narratives contain contextually rich system information beneficial for ABM conceptualization. Traditional qualitative data extraction is manual, complex, and time- and resource-consuming. Moreover, manual data extraction is often biased and may produce questionable and unreliable models. A possible alternative is to employ automated approaches borrowed from Artificial Intelligence. This study presents a largely unsupervised qualitative data extraction framework for ABM development. Using semantic and syntactic Natural Language Processing tools, our methodology extracts information on system agents, their attributes, and actions and interactions. In addition to expediting information extraction for ABM, the largely unsupervised approach also minimizes biases arising from modelers’ preconceptions about target systems. We also introduce automatic and manual noise-reduction stages to make the framework usable on large semi-structured datasets. We demonstrate the approach by developing a conceptual ABM of household food security in rural Mali. The data for the model contain a large set of semi-structured qualitative field interviews. The data extraction is swift, predominantly automatic, and devoid of human manipulation. We contextualize the model manually using the extracted information. We also put the conceptual model to stakeholder evaluation for added credibility and validity.

Tweeting for Health Using Real-time Mining and Artificial Intelligence-Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on Twitter.

Digital misinformation, primarily on social media, has led to harmful and costly beliefs in the general population. Notably, these beliefs have resulted in public health crises to the detriment of governments worldwide and their citizens. However, public health officials need access to a comprehensive system capable of mining and analyzing large volumes of social media data in real time. This study aimed to design and develop a big data pipeline and ecosystem (UbiLab Misinformation Analysis System [U-MAS]) to identify and analyze false or misleading information disseminated via social media on a certain topic or set of related topics. U-MAS is a platform-independent ecosystem developed in Python that leverages the Twitter V2 application programming interface and the Elastic Stack. The U-MAS expert system has 5 major components: data extraction framework, latent Dirichlet allocation (LDA) topic model, sentiment analyzer, misinformation classification model, and Elastic Cloud deployment (indexing of data and visualizations). The data extraction framework queries the data through the Twitter V2 application programming interface, with queries identified by public health experts. The LDA topic model, sentiment analyzer, and misinformation classification model are independently trained using a small, expert-validated subset of the extracted data. These models are then incorporated into U-MAS to analyze and classify the remaining data. Finally, the analyzed data are loaded into an index in the Elastic Cloud deployment and can then be presented on dashboards with advanced visualizations and analytics pertinent to infodemiology and infoveillance analysis. U-MAS performed efficiently and accurately. Independent investigators have successfully used the system to extract significant insights into a fluoride-related health misinformation use case (2016 to 2021). The system is currently used for a vaccine hesitancy use case (2007 to 2022) and a heat wave-related illnesses use case (2011 to 2022). Each component in the system for the fluoride misinformation use case performed as expected. The data extraction framework handles large amounts of data within short periods. The LDA topic models achieved relatively high coherence values (0.54), and the predicted topics were accurate and befitting to the data. The sentiment analyzer performed at a correlation coefficient of 0.72 but could be improved in further iterations. The misinformation classifier attained a satisfactory correlation coefficient of 0.82 against expert-validated data. Moreover, the output dashboard and analytics hosted on the Elastic Cloud deployment are intuitive for researchers without a technical background and comprehensive in their visualization and analytics capabilities. In fact, the investigators of the fluoride misinformation use case have successfully used the system to extract interesting and important insights into public health, which have been published separately. The novel U-MAS pipeline has the potential to detect and analyze misleading information related to a particular topic or set of related topics.

Digital Health Technologies for Maternal and Child Health in Africa and Other Low- and Middle-Income Countries: Cross-disciplinary Scoping Review With Stakeholder Consultation.

Maternal and child health (MCH) is a global health concern, especially impacting low- and middle-income countries (LMIC). Digital health technologies are creating opportunities to address the social determinants of MCH by facilitating access to information and providing other forms of support throughout the maternity journey. Previous reviews in different disciplines have synthesized digital health intervention outcomes in LMIC. However, contributions in this space are scattered across publications in different disciplines and lack coherence in what digital MCH means across fields. This cross-disciplinary scoping review synthesized the existing published literature in 3 major disciplines on the use of digital health interventions for MCH in LMIC, with a particular focus on sub-Saharan Africa. We conducted a scoping review using the 6-stage framework by Arksey and O'Malley across 3 disciplines, including public health, social sciences applied to health, and human-computer interaction research in health care. We searched the following databases: Scopus, PubMed, Google Scholar, ACM Digital Library, IEEE Xplore, Web of Science, and PLOS. A stakeholder consultation was undertaken to inform and validate the review. During the search, 284 peer-reviewed articles were identified. After removing 41 duplicates, 141 articles met our inclusion criteria: 34 from social sciences applied to health, 58 from public health, and 49 from human-computer interaction research in health care. These articles were then tagged (labeled) by 3 researchers using a custom data extraction framework to obtain the findings. First, the scope of digital MCH was found to target health education (eg, breastfeeding and child nutrition), care and follow-up of health service use (to support community health workers), maternal mental health, and nutritional and health outcomes. These interventions included mobile apps, SMS text messaging, voice messaging, web-based applications, social media, movies and videos, and wearable or sensor-based devices. Second, we highlight key challenges: little attention has been given to understanding the lived experiences of the communities; key role players (eg, fathers, grandparents, and other family members) are often excluded; and many studies are designed considering nuclear families that do not represent the family structures of the local cultures. Digital MCH has shown steady growth in Africa and other LMIC settings. Unfortunately, the role of the community was negligible, as these interventions often do not include communities early and inclusively enough in the design process. We highlight key opportunities and sociotechnical challenges for digital MCH in LMIC, such as more affordable mobile data; better access to smartphones and wearable technologies; and the rise of custom-developed, culturally appropriate apps that are more suited to low-literacy users. We also focus on barriers such as an overreliance on text-based communications and the difficulty of MCH research and design to inform and translate into policy.