Designing a model for implementing diagnosis-related groups in Iran: An action plan approach

Background and Aims

Implementing diagnosis-related groups (DRGs) in different countries increases the efficiency of healthcare services, improves treatment quality, and reduces treatment costs. Due to the lack of a coherent model for its implementation, the present study aimed to develop a DRGs-based implementation action plan Model for Iran.

Methods

The present study was an applied, descriptive cross-sectional study conducted in three stages. In the first stage, a review of studies conducted in different countries was carried out. In the second stage, a model was designed for an action plan to implement the DRGs in Iran. In the third stage, the model was validated based on the Delphi technique.

Results

The DRGs-based implementation action plan model in Iran was designed in three primary axes, including the strategic approach of the DRGs-based implementation action plan, technical dimensions, and executive institutions involved in the DRGs-based implementation action plan. Validation of the designed model showed the agreement of experts (94%) for the mentioned axes.

Conclusion

The significance of tailoring a DRGs-based implementation action plan to each country's unique context is well-established. Given the intricacies of the Iranian healthcare system, we recommend an initial pilot implementation of DRGs at the hospital level, followed by a gradual national rollout.

Promotion of training course on ICD-10 Poisoning coding : necessity to adopt preventive strategies

BACKGROUND

Poisoning is considered the most common cause of referral to emergency departments and hospitalization in the intensive care unit (ICU). Training or retraining of coders and ensuring the positive impact of these trainings in assigning accurate codes to poisoning cases is necessary to adopt practical health measures for optimal management of this disease. The present study aimed to evaluate the impact of holding a training course on poisoning coding rules based on ICD-10 in clinical coders.

METHODS

This study is descriptive and analytical. With the target population included the coders of hospitals affiliated with Shahid Beheshti University of Medical Sciences (N = 45). In order to evaluate the training course on poisoning coding rules, the Conex Input Process Product (CIPP) evaluation model was used. This model was the first goal-oriented approach evaluation model. According to the CIPP model, evaluation of the training course held in four components, including Context factors (course objectives and priority of objectives), Input factors (instructor, curriculum, facilities, equipment, and training location), Process factors (teaching process, learning, management, and support), and Product factors (feedback, knowledge, and skills). A researcher-made questionnaire containing 39 questions with a 5-point Likert scale was used to collect data. The validity of the questionnaire was calculated through content validity, and its reliability was calculated using Cronbach's alpha coefficient (alpha = 90% in all components). In order to analyze the data, descriptive statistics (frequency percentage distribution) and inferential statistics (one-sample t-test) were used.

RESULTS

The findings of this study were presented in four components of context, input, process, and product evaluation. The average criterion for all questions in the questionnaire was considered 3. As a result, the significance level obtained from the one sample t-test was equal to P = 0. 0001.The training course had a favorable effect in terms of context, input, process and products.

CONCLUSION

The knowledge and skills of clinical coders can be enhanced by updating medical knowledge, holding training courses, workshops, seminars, and conducting clinical coder accreditation. Extensive and continuous training for clinical coders is essential due to the impact of code quality on financial forecasting, electronic health records, and conducting research.

Situation analysis model of hospital emergency department promotion in Iran: A cross-sectional study

Background and Aim

The present study was conducted to develop a situation analysis model for Iran's hospitals' emergency departments (EDs).

Methods

The current research was a descriptive cross-sectional applied study in three stages. The studies were reviewed in various library resources and valid sites in the first stage. In the second stage, the analysis model of the ED in Iran was presented. In the third stage, the model was validated based on the Delphi technique, and the final model was presented.

Results

The final situation analysis model of ED in Iran was approved in four main aspects, including goals, internal factors, external factors, and organizations and institutions participating in the situation analysis, and its implementation schedule was approved by 90% of experts.

Conclusion

Considering the importance of situation analysis in developing a strategic plan and improving the quality of health services in the ED of hospitals, implementing a coherent situation analysis model that includes all aspects leading to improving the ED quality and analyzing the internal and external factors is vital.

