Assessing the e-health literacy level and its predictors in heart patients: A Case Study in a Heart Hospital Center in Iran.

BACKGROUND: eHealth literacy has many benefits for patients and community members, including the direct impact on improving the quality of patient education and reducing direct and indirect healthcare costs. Benefiting from eHealth literacy in patients with cardiovascular diseases can effectively provide healthcare services and manage these patients. This study aimed to evaluate eHealth literacy level and its factors affecting patients with cardiovascular diseases in a Heart Center Hospital. MATERIALS AND METHODS: This cross-sectional study was conducted in 2022. A valid and reliable questionnaire has been used for data gathering. From 147 distributed questionnaires among patients with cardiovascular diseases at Madani Heart Center Hospital in Khorramabad city, finally, 86 questionnaires have been collected completely. Data analyses were done using IBM Statistical Package for the Social Sciences (SPSS) software version 22 descriptive and analytical tests such as one-way ANOVA, independent sample t-test, and Spearman correlation coefficient based on the study objectives. RESULTS: The study showed that heart patients' eHealth literacy status is moderate (3.38 out of 5). The awareness of the availability of resources on the Internet had the highest score (3.79). The importance of using the Internet to obtain health information (r = 0.62, P < 0.001) and the ability to use the Internet (r = 0.62, P < 0.001) had the most significant relationship with eHealth literacy among patients with cardiovascular diseases. CONCLUSION: It is suggested that by increasing the level of Internet skills, expanding the use of the Internet for health-related services, using the Internet to make accurate health decisions among patients, extending the use of the Internet to access health resources, and reducing the patient's level of concern about their health status to improve the level of eHealth literacy of cardiovascular patients.

Development and pilot implementation of Iranian Hemolytic Uremic Syndrome Registry.

BACKGROUND: Patients with Hemolytic Uremic Syndrome (HUS) face late diagnosis and lack of appropriate treatment because of a lack of knowledge and experience in this field. A prerequisite for such knowledge is the development of research infrastructures such as a registry system. Therefore, this study aimed to develop and describe the HUS registry in accordance with the Iranian health system and implement its software system. METHODS: We first interviewed 10 pediatric nephrologists and after analyzing the interviews, we identified the features and requirements and the data related to HUS. Then, during two rounds of the Delphi technique (the first round with 23 participants and the second round with 18 participants), the model of this registry was finalized based on the agreement of at least 75% of specialists. At the next step, based on the agreed requirements, IRI.HUS.Reg (Iranian Hemolytic Uremic Syndrome Registry) software was developed and implemented in a pediatric hospital. RESULTS: We classified 369 meaning units of interviews in 41 codes and 7 final themes including purposes of the registry (10 codes), inclusion criteria (7 codes), data collection method (4 codes), data quality control (6 codes), data sources (4 codes), data analysis (3 codes) and software features (7 codes). These 7 feature groups (67 subgroups) and 12 data classes (138 data elements) include demographic data, referrals, examinations, clinical signs, causes, laboratory tests, medical histories, paraclinical measures, treatments, outcomes, patient's status at discharge, and follow-up data were reviewed by the Delphi panelists, and finally, 64 features and 131 data elements were accepted by at least 78% agreement. Then, we developed and implemented a registry software system in a hospital. CONCLUSION: We implemented IRI.HUS.Reg based on related features, 12 data classes agreed by specialists, literature review, and comparison with other existing registries. Therefore, the data collected in this registry can be compared with other data from existing registries in other countries.

Barriers and facilitators for disease registry systems: a mixed-method study.

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.

Barriers and facilitators for the implementation of health condition and outcome registry systems: a systematic literature review.

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.

Lessons learned from hemolytic uremic syndrome registries: recommendations for implementation.

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.