Digital Health Systems Research Articles

Partnering With Nurse Inventors and Entrepreneurs: A Guide for Nursing Professional Development Leaders.

Health care innovation and entrepreneurship are key drivers of transformation in health care, re-shaping the way care is delivered, managed, and experienced. Innovation in health care spans a range of areas, including digital health, bio-technology, medical devices, tele-health, and health information systems. Entrepreneurs are leveraging technology to create solutions to improve patient outcomes, reduce costs, and address inefficiencies. Nurse inventors and entrepreneurs are blazing new trails that require nursing professional development leaders to consider new opportunities for collaboration. For nursing professional development leaders, fostering relationships with nurse entrepreneurs can unlock numerous benefits, from improving patient safety to enhancing nursing practice and facilitating advisory board appointments. [J Contin Educ Nurs. 2024;55(11):517-519.].

How advanced is your digital public health system? A qualitative analysis of suitable indicators

Abstract Background Revealing the full potential of digital public health (DiPH) systems necessitates a wide-ranging tool to assess their maturity. Essential domains that need to be considered include the literacy and interest in DiPH tool application by society and the workforce and the legal and ICT maturity of DiPH systems. No review has investigated indicators on these national DiPH system maturity (DiPHSM) domains yet. Our narrative review and qualitative analysis aimed to map the landscape of indicators related to DiPHSM measurement and rank them based on their importance for such assessments. Methods As original indicators were not published in scientific databases but as grey literature, we used DuckDuckGo to apply a pre-defined search strategy for 11 countries from all continents classified as having reached level 4 of 5 in the Global Digital Health Monitor. Of the 1484 identified references, 137 were included which named 15806 indicators. Consensus on importance was defined as at least 3 or 4 authors rating an indicator as important. Results We recognized 180 indicators on different constructs with importance for DiPHSM analysis, including the availability and use of Smartphones, computers, the Internet or the DiPH intervention, the existence of skilled workforce, infrastructure (investment), and interoperability between interventions, the secondary use of health data, a DiPH strategy and controlling agency, or the application of big data and artificial intelligence for health data collection, analysis, and sharing. Conclusions Our study holds the potential to develop more comprehensive tools for DiPHSM assessments. Further examination is required to analyze the suitability and applicability of all identified indicators for diverse healthcare settings. By working towards a uniform evaluation of DiPHSM, we foster informed decision-making among healthcare planners and practitioners, improve resource distribution, and continue to drive innovation in healthcare delivery. Key messages • Maturity assessment tools need to consider the complexity of DiPH systems. Thereby, DiPH system evaluations need to be accompanied by analyses of the legal, ICT, and literacy perspective maturities. • New methods are needed to systematically assess and use multidisciplinary grey literature for research.

Capabilities for Using Telemonitoring in Physiotherapy Treatment: Exploratory Qualitative Study.

Telemonitoring (TM), as part of telehealth, allows physiotherapists to monitor and coach their patients using remotely collected data. The use of TM requires a different approach compared with face-to-face treatment. Although a telehealth capability framework exists for health care professionals, it remains unclear what specific capabilities are required to use TM during physiotherapy treatments. This study aims to identify the capabilities required to use TM in physiotherapy treatment. An exploratory qualitative study was conducted following a constructivist semistructured grounded theory approach. Three heterogeneous focus groups were conducted with 15 lecturers of the School of Physiotherapy (Bachelor of Science Physiotherapy program) from the Amsterdam University of Applied Sciences. Focus group discussions were audiotaped and transcribed verbatim. Capabilities for using TM in physiotherapy treatment were identified during an iterative process of data collection and analysis, based on an existing framework with 4 different domains. Team discussions supported further conceptualization of the findings. Sixteen capabilities for the use of TM in physiotherapy treatment were found addressing 3 different domains. Four capabilities were identified in the "digital health technologies, systems, and policies" domain, 7 capabilities in the "clinical practice and application" domain, and 5 capabilities in the "data analysis and knowledge creation" domain. No capabilities were identified in the "system and technology implementation" domain. The use of TM in physiotherapy treatment requires specific skills from physiotherapists. To best use TM in physiotherapy treatment, it is important to integrate these capabilities into the education of current and future physiotherapists.