COVID-19 vaccine registry for pregnant women: policy to control complications of vaccination in pregnant women in 2021-2022

BACKGROUND

Data management related to COVID-19 vaccination in pregnant women is vital to improve the treatment process and to establish preventive programs. Implementing a registry to manage data is an essential part of this process. This study aims to design a national model of the COVID-19 vaccination registry for pregnant women in Iran.

METHODS

The present study is an applied descriptive study conducted in 2021 and 2022 in two stages. In the first stage, the coordinates of the National Registry of COVID-19 vaccination of pregnant women from related references and articles, as well as the comparative study of the National Registry of COVID-19 vaccination of pregnant women in the United States, Canada, and the United Kingdom was done. In the second stage, the preliminary model was designed. The model was validated using the Delphi technique and questionnaire tools and analyzing the data.

RESULTS

The presented national COVID-19 vaccination registry model of pregnant women's main components consist of objectives, data sources, structure, minimum data set, standards, and registry processes, all of which received 100% expert consensus.

CONCLUSION

The vaccination registry of pregnant women has a major role in managing COVID-19 vaccination data of pregnant women and can be one of the Ministry of Health and Medical Education priorities.

Machine learning applications for early detection of esophageal cancer: a systematic review

INTRODUCTION

Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC.

METHODS

We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL (", "Neural Networks (NN)", "Esophagus", "EC" and "Early Detection". After applying inclusion and exclusion criteria, 31 articles were retained for full review.

RESULTS

The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods.

CONCLUSION

Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

Development and usability evaluation of a mHealth application for albinism self-management

BACKGROUND

Reduced or absence of melanin poses physical, social, and psychological challenges to individuals with albinism. Mobile health (mHealth) applications have the potential to improve the accessibility of information and services while reducing time and costs. This study aimed to develop and evaluate a mHealth application for self-management of albinism.

METHODS

This applied study was conducted in two stages (development and evaluation) in 2022. Initially, the functional requirements were determined, and the conceptual model of the application was then developed using Microsoft Visio 2021. In the second phase, the application was evaluated using the Mobile Application Usability Questionnaire (MAUQ) involving patients with albinism to reflect their views on the usability of the application.

RESULTS

The key capabilities of the application included: reminders, alerts, educational content, useful links, storage and exchange of images of skin lesions, specialist finder, and notifications for albinism-relevant events. Twenty-one users with albinism participated in the usability testing of the application. The users were predominantly satisfied with the application (5.53 ± 1.10; Max: 7.00).

CONCLUSIONS

The findings of this study suggest that the developed mobile application could assist individuals with albinism to effectively manage their condition by considering the users' requirements and services that the application should deliver.

Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models

Background

Breast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.

Materials and methods

This study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.

Results

The random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.

Conclusion

This study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.

Cervical cancer survival prediction by machine learning algorithms: a systematic review

BACKGROUND

Cervical cancer is a common malignant tumor of the female reproductive system and is considered a leading cause of mortality in women worldwide. The analysis of time to event, which is crucial for any clinical research, can be well done with the method of survival prediction. This study aims to systematically investigate the use of machine learning to predict survival in patients with cervical cancer.

METHOD

An electronic search of the PubMed, Scopus, and Web of Science databases was performed on October 1, 2022. All articles extracted from the databases were collected in an Excel file and duplicate articles were removed. The articles were screened twice based on the title and the abstract and checked again with the inclusion and exclusion criteria. The main inclusion criterion was machine learning algorithms for predicting cervical cancer survival. The information extracted from the articles included authors, publication year, dataset details, survival type, evaluation criteria, machine learning models, and the algorithm execution method.

RESULTS

A total of 13 articles were included in this study, most of which were published from 2018 onwards. The most common machine learning models were random forest (6 articles, 46%), logistic regression (4 articles, 30%), support vector machines (3 articles, 23%), ensemble and hybrid learning (3 articles, 23%), and Deep Learning (3 articles, 23%). The number of sample datasets in the study varied between 85 and 14946 patients, and the models were internally validated except for two articles. The area under the curve (AUC) range for overall survival (0.40 to 0.99), disease-free survival (0.56 to 0.88), and progression-free survival (0.67 to 0.81), respectively from (lowest to highest) received. Finally, 15 variables with an effective role in predicting cervical cancer survival were identified.

