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Health information on the Internet has a ubiquitous influence on health consumers' behaviour. Searching and evaluating online health information poses a real challenge for many health consumers. To our knowledge, our systematic review paper is the first to explore the interventions targeting lay people to improve their e-health literacy skills. Our paper aims to explore interventions to improve laypeople ability to identify trustworthy online health information. The search was conducted on Ovid Medline, Embase, Cochrane database, Academic Search Complete, and APA psych info. Publications were selected by screening title, abstract, and full text, then manual review of reference lists of selected publications. Data was extracted from eligible studies on an excel sheet about the types of interventions, the outcomes of the interventions and whether they are effective, and the barriers and facilitators for using the interventions by consumers. A mixed-methods appraisal tool was used to appraise evidence from quantitative, qualitative, and mixed-methods studies. Whittemore and Knafl's integrative review approach was used as a guidance for narrative synthesis. The total number of included studies is twelve. Media literacy interventions are the most common type of interventions. Few studies measured the effect of the interventions on patient health outcomes. All the procedural and navigation/ evaluation skills-building interventions are significantly effective. Computer/internet illiteracy and the absence of guidance/facilitators are significant barriers to web-based intervention use. Few interventions are distinguished by its implementation in a context tailored to consumers, using a human-centred design approach, and delivery through multiple health stakeholders' partnership. There is potential for further research to understand how to improve consumers health information use focusing on collaborative learning, using human-centred approaches, and addressing the social determinants of health., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mohamed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Health equity in Bangladesh faces a large chasm over the economic conditions, socio-cultural factors and geographic location despite the push for digitalization of the health sector. While some research has been conducted assessing the viability of digital health solutions in Bangladesh, gender dynamics of digital healthcare have been absent. This study dived into healthcare equity for women with a focus on reproductive health services delivered through mobile devices. This paper reported the findings of a qualitative study employing in-depth interviews conducted among 26 women about their behavioral intention to use mHealth services for reproductive health and the underlying factors influencing this intention with the help of the Integrative Model of Planned Behavior (IMPB). A snowball sampling technique were used to interview those university educated women, aged 21-31, based on their familiarity and exposure of mHealth services from seven universities in Bangladesh. The findings suggested that users of mHealth services find it more convenient and secure compared to visiting healthcare facilities, especially for trivial issues and inquiries regarding their reproductive health. Although promoting such services is lagging behind traditional healthcare, the attitude toward reproductive health services in Bangladesh is generally favorable resulting increasing adoption and use. Because such information-related mobile services (apps, websites, and social media) served as a first base of knowledge on reproductive health among many young girls and women in Bangladesh, who are generally shy to share or talk about their menstruation or personal health problems with family members, peers, or even health professionals due to socio-cultural factors and stigmatization. Conversely, urban centric services, availability of experts, quality management, security of privacy, authenticity of the information, digital divide, lack of campaign initiatives, lack of equipment and technology, lack of sex education, and outdated apps and websites were identified as obstacles that constrain the widespread use of reproductive mHealth services in Bangladesh. This study also concluded that promotion will be crucial in reforming conservative norms, taboos, and misconceptions about women's health and recommended such endeavors to be initiated by the policy makers as there is a substantive need for a specific policy regulating emerging digital health market in Bangladesh. Notwithstanding, women-only sample, low sample size, narrow focus on mHealth users and absence of perspectives from healthcare providers were among shortcomings of this study which could be addressed in future research. Further quantitative explorations are must to determine the usage patterns of reproductive mHealth services and their effectiveness that would identify implementation challenges in terms of customization and personalization in reproductive healthcare in a developing country like Bangladesh., Competing Interests: The authors declare that no competing interests exist., (Copyright: © 2024 Jonayed, Rumi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Chronic Kidney Disease (CKD) is a long-term condition and a major health problem, which affects over 3.5 million adults in the UK. Use of digital technology has been proposed as a means of improving patient management. It is important to understand the factors that affect the acceptability of this technology to people living with chronic kidney disease. This study used the Technology Acceptance Model 3 (TAM) to investigate whether perceived ease of use and perceived usefulness could predict intention behaviour. It then investigated if intention to use digital technology predicted actual use., Methodology: This was a cross-sectional study whereby the TAM3 questionnaire was sent online to people known to have chronic kidney disease via Kidney Care UK. The characteristics of the respondents (age, sex, CKD stage) were recorded., Principal Findings: The questionnaire was sent to 12,399 people, of which 229 (39% drop out) completed it. The respondents' age ranged from 24-90 years and 45% (n = 102) were male. Thirty-five percent of participants had advanced kidney care, 33% (n = 76) had kidney transplant and 22% (n = 51) had CKD. A multiple regression analysis showed a perceived ease of use and perceived usefulness of the technology predicted behaviour intention to use digital health technology. Behaviour intention did not significantly predict actual use behaviour., Conclusion: Perceived usefulness and perceived ease of use are important factors in determining the intention of people with CKD to use digital healthcare. However, a gap exists between this intention and readiness to actually use the technology. This needs to be overcome if digital healthcare is to gain future traction in the clinical scenario., Competing Interests: The grant that was used to fund the current work by NHS England (nee NHSx), specifically wanted to bring Small-Medium Enterprises (SMEs), NHS trusts and Universities to work together. However, the research element was done independently from the SMEs and NHS Trust. The University team only reported our findings to the project partners. For subsequent parts of the research, we conducted, the SME supplied the technology and provide technology support but throughout all of this it has remained completely independent of the research. Nick Sangala a co-author to the current paper is the Director of Ardia Digital Health that is part of the MyRenalCare app that part of the grant was to scale up its use. He had no role in study design, data collection and analysis, or decision to publish., (Copyright: © 2024 Kaklamanou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Katebi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

A vision-based patient monitoring system (VBPMS), Oxevision, has been introduced in approximately half of National Health Service (NHS) mental health trusts in England. A VBPMS is an assistive tool that supports patient safety by enabling non-contact physiological and physical monitoring. The system aims to help staff deliver safer, higher-quality and more efficient care. This paper summarises the potential health economic impact of using a VBPMS to support clinical practice in two inpatient settings: acute mental health and older adult mental health services. The economic model used a cost calculator approach to evaluate the potential impact of introducing a VBPMS into clinical practice, compared with clinical practice without a VBPMS. The analysis captured the cost differences in night-time observations, one-to-one continuous observations, self-harm incidents, and bedroom falls at night, including those resulting in A&E visits and emergency service callouts. The analysis is based on before and after studies conducted at five mental health NHS trusts, including acute mental health and older adult mental health services. Our findings indicate that the use of a VBPMS results in more efficient night-time observations and reductions in one-to-one observations, self-harm incidents, bedroom falls at night, and A&E visits and emergency service callouts from night-time falls. Substantial staff time in acute mental health and older adult mental health services is spent performing night-time observations, one-to-one observations, and managing incidents. The use of a VBPMS could lead to cost savings and a positive return on investment for NHS mental health trusts. The results do not incorporate all of the potential benefits associated with the use of a VBPMS, such as reductions in medication and length of hospital stay, plus the potential to avoid adverse events which would otherwise have a detrimental impact on a patient's quality of life., Competing Interests: Ciara Buckley, Robert Malcolm, and Jo Hanlon are employed by York Health Economics Consortium (YHEC). YHEC was funded by Oxehealth to develop the economic model and manuscript. Oxehealth conducted the data analysis which was used to populate the economic model. Oxehealth reviewed the final manuscript., (Copyright: © 2024 Buckley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

There is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring urgent surgical repair to prevent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT B-scans around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 B-scans from each eye). On three replicate data splits, 3D spatial contrast pre-training yields a model with an average F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared to an average F1-score of 0.831 for FTMH detection by ImageNet pre-trained models. These results demonstrate that even limited data may be applied toward self-supervised pre-training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Wheeler et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

During pregnancy and early motherhood, the perinatal period, women use a variety of resources including digital resources to support social interactions, information seeking and health monitoring. While previous studies have investigated specific timepoints, this study takes a more holistic approach to understand how information needs and resources change over the perinatal period. Furthermore, we include the perspective of maternity healthcare professionals to better understand the relationship between different stakeholders in the information work of perinatal women. A total of 25 interviews with 10 UK based mothers and 5 healthcare professionals (3 Midwives and 2 Health visitors) were conducted. Perinatal women were asked about their information and support needs throughout pregnancy and the postnatal period, healthcare professionals were asked about information and support provision to perinatal women. Information work activities were grouped along stages of the perinatal timeline from pre-pregnancy to the postanal period to illustrate the work and perspectives of the women and the healthcare professionals. Information work varies considerably over the timeline of the perinatal period, shifting back and forth in focus between mother and baby. information work during this period consists of many information related activities including seeking, monitoring, recording, questioning, sharing and checking. The importance of the HCPs as stakeholders in this work is notable as is the digital support for information work. Importantly, paper-based resources are