The Red Cross Red Crescent Health Information System (RCHIS): an electronic medical records and health information management system for the red cross red crescent emergency response units.

BACKGROUND: The Red Cross and Red Crescent Movement (RCRC) utilizes specialized Emergency Response Units (ERUs) for international disaster response. However, data collection and reporting within ERUs have been time-consuming and paper-based. The Red Cross Red Crescent Health Information System (RCHIS) was developed to improve clinical documentation and reporting, ensuring accuracy and ease of use while increasing compliance with reporting standards. CASE PRESENTATION: RCHIS is an Electronic Medical Record (EMR) and Health Information System (HIS) designed for RCRC ERUs. It can be accessed on Android tablets or Windows laptops, both online and offline. The system securely stores data on Microsoft Azure cloud, with synchronization facilitated through a local ERU server. The functional architecture covers all clinical functions of ERU clinics and hospitals, incorporating user-friendly features. A pilot study was conducted with the Portuguese Red Cross (PRC) during a large-scale event. Thirteen super users were trained and subsequently trained the staff. During the four-day pilot, 77 user accounts were created, and 243 patient files were documented. Feedback indicated that RCHIS was easy to use, requiring minimal training time, and had sufficient training for full utilization. Real-time reporting facilitated coordination with the civil defense authority. CONCLUSIONS: The development and pilot use of RCHIS demonstrated its feasibility and efficacy within RCRC ERUs. The system addressed the need for an EMR and HIS solution, enabling comprehensive clinical documentation and supporting administrative reporting functions. The pilot study validated the training of trainers' approach and paved the way for further domestic use of RCHIS. RCHIS has the potential to improve patient safety, quality of care, and reporting efficiency within ERUs. Automated reporting reduces the burden on ERU leadership, while electronic compilation enhances record completeness and correctness. Ongoing feedback collection and feature development continue to enhance RCHIS's functionality. Further trainings took place in 2023 and preparations for international deployments are under way. RCHIS represents a significant step toward improved emergency medical care and coordination within the RCRC and has implications for similar systems in other Emergency Medical Teams.

Tanzania's and Germany's Digital Health Strategies and Their Consistency With the World Health Organization's Global Strategy on Digital Health 2020-2025: Comparative Policy Analysis.

BACKGROUND: In recent years, the fast-paced adoption of digital health (DH) technologies has transformed health care delivery. However, this rapid evolution has also led to challenges such as uncoordinated development and information silos, impeding effective health care integration. Recognizing these challenges, nations have developed digital health strategies (DHSs), aligning with their national health priorities and guidance from global frameworks. The World Health Organization (WHO)'s Global Strategy on Digital Health 2020-2025 (GSDH) guides national DHSs. OBJECTIVE: This study analyzes the DHSs of Tanzania and Germany as case studies and assesses their alignment with the GSDH and identifies strengths, shortcomings, and areas for improvement. METHODS: A comparative policy analysis was conducted, focusing on the DHSs of Tanzania and Germany as case studies, selected for their contrasting health care systems and cooperative history. The analysis involved a three-step process: (1) assessing consistency with the GSDH, (2) comparing similarities and differences, and (3) evaluating the incorporation of emergent technologies. Primary data sources included national eHealth policy documents and related legislation. RESULTS: Both Germany's and Tanzania's DHSs align significantly with the WHO's GSDH, incorporating most of its 35 elements, but each missing 5 distinct elements. Specifically, Tanzania's DHS lacks in areas such as knowledge management and capacity building for leaders, while Germany's strategy falls short in engaging health care service providers and beneficiaries in development phases and promoting health equity. Both countries, however, excel in other aspects like collaboration, knowledge transfer, and advancing national DHSs, reflecting their commitment to enhancing DH infrastructures. The high ratings of both countries on the Global Digital Health Monitor underscore their substantial progress in DH, although challenges persist in adopting the rapidly advancing technologies and in the need for more inclusive and comprehensive strategies. CONCLUSIONS: This study reveals that both Tanzania and Germany have made significant strides in aligning their DHSs with the WHO's GSDH. However, the rapid evolution of technologies like artificial intelligence and machine learning presents challenges in keeping strategies up-to-date. This study recommends the development of more comprehensive, inclusive strategies and regular revisions to align with emerging technologies and needs. The research underscores the importance of context-specific adaptations in DHSs and highlights the need for broader, strategic guidelines to direct the future development of the DH ecosystem. The WHO's GSDH serves as a crucial blueprint for national DHSs. This comparative analysis demonstrates the value and challenges of aligning national strategies with global guidelines. Both Tanzania and Germany offer valuable insights into developing and implementing effective DHSs, highlighting the importance of continuous adaptation and context-specific considerations. Future policy assessments require in-depth knowledge of the country's health care needs and structure, supplemented by stakeholder input for a comprehensive evaluation.

