Attitudes of Covid-19 patients toward sharing their health data: A survey-based study to understand security and privacy concerns.

Background and Aims: Many people around the world, especially at the time of the Covid-19 outbreak, are concerned about their e-health data. The aim of this study was to investigate the attitudes of patients with Covid-19 toward sharing their health data for research and their concerns about security and privacy. Methods: This survey is a cross-sectional study conducted through an electronic researcher-made questionnaire from February to May 2021. Convenience sampling was applied to select the participants and all 475 patients were referred to two to Afzalipour and Shahid Bahonar hospitals were invited to the study. According to the inclusion and exclusion criteria, 204 patients were included in the study and completed the questionnaire. Descriptive statistics (frequency, mean, and standard deviation) were used to analyze the questionnaire data. SPSS 23.0 was used for data analysis. Results: Participants tended to share information about "comments provided by individuals on websites" (68.6%), "fitness tracker data" (64.19%), and "online shopping history" (63.21%) before death. Participants also tended to share information about "electronic medical records data" (36.75%), "genetic data" (24.99%), and "Instagram data" (24.99%) after death. "Fraud or misuse of personal information" (4.48 [±1.27]) was the most common concern of participants regarding the virtual world. "Unauthorized access to the account" (4.38 [±0.73]), "violation of the privacy of personal information" (4.26 [±0.85]), and "violation of the patient privacy and personal information confidentially" (4.26 [±0.85]) were the most of the unauthorized security incidents that occurred online for participants. Conclusion: Patients with Covid-19 were concerned about releasing information they shared on websites and social networks. Therefore, people should be made aware of the reliability of websites and social media so that their security and privacy are not affected.

The information-seeking behavior of medical sciences students toward COVID-19 in mass and social media: A cross-sectional study.

Background and Aims: During the COVID-19 pandemic, college students can access health-related information on the Internet to improve preventative behaviors, but they often judge the merits of such information and create challenges in the community. The aim of this study was to investigate information-seeking behaviors in regard to COVID-19 among students at Kerman University of Medical Sciences (KUMS) with the help of mass and social media. Methods: The present study is a cross-sectional study, which was conducted using an online researcher-made questionnaire. An invitation to participate in the study was sent to 500 students at KUMS, of which 203 were selected according to the inclusion criteria and completed the questionnaire. Descriptive statistics were used to analyze the data. Results: COVID-19 news was mostly obtained through social media platforms such as WhatsApp, Telegram, Instagram, radio, and television, as well as online publications and news agencies. Social media platforms such as WhatsApp, Telegram, Instagram, and satellite networks such as BBC contained the most rumors about COVID-19. Some of the most common misconceptions regarding COVID-19 were as follows: "COVID-19 is the deadliest disease in the world," "COVID-19 is a biological attack," and "COVID-19 disappears as the air temperature rises." In addition, most of the virtual training provided through mass media focused on "refraining from visiting holy places and crowded locations such as markets," "observing personal hygiene and refraining from touching the eyes, nose, and mouth with infected hands," and "the role of quarantine in reducing the incidence of COVID-19." Conclusion: Our findings demonstrated that during the pandemic, students used social media platforms the most to obtain health-related information and these media have a significant impact on their willingness to engage in preventative behaviors and take the COVID-19 risk seriously.

The information‐seeking behavior of medical sciences students toward COVID‐19 in mass and social media: A cross‐sectional study