still an important shared resource allowing reflection and supporting communication. Information work for women varies across the perinatal timeline. Particular challenges exist at key transition points, and we suggest design considerations for more integrated digital resources that support information work focused on mother and baby to enhance communication between perinatal women and healthcare professionals., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kemp et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper presents results from the Smart Healthy Campus 2.0 study/smartphone app, developed and used to collect mental health-related lifestyle data from 86 Canadian undergraduates January-August 2021. Objectives of the study were to 1) address the absence of longitudinal mental health overview and lifestyle-related data from Canadian undergraduate students, and 2) to identify associations between these self-reported mental health overviews (questionnaires) and lifestyle-related measures (from smartphone digital measures). This was a longitudinal repeat measures study conducted over 40 weeks. A 9-item mental health questionnaire was accessible once daily in the app. Two variants of this mental health questionnaire existed; the first was a weekly variant, available each Monday or until a participant responded during the week. The second was a daily variant available after the weekly variant. 6518 digital measure samples and 1722 questionnaire responses were collected. Mixed models were fit for responses to the two questionnaire variants and 12 phone digital measures (e.g. GPS, step counts). The daily questionnaire had positive associations with floors walked, installed apps, and campus proximity, while having negative associations with uptime, and daily calendar events. Daily depression had a positive association with uptime. Daily resilience appeared to have a slight positive association with campus proximity. The weekly questionnaire variant had positive associations with device idling and installed apps, and negative associations with floors walked, calendar events, and campus proximity. Physical activity, weekly, had a negative association with uptime, and a positive association with calendar events and device idling. These lifestyle indicators that associated with student mental health during the COVID-19 pandemic suggest directions for new mental health-related interventions (digital or otherwise) and further efforts in mental health surveillance under comparable circumstances., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Brogly et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Despite significant technical advances in machine learning (ML) over the past several years, the tangible impact of this technology in healthcare has been limited. This is due not only to the particular complexities of healthcare, but also due to structural issues in the machine learning for healthcare (MLHC) community which broadly reward technical novelty over tangible, equitable impact. We structure our work as a healthcare-focused echo of the 2012 paper "Machine Learning that Matters", which highlighted such structural issues in the ML community at large, and offered a series of clearly defined "Impact Challenges" to which the field should orient itself. Drawing on the expertise of a diverse and international group of authors, we engage in a narrative review and examine issues in the research background environment, training processes, evaluation metrics, and deployment protocols which act to limit the real-world applicability of MLHC. Broadly, we seek to distinguish between machine learning ON healthcare data and machine learning FOR healthcare-the former of which sees healthcare as merely a source of interesting technical challenges, and the latter of which regards ML as a tool in service of meeting tangible clinical needs. We offer specific recommendations for a series of stakeholders in the field, from ML researchers and clinicians, to the institutions in which they work, and the governments which regulate their data access., Competing Interests: L.A.C. is the Editor-in Chief of PLOS Digital Health., (Copyright: © 2024 Balagopalan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Conceptually, this study aimed to 1) identify the challenges and drivers encountered by England's Electronic Palliative Care Coordination System (EPaCCS) projects in the context of challenges and drivers in other projects on data sharing for individual care (also referred to as Health Information Exchange, HIE) and 2) organise them in a comprehensive yet parsimonious framework. The study also had a strong applied goal: to derive specific and non-trivial recommendations for advancing data sharing projects, particularly ones in early stages of development and implementation. Primary data comprised 40 in-depth interviews with 44 healthcare professionals, patients, carers, project team members and decision makers in Cambridgeshire, UK. Secondary data were extracted from four pre-existing literature reviews on Health Information Exchange and Health Information Technology implementation covering 135 studies. Thematic and framework analysis underpinned by "pluralist" coding were the main analytical approaches used. We reduced an initial set of >1,800 parameters into >500 challenges and >300 drivers to implementing EPaCCS and other data sharing projects. Less than a quarter of the 800+ parameters were associated primarily with the IT solution. These challenges and drivers were further condensed into an action-guiding, strategy-informing framework of nine types of "pure challenges", four types of "pure drivers", and nine types of "oppositional or ambivalent forces". The pure challenges draw parallels between patient data sharing and other broad and complex domains of sociotechnical or social practice. The pure drivers differ in how internal or external to the IT solution and project team they are, and thus in the level of control a project team has over them. The oppositional forces comprise pairs of challenges and drivers where the driver is a factor serving to resolve or counteract the challenge. The ambivalent forces are factors perceived simultaneously as a challenge and a driver depending on context, goals and perspective. The framework is