Methodological challenges in systematic reviews of mHealth interventions: Survey and consensus-based recommendations.

OBJECTIVE: Mobile Health (mHealth) refers to using mobile devices to support health. This study aimed to identify specific methodological challenges in systematic reviews (SRs) of mHealth interventions and to develop guidance for addressing selected challenges. STUDY DESIGN AND SETTING: Two-phase participatory research project. First, we sent an online survey to corresponding authors of SRs of mHealth interventions. On a five-category scale, survey respondents rated how challenging they found 24 methodological aspects in SRs of mHealth interventions compared to non-mHealth intervention SRs. Second, a subset of survey respondents participated in an online workshop to discuss recommendations to address the most challenging methodological aspects identified in the survey. Finally, consensus-based recommendations were developed based on the workshop discussion and subsequent interaction via email with the workshop participants and two external mHealth SR authors. RESULTS: We contacted 953 corresponding authors of mHealth intervention SRs, of whom 50 (5 %) completed the survey. All the respondents identified at least one methodological aspect as more or much more challenging in mHealth intervention SRs than in non-mHealth SRs. A median of 11 (IQR 7.25-15) out of 24 aspects (46 %) were rated as more or much more challenging. Those most frequently reported were: defining intervention intensity and components (85 %), extracting mHealth intervention details (71 %), dealing with dynamic research with evolving interventions (70 %), assessing intervention integrity (69 %), defining the intervention (66 %) and maintaining an updated review (65 %). Eleven survey respondents participated in the workshop (five had authored more than three mHealth SRs). Eighteen consensus-based recommendations were developed to address issues related to mHealth intervention integrity and to keep mHealth SRs up to date. CONCLUSION: mHealth SRs present specific methodological challenges compared to non-mHealth interventions, particularly related to intervention integrity and keeping SRs current. Our recommendations for addressing these challenges can improve mHealth SRs.

Mobile Apps for COVID-19: A Systematic Review of Reviews.

BACKGROUND: One measure national governments took to react to the acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic was mobile applications (apps). This study aims to provide a high-level overview of published reviews of mobile apps used in association with coronavirus disease 19 (COVID-19), examine factors that contributed to the success of these apps, and provide data for further research into this topic. METHODS: We conducted a systematic review of reviews (also referred to as an umbrella review) and searched two databases, Medline and Embase, for peer-reviewed reviews of COVID-19 mobile apps that were written in English and published between January 1st 2020 and April 25th 2022. RESULTS: Out of the initial 17,611 studies, 24 studies were eligible for the analysis. Publication dates ranged from May 2020 to January 2022. In total, 54% (n = 13) of the studies were published in 2021, and 33% (n = 8) were published in 2020. Most reviews included in our review of reviews analyzed apps from the USA, the UK, and India. Apps from most of the African and Middle and South American countries were not analyzed in the reviews included in our study. Categorization resulted in four clusters (app overview, privacy and security, MARS rating, and miscellaneous). CONCLUSIONS: Our study provides a high-level overview of 24 reviews of apps for COVID-19, identifies factors that contributed to the success of these apps, and identifies a gap in the current literature. The study provides data for further analyses and further research.

Brain Tumor Classification and Segmentation Using Dual-Outputs for U-Net Architecture: O2U-Net.