CONCLUSION

Combining heterogeneous multidimensional data with machine learning techniques can play a very influential role in predicting cervical cancer survival. Despite the benefits of machine learning, the problem of interpretability, explainability, and imbalanced datasets is still one of the biggest challenges. Providing machine learning algorithms for survival prediction as a standard requires further studies.

CARDIOPROTECTIVE EFFECT OF DICHLOROMETHANE VALERIAN (VALERIANA OFFICINALIS) EXTRACT ON ISCHEMIA-REPERFUSION-INDUCED CARDIAC INJURIES IN RATS

Background

Valepotriate is an active ingredient of valerian (Valeriana officinalis) with strong antioxidant activity that is effective for numerous cardiovascular diseases.

Objective

The aim of this study was to investigate the effect of an active ingredient of V. officinalis extract on ischemia-reperfusion-induced cardiac injuries in male rats.

Methods

Thirty-two male rats were subjected to ischemia for 40 minutes and reperfusion for five days. The rats were divided into 4 groups of 8 each; group 1 (control) was given normal saline, and groups 2-4 were gavaged with 0.2, 0.1, 0.05 mg/kg of valepotriate extract, respectively, and received extract (0.2 mg/kg ip) two weeks before ischemia induction.

Results

Dichloromethane V. officinalis (valepotriate) extract exerted a protective effect against ischemia-reperfusion-induced injuries. So that infarct size and number of ventricular arrhythmia and ventricular escape beats decreased compared to the control group. Moreover, ST segment amplitude, QTC interval, and heart rate decreased in the injured hearts and serum levels of antioxidant enzymes glutathione peroxidase, catalase, and superoxide dismutase increased. Biochemical markers malondialdehyde and lactate dehydrogenase also decreased on day 5 after the onset of reperfusion.

Conclusion

V. officinalis extract may have a protective effect against myocardial ischemia-reperfusion by producing antioxidant effects.

Self-care for coronavirus disease through electronic health technologies: A scoping review

Background and Aims

Considering the rapid spread and transmission of COVID-19 and its high mortality rate, self-care practices are of special importance during this pandemic to prevent and control the spread of the virus. In this regard, electronic health systems can play a major role in improving self-care practices related to coronavirus disease. This study aimed to review the electronic health technologies used in each of the constituent elements of the self-care (self-care maintenance, self-care monitoring, and self-care management) during the COVID-19 pandemic.

Methods

This scoping review was conducted based on Arksey and O'Malley's framework. In this study, the specific keywords related to "electronic health," "self-care," and "COVID-19" were searched on PubMed, Web of Science, Scopus, and Google.

Results

Of the 47 articles reviewed, most articles (27 articles) were about self-care monitoring and aimed to monitor the vital signs of patients. The results showed that the use of electronic health tools mainly focuses on training in the control and prevention of coronavirus disease during this pandemic, in the field of self-care maintenance, and medication management, communication, and consultation with healthcare providers, in the field of self-care management. Moreover, the most commonly used electronic health technologies were mobile web applications, smart vital signs monitoring devices, and social networks, respectively.

Conclusion

The study findings suggested that the use of electronic health technologies, such as mobile web applications and social networks, can effectively improve self-care practices for coronavirus disease. In addition, such technologies can be applied by health policymakers and disease control and prevention centers to better manage the COVID-19 pandemic.

Challenges of implementing diagnostic-related groups and healthcare promotion in Iran: A strategic applied research

Background and Aim

Implementing the diagnostic-related groups (DRGs) promotes the efficiency of healthcare. Therefore, the present study aimed to identify the challenges facing implementing the DRGs in Iran.