distinctive in its emphasis on: 1) the form of challenges and drivers; 2) ambivalence, ambiguity and persistent tensions as fundamental forces in the field of innovation implementation; and 3) the parallels it draws with a variety of non-IT, non-health domains of practice as a source of fruitful learning. Teams working on data sharing projects need to prioritise further the shaping of social interactions and structural and contextual parameters in the midst of which their IT tools are implemented. The high number of "ambivalent forces" speaks of the vital importance for data sharing projects of skills in eliciting stakeholders' assumptions; managing conflict; and navigating multiple needs, interests and worldviews., Competing Interests: We have read the journal’s policy and the authors of this manuscript have the following competing interests: Both authors were members of the project development team for the Cambridgeshire & Peterborough CCG Project for Data Sharing in End of Life Care (during the stage of the work described in this paper; currently this is the Cambridgeshire and Peterborough ICS Project for Data Sharing in End of Life Care). SB was the project Clinical Lead. The HIEC grant, through which the service development project was originally funded, has contributed to the salaries of both MP and SB in early stages of this research. The data sharing project is a not-for-profit project, currently funded by the Cambridgeshire and Peterborough Integrated Care System. It aims to improve health services for end of life care patients in the locality, as part of the provision of services by the NHS (the UK National Health Service), which is publicly funded and free at the point of care., (Copyright: © 2024 Petrova, Barclay. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Childhood obesity is a growing global health concern. Although mobile health apps have the potential to deliver behavioural interventions, their impact is commonly limited by a lack of sufficient engagement. The purpose of this study was to explore barriers and facilitators to engagement with a family-focused app and its perceived impact on motivation, self-efficacy, and behaviour. Parents with at least one child under 18 and healthcare professionals working with children were recruited; all participants were allocated to use the NoObesity app over a 6-month period. The mixed-methods design was based on the Non-adoption, Abandonment, Scale-Up, Spread, and Sustainability and Reach, Effectiveness, Adoption, Implementation, and Maintenance frameworks. Qualitative and quantitative data were gathered through semi-structured interviews, questionnaires, and app use data (logins and in-app self-reported data). 35 parents were included in the final analysis; quantitative results were analysed descriptively and thematic analysis was conducted on the qualitative data. Key barriers to engagement were boredom, forgetting, and usability issues and key barriers to potential impact on behaviours were accessibility, lack of motivation, and family characteristics. Novelty, gamification features, reminders, goal setting, progress monitoring and feedback, and suggestions for healthy foods and activities were key facilitators to engagement with the app and behaviours. A key observation was that intervention strategies could help address many motivation and capability barriers, but there was a gap in strategies addressing opportunity barriers. Without incorporating strategies that successfully mitigate barriers in all three determinants of behaviour, an intervention is unlikely to be successful. We highlight key recommendations for developers to consider when designing the features and implementation of digital health interventions. Trial Registration: ClinicalTrials.gov (NCT05261555)., Competing Interests: ER and AP are employees of the former Health Education England (now the South East School of Public Health, Workforce Training and Education Directorate, NHS England) and were involved in the development of the NoObesity apps. ER and AP contributed qualitative data and reviewed the final manuscript prior to submission, but the academic authors retained editorial control. MMI and EM received research grant funding for this study from the former Health Education England, which is now the South East School of Public Health, Workforce Training and Education Directorate, NHS England (grant reference number: AM1000393). EM and MMI are supported by the NIHR Newcastle Biomedical Research Council (BRC). The views expressed in the paper belong to the authors and not necessarily those of the South East School of Public Health, NHS England, the University of Plymouth, the University of Liverpool, the University of Southampton, the University of Oxford, Newcastle University, the NIHR Newcastle BRC, or Imperial College London. The funding body had no editorial control and was not involved in the decision to submit the article for publication. The other authors have declared that no competing interests exist., (Copyright: © 2024 Milne-Ives et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The StayWell at Home intervention, a 60-day text-messaging program based on Cognitive Behavioral Therapy (CBT) principles, was developed to help adults cope with the adverse effects of the global pandemic. Participants in StayWell at Home were found to show reduced depressive and anxiety symptoms after participation. However, it remains unclear whether the intervention improved mood and which intervention components were most effective at improving user mood during the pandemic. Thus, utilizing a micro-randomized trial (MRT) design, we examined two intervention components to inform the mechanisms of action that improve mood: 1) text messages delivering CBT-informed coping strategies (i.e., behavioral activation, other coping skills, or social support); 2) time at which messages were sent. Data from two independent trials of StayWell are included in this paper. The first trial included 303 adults aged 18 or older, and the second included 266 adults aged 18 or older. Participants were recruited via online platforms (e.g., Facebook ads) and partnerships with community-based agencies aiming to reach diverse populations, including low-income individuals and people of color. The results of this paper indicate