We propose a modified version of the U-Net architecture for segmenting and classifying brain tumors, introducing another output between down- and up-sampling. Our proposed architecture utilizes two outputs, adding a classification output beside the segmentation output. The central idea is to use fully connected layers to classify each image before applying U-Net's up-sampling operations. This is achieved by utilizing the features extracted during the down-sampling procedure and combining them with fully connected layers for classification. Afterward, the segmented image is generated by U-Net's up-sampling process. Initial tests show competitive results against comparable models with 80.83%, 99.34%, and 77.39% for the dice coefficient, accuracy, and sensitivity, respectively. The tests were conducted on the well-established dataset from Nanfang Hospital, Guangzhou, China, and General Hospital, Tianjin Medical University, China, from 2005 to 2010 containing MRI images of 3064 brain tumors.

Machine Learning Approaches for Detecting Coronary Artery Disease Using Angiography Imaging: A Scoping Review.

This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies that met the inclusion criteria. They employed different types of angiography imaging including computed tomography and invasive coronary angiography. Several studies have used deep learning algorithms for image classification and segmentation, and our findings show that various machine learning algorithms, such as convolutional neural networks, different types of U-Net, and hybrid approaches. Studies also varied in the outcomes measured, identifying stenosis, and assessing the severity of CAD. ML approaches can improve the accuracy and efficiency of CAD detection by using angiography. The performance of the algorithms differed depending on the dataset used, algorithm employed, and features selected for analysis. Therefore, there is a need to develop ML tools that can be easily integrated into clinical practice to aid in the diagnosis and management of CAD.

Usability Assessment of an Electronic Medical Record-Embedded Clinical Decision Support System for Arterial Blood Gas Interpretation.

Since usability is considered a significant success factor for Clinical decision support systems (CDSSs), this study seeks to assess the usability of an electronic medical records-embedded CDSS for arterial blood gas (ABG) interpretation and ordering. The current study was conducted in the general ICU of a teaching hospital, using the System Usability Scale (SUS) and interviews with all anesthesiology residents and intensive care fellows in two rounds of CDSS usability testing. The feedback from the participants was discussed with the research team across a series of meetings, and the second version of CDSS was designed and tailored to participants' feedbacks. Subsequently, the CDSS usability score increased from 67.22±4.58 to 80.00±4.84 (P-value<0.001) through participatory, iterative design and the users' usability testing feedbacks.

Examining standardized tools used for the evaluation of mobile health applications for cardiovascular disease.

Cardiovascular disease is one of the leading causes of death worldwide. Scarce resources and rising costs are pushing healthcare systems to their limits. There is an urgency to develop, optimize and evaluate technologies that provide more effective care for patients. Modern technologies, such as mobile health (mHealth) applications, can provide relief as a key strategy. To integrate digital interventions into care structures, a detailed impact assessment of all professional mHealth applications is needed. The aim of this study is to analyze the standardized tools used in the field of cardiovascular disease. The results show that questionnaires, usage logs, and key indicators are predominantly used. Although the identified mHealth interventions are specific to cardiovascular disease and thus require particular questions to evaluate apps, the user readiness, usability, or quality of life criteria are non-specific. Therefore, the results contribute to understanding how different mHealth interventions can be assessed, categorized, evaluated, and accepted.

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale.

Objective: The purpose of this study is to provide an overview of apps to help control the spread of Covid-19 in Germany and rate them according to standardized instruments. Materials and methods: The Apple App Store and Google Play Store were systematically searched to identify apps dealing with Covid-19 in Germany. The German Mobile App Rating Scale (MARS-G) was used to independently assess app quality by 2 trained reviewers. Results: Overall, the quality of the 6 rated apps was good with a mean score of 4.15 (3.88-4.34). The best-rated apps were NINA app (4.34) and Corona Health App (4.29). The best-rated sections were functionality (4.40), aesthetic (4.25), and information (4.25). In contrast, the worst-rated section was engagement (3.63). Even though some of the apps were used by more people than others, there was no correlation between the MARS-G rating and app store rating. In addition, the MARS-G proved to be effective even with rating apps, which have different goals and methods to achieve them. Conclusions: To our knowledge, this is the first study that identified and evaluated German Covid-19 mobile health apps available in the German app stores. The review shows that despite the excellent quality in aspects like information and functionality, there is still a gap in the engagement section. To motivate more people to use the Covid-19 apps, new ideas are needed, besides more information and education about the functionality of the apps, to gain trust in app developers and raise the number of downloads.