The Role of Visualization in Estimating Cardiovascular Disease Risk: Scoping Review.

Supporting and understanding the health of patients with chronic diseases and cardiovascular disease (CVD) risk is often a major challenge. Health data are often used in providing feedback to patients, and visualization plays an important role in facilitating the interpretation and understanding of data and, thus, influencing patients' behavior. Visual analytics enable efficient analysis and understanding of large datasets in real time. Digital health technologies can promote healthy lifestyle choices and assist in estimating CVD risk. This review aims to present the most-used visualization techniques to estimate CVD risk. In this scoping review, we followed the Joanna Briggs Institute PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search strategy involved searching databases, including PubMed, CINAHL Ultimate, MEDLINE, and Web of Science, and gray literature from Google Scholar. This review included English-language articles on digital health, mobile health, mobile apps, images, charts, and decision support systems for estimating CVD risk, as well as empirical studies, excluding irrelevant studies and commentaries, editorials, and systematic reviews. We found 774 articles and screened them against the inclusion and exclusion criteria. The final scoping review included 17 studies that used different methodologies, including descriptive, quantitative, and population-based studies. Some prognostic models, such as the Framingham Risk Profile, World Health Organization and International Society of Hypertension risk prediction charts, Cardiovascular Risk Score, and a simplified Persian atherosclerotic CVD risk stratification, were simpler and did not require laboratory tests, whereas others, including the Joint British Societies recommendations on the prevention of CVD, Systematic Coronary Risk Evaluation, and Framingham-Registre Gironí del COR, were more complex and required laboratory testing-related results. The most frequently used prognostic risk factors were age, sex, and blood pressure (16/17, 94% of the studies); smoking status (14/17, 82%); diabetes status (11/17, 65%); family history (10/17, 59%); high-density lipoprotein and total cholesterol (9/17, 53%); and triglycerides and low-density lipoprotein cholesterol (6/17, 35%). The most frequently used visualization techniques in the studies were visual cues (10/17, 59%), followed by bar charts (5/17, 29%) and graphs (4/17, 24%). On the basis of the scoping review, we found that visualization is very rarely included in the prognostic models themselves even though technology-based interventions improve health care worker performance, knowledge, motivation, and compliance by integrating machine learning and visual analytics into applications to identify and respond to estimation of CVD risk. Visualization aids in understanding risk factors and disease outcomes, improving bioinformatics and biomedicine. However, evidence on mobile health's effectiveness in improving CVD outcomes is limited.

Development of a Theoretical Continuous Glucose Monitoring Module for Pharmacy Students: Preparing Pharmacists for the Future.

To enhance the digital competencies of pharmacy students, the Institute of Clinical Pharmacy and Pharmacotherapy at Heinrich-Heine University Duesseldorf developed and evaluated a theoretical module on digital health and data analysis. This innovative module integrated a continuous glucose-monitoring (CGM) wearable device into teaching, providing students with in-depth practical experience and a 2.5 h seminar on digital health and CGM systems. Students' knowledge of CGM and self-assessment of their CGM competencies were assessed in a pre-post manner. Additionally, students' satisfaction with the module and their perceptions of the future integration of digital health training and the role of wearables in pharmacy practice were also assessed after the module. A total of 39 final-year pharmacy students completed the module conducted in June 2024 as part of the clinical pharmacy seminar. In total, 32 students completed the pre- and post-knowledge tests and self-assessment questionnaires. Both the knowledge and the students' self-assessment of CGM-related skills after the module increased significantly (p < 0.05). Students expanded their knowledge regarding digital health solutions, in particular the CGM systems, and increased their self-reported competence in CGM-related skills. With this module, an important foundation was laid, as this is the first theoretical module including the essentials of CGM digital health tools for pharmacy students in Germany.