Methods

The present study is a strategic applied research conducted in two phases. In the first phase, the challenges facing DRGs were extracted through a literature review. Then the collected data is entered into a checklist consisting of five sections including technological, cultural, organizational, strategic, and natural challenges. In the second phase, data were collected by purposive sampling and semistructured interviews with 10 managers of the Medical Services Organization of Tehran, Iran. Data analysis was performed by conventional content analysis using MAXQDA software and descriptive using SPSS software version 19.

Results

The challenges facing the implementing DGRs from the experts' perspective included technological, organizational, nature, strategic, and cultural in order of priority. The three main fundamental challenges were reported; lack of integrating the DGRs with health information system (70%), frequent changes of management (70%), reducing the quality of care following early patient discharge (60%).

Conclusion

The results of the present study showed that the DRG system faced with challenges and healthcare officials should apply policies and guidelines to reform the system before changing the reimbursement system in Iran. By considering the leading countries experiences in the nationalizing the DRG system field, the problems and solutions of the system can be identified and aid in the more successful implementation of these systems.

Artificial intelligence in screening, diagnosis, and classification of diabetic macular edema: A systematic review

We review the application of artificial intelligence (AI) techniques in the screening, diagnosis, and classification of diabetic macular edema (DME) by searching six databases- PubMed, Scopus, Web of Science, Science Direct, IEEE, and ACM- from January 1, 2005 to July 4, 2021. A total of 879 articles were extracted, and by applying inclusion and exclusion criteria, 38 articles were selected for more evaluation. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We provide an overview of the current state of various AI techniques for DME screening, diagnosis, and classification using retinal imaging modalities such as optical coherence tomography (OCT) and color fundus photography (CFP). Based on our findings, deep learning models have an extraordinary capacity to provide an accurate and efficient system for DME screening and diagnosis. Using these in the processing of modalities leads to a significant increase in sensitivity and specificity values. The use of decision support systems and applications based on AI in processing retinal images provided by OCT and CFP increases the sensitivity and specificity in DME screening and detection.

Oncological Applications of Quantum Machine Learning

Background: Cancer is a leading cause of death worldwide. Machine learning (ML) and quantum computers (QCs) have recently advanced significantly. Numerous studies have examined the application of quantum machine learning (QML) in healthcare and validated its superiority over classical ML algorithms. Objectives: This review investigates and reports the oncological applications of QML. Methods: In March 2023, an electronic investigation of PubMed, Scopus, Web of Science, IEEE, and Cochrane databases was performed. The articles were screened based on titles and abstracts, and their full texts were examined. Results: Initially, a total of 207 articles were retrieved. Thereafter, 9 articles were included in the study, most of which were published from 2020 onwards. The results indicated the implementation of various QML techniques in different aspects of oncology, such as reducing mammography image noise, edge detection of breast cancer, clinical decision support in radiotherapy treatment, and cancer classification. Conclusion: These studies revealed that integrating quantum science with ML can significantly improve patient care and clinical outcomes. Future studies should explore the integration of QC and ML and the development of novel algorithms to enhance cancer prognosis, diagnosis, and treatment planning.

Providing a Population Based Registry Model of Drug Poisoning in Iran

Background

The prevalence of drug poisoning is on the rise in Iran due to the increased public access to drugs. A national drug poisoning registry system is a suitable tool for better management, control, and prevention of drug poisoning.

Objectives

This study aimed to propose a national drug poisoning registry model for Iran.

Methods

This was an applied research conducted in two major phases. In the first phase, all sources pertaining to drug poisoning registries were reviewed, and a national drug poisoning registry model was proposed. In the second phase, this model was validated and finalized using a researcher-made questionnaire and through a two-stage Delphi technique.

Results

The focus of national drug poisoning activities and registry management reached the 100% consensus of experts at the Drug and Poison Information Center of the Food and Drug Organization (Ministry of Health and Medical Education). Goals, data sources, registry system structure, data set, standards, data exchange, registry features, and processes of the proposed model also achieved unanimous expert consensus.

Conclusions

Given the importance of a national drug poisoning registry in gathering, storing, analyzing, and reporting the data of patients, it is essential to provide a framework for evaluating and controlling drug poisoning and for generating valuable data for decision-making. The model proposed herein can offer the information infrastructure for designing and implementing such a system.