Enhancing mHealth data collection applications with sensing capabilities.

Smart mobile devices such as smartphones or tablets have become an important factor for collecting data in complex health scenarios (e.g., psychological studies, medical trials), and are more and more replacing traditional pen-and-paper instruments. However, simply digitizing such instruments does not yet realize the full potential of mobile devices: most modern smartphones have a variety of different sensor technologies (e.g., microphone, GPS data, camera, ...) that can also provide valuable data and potentially valuable insights for the medical purpose or the researcher. In this context, a significant development effort is required to integrate sensing capabilities into (existing) data collection applications. Developers may have to deal with platform-specific peculiarities (e.g., Android vs. iOS) or proprietary sensor data formats, resulting in unnecessary development effort to support researchers with such digital solutions. Therefore, a cross-platform mobile data collection framework has been developed to extend existing data collection applications with sensor capabilities and address the aforementioned challenges in the process. This framework will enable researchers to collect additional information from participants and environment, increasing the amount of data collected and drawing new insights from existing data.

Evaluating the Quality of a Clinical Mobile App for Physicians' CT Scan Ordering Using the MARS Rating Scale.

Given the lack of literature on the contributing factors to adopt mobile applications (apps) among physicians and the crucial role of the quality of the apps in their widespread use, the aim of this study is using the Mobile App Rating Scale (MARS) to evaluate the quality of the head CT scan appropriateness criteria app(HAC app). It was developed to assist medical interns and residents in ordering head CT scans. MARS is internationally recognized as an app rating tool and consists of four objective and subjective quality subscales quality subscale. Although the overall quality score of the HAC app was favorable (82 out of 100), it had low quality scores in the "information" (73.37 out of 100) and the "engagement" (73.48 out of 100) subscales. The HAC app appears to be functional to the physicians; however, it needs to improve its quality in terms of interactivity and effectiveness.

Chances and Risks of Using Robotic Assistance Systems in Early Neurological Rehabilitation: A Qualitative Analysis.

Robotic assistance systems offer new therapeutic perspectives for patient mobilization. This work aims to investigate the chances and risks of robotic assistance systems in early neurological rehabilitation. Nine professionals working in physiotherapy and nursing were interviewed on their opinion on robotic assistance systems. The experts were recruited in three different clinics, one of which has already established robot-assisted rehabilitation. 171 individual codes were extracted from the interviews. Based on the professionals' statements and the literature, the most significant added value of robotic assistance systems is seen in the expected relief of employees. The study results and the literature confirm the potential of robotic systems for early neurological rehabilitation.

Key Elements for the Evaluation of mHealth Applications: Results from a Delphi Survey.

Mobile Health (mHealth) applications have seen strong growth in recent years, but they are often not systematically evaluated. A Delphi survey was conducted to identify key elements for the evaluation of mHealth applications. Sixteen experts participated in the study, and the study yielded a list of 79 key elements with expert consensus. Thirty-two elements were in the category of structure quality, 29 in process quality, and 18 in outcome quality. The number of key elements highlights the complexity of conducting systematic evaluations of mHealth applications.

Development and Testing Requirements for an Integrated Maternal and Child Health Information System in Iran: A Design Thinking Case Study.

BACKGROUND: Management of child health care can be negatively affected by incomplete recording, low data quality, and lack of data integration of health management information systems to support decision making and public health program needs. Given the importance of identifying key determinants of child health via capturing and integrating accurate and high-quality information, we aim to address this gap through the development and testing requirements for an integrated child health information system. SUBJECTS AND METHODS: A five-phase design thinking approach including empathizing, defining, ideation, prototyping, and testing was applied. We employed observations and interviews with the health workers at the primary health care network to identify end-users' challenges and needs using tools in human-centered design and focus group discussion. Then, a potential solution to the identified problems was developed as an integrated maternal and child health information system (IMCHIS) prototype and tested using Software Quality Requirements and Evaluation Model (SQuaRE) ISO/IEC 25000. RESULTS: IMCHIS was developed as a web-based system with 74 data elements and seven maternal and child health care requirements. The requirements of "child disease" with weight (0.26), "child nutrition" with weight (0.20), and "prenatal care" with weight (0.16) acquired the maximum weight coefficient. In the testing phase, the highest score with the weight coefficient of 0.48 and 0.73 was attributed to efficiency and functionality characteristics, focusing on software capability to fulfill the tasks that meet users' needs. CONCLUSION: Implementing a successful child health care system integrates both maternal and child health care information systems to track the effect of maternal conditions on child health and support managing performance and optimizing service delivery. The highest quality score of IMCHIS in efficiency and functionality characteristics confirms that it owns the capability to identify key determinants of child health.