Background and AimsDuring the COVID‐19 pandemic, college students can access health‐related information on the Internet to improve preventative behaviors, but they often judge the merits of such information and create challenges in the community. The aim of this study was to investigate information‐seeking behaviors in regard to COVID‐19 among students at Kerman University of Medical Sciences (KUMS) with the help of mass and social media.MethodsThe present study is a cross‐sectional study, which was conducted using an online researcher‐made questionnaire. An invitation to participate in the study was sent to 500 students at KUMS, of which 203 were selected according to the inclusion criteria and completed the questionnaire. Descriptive statistics were used to analyze the data.ResultsCOVID‐19 news was mostly obtained through social media platforms such as WhatsApp, Telegram, Instagram, radio, and television, as well as online publications and news agencies. Social media platforms such as WhatsApp, Telegram, Instagram, and satellite networks such as BBC contained the most rumors about COVID‐19. Some of the most common misconceptions regarding COVID‐19 were as follows: “COVID‐19 is the deadliest disease in the world,” “COVID‐19 is a biological attack,” and “COVID‐19 disappears as the air temperature rises.” In addition, most of the virtual training provided through mass media focused on “refraining from visiting holy places and crowded locations such as markets,” “observing personal hygiene and refraining from touching the eyes, nose, and mouth with infected hands,” and “the role of quarantine in reducing the incidence of COVID‐19.”ConclusionOur findings demonstrated that during the pandemic, students used social media platforms the most to obtain health‐related information and these media have a significant impact on their willingness to engage in preventative behaviors and take the COVID‐19 risk seriously.

Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications.

Coronavirus disease (COVID-19) as an emerging disease decreases security among people from different countries. Sense of security can be raised via quick diagnosis of COVID-19, and its management and control using clinical decision support systems (CDSS) to prevent further spread of the disease. So, the aim of this study is to identify and introduce the applications of a CDSS in the diagnosis, management, and control of COVID-19. This cross-sectional study was conducted to identify and introduce the applications of CDSS in the diagnosis, management, and control of COVID-19. Based on the results of some meetings with infectious disease specialists and a general practitioner as well as reviewing the related literature, information about COVID-19 and CDSS was obtained. Then based on the information obtained, a questionnaire was designed electronically and distributed in a two-round Delphi method among 19 experts in the three fields of medical informatics, health information management, and infectious disease specialists. According to the literature and expert opinions, 35 applications of CDSS applications were identified in the four main groups of "diagnosis", "medication", "monitoring", and "health services". Eventually, a collective agreement was reached on 30 applications in the first and second rounds of Delphi. Among all the applications, the highest means were assigned to "monitoring the vital signs" and "helping diagnose infections and damaged lung tissue through CT scan". Introducing these applications can provide general, basic knowledge of the design and implementation of clinical decision support systems in the real world to prevent irreversible complications and even many people's death.

Survey of the patients' perspectives and preferences in adopting telepharmacy versus in-person visits to the pharmacy: a feasibility study during the COVID-19 pandemic.

BACKGROUND: Following the coronavirus disease 2019 (COVID-19) pandemic, the health authorities recommended the implementation of strict social distancing and complete lockdown regulations to reduce disease spread. The pharmacists quickly adopted telemedicine (telepharmacy) as a solution against this crisis, but awareness about this technology is lacking. Therefore, the purpose of this research was to explore the patients' perspectives and preferences regarding telepharmacy instead of traditional in-person visits. METHODS: An electronic questionnaire was designed and sent to 313 patients who were eligible for the study (from March to April 2021). The questionnaire used five-point Likert scales to inquire about motivations for adopting telepharmacy and in-person visits, their perceived advantages and disadvantages, and the declining factors of telepharmacy. Finally, the results were descriptively analyzed using SPSS 22. RESULTS: Of all 313 respondents, a total of 241 (77%) preferred appointments via telepharmacy while 72 (23%) preferred in-person services. There was a significant difference between the selection percentage of telepharmacy and in-person services (chi-square 91.42; p < 0.0001). Preference bout the telepharmacy system versus in-person visits to the pharmacy was associated with factors such as "reducing the incidence of contagious disease" (4.41; ± 0.78), "spending less time receiving pharmaceutical services" (4.24; ± 0.86)), and "traveling a shorter distance for receiving pharmaceutical services" (4.25; ± 0.86). "Reducing costs" (90.87%), "saving time" (89.21%), and "reducing the incidence of contagious disease" (87.13%) were the most important reasons for choosing telepharmacy services. Also, "face-to-face communication with the pharmacist" (25%), "low internet bandwidth" (25%), and "reduction of patients' anxiety and the increase of their peace of mind" (23.61%) were the most important reasons for choosing in-person visits. CONCLUSION: Survey data indicate that most participants are likely to prefer the use of telepharmacy, especially during crises such as the current COVID-19 pandemic. Telepharmacy can be applied as an important means and a crucial service to lessen the load on healthcare organizations and expand drug supply shelters in pharmacies. However, there are still substantial hurdles to overcome in order to successfully implement the telemedicine platform as part of mainstream practice.