that participating in the program improved and sustained self-reported mood ratings among participants. We did not find significant differences between the type of message delivered and mood ratings. On the other hand, the results from Phase 1 indicated that delivering any type of message in the 3 pm-6 pm time window improved mood significantly over sending a message in the 9 am-12 pm time window. The StayWell at Home program increases in mood ratings appeared more pronounced during the first two to three weeks of the intervention and were maintained for the remainder of the study period. The current paper provides evidence that low-burden text-message interventions may effectively address behavioral health concerns among diverse communities., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Adrian Aguilera is the creator and owner of HealthySMS, the system utilized for delivering text messages and collecting mood data. The rest of the authors have declared that no competing interests exist, (Copyright: © 2024 Arévalo Avalos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Healthcare systems have made rapid progress towards combining data science with precision medicine, particularly in pharmacogenomics. With the lack of predictability in medication effectiveness from patient to patient, acquiring the specifics of their genotype would be highly advantageous for patient treatment. Genotype-guided dosing adjustment improves clinical decision-making and helps optimize doses to deliver medications with greater efficacy and within safe margins. Current databases demand extensive effort to locate relevant genetic dosing information. To address this problem, Patient Optimization Pharmacogenomics (POPGx) was constructed. The objective of this paper is to describe the development of POPGx, a tool to simplify the approach for healthcare providers to determine pharmacogenomic dosing recommendations for patients taking multiple medications. Additionally, this tool educates patients on how their allele variations may impact gene function in case they need further healthcare consultations. POPGx was created on Konstanz Information Miner (KNIME). KNIME is a modular environment that allows users to conduct code-free data analysis. The POPGx workflow can access Clinical Pharmacogenomics Implementation Consortium (CPIC) guidelines and subsequently be able to present relevant dosing and counseling information. A KNIME representational state transfer (REST) application program interface (API) node was established to retrieve information from CPIC and drugs that are exclusively metabolized through CYP450, and these drugs were processed simultaneously to demonstrate competency of the workflow. The POPGx program provides a time-efficient method for users to retrieve relevant, patient-specific medication selection and dosing recommendations. Users input metabolizer gene, genetic allele data, and medication list to retrieve clear dosing information. The program is automated to display current guideline recommendations from CPIC. The integration of this program into healthcare systems has the potential to revolutionize patient care by giving healthcare practitioners an easy way to prescribe medications with greater efficacy and safety by utilizing the latest advancements in the field of pharmacogenomics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Behdani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Low-middle income countries like India bear a heavier burden of maternal, childcare, and child mortality rates when compared with high-income countries, which highlights the disparity in global health. Numerous societal, geopolitical, economic, and institutional issues have been linked to this inequality. mHealth has the potential to ameliorate these challenges by providing health services and health-related information with the assistance of frontline workers in the provision of prepartum, delivery, and postnatal care to improve maternal and child health outcomes in hard-to-reach areas in low- and middle-income countries (LMICs). However, there is limited evidence to support how mHealth can strengthen maternal and child health in India. The scoping review guideline in the Cochrane Handbook was used to retrieve studies from 4 international databases: CINAHL, Embase, Medline Ovid, and PubMed. This search strategy used combined keywords (MeSH terms) related to maternal and child healthcare, mHealth, and BIMARU in conjunction with database-controlled vocabulary. Out of 278 records, 8 publications were included in the review. The included articles used mHealth for data collection, eLearning, communication, patient monitoring, or tracking to deliver maternal and neonatal care. The results of these papers reflected a favourable effect of mHealth on the target population and found that it altered their attitudes and behaviours about healthcare. Higher job satisfaction and self-efficiency were reported by mHealth user care providers. Multiple barriers to the acceptance of mHealth exist, but the majority of the evidence points towards the feasibility of the intervention in a clinical setting. The mHealth has positive potential for improving maternal and child health outcomes in low-resource settings in India's BIMARU states by strengthening the healthcare system. The results of the study could be used in the tailoring of an effective mHealth intervention and implementation strategy in a similar context. However, there is a need for economic evaluation in the future to bridge the knowledge gap regarding the cost-effectiveness of mHealth interventions., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Singh, Walters. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Hospital-based cancer registries (HBCRs) record data on all patients diagnosed and/or treated for cancer at healthcare facilities and evaluate the burden of the disease and the quality of healthcare services at that hospital, helping improve patient care, and providing an assessment of healthcare quality. The CARE PH app was created as a tool to facilitate a system of hospital-based cancer registries in the Philippines, a lower middle-income country. From 2017 to 2022, a total of 60,021 cancer registrants from 44 CARE PH hospitals were entered into the database. Breast cancer was the most common primary site, accounting for 17,660 cases (29.4%). This was followed by colorectal cancer at 11.1%, cervical cancer at 6.2%, head and neck cancer at 5.9%, and prostate and other male genital cancer at 5.1%.Among the 30 data fields collected, 17 exhibited 0-20% missing data, eight displayed 21%-90% missing data, while five depicted 91%-100% missing data. Most of the data fields with missing data are in the treatment and follow-up modules, which are stored in separate forms in a patient's record. Digital transformation of hospitals from paper-based charts to electronic medical records, and the integration of the HBCR to the EMR and hospital information system, will likely be the best solution for these limitations. It is recommended that the creation and maintenance of HBCRs nationwide must be harmonized, and embedded in all relevant national programs and legislations. The development of an information technology process that is based on a cancer patient's journey, should be built on an open system embedded in a well designed enterprise architecture, functioning under the guidance of a strong leadership and governance team. All these must be present in order to create and maintain a robust HBCR that is useful for furthering cancer registry and research in the country., Competing Interests: Leo Anthony Celi is on the PLOS Computational Biology editorial board. The authors declare no other competing interests in this work., (Copyright: © 2024 Tiangco et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The study provides a comprehensive review of OpenAI's Generative Pre-trained Transformer 4 (GPT-4) technical report, with an emphasis on applications in high-risk settings like healthcare. A diverse team, including experts in artificial intelligence (AI), natural language processing, public health, law, policy, social science, healthcare research, and bioethics, analyzed the report against established peer review guidelines. The GPT-4 report shows a significant commitment to transparent AI research, particularly in creating a systems card for risk assessment and mitigation. However, it reveals limitations such as restricted access to training data, inadequate confidence and uncertainty estimations, and concerns over privacy and intellectual property rights. Key strengths identified include the considerable time and economic investment in transparent AI research and the creation of a comprehensive systems card. On the other hand, the lack of clarity in training processes and data raises concerns about encoded biases and interests in GPT-4. The report also lacks confidence and uncertainty estimations, crucial in high-risk areas like healthcare, and fails to address potential privacy and intellectual property issues. Furthermore, this study emphasizes the need for diverse, global involvement in developing and evaluating large language models (LLMs) to ensure broad societal benefits and mitigate risks. The paper presents recommendations such as improving data transparency, developing accountability frameworks, establishing confidence standards for LLM outputs in high-risk settings, and enhancing industry research review processes. It concludes that while GPT-4's report is a step towards open discussions on LLMs, more extensive interdisciplinary reviews are essential for addressing bias, harm, and risk concerns, especially in high-risk domains. The review aims to expand the understanding of LLMs in general and highlights the need for new reflection forms on how LLMs are reviewed, the data required for effective evaluation, and addressing critical issues like bias and risk., Competing Interests: I have read the journal’s policy, and the authors of this manuscript have the following competing interests: LAC is the Editor-In-Chief of PLOS DH; JWG is an associate editor of PLOS DH and program director for the Radiology:AI trainee editorial board. All other authors declare no competing interests., (Copyright: © 2024 Gallifant et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

In recent years, technology has been increasingly incorporated within healthcare for the provision of safe and efficient delivery of services. Although this can be attributed to the benefits that can be harnessed, digital technology has the potential to exacerbate and reinforce preexisting health disparities. Previous work has highlighted how sociodemographic, economic, and political factors affect individuals' interactions with digital health systems and are termed social determinants of health [SDOH]. But, there is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health [DDOH]. This paper will, for the first time, propose a definition of DDOH and provide a conceptual model characterizing its influence on healthcare outcomes. Specifically, DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology. A better appreciation of DDOH by the various stakeholders at the individual and societal levels can be channeled towards policies that are more digitally inclusive. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity., Competing Interests: Leo Anthony Celi is the Editor-in-Chief of PLOS Digital Health., (Copyright: © 2024 Chidambaram et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Materially deprived communities in the UK have excess morbidity and mortality from cardiovascular disease (CVD) but are less likely to engage with formal care pathways. Community engagement and e-health may be more effective ways to promote risk-reducing lifestyle change. The "Healthy Hearts Project" website was designed for use by community health workers (CHWs) for cardiovascular risk assessment and lifestyle goal setting, or for independent use by community members. This paper describes the website's development and evaluation. The website was developed using interactive wire frame prototypes in a user-led approach. Qualitative evaluation of the completed website's usability and acceptability was conducted using the "Thinking Aloud" method in a purposive sample of 10 participants (one voluntary sector employee, three CHWs, two community members and four healthcare professionals). Thinking Aloud interview transcripts were thematically analysed using an inductive approach. A separate quantitative evaluation of usability and the effect of using the website on