Development of clinical-guideline-based mobile application and its effect on head CT scan utilization in neurology and neurosurgery departments.

BACKGROUND: There is little evidence regarding the adoption and intention of using mobile apps by health care professionals (HCP) and the effectiveness of using mobile apps among physicians is still unclear. To address this challenge, the current study seeks two objectives: developing and implementing a head CT scan appropriateness criteria mobile app (HAC app), and investigating the effect of HAC app on CT scan order. METHODS: A one arm intervention quasi experimental study with before/after analysis was conducted in neurology & neurosurgery (N&N) departments at the academic hospital. We recruited all residents' encounters to N&N departments with head CT scan to examine the effect of HAC app on residents' CT scan utilization. The main outcome measure was CT scan order per patient for seven months at three points, before the intervention, during the intervention, after cessation of the intervention -post-intervention follow-up. Data for CT scan utilization were collected by reviewing medical records and then analyzed using descriptive statistics, Kruskal-Wallis, and Mann-Whitney tests. A focus group discussion with residents was performed to review and digest residents' experiences during interaction with the HAC app. RESULTS: Sixteen residents participated in this study; a total of 415 N&N encounters with CT scan order, pre-intervention 127 (30.6%), intervention phase 187 (45.1%), and 101 (24.3%) in the post-intervention follow-up phase were included in this study. Although total CT scan utilization was statistically significant during three-time points of the study (P = 0.027), no significant differences were found for CT utilization after cessation of the intervention (P = 1). CONCLUSION: The effect of mobile devices on residents' CT scan ordering behavior remains open to debate since the changes were not long-lasting. Further studies based on real interactive experiences with mobile devices is advisable before it can be recommended for widespread use by HCP.

The Effectiveness of Assistive Technologies for Older Adults and the Influence of Frailty: Systematic Literature Review of Randomized Controlled Trials.

BACKGROUND: The use of assistive technologies (ATs) to support older people has been fueled by the demographic change and technological progress in many countries. These devices are designed to assist seniors, enable independent living at home or in residential facilities, and improve quality of life by addressing age-related difficulties. OBJECTIVE: We aimed to evaluate the effectiveness of ATs on relevant outcomes with a focus on frail older adults. METHODS: A systematic literature review of randomized controlled trials evaluating ATs was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Ovid Medline, PsycINFO, SocIndex, CINAHL (Cumulative Index to Nursing and Allied Health Literature), CENTRAL (Cochrane Central Register of Controlled Trials), and IEEEXplore databases were searched from January 1, 2009, to March 15, 2019. ATs were included when aiming to support the domains autonomy, communication, or safety of older people with a mean age ≥65 years. Trials performed within a laboratory setting were excluded. Studies were retrospectively categorized according to the physical frailty status of participants. RESULTS: A total of 19 trials with a high level of heterogeneity were included in the analysis. Six device categories were identified: mobility, personal disease management, medication, mental support, hearing, and vision. Eight trials showed significant effectiveness in all or some of the primary outcome measures. Personal disease management devices seem to be the most effective, with four out of five studies showing significant improvement of disease-related outcomes. Frailty could only be assessed for seven trials. Studies including participants with significant or severe impairment showed no effectiveness. CONCLUSIONS: Different ATs show some promising results in well-functioning but not in frail older adults, suggesting that the evaluated ATs might not (yet) be suitable for this subgroup. The uncertainty of the effectiveness of ATs and the lack of high-quality research for many promising supportive devices were confirmed in this systematic review. Large studies, also including frail older adults, and clear standards are needed in the future to guide professionals, older users, and their relatives. TRIAL REGISTRATION: PROSPERO CRD42019130249; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=130249.