Feasibility of a digital palliative care intervention (Convoy-Pal) for older adults with heart failure and multiple chronic conditions and their caregivers: a waitlist randomized control trial

BackgroundAlthough older adults with heart failure (HF) and multiple chronic conditions (MCC) frequently rely on caregivers for health management, digital health systems, such as patient portals and mobile apps, are designed for individual patients and often exclude caregivers. There is a need to develop approaches that integrate caregivers into care. This study tested the feasibility of the Social Convoy Palliative Care intervention (Convoy-Pal), a 12-week digital self-management program that includes assessment tools and resources for clinical palliative care, designed for both patients and their caregivers.MethodsA randomized waitlist control feasibility trial involving patients over 65 years old with MCC who had been hospitalized two or more times for HF in the past 12 months and their caregivers. Descriptive statistics were used to evaluate recruitment, retention, missing data, self-reported social functioning, positive aspects of caregiving, and the acceptability of the intervention.ResultsOf 126 potentially eligible patients, 11 were ineligible and 69 were deceased. Of the 46 eligible patients, 31 enrolled in the trial. Although 48 caregivers were identified, only 15 enrolled. The average age was 76.3 years for patients and 71.6 years for caregivers, with most participants being non-Hispanic White. Notably, 4% did not have access to a personal mobile device or computer. Retention rates were 79% for intervention patients, 57% for intervention caregivers, and 60% for control participants. Only 4.6% of survey subscales were missing, aided by robust technical support. Intervention patients reported improved social functioning (SF-36: 64.6 ± 25.8 to 73.2 ± 31.3) compared to controls (64.6 ± 27.1 to 67.5 ± 24.4). Intervention caregivers also reported increased positive perceptions of caregiving (29.5 ± 5.28 to 35.0 ± 5.35) versus control caregivers (29.4 ± 8.7 to 28.0 ± 4.4). Waitlist control participants who later joined the Convoy-Pal program showed similar improvements. The intervention was well-rated for acceptability, especially regarding the information provided (3.96 ± .57 out of 5).ConclusionsRecruiting informal caregivers proved challenging. Nonetheless, Convoy-Pal retained patients and collected meaningful self-reported outcomes, showing potential benefits for both patients and caregivers. Given the importance of a patient and caregiver approach in palliative care, further research is needed to design digital tools that cater to multiple simultaneous users.Trial registrationClinicalTrials.gov Identifier NCT04779931. Date of registration: March 3, 2021.

Role Of Digital Image Processing In Education And Medical Field

Digital image processing is a technique that uses computer algorithms to analyze images and transform images into better ones.It is an important branch of Telecommunication engineering that deals with the improvisation of reliability and accuracy of the digital communication by employing multiple techniques. Image processing involves many processes such as taking images, preprocessing, image enhancement, transformation and analysis. Variations in pixel values are one of the major challenges faced by image processing. Digital Signal Processing is at the core of virtually all of today's information technology, and its impact is felt everywhere in telecommunications, medical technology, radar and sonar, and in seismic data analysis.Digital Image Processing plays an important role in medical field and education. Various researches has been done and currently popularly used. In education, pictures can be used to enhance student learning and to develop students’ self-esteem, or their utilization can be of little or no value and, if used carelessly, can, in fact, undermine students' self-confidence. Digital health technologies include both hardware and software solutions and services, including telemedicine, wearable devices, augmented reality, and virtual reality. Generally, digital health interconnects health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their patients manage illnesses and health risks, as well as promote health and wellbeing. Digital imaging gives healthcare providers better and more accurate images, which leads to establishing diagnoses sooner, making treatment of the patient more successful.

Contextualised digital health communication infrastructure standards for resource-constrained settings: Perception of digital health stakeholders regarding suitability for Uganda's health system.

Ignoring the need to contextualise international standards has caused low-resourced countries to implement digital health systems on the ad-hoc, thereby often failing to meet the local needs or scale up. Authors have recommended adapting standards to a country's context. However, to date, most resources constrained countries like Uganda have not done so, affecting their success in attaining the full benefits of using ICT to support their health systems. They apply the standards 'as is' with little regard for their fitness for potential use and ability to fulfil the country's digital health needs. A design science approach was followed to elicit digital health communication infrastructure (DHCI) requirements and develop the contextual DHCI standards for Uganda. The design science methodology's design cycle supported DHCI standards' construction and evaluation activities. Whereas two workgroup sessions were held to craft the standards, three cycles of evaluation and refinement were performed. The final refinement produces the contextualised DHCI standards approved by Uganda's DH stakeholders through summative evaluation. Results of the summative evaluation show that DH stakeholders agree that the statement of the standards and the requirements specification are suitable to guide DHCI standards implementation in Uganda. Stakeholders agreed that the standards are complete, have the potential to realise DHCI requirements in Uganda, that have been well structured and follow international style for standards, and finally, that the standards are fit to realise their intended use in Uganda. Having been endorsed by DH stakeholders in Uganda's health system, the standards should be piloted to establish their potency to improve health information exchange and healthcare outcomes. Also, we recommend other low middle income countries (LMICs) with similar challenges to those in Uganda adopt the same set of contextualised DHCI standards.