CVD knowledge and beliefs was conducted. A random sample of 134 participants, recruited using the online platform Prolific, completed the "Attitudes and Beliefs About Cardiovascular Disease" (ABCD) questionnaire before and after using the website, along with the System Usability Scale (SUS). Qualitative evaluation-Four key themes were identified: 1) Website functionality and design-participants generally found the website easy to use and understood the risk communication graphics and the feedback and goal-setting features,; 2) Inclusivity and representation-most participants considered the website inclusive of a range of users/cultures; 3) Language and comprehension-participants found the language used easy to understand but suggested reducing the amount of text; 4) Motivation and barriers to change-participants liked the personalized feedback and empowerment offered by goal-setting but commented on the need for self-motivation. Quantitative evaluation-The mean score across all domains of the ABCD questionnaire (from 2.99 to 3.11, p<0.001) and in the sub-domains relating to attitudes and beliefs around healthy eating and exercise increased after using the website. The mean(sd) score on the SUS was 77.5 (13.5). The website's usability was generally rated well by both quantitative and qualitative measures, and measures of CVD knowledge improved after use. A number of general recommendations for the design of eHealth behaviour change tools are made based on participants' suggestions to improve the website., Competing Interests: Nicola O’Connor is employed by William Joseph Ltd. The remaining authors have no competing interests., (Copyright: © 2023 Rogers et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Numerous ethics guidelines have been handed down over the last few years on the ethical applications of machine learning models. Virtually every one of them mentions the importance of "fairness" in the development and use of these models. Unfortunately, though, these ethics documents omit providing a consensually adopted definition or characterization of fairness. As one group of authors observed, these documents treat fairness as an "afterthought" whose importance is undeniable but whose essence seems strikingly elusive. In this essay, which offers a distinctly American treatment of "fairness," we comment on a number of fairness formulations and on qualitative or statistical methods that have been encouraged to achieve fairness. We argue that none of them, at least from an American moral perspective, provides a one-size-fits-all definition of or methodology for securing fairness that could inform or standardize fairness over the universe of use cases witnessing machine learning applications. Instead, we argue that because fairness comprehensions and applications reflect a vast range of use contexts, model developers and clinician users will need to engage in thoughtful collaborations that examine how fairness should be conceived and operationalized in the use case at issue. Part II of this paper illustrates key moments in these collaborations, especially when inter and intra disagreement occurs among model developer and clinician user groups over whether a model is fair or unfair. We conclude by noting that these collaborations will likely occur over the lifetime of a model if its claim to fairness is to advance beyond "afterthought" status., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Banja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient's presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ghosheh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Digital health interventions have enormous potential to support patients and the public in achieving their health goals. Nonetheless, many digital health interventions are failing to effectively engage patients and the public. One solution that has been proposed is to directly involve patients and the public in the design process of these digital health interventions. Although there is consensus that involving patients and the public in collaborative design is valuable, design teams have little guidance on how to maximize the value of their collaborative design work. The main objective of this study was to understand how the value of patient and public involvement in digital health design can be maximized, from the perspective of design leaders and patient-public partners. Using a qualitative descriptive methodology, we conducted semi-structured interviews with 19 design leaders and 9 patient-public partners. Interviewees agreed that involving patients and the public was valuable, however, they questioned if current collaborative methods were optimized to ensure maximal value. Interviewees suggested that patient and public collaborative design can add value through four different mechanisms: (1) by allowing the design process to be an empowering intervention itself, (2) by ensuring that the digital health intervention will be effectively engaging for users, (3) by ensuring that the digital health intervention will be seamlessly implemented in practice, and (4) by allowing patient-public collaborations extend beyond the initial product design. Overall, interviewees emphasized that although collaborative design has historically focused on improving the digital health product itself, patients and the public have crucial insights on implementation planning as well as how collaborative design can be used as its own empowering intervention. The results of this paper provide clarity about the ways that patient and public collaborative design can be made more valuable. Digital health design teams can use these results to be more intentional about their collaborative design approaches., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Voorheis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Diabetes mellitus type 2 is increasingly being called a modern preventable pandemic, as even with excellent available treatments, the rate of complications of diabetes is rapidly increasing. Predicting diabetes and identifying it in its early stages could make it easier to prevent, allowing enough time to implement therapies before it gets out of control. Leveraging longitudinal electronic medical record (EMR) data with deep learning has great potential for diabetes