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Considering the worldwide spread of the COVID-19 pandemic, it is critical to use electronic health (e-health) to prevent, diagnose, and treat this disease. According to reports on the use of e-health technology in past and present crises, this technology can have the potential to improve the quality and the quantity of provided services and control and manage diseases in epidemic conditions. The important issue is how to implement this technology fairly and facilitate the use of this technology by health care providers and the general public. Moreover, the concerns about the physician-patient relationship, patient privacy and health costs should be addressed. Therefore, it is necessary for health policymakers and planners to develop laws and guidelines to address legal and ethical barriers to the use of this technology, focusing on vulnerable populations, to manage the crisis and also determine the role of insurers in this area.

Introduction: Various training methods such as web-based training tools have been developed to achieve the potential benefits of classification systems developed by the World Health Organization (WHO). Given that users of these tools have different levels of capability, usability problems could reduce the speed and accuracy of learning among users interacting with these tools. This study aims to identify usability problems of web-based training tools under the WHO family of international classifications (WHO-FIC). Methods: In this descriptive and cross-sectional study, ten trained evaluators independently examined WHO-FIC training tools using the heuristic evaluation method. The identified problems were classified into 10 Nielsen’s usability heuristics. Then, their average severity was calculated. Results: In total, 40 usability problems were identified after merging and eliminating the duplicates. The highest number of problems was related to ICD-10 training tool (n=20). The highest number of problems was related to heuristics of aesthetic and minimalist design (25.0%), and user control and freedom (17.5%). Heuristics of flexibility and efficiency of use and helping users recognize, diagnose and recover from errors had the highest average severity of problems. Conclusion: Violating heuristics of aesthetic and minimalist design, user control and freedom and recognition rather than recall were among the most common problems of WHO-FIC training tools. Evaluators reported that half of the user interface problems of WHO-FIC training tools were of major and catastrophe type. Solving the usability problems of these tools could lead to ease of work, increased speed of learning and acceptance of these systems among users.

Introduction: One way to reduce medication errors in the cardiovascular settings is to electronically prescribe medication through the computerized physician order entry system (CPOE). Improper design and non-compliance with users' needs are obstacles to implementing this system. Therefore, it is necessary to consider the standard minimum data set (MDS) of this system in order to meet the basic needs of its users. The aim of this study was to introduce MDS in the cardiovascular CPOE drug system to standardize data items as well as to facilitate data sharing and integration with other systems. Material and Methods: This study was a survey study conducted in 1399 in Iran. The study population was all cardiologists in Iran. The data collection tool was a researcher-made questionnaire consisting of 33 questions. Data were analyzed in SPSS-24 using descriptive statistics. Results: A total of 31 cardiologists participated in this study. The participants identified 19 of the 25 drug data items as essential for drug MDS. Five data items (Medication name, Medication dosage, Medication frequency, Medication start date and Patient medication history) were considered essential by more than 90% of the participants. Conclusion: The results of this study identified drug MDS for the cardiovascular CPOE system. The results of this study can be a model for CPOE system designers to develop new systems or upgrade existing systems.

Introduction: Our aim in this study was to summarize information on the use of intelligent models for predicting and diagnosing the Coronavirus disease 2019 (COVID-19) to help early and timely diagnosis of the disease. Material and Methods: A systematic literature search included articles published until 20 April 2020 in PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases. The search strategy consisted of two groups of keywords: A) Novel coronavirus, B) Machine learning. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. Studies were critically reviewed for risk of bias using prediction model risk of bias assessment tool. Results: We gathered 1650 articles through database searches. After the full-text assessment 31 articles were included. Neural networks and deep neural network variants were the most popular machine learning type. Of the five models that authors claimed were externally validated, we considered external validation only for four of them. Area under the curve (AUC) in internal validation of prognostic models varied from .94 to .97. AUC in diagnostic models varied from 0.84 to 0.99, and AUC in external validation of diagnostic models varied from 0.73 to 0.94. Our analysis finds all but two studies have a high risk of bias due to various reasons like a low number of participants and lack of external validation. Conclusion: Diagnostic and prognostic models for COVID-19 show good to excellent discriminative performance. However, these models are at high risk of bias because of various reasons like a low number of participants and lack of external validation. Future studies should address these concerns. Sharing data and experiences for the development, validation, and updating of COVID-19 related prediction models is needed.

Introduction: Speech recognition(SR) technology has been existing for more than two decades. But, it has been rarely used in health care institutions and not applied uniformly in all the clinical domains. The aim of this study was to investigate the accuracy of speech recognition system in four different situations in the real environment of health services. We also report physicians' experience of using speech recognition technology. Method:. To do this study, NEVISA SR software professional v.3 was installed on the computers of expert physicians. The pre-designated medical report was tested by the physicians in four different modes including slow expression in a silent environment, slow expression in crowded environments, rapid expression in a silent environment and rapid expression in a busy environment. After using the speech recognition software by 15 physicians in hospitals, a designed questionnaire was distributed among them. . Results: The results showed that the highest average accuracy of speech recognition software was in the silent environment by slow expression and the minimum average accuracy was in the busy environment by rapid expression. Of all the participants in the study, 53.3% of the physicians believed that the use of speech recognition system promoted the workflow. Conclusion: We found that software accuracy was generally higher than the expectation and its use required to upgrade the system and its operation. In order to achieve the highest level of recognition rate and error reduction by speech recognition, influential factors such as environmental noise, type of software or hardware, training and experience of participants can be also considered.