Radiological assessments on zones to take protective actions in case of a nuclear or radiological emergency involve a series of real-time forecasts of radiological impact on the public at various distances from the release point, using actual weather or forecast data, information on the source term or facility status, and primary radiation monitoring data. This practice is implemented during the operation of emergency centers around the world in order to promptly report the occurrence and possible consequences of radiological accidents in the country and abroad in the event of a possible transboundary impact. Since the Chornobyl disaster, a lot of emergency exercises, research programs and projects, in particular, benchmarking, have served as international platforms for improving modeling capacity in atmospheric dispersion. This activity is carried out both on the basis of past severe accidents with significant atmospheric releases and corresponding radiological consequences, and on the basis of specific conditional (hypothetical) events that are developed in accordance with the purpose of the study. The paper is focused on the comparison results performed under the international project \"Benchmarking on Assessment of Radiological COnsequences\" (BARCO) conducted in 2020-2021 between five technical support organisations - members of the European Technical Safety Organisations Network (ETSON). The work contains a short overview of relevant international activity conducted in the past, a description of the BARCO project and its objectives, a list of participants, project tasks, initial data (source term, meteorology, list of benchmarking quantities, approach to data exchange, codes used). The study presents some of comparative analysis results obtained via two techniques such as code-to-code analysis (CTCA) and matched-pair analysis (MPA). The results discussion concentrates on the overall recommendations for code users. Conclusions provide the main outputs of the project.

UNASSIGNED: Electronic health records (EHRs) are live, digital patient records that provide a thorough overview of a person\'s complete health data. Electronic health records (EHRs) provide better healthcare decisions and evidence-based patient treatment and track patients\' clinical development. The EHR offers a new range of opportunities for analyzing and contrasting exam findings and other data, creating a proper information management mechanism to boost effectiveness, quick resolutions, and identifications.
UNASSIGNED: The aim of this studywas to implement an interoperable EHR system to improve the quality of care through the decision support system for the identification of lung cancer in its early stages.
UNASSIGNED: The main objective of the proposed system was to develop an Android application for maintaining an EHR system and decision support system using deep learning for the early detection of diseases. The second objective was to study the early stages of lung disease to predict/detect it using a decision support system.
UNASSIGNED: To extract the EHR data of patients, an android application was developed. The android application helped in accumulating the data of each patient. The accumulated data were used to create a decision support system for the early prediction of lung cancer. To train, test, and validate the prediction of lung cancer, a few samples from the ready dataset and a few data from patients were collected. The valid data collection from patients included an age range of 40 to 70, and both male and female patients. In the process of experimentation, a total of 316 images were considered. The testing was done by considering the data set into 80:20 partitions. For the evaluation purpose, a manual classification was done for 3 different diseases, such as large cell carcinoma, adenocarcinoma, and squamous cell carcinoma diseases in lung cancer detection.
UNASSIGNED: The first model was tested for interoperability constraints of EHR with data collection and updations. When it comes to the disease detection system, lung cancer was predicted for large cell carcinoma, adenocarcinoma, and squamous cell carcinoma type by considering 80:20 training and testing ratios. Among the considered 336 images, the prediction of large cell carcinoma was less compared to adenocarcinoma and squamous cell carcinoma. The analysis also showed that large cell carcinoma occurred majorly in males due to smoking and was found as breast cancer in females.
UNASSIGNED: As the challenges are increasing daily in healthcare industries, a secure, interoperable EHR could help patients and doctors access patient data efficiently and effectively using an Android application. Therefore, a decision support system using a deep learning model was attempted and successfully used for disease detection. Early disease detection for lung cancer was evaluated, and the model achieved an accuracy of 93%. In future work, the integration of EHR data can be performed to detect various diseases early.