Strategies to Address Statin Medication Intolerance Among Patients at Risk of Cardiovascular Disease Identified Through Electronic Health Records: A Literature Review and Pooled Analysis.

Statins are a group of medications that lower lipid and are used for primary and secondary prevention of cardiovascular diseases (CVD). Patients can either be partially (15%) or completely (5%) intolerant to statins. Symptoms of statin intolerance can include muscle aches (myalgia), weakness, cramps, myopathy, diabetes mellitus, and elevated creatine kinase levels. Decreasing statin intolerance also improves statin adherence, which in turn results in lower number of CVD events among patients. Studies on statin intolerance is often embedded within studies of statin adherence. However, relevant data can be obtained from digital health systems. This preliminary literature review looks at studies from the past 10 years to identify and determine the effectiveness of strategies to address statin intolerance. The NLA definition for statin intolerance was used in this review. The initial search results on EMBASE, PubMed, SCOPUS, and CINAHL showed 91 articles and applying the inclusion and exclusion criteria, four articles were used in this review and pooled analysis. The study patients were identified through electronic health records. The pooled analysis was done using the Metafor package in R, applying a random-effect model to estimate pooled effect size. The findings suggest that using fixed dose combination therapy and switching from a lipophilic statin to a hydrophilic statin, while correcting metabolic abnormalities, or initiating evolocumab alongside statin can address statin intolerance. The overall relative risk (RR) was 0.40 (95% CI, 0.09 to 1.70) with I2 90%, and the overall odds ratio (OR) was 0.11 (95% CI, 0.01 to 1.59) with I2 94%, suggesting that the interventions work well in addressing statin intolerance. Since statin intolerance is has a vast range of effects, further research works may be done on exploring the possibility of using digital health systems to identify and provide targeted interventions to patients.

Maturity Assessment of District Health Information System Version 2 Implementation in Ethiopia: Current Status and Improvement Pathways.

Although Ethiopia has made remarkable progress in the uptake of the District Health Information System version 2 (DHIS2) for national aggregate data reporting, there has been no comprehensive assessment of the maturity level of the system. This study aims to assess the maturity level of DHIS2 implementation in Ethiopia and propose a road map that could guide the progress toward a higher level of maturity. We also aim to assess the current maturity status, implementation gaps, and future directions of DHIS2 implementation in Ethiopia. The assessment focused on digital health system governance, skilled human resources, information and communication technology (ICT) infrastructure, interoperability, and data quality and use. A collaborative assessment was conducted with the engagement of key stakeholders through consultative workshops using the Stages of Continuous Improvement tool to measure maturity levels in 5 core domains, 13 components, and 39 subcomponents. A 5-point scale (1=emerging, 2=repeatable, 3=defined, 4=managed, and 5=optimized) was used to measure the DHIS2 implementation maturity level. The national DHIS2 implementation's maturity level is currently at the defined stage (score=2.81) and planned to move to the manageable stage (score=4.09) by 2025. The domain-wise maturity score indicated that except for ICT infrastructure, which is at the repeatable stage (score=2.14), the remaining 4 domains are at the defined stage (score=3). The development of a standardized and basic DHIS2 process at the national level, the development of a 10-year strategic plan to guide the implementation of digital health systems including DHIS2, and the presence of the required competencies at the facility level to accomplish specific DHIS2-related tasks are the major strength of the Ministry of Health of Ethiopia so far. The lack of workforce competency guidelines to support the implementation of DHIS2; the unavailability of core competencies (knowledge, skills, and abilities) required to accomplish DHIS2 tasks at all levels of the health system; and ICT infrastructures such as communication network and internet connectivity at the district, zonal, and regional levels are the major hindrances to effective DHIS2 implementation in the country. On the basis of the Stages of Continuous Improvement maturity model toolkit, the implementation status of DHIS2 in Ethiopia is at the defined stage, with the ICT infrastructure domain being at the lowest stage as compared to the other 4 domains. By 2025, the maturity status is planned to move from the defined stage to the managed stage by improving the identified gaps. Various action points are suggested to address the identified gaps and reach the stated maturity level. The responsible body, necessary resources, and methods of verification required to reach the specified maturity level are also listed.