Findings from a Panel Discussion on Evaluation Methods in Medical Informatics.

Healthcare systems are challenged by increasing costs. Digital technology can help to combat this trend. Evaluation of these technologies is uncommon or incomplete. Scholars have called for a standardized and holistic evaluation. We provide a synthesis of an online panel on medical informatics (MI) and stipulate a discussion on new guidelines for medical informatics project evaluations. The panel consisted of presentations and a discussion. The presentations gave the participants an overview of evaluation methods currently used in different medical informatics domains and their shortcomings. The presenters highlighted new evaluation methods such as a roadmap for economic analysis of eHealth projects and the German Digital Healthcare Act methods. Participants discussed the shortcomings of RCTs and methods that need to be included in eHealth evaluation and called for new evaluation methods. The discussion showed weaknesses of the currently used methods and underlined the need for a new, holistic evaluation standard for MI.

The Effect of Short Messaging System-Based Feedback on Physicians' Head CT Scan Ordering Behavior in Neurology and Neurosurgery Departments.

Short messaging system (SMS) works as one of the most popular strategies for physicians' behavior change via sending feedback and reminder messages. One of the areas in which SMS feedback can be effective on physicians' behavior is CT scan ordering. This study investigates the effect of mobile phone SMS feedback on residents' head CT scan ordering at a general teaching hospital in Iran. Through a three-month before-after experimental study, an intervention was conducted, and the CT scans ordered by an individual resident were evaluated every two weeks. Consequently, personal SMS-based feedback was provided to the residents, and the rate of CT per patient in the two phases of the study was analyzed. The mean CT scan ordered per patient decreased from 1.98 ± 1.09 to 1.74 ± 1.45, and this decrease was insignificant (P = 0.106). SMS-based feedback can reduce head CT scan ordering among residents; whereas this decline was not significant further studies are required to investigate its effectiveness.

Effects of Computer-Aided Decision Support Systems on Appropriate Antibiotic Prescribing by Medical Interns: A Quasi-Experimental Study.

Literature suggests that the adoption of guidelines for antibiotic prescribing has a significant impact on improving prescription practices of physicians; thus, this study aimed to assess the effectiveness of computer-aided decision support systems (CA-DSS) on antibiotic prescribing among medical interns. A prospective before-and-after interventional study was conducted on 40 medical interns. The interns were asked to use the CA-DSS during a one-month internship course at the infectious disease department. The main outcome measure was the knowledge of medical interns regarding the type, name, volume, usual dosages, and administration route of antibiotics prescribed. Paired t-test was applied to assess the change of medical interns' knowledge before and after the study. There was a statistically significant difference between the mean score of interns' medical knowledge before 5.4±2 and after 9.1±2.8 using the CA-DSS (p = 0.000). CA-DSS as an IT-based training intervention was effective for the knowledge of medical interns to prescribe the right antibiotics for acute respiratory infections.

Methods Used to Evaluate mHealth Applications for Cardiovascular Disease: A Quasi-Systematic Scoping Review.

In the face of demographic change and constantly increasing health care costs, health care system decision-makers face ever greater challenges. Mobile health applications (mHealth apps) have the potential to combat this trend. However, in order to integrate mHealth apps into care structures, an evaluation of such apps is needed. In this paper, we focus on the criteria and methods of evaluating mHealth apps for cardiovascular disease and the implications for developing a widely applicable evaluation framework for mHealth interventions. Our aim is to derive substantiated patterns and starting points for future research by conducting a quasi-systematic scoping review of relevant peer-reviewed literature published in English or German between 2000 and 2021. We screened 4066 articles and identified n = 38 studies that met our inclusion criteria. The results of the data derived from these studies show that usability, motivation, and user experience were evaluated primarily using standardized questionnaires. Usage protocols and clinical outcomes were assessed primarily via laboratory diagnostics and quality-of-life questionnaires, and cost effectiveness was tested primarily based on economic measures. Based on these findings, we propose important considerations and elements for the development of a common evaluation framework for professional mHealth apps, including study designs, data collection tools, and perspectives.