eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.

Evaluating the Performance of the SIB System of Health Centers in Bojnourd and Neishabour from the Users Perspective in 2020

Introduction: Nurses and medical staff and health technologists as the largest segment of the health system are the main users of health information systems that understanding the perspective and how to use this system can be effective in improving the quality of community health. The aim of this study was to evaluate the performance of the Sib system of health centers in Bojnourd and Neishabour.Material and Methods: This is an applied study and was performed by descriptive cross-sectional method. The study population included all users of the Sib system in the health centers of Bojnourd and Neishabour who used the Sib system. Sampling was available and data were collected using a researcher-made questionnaire and data were analyzed using SPSS software version 21.Results: According to the findings of the study, the majority of users were 70% female and 30% male, 58% were in the age group of 30-39 years, and 40% of them had 5-9 years of work experience and also 63% of System users have a bachelor's degree. In the technical field, from the point of view of 40% of users, the ease of using the system is moderate.Conclusion: Based on the identified factors, by strengthening the advantages of the system and also trying to eliminate or reduce the shortcomings in it, it is possible to institutionalize and use the system more practically in order to solve health problems.

Comparison of the Performance of Machine Learning Algorithms in Predicting Heart Disease

Introduction: Heart disease is often associated with conditions such as clogged arteries due to the sediment accumulation which causes chest pain and heart attack. Many people die due to the heart disease annually. Most countries have a shortage of cardiovascular specialists and thus, a significant percentage of misdiagnosis occurs. Hence, predicting this disease is a serious issue. Using machine learning models performed on multidimensional dataset, this article aims to find the most efficient and accurate machine learning models for disease prediction.Material and Methods: Several algorithms were utilized to predict heart disease among which Decision Tree, Random Forest and KNN supervised machine learning are highly mentioned. The algorithms are applied to the dataset taken from the UCI repository including 294 samples. The dataset includes heart disease features. To enhance the algorithm performance, these features are analyzed, the feature importance scores and cross validation are considered.Results: The algorithm performance is compared with each other, so that performance based on ROC curve and some criteria such as accuracy, precision, sensitivity and F1 score were evaluated for each model. As a result of evaluation, Accuracy, AUC ROC are 83% and 99% respectively for Decision Tree algorithm. Logistic Regression algorithm with accuracy and AUC ROC are 88% and 91% respectively has better performance than other algorithms. Therefore, these techniques can be useful for physicians to predict heart disease patients and prescribe them correctly.Conclusion: Machine learning technique can be used in medicine for analyzing the related data collections to a disease and its prediction. The area under the ROC curve and evaluating criteria related to a number of classifying algorithms of machine learning to evaluate heart disease and indeed, the prediction of heart disease is compared to determine the most appropriate classification. As a result of evaluation, better performance was observed in both Decision Tree and Logistic Regression models.

Challenges and Opportunities of Using Telenursing During COVID-19 Pandemic: An Integrative Review

Introduction: Telenursing during the COVID-19 pandemic with an emphasis on self-care is an effective approach to help patients, hospitals, as well as community. Despite the many challenges and benefits, tele-nursing can be used to help COVID 19 patients with new technologies. This study aimed to explore the challenges and opportunities of using tele-nursing in the COVID 19 Pandemic for helping patients with COVID 19 to gain better care.Material and Methods: An integrative review was conducted from December, 2019 to January, 2021. Databases of PubMed, MEDLINE, Web of Science, Scopus, CINHAL, and google scholar were searched on the concept of tele-nursing by using the following keywords, of COVID-19, Coronavirus, Telenursing, nurse roles, technology, Pandemics and Internet. DaA ta were analyzed according to Broome method.Results: The main results of tele-nursing in COVID 19 includes: implementation problems, insurance coverage, prevention of nurses, the problem of continuing care, and changing the roles of nurses’ infections, development of nursing knowledge, the emergence of technological care providing, emphasis on patient independence and transmission cycle control.Conclusion: Tele-nursing, this, despite the challenges, has many benefits that are effective in the current situation and effective, and reliable measure, through effective planning and implementation, help control COVID-19.

Data Incompleteness Preventing Information Communication from Hospital Information Systems to the Iranian National Electronic Health Record (SEPAS)

Introduction: One of the well-known problems related to the information quality is the information incompleteness in health information systems. The purpose of this study was to investigate the completeness rate of patients’ information recorded in the hospital information system, sending information from which to Iranian electronic health record system (SEPAS) seemed to be unsuccessful.Methods: This study was conducted in six hospitals associated with Kerman University of Medical Sciences (KUMS) in Iran. In this study, 882 records which had failed to be sent from three hospital information systems to SEPAS were reviewed and the data were collected using a checklist. Data were analyzed using the descriptive and inferential statistics with SPSS.18.Results: A total of 18758 demographic and clinical information elements were examined. The rate of completeness was 55%. The highest completeness rate of demographic information was related to name, surname, gender, nationality, date of birth, father's name, marital status, place of residence, telephone number (79-100%), and in clinical information it was related to the final diagnosis (74%). The completeness rate of some information elements was significantly different among the hospitals (p <0.05). The completeness rate of information communicated to the Iranian national electronic health record was at a moderate level.Conclusion: This study showed that completeness rate is different among hospitals using the same hospital information system. The results of this study can help the health policymakers and developers of the national electronic health record in developing countries to improve completeness rate and also information quality in health information systems.