Enhanced Medical Image Segmentation using Transfer Learning with Res101_UNet: Experimental Insights and Comparative Performance Analysis

Throughout the past few decades, artificial intelligence and machine learning have seen a lot of active research in areas such as computer vision, natural language processing, and speech processing. As a result, deep learning models became state-of-the-art for computer vision tasks such as object detection, classification, segmentation, and other allied tasks. Of course, the fruits of this research are extended to the design of robust and reliable digital health systems as well as other applications in the healthcare sector. Many clinical applications require the automatic segmentation of medical images. Recent deep learning-based approaches have demonstrated state-of-the-art performance in medical image segmentation tasks. In addition to their ability to automatically extract features and generalize over large amounts of data, transfer learning based deep learning models have proven to be handy for data scared areas like medical domains. In this research, we investigate and demonstrate the efficacy of a DCNN-based transfer learning model -Res101_Unet, which has been trained and/or fine-tuned to execute tumor tissue segmentation tasks in MRI, CT, PET, and X-RAY pictures of medical organ scans with little data. For our experimental study, we employed two image datasets: 'Liver Tumor' and 'Gland Colon Cancer', both obtained from the Kaggle portal. This experimental setup includes an Open-Source segmentation model API. Our findings indicate that domain similarity-based transfer learning can be used to data-scarce sectors. We achieved 98.47% accuracy and a IoU score of 0.9891 on Liver Tumor data and 0.6956 accuracy and a IoU score of 0.7043 on gland colon dataset.

Evaluation of a digital tool for detecting stress and craving in SUD recovery: An observational trial of accuracy and engagement

BackgroundDigital health interventions offer opportunities to expand access to substance use disorder (SUD) treatment, collect objective real-time data, and deliver just-in-time interventions: however implementation has been limited. RAE (Realize, Analyze, Engage) Health is a digital tool which uses continuous physiologic data to detect high risk behavioral states (stress and craving) during SUD recovery. MethodsThis was an observational study to evaluate the digital stress and craving detection during outpatient SUD treatment. Participants were asked to use the RAE Health app, wear a commercial-grade wrist sensor over a 30-day period. They were asked to self-report stress and craving, at which time were offered brief in-app de-escalation tools. Supervised machine learning algorithms were applied retrospectively to wearable sensor data obtained to develop group-based digital biomarkers for stress and craving. Engagement was assessed by number of days of utilization, and number of hours in a given day of connection. ResultsSixty percent of participants (N=30) completed the 30-day protocol. The model detected stress and craving correctly 76 % and 69 % of the time, respectively, but with false positive rates of 33 % and 28 % respectively. All models performed close to previously validated models from a research grade sensor. Participants used the app for a mean of 14.2 days (SD 10.1) and 11.7 h per day (SD 8.2). Anxiety disorders were associated with higher mean hours per day connected, and return to drug use events were associated with lower mean hours per day connected. ConclusionsFuture work should explore the effect of similar digital health systems on treatment outcomes and the optimal dose of digital interventions needed to make a clinically significant impact.

