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Evaluation criteria for health information systems (HIS) and health information technologies (HIT) is broad, diverse and lacks a gold standard approach that could be leveraged, to evaluate clinical systems at various stages of their system development life cycle (SDLC). Without generalizable tools such as frameworks or models, comparative analysis across HIS and HIT is not possible. This paper presents the findings from a scoping review, utilizing the Arksey and O'Malley methodology [1]. The objective of this review is two-fold 1) to classify models and frameworks published between the years 2010-2020 according to their level of evaluative focus (e.g. micro, meso, macro, multi), 2) to identify the countries where these models and frameworks have been employed for the purpose of evaluation, using the International Medical Informatics Association (IMIA) Represented Regions [3]. The results demonstrated the heterogeneity of evaluation models and frameworks currently used in health informatics and reflected the necessity for more adaptive approaches to HIS and HIT evaluation.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.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.Artificial intelligence (AI) techniques can contribute to the early diagnosis of prostate cancer. Recently, there has been a sharp increase in the literature on AI techniques for prostate cancer diagnosis. This review article presents a summary of the AI methods that detect and diagnose prostate cancer using different medical imaging modalities. Following the PRISMA-ScR principle, this review covers 69 studies selected from 1441 searched papers published in the last three years. The application of AI methods reported in these articles can be divided into three broad categories diagnosis, grading, and segmentation of tissues that have prostate cancer. Most of the AI methods leveraged convolutional neural networks (CNNs) due to their ability to extract complex features. Some studies also reported traditional machine learning methods, such as support vector machines (SVM), decision trees for classification, LASSO, and Ridge regression methods for features extraction. We believe that the implementation of AI-based tools will support clinicians to provide better diagnosis plans for prostate cancer.Echocardiography (echo) is a non-invasive, safe, widely available imaging modality that is frequently used to assess the heart structure and function. Accurate heart chamber segmentation is an essential step to quantify certain parameters, including heart chamber volumes. In clinical practice, this task is manually done by echo experts, where it consumes considerable time and is subjective to both errors as well as intra-operator variability. Artificial intelligence (AI) models have been used to automatically segment heart chambers. We conducted a scoping review to provide an overview of the AI models used for this task. Three bibliographic databases; PubMed, Embase, and Google Scholar were explored. Out of 640 initially retrieved studies, 36 studies were included. Multiple AI models used for echo images segmentation were identified, which can be broadly categorized into five methods low-level image processing, deformable-based, statistical techniques, machine learning (ML), and deep learning-based (DL) techniques. The initial three categories were relatively simple and required less computational complexity compared to the ML and DL models. The convolutional neural network was the most widely used DL-based technique in most-recent publications. Generalizability of the models is a major concern that needs to be addressed in the future. Well-annotated larger 2D echo image datasets would be required to mitigate the challenges to some extent.The Normandy health data warehouse EDSaN integrates the medication orders from the University Hospital of Rouen (France). This study aims at describing the design and the evaluation of an information retrieval system founded on a complex and semantically augmented knowledge graph dedicated to EDSaN drugs' prescriptions. The system is intended to help the selection of drugs in the search process by health professionals. The manual evaluation of the relevance of the returned drugs showed encouraging results as expected. A deeper analysis in order to improve the ranking method is needed and will be performed in a future work.Electronic personal health records (ePHR) are web-based tools that enable patients to access their personal health data. Since the data in PHR are systematized, they can be used in scientific research with the patient's consent. Despite the potential benefits of using ePHR, their adoption in Croatia remains low. Multiple factors are influencing the use of PHR and willingness to share personal health data (PHD). The purpose of this paper was to determine familiarity with the e-health system among Croatian citizens, trustfulness in the system itself, and willingness to share PHD with physicians or researchers. Results showed that 34% of respondents use ePHR, and have less confidence in the electronic system than doctors' confidentiality. However, health professionals have lover trust in doctors' confidentiality compared to non-health workers. Respondents rated mental health data and STD data as most sensitive but are overall willing to share that data with doctors and researchers.eÎŮealth literacy is a necessary skill to find and make good use of online health information. However, the general public lacks this skill; it is essential for future health professionals to be able to guide and facilitate the public. The study aimed to examine the perceived eHealth literacy level of health sciences students in Greece. A cross-sectional, online survey was conducted (N=113 students). The questionnaire included socio-demographic data and the eHealth Literacy Scale (eHEALS). Participants' mean eHEALS score was 31.9 with medicine and dentistry students having the highest score (33.7) and other health and caring sciences students the lowest (29.8). There was no statistically significant difference at eHEALS score among participants of different academic year. However, there was a statistically significant difference at eHEALS score among University Departments (p=0.009). Further research in representative samples is required to assess specific needs and improve current educational curricula.Radiotherapy is one of the main means of treating cancer patients. Its application has grown worldwide. Around 50% of all cancer patients should receive radiation. Brazil faces a shortage of radiotherapy treatment because of a lack of enough treatment units, equipment availability, well-trained staff, and fair reimbursement. The Radiotherapy Information System (RIS) implementation to manage information about patient scheduling is vital to improve the efficiency of care and reduce the waiting time to start cancer treatment. The information system deployed can be indicated as a disruptive innovation in the Brazilian public health system, considering the radical improvement in the cancer treatment process at the Brazilian National Cancer Institute.Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. [https://www.selleckchem.com/products/alexidine-dihydrochloride.html read more] Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases.Health data from hospital information systems are valuable sources for medical research but have known issues in terms of data quality. In a nationwide data integration project in Germany, health care data from all participating university hospitals are being pooled and refined in local centers. As there is currently no overarching agreement on how to deal with errors and implausibilities, meetings were held to discuss the current status and the need to develop consensual measures at the organizational and technical levels. This paper analyzes the discovered similarities and differences. The result shows that although data quality checks are carried out at all sites, there is a lack of both centrally coordinated data quality indicators and a formalization of plausibility rules as well as a repository for automatic querying of the rules, for example in ETL processes.