Usability evaluation and Compatibility test of digital self-management support system for children with cancer and their caregivers: using cloud automation testing platform (Preprint)

BACKGROUND

Despite the increasing development of different smartphone apps in the health care domain, most of these apps lack proper evaluation. In fact, with the rapid development of smartphones and wireless communication infrastructure, many health care systems around the world are using these apps to provide health services for people without sufficient scientific efforts to design, develop, and evaluate them.

OBJECTIVE

The objective of this study was to evaluate the usability of CanSelfMan, a self-management app that provides access to reliable information to improve communication between health care providers and children with cancer and their parents/caregivers, facilitating remote monitoring and promoting medication adherence.

METHODS

We performed debugging and compatibility tests in a simulated environment to identify possible errors. Then, at the end of the 3-week period of using the app, children with cancer and their parents/caregivers filled out the User Experience Questionnaire (UEQ) to evaluate the usability of the CanSelfMan app and their level of user satisfaction.

RESULTS

During the 3 weeks of CanSelfMan use, 270 cases of symptom evaluation and 194 questions were recorded in the system by children and their parents/caregivers and answered by oncologists. After the end of the 3 weeks, 44 users completed the standard UEQ user experience questionnaire. According to the children's evaluations, attractiveness (mean 1.956, SD 0.547) and efficiency (mean 1.934, SD 0.499) achieved the best mean results compared with novelty (mean 1.711, SD 0.481). Parents/caregivers rated efficiency at a mean of 1.880 (SD 0.316) and attractiveness at a mean of 1.853 (SD 0.331). The lowest mean score was reported for novelty (mean 1.670, SD 0.225).

CONCLUSIONS

In this study, we describe the evaluation process of a self-management system to support children with cancer and their families. Based on the feedback and scores obtained from the usability evaluation, it seems that the children and their parents find CanSelfMan to be an interesting and practical idea to provide reliable and updated information on cancer and help them manage the complications of this disease.

Performance of machine learning techniques on prediction of esophageal varices grades among patients with cirrhosis

OBJECTIVES

All patients with cirrhosis should be periodically examined for esophageal varices (EV), however, a large percentage of patients undergoing screening, do not have EV or have only mild EV and do not have high-risk characteristics. Therefore, developing a non-invasive method to predict the occurrence of EV in patients with liver cirrhosis as a non-invasive method with high accuracy seems useful. In the present research, we compared the performance of several machine learning (ML) methods to predict EV on laboratory and clinical data to choose the best model.

METHODS

Four-hundred-and-ninety data from the Liver and Gastroenterology Research Center of Shahid Beheshti University of Medical Sciences in the period 2014-2021, were analyzed applying models including random forest (RF), artificial neural network (ANN), support vector machine (SVM), and logistic regression.

RESULTS

RF and SVM had the best results in general for all grades of EV. RF showed remarkably better results and the highest area under the curve (AUC). After that, SVM and ANN had the AUC of 98%, for grade 3, the SVM algorithm had the highest AUC after RF (89%).

CONCLUSIONS

The findings may help to better predict EV with high precision and accuracy and also can help reduce the burden of frequent visits to endoscopic centers. It can also help practitioners to manage cirrhosis by predicting EV with lower costs.

Usability of Emergency Department Information System Based on Users' Viewpoint; a Cross-Sectional Study

INTRODUCTION

The emergency department is of special importance due to its emergency and vital services, the high volume of referrals, and the patients' physical condition. Thus, it requires a well-designed information system with no usability problems. This study aimed to evaluate the usability of the emergency department information system from users' perspectives.

METHODS

This was a cross-sectional study. The research setting was the emergency department of 3 hospitals. The research instrument was a 37-item questionnaire adapted from the USE and ISO Metrics questionnaires, consisting of five dimensions measuring the usefulness of the system, ease of use, ease of learning, user satisfaction, and suitability for the task. The content validity of the questionnaire was examined using the content validity ratio and content validity index, and its reliability was assessed using Cronbach's alpha (α = 0.88).

