Context: Using smart mobile devices, called mobile health (mHealth), facilitates providing health services, speeds up the process, and reduces the costs and complications of direct services. Also, mHealth has many capabilities and applications in epidemic and pandemic outbreaks. This study aimed to identify mHealth applications in epidemic/pandemic outbreaks and provide some suggestions for tackling COVID-19. Methods: To find the relevant studies, searches were done in PubMed and Scopus by related keywords during 2014 - 2020 (March 10). After selecting the studies based on the inclusion and exclusion criteria, data were collected by a data-gathering form. Results: Of the 727 retrieved studies, 17 studies were included. All studies emphasized the positive effect of mHealth for use in epidemic/pandemic outbreaks. The main applications of mHealth for epidemic/pandemic outbreaks included public health aspects, data management, educational programs, diagnosis, and treatment. Conclusions: mHealth is an appropriate method for encountering epidemic/pandemic outbreaks due to its extensive applications. In the pandemic outbreak of COVID-19, mHealth is one of the best choices to use in the patient-physician relationship as tele-visits, using in fever coach, providing real-time information for healthcare providers, population monitoring, and detecting the disease based on obtained data from different locations.

Background A Disease Registry System (DRS) is a system that collects standard data on a specific disease with an organized method for specific purposes in a population. Barriers and facilitators for DRSs are different according to the health system of each country, and identifying these factors is necessary to improve DRSs, so the purpose of this study was to identify and prioritize these factors. Methods First, by conducting 13 interviews with DRS specialists, barriers and facilitators for DRSs were identified and then, a questionnaire was developed to prioritize these factors. Then, 15 experts answered the questionnaires. We prioritized these factors based on the mean of scores in four levels including first priority (3.76–5), second priority (2.51–3.75), third priority (1.26–2.50), and the fourth priority (1–1.25). Results At first, 139 unique codes (63 barriers and 76 facilitators) were extracted from the interviews. We classified barriers into 9 themes, including management problems (24 codes), data collection-related problems (8 codes), poor cooperation/coordination (7 codes), technological problems and lack of motivation/interest (6 codes for each), threats to ethics/data security/confidentiality (5 codes), data quality-related problems (3 codes), limited patients’ participation and lack of or non-use of standards (2 codes for each). We also classified facilitators into 9 themes including management facilitators (36 codes), improving data quality (8 codes), proper data collection and observing ethics/data security/confidentiality (7 codes for each), appropriate technology (6 codes), increasing patients’ participation, increasing motivation/interest, improving cooperation/coordination, and the use of standards (3 codes for each). The first three ranked barriers based on mean scores included poor stakeholder cooperation/coordination (4.30), lack of standards (4.26), and data quality-related problems (4.06). The first three ranked facilitators included improving data quality (4.54), increasing motivation/interest (4.48), and observing ethics/data security/confidentiality (4.36). Conclusion Stakeholders’ coordination, proper data management, standardization and observing ethics, security/confidentiality are the most important areas for planning and investment that managers must consider for the continuation and success of DRSs.

Background Hemolytic uremic syndrome (HUS) is a rare condition which diagnosed with the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal injury. There is a high requirement for research to discover treatments. HUS registries can be used as an important information infrastructure. In this study, we identified and compared the different features of HUS registries to present a guide for the development and implementation of HUS registries. Results The purposes of registries were classified as clinical (9 registries), research (7 registries), and epidemiological (5 registries), and only 3 registries pursued all three types of purposes. The data set included demographic data, medical and family history, para-clinical and diagnostic measures, treatment and pharmacological data, complications, and outcomes. The assessment strategies of data quality included monthly evaluation and data audit, the participation of physicians to collect data, editing and correcting data errors, increasing the rate of data completion, following guidelines and data quality training, using specific data quality indicators, and real-time evaluation of data at the time of data entry. 8 registries include atypical HUS patients, and 7 registries include all patients regardless of age. Only two registries focused on children. 4 registries apply prospective and 4 applied both prospective, and retrospective data collection. Finally, specialized hospitals were the main data source for these registries. Conclusion Based on the findings, we suggested a learning framework for developing and implementing an HUS registry. This framework includes lessons learned and suggestions for HUS registry purposes, minimum data set, data quality assurance, data collection methods, inclusion and exclusion criteria as well as data sources. This framework can help researchers develop HUS registries.

Background: One of the effective factors in successful implementation of health information technology, especially electronic medical record, is investigation of adoption and its use by users. Therefore, the aim of this study was to investigate factors affecting adoption and use of electronic medical record in Shiraz teaching hospitals from the perceptive of top and middle managers.

Objective Health condition and outcome registry systems (registries) are used to collect data related to diseases and other health-related outcomes in specific populations. The implementation of these programs encounters various barriers and facilitators. Therefore, the present review aimed to identify and classify these barriers and facilitators. Materials and Methods Some databases, including PubMed, Embase, ISI Web of Sciences, Cochrane Library, Scopus, Ovid, ProQuest, and Google Scholar, were searched using related keywords. Thereafter, based on the inclusion and exclusion criteria, the required data were collected using a data extraction form and then analyzed by the content analysis method. The obtained data were analyzed separately for research and review studies, and the developed and developing countries were compared. Results Forty-five studies were reviewed and 175 unique codes were identified, among which 93 barriers and 82 facilitators were identified. Afterward, these factors were classified into the following 7 categories: barriers/facilitators to management and data management, poor/improved collaborations, technological constraints/appropriateness, barriers/facilitators to legal and regulatory factors, considerations/facilitators related to diseases, and poor/improved patients’ participation. Although many of these factors have been more cited in the literature related to the developing countries, they were found to be common in both developed and developing countries. Conclusion Lack of budget, poor performance of managers, low data quality, and low stakeholders’ interest/motivation on one hand, and financing, providing adequate training, ensuring data quality, and appropriate data collection on the other hand were found as the most common barriers or facilitators for the success of the registry implementation.