Protocol of a Systematic Review on Telemedicine Solutions in COVID-19 Pandemic

Introduction: On March 20, 2020, the World Health Organization (WHO) announced the spread of SARS-CoV-2 infection in most countries worldwide as a pandemic. COVID-19 is mainly disseminated through human-to-human transmission route via direct contact and respiratory droplets. Telehealth and/or telemedicine technologies are beneficial methods that could be employed to deal with pandemic situation of communicable infections. The purpose of this proposed systematic review study is to sum up the functionalities, applications, and technologies of telemedicine during COVID-19 outbreak.Material and Methods: This review will be carried out in accordance with the Cochrane Handbook and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. PubMed and Scopus databases were searched for related articles. Randomized and non-randomized controlled trials published in English in scientific journals were identified to be evaluated for eligibility. Articles conducted on telemedicine services (TMS) during COVID-19 outbreak (2019-2020) were identified to be evaluated.Results: The literature search for related articles in PubMed and Scopus databases led to the identification and retrieval of a total of 1118 and 485 articles, respectively. After eliminating duplicate articles, title and abstract screening process was performed for the remaining 1440 articles. The current study findings are anticipated to be used as a guide by researchers, decision makers, and managers to design, implement, and assess TMS during COVID-19 crisis.Conclusion: As far as we know, this systematic review is conducted to comprehensively evaluate TM methods and technologies developed with the aim of controlling and managing COVID-19 pandemic. This study highlights important applications of telemedicine in pandemic conditions, which could be employed by future health systems in controlling and managing communicable infections when an outbreak occurs.

Development of a Minimum Data Set for Drug Module of Computerized Physician Order Entry System

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.

Designing a Profit and Loss Prediction Model for Health Companies Using Data Mining

Introduction: Health companies need investment for development. Due to the high risk of their activities, it is very difficult to attract investment for this field, but this lack of financial resources leads to the failure of these companies, so providing a model for predicting profits and losses in companies is very important and functional.Materials and Method: In this study, a combination of two logistic regression algorithms and differential analysis were used to design a profit and loss forecasting model. Also, the information of 20 companies in the field of health was used to evaluate the proposed model. 10 profitable companies and 10 loss-making companies were selected and for each company, nine variables independent of the financial information of these companies were collected.Results: The designed prediction model was implemented on the data in this study. To do this, the data were divided into two sets: training and testing. The prediction model was implemented on training data and evaluated by test data and reached 99.65% sensitivity, 94.75% specificity and 96.28% accuracy. The proposed model was then compared with the methods of decision tree C4.5, Bayesian, support vector machine, nearest neighborhood and multilayer neural network and it was found to have a better output.Conclusion: In this study, it was found that the risk in the field of health investment can be reduced, so the profit and loss situation of health companies can be predicted with appropriate accuracy. It was also found that the combination of logistic regression and differential analysis algorithms can increase the accuracy of the prediction model.

Applications of Artificial Intelligence and Machine Learning in Diagnosis and Prognosis of COVID-19 infection: A systematic review

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.

The Effect of Telemedicine and Social Media on Cancer patients' Self-Care: A Systematic Review

Introduction: Cancer is an incurable disease that affects people regardless of age, sex, race and social, economic and cultural status. Most cancer patients are treated with a combination of treatments based on the type of tumor, the extent of the disease, and their physical condition. Self-management programs empower people to deal with illness and improve their quality of life. Telemedicine in the form of mobile applications, websites and social networks is one of the effective tools to achieve this goal. The aim of this study was to investigate the impact of telemedicine and social media on self-care of cancer patients.Method: English related articles were searched based on keywords in the title and abstract using PubMed and Scopus databases (from 1963 to December 2020). Keywords included telemedicine, social networking, self-care and m-health. Inclusion criteria included all studies published in English that examined the impact of telemedicine and social media on cancer patients' self-care. Review articles and non-intervention articles were excluded from the study.Results: A total of 516 articles were selected by title. After reviewing the abstract, 80 articles remained to be reviewed. After evaluating the full text of these articles, 9 eligible articles were selected for final review. In terms of the type of cancer among these studies, prostate cancer had the largest share (33%). In line with the main purpose of this study, in 7 articles (77.8%) telemedicine had a significant positive effect on self-care of cancer patients and increased self-care. In one article (11.1%) this effect was negative and reduced self-care. In 1 article (11.1%) no effect was observed.Conclusion: According to the results of the present study, it seems that web-based interventions and mobile health in most articles have been effective in increasing patients' self-care. However, due to the increasing number of cancers as well as the increasing use of telemedicine in the field of chronic diseases and cancer, the need for further studies is felt in this field.