prediction. This paper examines the predictive competency of deep learning models in contrast to state-of-the-art machine learning models to incorporate the time dimension of risk. The proposed research investigates a variety of deep learning models and features for predicting diabetes. Model performance was appraised and compared in relation to predominant features, risk factors, training data density and visit history. The framework was implemented on the longitudinal EMR records of over 19K patients extracted from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Empirical findings demonstrate that deep learning models consistently outperform other state-of-the-art competitors with prediction accuracy of above 91%, without overfitting. Fasting blood sugar, hemoglobin A1c and body mass index are the key predictors of future onset of diabetes. Overweight, middle aged patients and patients with hypertension are more vulnerable to developing diabetes, consistent with what is already known. Model performance improves as training data density or the visit history of a patient increases. This study confirms the ability of the LSTM deep learning model to incorporate the time dimension of risk in its predictive capabilities., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Naveed et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Premature birth and neonatal mortality are significant global health challenges, with 15 million premature births annually and an estimated 2.5 million neonatal deaths. Approximately 90% of preterm births occur in low/middle income countries, particularly within the global regions of sub-Saharan Africa and South Asia. Neonatal hypothermia is a common and significant cause of morbidity and mortality among premature and low birth weight infants, particularly in low/middle-income countries where rates of premature delivery are high, and access to health workers, medical commodities, and other resources is limited. Kangaroo Mother Care/Skin-to-Skin care has been shown to significantly reduce the incidence of neonatal hypothermia and improve survival rates among premature infants, but there are significant barriers to its implementation, especially in low/middle-income countries (LMICs). The paper proposes the use of a multidisciplinary approach to develop an integrated mHealth solution to overcome the barriers and challenges to the implementation of Kangaroo Mother Care/Skin-to-skin care (KMC/STS) in LMICs. The innovation is an integrated mHealth platform that features a wearable biomedical device (NeoWarm) and an Android-based mobile application (NeoRoo) with customized user interfaces that are targeted specifically to parents/family stakeholders and healthcare providers, respectively. This publication describes the iterative, human-centered design and participatory development of a high-fidelity prototype of the NeoRoo mobile application. The aim of this study was to design and develop an initial ("A") version of the Android-based NeoRoo mobile app specifically to support the use case of KMC/STS in health facilities in Kenya. Key functions and features are highlighted. The proposed solution leverages the promise of digital health to overcome identified barriers and challenges to the implementation of KMC/STS in LMICs and aims to equip parents and healthcare providers of prematurely born infants with the tools and resources needed to improve the care provided to premature and low birthweight babies. It is hoped that, when implemented and scaled as part of a thoughtful, strategic, cross-disciplinary approach to reduction of global rates of neonatal mortality, NeoRoo will prove to be a useful tool within the toolkit of parents, health workers, and program implementors., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: Dr. Sherri Bucher has been awarded intellectual property protection for invention of the NeoWarm biomedical device. This includes US patent US10390630B2, Nigeria NG/PT/C/2018/2802 and ARIPO patent PT/C/2018/2802. The authors declare no other potential conflicts of interest with respect to research, authorship, financial relationships, and/or publication of the article., (Copyright: © 2023 Bucher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Introduction: Harnessing new digital technologies can improve access to health care but can also widen the health divide for those with poor digital literacy. This scoping review aims to assess the current situation of low digital health literacy in terms of its definition, reach, impact on health and interventions for its mitigation., Methods: A comprehensive literature search strategy was composed by a qualified medical librarian. Literature databases [Medline (Ovid), Embase (Ovid), Scopus, and Google Scholar] were queried using appropriate natural language and controlled vocabulary terms along with hand-searching and citation chaining. We focused on recent and highly cited references published in English. Reviews were excluded. This scoping review was conducted following the methodological framework of Arksey and O'Malley., Results: A total of 268 articles were identified (263 from the initial search and 5 more added from the references of the original papers), 53 of which were finally selected for full text analysis. Digital health literacy is the most frequently used descriptor to refer to the ability to find and use health information with the goal of addressing or solving a health problem using technology. The most utilized tool to assess digital health literacy is the eHealth literacy scale (eHEALS), a self-reported measurement tool that evaluates six core dimensions and is available in various languages. Individuals with higher digital health literacy scores have better self-management and participation in their own medical decisions, mental and psychological state and quality of life. Effective interventions addressing poor digital health literacy included education/training and social support., Conclusions: Although there is interest in the study and impact of poor digital health literacy, there is still a long way to go to improve measurement tools and find effective interventions to reduce the digital health divide., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Arias López et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)