Comparing machine learning algorithms for predicting COVID-19 mortality.

BACKGROUND: The coronavirus disease (COVID-19) hospitalized patients are always at risk of death. Machine learning (ML) algorithms can be used as a potential solution for predicting mortality in COVID-19 hospitalized patients. So, our study aimed to compare several ML algorithms to predict the COVID-19 mortality using the patient's data at the first time of admission and choose the best performing algorithm as a predictive tool for decision-making. METHODS: In this study, after feature selection, based on the confirmed predictors, information about 1500 eligible patients (1386 survivors and 144 deaths) obtained from the registry of Ayatollah Taleghani Hospital, Abadan city, Iran, was extracted. Afterwards, several ML algorithms were trained to predict COVID-19 mortality. Finally, to assess the models' performance, the metrics derived from the confusion matrix were calculated. RESULTS: The study participants were 1500 patients; the number of men was found to be higher than that of women (836 vs. 664) and the median age was 57.25 years old (interquartile 18-100). After performing the feature selection, out of 38 features, dyspnea, ICU admission, and oxygen therapy were found as the top three predictors. Smoking, alanine aminotransferase, and platelet count were found to be the three lowest predictors of COVID-19 mortality. Experimental results demonstrated that random forest (RF) had better performance than other ML algorithms with accuracy, sensitivity, precision, specificity, and receiver operating characteristic (ROC) of 95.03%, 90.70%, 94.23%, 95.10%, and 99.02%, respectively. CONCLUSION: It was found that ML enables a reasonable level of accuracy in predicting the COVID-19 mortality. Therefore, ML-based predictive models, particularly the RF algorithm, potentially facilitate identifying the patients who are at high risk of mortality and inform proper interventions by the clinicians.

Predicting Mortality of COVID-19 Patients based on Data Mining Techniques.

If Coronavirus (COVID-19) is not predicted, managed, and controlled timely, the health systems of any country and their people will face serious problems. Predictive models can be helpful in health resource management and prevent outbreak and death caused by COVID-19. The present study aimed at predicting mortality in patients with COVID-19 based on data mining techniques. To do this study, the mortality factors of COVID-19 patients were first identified based on different studies. These factors were confirmed by specialist physicians. Based on the confirmed factors, the data of COVID-19 patients were extracted from 850 medical records. Decision tree (J48), MLP, KNN, random forest, and SVM data mining models were used for prediction. The models were evaluated based on accuracy, precision, specificity, sensitivity, and the ROC curve. According to the results, the most effective factor used to predict the death of COVID-19 patients was dyspnea. Based on ROC (1.000), accuracy (99.23%), precision (99.74%), sensitivity (98.25%) and specificity (99.84%), the random forest was the best model in predicting of mortality than other models. After the random forest, KNN5, MLP, and J48 models were ranked next, respectively. Data analysis of COVID-19 patients can be a suitable and practical tool for predicting the mortality of these patients. Given the sensitivity of medical science concerning maintaining human life and lack of specialized human resources in the health system, using the proposed models can increase the chances of successful treatment, prevent early death and reduce the costs associated with long treatments for patients, hospitals and the insurance industry.

The Design and Evaluation of a Mobile based Application to Facilitate Self-care for Pregnant Women with Preeclampsia during COVID-19 Prevalence.