International perspectives on measuring national digital public health system maturity through a multidisciplinary Delphi study

Unlocking the full potential of digital public health (DiPH) systems requires a comprehensive tool to assess their maturity. While the World Health Organization and the International Telecommunication Union released a toolkit in 2012 covering various aspects of digitalizing national healthcare systems, a holistic maturity assessment tool has been lacking ever since. To bridge this gap, we conducted a pioneering Delphi study, to which 54 experts from diverse continents and academic fields actively contributed to at least one of three rounds. 54 experts participated in developing and rating multidisciplinary quality indicators to measure the maturity of national digital public health systems. Participants established consensus on these indicators with a threshold of 70% agreement on indicator importance. Eventually, 96 indicators were identified and agreed upon by experts. Notably, 48% of these indicators were found to align with existing validated tools, highlighting their relevance and reliability. However, further investigation is required to assess the suitability and applicability of all the suggestions put forward by our participants. Nevertheless, this Delphi study is an essential initial stride toward a comprehensive measurement tool for DiPH system maturity. By working towards a standardized assessment of DiPH system maturity, we aim to empower decision-makers to make informed choices, optimize resource allocation, and drive innovation in healthcare delivery. The results of this study mark a significant milestone in advancing DiPH on a global scale.

Information sharing across institutions: Practices and barriers during public health emergencies in Ethiopia

BackgroundRapid, integrated information exchange between stakeholders is critical for effective emergency preparedness and response. However, many low- and middle-income countries face barriers to seamless data sharing. While information accessibility is recognized as important for evidence-based decision-making and resource allocation in Ethiopia, factors influencing current health information sharing practices among stakeholders involved in public health emergency management programs are unclear. This study aims to examine multi-sectoral stakeholders' perspectives and experiences with health data sharing during emergencies in Ethiopia, to identify opportunities and challenges influencing practices to strengthen the national public health emergency response system. MethodsA mixed-methods study was conducted between June and August 2023, involving a survey of 169 stakeholders actively involved in PHEM programs in Ethiopia as well as 23 in-depth interviews with key informants in senior leadership or advisory roles. The data was analyzed using descriptive statistics in SPSS and thematic analysis of qualitative transcripts. ResultsDuring emergencies, it was observed that data sharing between different entities occurred. Quantitative findings showed the predominant types of health data shared between stakeholders during emergencies included hospital data (109, 64.5 %), clinical case information, and laboratory results. Challenges limiting effective coordination included issues like limited functionality of digital health systems (75, 44 %), incompatible data formats (13, 34 %), and financial constraints (83, 49 %) and and socio-cultural barriers constrain current practices in Ethiopia. Qualitative interviews identified five themes around risk communication and inclusive alert systems. Experts emphasized tailored, multichannel outreach but noted infrastructure gaps and digital divides currently limit poorer communities' engagement. ConclusionWhile collaborative health information exchange during emergencies is recognized as important, systemic, financial, and socio-cultural barriers constrain current practices in Ethiopia. Targeted strategies including capacity building, investment in integrated data infrastructure, economic optimization through innovative financing models, trust-based relationship development, and locally relevant communication channels informed by stakeholder perspectives can optimize information accessibility, coordination, quality, and equity of healthcare services during public health emergencies.

Assessment of the Barriers and Enablers of the Use of mHealth Systems in Sub-Saharan Africa According to the Perceptions of Patients, Physicians, and Health Care Executives in Ethiopia: Qualitative Study.

Digital technologies are increasingly being used to deliver health care services and promote public health. Mobile wireless technologies or mobile health (mHealth) technologies are particularly relevant owing to their ease of use, broad reach, and wide acceptance. Unlike developed countries, Sub-Saharan Africa experiences more challenges and obstacles when it comes to deploying, using, and expanding mHealth systems. In addition to barriers, there are enabling factors that could be exploited for the design, implementation, and scaling up of mHealth systems. Sub-Saharan Africa may require tailored solutions that address the specific challenges facing the region. The overall aim of this study was to identify the barriers and enablers for using mHealth systems in Sub-Saharan Africa from the perspectives of patients, physicians, and health care executives. Multi-level and multi-actor in-depth semistructured interviews were employed to qualitatively explore the barriers and enablers of the use of mHealth systems. Data were collected from patients, physicians, and health care executives. The interviews were audio recorded, transcribed verbatim, translated, and coded. Thematic analysis methodology was adopted, and NVivo software was used for the data analysis. Through this rigorous study, a total of 137 determinants were identified. Of these determinants, 68 were identified as barriers and 69 were identified as enablers. Perceived barriers in patients included lack of awareness about mHealth systems and language barriers. Perceived enablers in patients included need for automated tools for health monitoring and an increasing literacy level of the society. According to physicians, barriers included lack of available digital health systems in the local context and concern about patients' mHealth capabilities, while enablers included the perceived usefulness in reducing workload and improving health care service quality, as well as the availability of mobile devices and the internet. As perceived by health care executives, barriers included competing priorities alongside digitalization in the health sector and lack of interoperability and complete digitalization of implemented digital health systems, while enablers included the perceived usefulness of digitalization for the survival of the highly overloaded health care system and the abundance of educated manpower specializing in technology. mHealth systems in Sub-Saharan Africa are hindered and facilitated by various factors. Common barriers and enablers were identified by patients, physicians, and health care executives. To promote uptake, all relevant stakeholders must actively mitigate the barriers. This study identified a promising outlook for mHealth in Sub-Saharan Africa, despite the present barriers. Opportunities exist for successful integration into health care systems, and a user-centered design is crucial for maximum uptake.