BACKGROUND: Type-2 Diabetes Mellitus (T2D) is a growing concern worldwide, and family doctors are called to help diabetic patients manage this chronic disease, also with Medical Nutrition Therapy (MNT). However, MNT for Diabetes is usually standardized, while it would be much more effective if tailored to the patient. There is a gap in patient-tailored MNT which, if addressed, could support family doctors in delivering effective recommendations. In this context, decision support systems (DSSs) are valuable tools for physicians to support MNT for T2D patients - as long as DSSs are transparent to humans in their decision-making process. Indeed, the lack of transparency in data-driven DSS might hinder their adoption in clinical practice, thus leaving family physicians to adopt general nutrition guidelines provided by the national healthcare systems.
METHODS: This work presents a prototypical ontology-based clinical Decision Support System (OnT2D- DSS) aimed at assisting general practice doctors in managing T2D patients, specifically in creating a tailored dietary plan, leveraging clinical expert knowledge. OnT2D-DSS exploits clinical expert knowledge formalized as a domain ontology to identify a patient\'s phenotype and potential comorbidities, providing personalized MNT recommendations for macro- and micro-nutrient intake. The system can be accessed via a prototypical interface.
RESULTS: Two preliminary experiments are conducted to assess both the quality and correctness of the inferences provided by the system and the usability and acceptance of the OnT2D-DSS (conducted with nutrition experts and family doctors, respectively).
CONCLUSIONS: Overall, the system is deemed accurate by the nutrition experts and valuable by the family doctors, with minor suggestions for future improvements collected during the experiments.

BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.
OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.
METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.
RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.
CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

UNASSIGNED: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered \"black boxes,\" and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms.
UNASSIGNED: The aim of this study is to explore the effect of user-interface design features on intensivists\' trust in an ML-based clinical decision support system.
UNASSIGNED: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants\' trust in the system was assessed by their agreement with the system\'s prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects.
UNASSIGNED: Participants\' agreement with the system\'s prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05).
UNASSIGNED: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists\' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.

UNASSIGNED: With a decreasing workforce of carers and a transition from care homes to home care, people with dementia (PwD) increasingly rely on informal caregivers (ICs) and assistive technologies (ATs). There is growing evidence that ATs in the home environment can reduce workload for formal carers (FCs) and ICs, reduce care costs, and can have a positive influence on quality of life (QoL) for PwD and their caregivers. In practice, using multiple ATs still often implies using different separate point solutions and applications. However, the integral, combined use of the data generated using various applications can potentially enhance the insight into the health and wellbeing status of PwD and can provide decision support for carers. The purpose of the current study was to evaluate the use of a DSS that integrated multiple ATs into one dashboard through a small-scale field study.
UNASSIGNED: The current study presents the formative evaluation of a Decision Support System (DSS) connected to multiple ATs. This DSS has been developed by means of co-creation during an international project. The DSS provides an insight into the physical and cognitive status of a PwD, as well as an insight into sleep activity and general wellbeing. Semi-structured interview sessions were held in three countries (Netherlands, Italy, and Taiwan) with 41 participants to gain insight into the experiences of formal and informal carers and PwD with both the ATs and the DSS Alpha prototype dashboard.
UNASSIGNED: The results showed that participants using the DSS were satisfied and perceived added value and a fit with certain care demands from the PwD. In general, ICs and FCs have limited insight into the status of PwD living independently at home, and in these moments, the DSS dashboard and AT bundle can provide valuable insights. Participants experienced the DSS dashboard as well-organized and easy to navigate. The accuracy of the data displayed in the dashboard is important, the context, and (perceived) privacy issues should be tackled according to all users. Furthermore, based in the insight gained during the evaluation a set of design improvements was composed which can be used to further improve the DSS for the Beta evaluation.
UNASSIGNED: The current paper evaluates a possible solution for excess AT usage and how the use of a DSS which integrated multiple AT into one single technology could support caregivers in providing care for PwD. The formative evaluation scrutinized the integration of the developed DSS and the composed bundle of ATs across diverse cultural contexts. Insights from multi-center observations shed light on user experiences, encompassing overall usability, navigational efficacy, and attitudes toward the system. FCs and ICs expressed positivity toward the DSS dashboard\'s design and functionalities, highlighting its utility in remote monitoring, tracking changes in the person\'s abilities, and managing urgent situations. There is a need for personalized solutions and the findings contribute to a nuanced understanding of DSS and AT integration, providing insights for future developments and research in the field of DSS for the care of PwD.

Medical support in crisis situations is a major challenge. Efficient implementation of the medical evacuation process especially in operations with limited human resources that may occur during armed conflicts can limit the loss of these resources. Proper evacuation of wounded soldiers from the battlefield can increase the chances of their survival and rapid return to further military operations. This paper presents the technical details of the decision support system for medical evacuation to support this process. The basis for the functioning of this system is the continuous measurement of vital signs of soldiers via a specialized measurement module with a set of medical sensors. Vital signs values are then transmitted via the communication module to the analysis and inference module, which automatically determines the color of medical triage and the soldier\'s chance of survival. This paper presents the results of tests of our system to validate it, which were carried out using test vectors of soldiers\' vital signs, as well as the results of the system\'s performance on a group of volunteers who performed typical activities of tactical operations. The results of this study showed the usefulness of the developed system for supporting military medical services in military operations.