Preeclampsia is one of the most common complications of pregnancy that is very difficult to control and manage during the outbreak of COVID-19. One way to control and manage this disease is to use self-care applications. Therefore, the aim of this study was to design and develop a mobile-based application to facilitate self-care for women, who suffer from pregnancy poisoning in the COVID-19 pandemic. This study was conducted in two stages: In the first stage, according to the opinion of 20 obstetricians and pregnant women, a needs assessment was performed. In the second stage, based on the identified needs, the application prototype was designed and then evaluated. For evaluation, 20 pregnant women were asked to use the application for 10 days. QUIS questionnaire version 5.5 was used for evaluation. Descriptive statistics and mann-whitney test in SPSS software version 23 were used for data analysis. Out of the 66 information needs that were identified via the questionnaire, 58 were considered in designing the application. Features of the designed application were placed in 5 categories: User's profile, lifestyle, disease prevention and control, application capabilities and user's satisfaction. The capabilities of the application consist of introducing specialized COVID-19 medical centers, search for the location of medical centers and doctors' offices, drug management, drug allergies, self-assessment, stress reduction and control, nutrition and diet management, sleep management, doctor's appointment reminders, communication with other patients and physicians, application settings. Pregnant women rated the usability of the application at a good level. The designed application can reduce the anxiety and stress due to preeclampsia feel and also improve their knowledge as well as attitude towards the COVID-19 pandemic and preeclampsia.

The Development and Usability Assessment of an mHealth Application to Encourage Self-Care in Pregnant Women against COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic has caused serious concerns in pregnant women. Self-care mHealth applications can provide helpful guidelines for COVID-19 prevention or management in case of infection. This study aimed to develop and then assess a self-care smartphone-based application to provide self-care for pregnant women against COVID-19. The present study was conducted in two phases. First, a needs assessment was performed based on the opinions of 30 obstetricians and pregnant women. Then, relying on the results, a smartphone-based application was prototyped and assessed in terms of its usability and user satisfaction. To assess the application, 36 pregnant women (11 infected with COVID-19) were asked to use the application for a week. The QUIS questionnaire 5.5 was used for assessment, and the results were analyzed via descriptive statistics in SPSS 23. According to the obstetricians and pregnant women, of the 41 information requirements, 35 data elements were noted to be essential in the needs assessment. Features of the application were placed in four categories of User's Profile, Lifestyle, Disease Management and Control, and Application Functions (e.g., introducing high-risk places in terms of COVID-19 prevalence in each city, introducing specialized COVID-19 medical centers to pregnant women to receive services, medication management, stress management and control, nutrition and diet management, sleep management, contacting physicians, doctor's appointment reminder, searching the available educational materials, and making application adjustments such as text font, size, and color). With an average score of 7.94 (out of 9), pregnant women rated the application at a good level. The application can be used to reduce anxiety and stress about COVID-19 in mothers, provide access to reliable information to answer possible questions, identify high-risk locations, and provide pregnant women with instant access to healthcare facilities and information related to COVID-19 self-care processes.

Design and development of a web-based registry for Coronavirus (COVID-19) disease.

Background: The 2019 coronavirus (COVID-19) is a highly contagious disease associated with a high morbidity and mortality worldwide. The accumulation of data through a prospective clinical registry enables public health authorities to make informed decisions based on real evidence obtained from surveillance of COVID-19. This registry is also fundamental to providing robust infrastructure for future research surveys. The purpose of this study was to design a registry and its minimum data set (MDS), as a valid and reliable data source for reporting and benchmarking COVID-19. Methods: This cross sectional and descriptive study provides a template for the required MDS to be included in COVID-19 registry. This was done by an extensive literature review and 2 round Delphi survey to validate the content, which resulted in a web-based registry created by Visual Studio 2019 and a database designed by Structured Query Language (SQL). Results: The MDS of COVID-19 registry was categorized into the administrative part with 3 sections, including 30 data elements, and the clinical part with 4 sections, including 26 data elements. Furthermore, a web-based registry with modular and layered architecture was designed based on final data classes and elements. Conclusion: To the best of our knowledge, COVID-19 registry is the first designed instrument from information management perspectives in Iran and can become a homogenous and reliable infrastructure for collecting data on COVID-19. We hope this approach will facilitate epidemiological surveys and support policymakers to better plan for monitoring patients with COVID-19.