Digital Health for Myocardial Infarction: Research Topics and Trends.

We aimed to map the topics and trends of research on digital health for myocardial infarction over the past ten years. This can inform future research directions and newly emerging topics for myocardial infarction care, diagnosis and monitoring. The Web of Science database was searched for papers related to digital health for myocardial infarction. 1,344 retrieved records were used for visualisation through bibliometrics and co-occurrence network analysis of keywords. Our mapping revealed several emerging topics in recent years, including artificial intelligence and deep learning. Higher emphasis on automated and artificially intelligent digital health systems in recent years can inform future clinical practice and research directions for myocardial infarction.

An introduction to digital determinants of health.

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.

Opportunities to Increase Science of Diversity and Inclusion in Clinical Trials: Equity and a Lack of a Control.

The United States witnessed a nearly 4-fold increase in personal health care expenditures between 1980 and 2010. Despite innovations and obvious benefits to health, participants enrolled in clinical trials still do not accurately represent the racial and ethnic composition of patients nationally or globally. This lack of diversity in cohorts limits the generalizability and significance of results among all populations and has deep repercussions for patient equity. To advance diversity in clinical trials, robust evidence for the most effective strategies for recruitment of diverse participants is needed. A major limitation of previous literature on clinical trial diversity is the lack of control or comparator groups for different strategies. To date, interventions have focused primarily on (1) community-based interventions, (2) institutional practices, and (3) digital health systems. This review article outlines prior intervention strategies across these 3 categories and considers health policy and ethical incentives for substantiation before US Food and Drug Administration approval. There are no current studies that comprehensively compare these interventions against one another. The American Heart Association Strategically Focused Research Network on the Science of Diversity in Clinical Trials represents a multicenter, collaborative network between Stanford School of Medicine and Morehouse School of Medicine created to understand the barriers to diversity in clinical trials by contemporaneous head-to-head interventional strategies accessing digital, institutional, and community-based recruitment strategies to produce informed recruitment strategies targeted to improve underrepresented patient representation in clinical trials.

FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database

IntroductionLinking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set.MethodsIn this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s (n=771,588) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB).ResultsWe achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled (n=3,876) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top n candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement.DiscussionIn conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.

Assessing the Utilization and User Satisfaction of Health Information Management and Record Systems in Saudi Health Centers

Health information management and record systems have become increasingly digitized and integrated across healthcare settings globally. However, implementation and adoption of these systems varies between countries and regions. This study aimed to assess the utilization and user satisfaction of existing health information management and record systems within primary healthcare centers in Saudi Arabia. An electronic survey was distributed to physicians, nurses and administrative staff at 10 randomly selected primary health centers in Riyadh, Saudi Arabia. The survey collected data on demographics, frequency and purpose of system use, perceived ease of use, usefulness and overall satisfaction. A total of 145 surveys were completed. Results showed moderate to high levels of utilization across job roles for administrative and clinical tasks. Ease of use, usefulness and overall satisfaction scores were moderately positive. Areas for improvement were also identified. This study provides valuable insight into adoption rates and user perceptions of digital health systems in Saudi primary care that can inform future optimization and implementation efforts.