BACKGROUND: Childhood tumors in the central nervous system (CNS) have longer diagnostic delays than other pediatric tumors. Vague presenting symptoms pose a challenge in the diagnostic process; it has been indicated that patients and parents may be hesitant to seek help, and health care professionals (HCPs) may lack awareness and knowledge about clinical presentation. To raise awareness among HCPs, the Danish CNS tumor awareness initiative hjernetegn.dk was launched.
OBJECTIVE: This study aims to present the learnings from designing and implementing a decision support tool for HCPs to reduce diagnostic delay in childhood CNS tumors. The aims also include decisions regarding strategies for dissemination and use of social media, and an evaluation of the digital impact 6 months after launch.
METHODS: The phases of developing and implementing the tool include participatory co-creation workshops, designing the website and digital platforms, and implementing a press and media strategy. The digital impact of hjernetegn.dk was evaluated through website analytics and social media engagement.
UNASSIGNED: hjernetegn.dk was launched in August 2023. The results after 6 months exceeded key performance indicators. The analysis showed a high number of website visitors and engagement, with a plateau reached 3 months after the initial launch. The LinkedIn campaign and Google Search strategy also generated a high number of impressions and clicks.
CONCLUSIONS: The findings suggest that the initiative has been successfully integrated, raising awareness and providing a valuable tool for HCPs in diagnosing childhood CNS tumors. The study highlights the importance of interdisciplinary collaboration, co-creation, and ongoing community management, as well as broad dissemination strategies when introducing a digital support tool.

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BACKGROUND: The diagnosis of inflammatory rheumatic diseases (IRDs) is often delayed due to unspecific symptoms and a shortage of rheumatologists. Digital diagnostic decision support systems (DDSSs) have the potential to expedite diagnosis and help patients navigate the health care system more efficiently.
OBJECTIVE: The aim of this study was to assess the diagnostic accuracy of a mobile artificial intelligence (AI)-based symptom checker (Ada) and a web-based self-referral tool (Rheport) regarding IRDs.
METHODS: A prospective, multicenter, open-label, crossover randomized controlled trial was conducted with patients newly presenting to 3 rheumatology centers. Participants were randomly assigned to complete a symptom assessment using either Ada or Rheport. The primary outcome was the correct identification of IRDs by the DDSSs, defined as the presence of any IRD in the list of suggested diagnoses by Ada or achieving a prespecified threshold score with Rheport. The gold standard was the diagnosis made by rheumatologists.
RESULTS: A total of 600 patients were included, among whom 214 (35.7%) were diagnosed with an IRD. Most frequent IRD was rheumatoid arthritis with 69 (11.5%) patients. Rheport\'s disease suggestion and Ada\'s top 1 (D1) and top 5 (D5) disease suggestions demonstrated overall diagnostic accuracies of 52%, 63%, and 58%, respectively, for IRDs. Rheport showed a sensitivity of 62% and a specificity of 47% for IRDs. Ada\'s D1 and D5 disease suggestions showed a sensitivity of 52% and 66%, respectively, and a specificity of 68% and 54%, respectively, concerning IRDs. Ada\'s diagnostic accuracy regarding individual diagnoses was heterogenous, and Ada performed considerably better in identifying rheumatoid arthritis in comparison to other diagnoses (D1: 42%; D5: 64%). The Cohen κ statistic of Rheport for agreement on any rheumatic disease diagnosis with Ada D1 was 0.15 (95% CI 0.08-0.18) and with Ada D5 was 0.08 (95% CI 0.00-0.16), indicating poor agreement for the presence of any rheumatic disease between the 2 DDSSs.
CONCLUSIONS: To our knowledge, this is the largest comparative DDSS trial with actual use of DDSSs by patients. The diagnostic accuracies of both DDSSs for IRDs were not promising in this high-prevalence patient population. DDSSs may lead to a misuse of scarce health care resources. Our results underscore the need for stringent regulation and drastic improvements to ensure the safety and efficacy of DDSSs.
BACKGROUND: German Register of Clinical Trials DRKS00017642; https://drks.de/search/en/trial/DRKS00017642.