RESULTS

Fifty questionnaires were administered in the three hospitals, and the response rate was 80%. According to the findings, 55% of the respondents were female. The highest mean scores belonged to usefulness in emergency department information system (EDIS) A, ease of use in EDIS B, ease of learning in EDIS A, user satisfaction in EDIS C, and suitability for the task in EDIS A. According to the usability evaluation criteria, ease of learning (3.66 ± 0.74), usefulness (3.53 ± 0.87), and suitability for the task (3.47 ± 0.96) received the highest scores, and the lowest scores belonged to user satisfaction (3.29 ± 1.01) and ease of use (3.12 ± 1.00).

CONCLUSION

In terms of usability criteria, the emergency department information system is at a relatively good level. The usability of these systems can be further enhanced by considering the users' working needs, improving software flexibility, customizing the software, using data visualization tools, observing consistency of features and standards, and increasing the quality of information and system services.

Identifying predictors of varices grading in patients with cirrhosis using ensemble learning

Objectives

The present study was conducted to improve the performance of predictive methods by introducing the most important factors which have the highest effects on the prediction of esophageal varices (EV) grades among patients with cirrhosis.

Methods

In the present study, the ensemble learning methods, including Catboost and XGB classifier, were used to choose the most potent predictors of EV grades solely based on routine laboratory and clinical data, a dataset of 490 patients with cirrhosis gathered. To increase the validity of the results, a five-fold cross-validation method was applied. The model was conducted using python language, Anaconda open-source platform. TRIPOD checklist for prediction model development was completed.

Results

The Catboost model predicted all the targets correctly with 100% precision. However, the XGB classifier had the best performance for predicting grades 0 and 1, and totally the accuracy was 91.02%. The most significant variables, according to the best performing model, which was CatBoost, were child score, white blood cell (WBC), vitalism K (K), and international normalized ratio (INR).

Conclusions

Using machine learning models, especially ensemble learning models, can remarkably increase the prediction performance. The models allow practitioners to predict EV risk at any clinical visit and decrease unneeded esophagogastroduodenoscopy (EGD) and consequently reduce morbidity, mortality, and cost of the long-term follow-ups for patients with cirrhosis.

Design and Evaluation of an Electronic Information Exchange System Connecting Laboratories and Physicians' Offices

Laboratory services are a crucial part of medical care and contribute to physicians' treatment-related decision-making. However, paper-based information exchanges between physicians' offices and laboratories waste physicians' time and prevent them from using outpatient test results in a timely and effective manner. To solve this problem, improve the safety and quality of patient care, and save patients' time and energy, the present study developed a web-based system for electronic information exchange between laboratories and offices in Microsoft Visual Studio with the ASP.net technology and the Microsoft SQL Server database. The developed web-based software met the needs of the users and stakeholders (physicians, laboratory personnel, and patients) in the laboratory service cycle. To evaluate the software, user satisfaction was assessed in terms of user interface, operational functionality, and system performance, indicating the acceptability of all the criteria from the viewpoint of the stakeholders. The developed web-based software enables electronic communication between offices and laboratories (two important healthcare bases), establishes information exchange (sending requests and receiving laboratory results) between these two bases, and also notifies the patients. The software gained the overall satisfaction of the users, and this highlights the need for electronic communications in the healthcare domain.

Machine learning-based system for prediction of ascites grades in patients with liver cirrhosis using laboratory and clinical data: design and implementation study

OBJECTIVES

The aim of the study was to implement a non-invasive model to predict ascites grades among patients with cirrhosis.

METHODS

In the present study, we used modern machine learning (ML) methods to develop a scoring system solely based on routine laboratory and clinical data to help physicians accurately diagnose and predict different degrees of ascites. We used ANACONDA3-5.2.0 64 bit, free and open-source platform distribution of Python programming language with numerous modules, packages, and rich libraries that provide various methods for classification problems. Through the 10-fold cross-validation, we employed three common learning models on our dataset, k-nearest neighbors (KNN), support vector machine (SVM), and neural network classification algorithms.

RESULTS

According to the data received from the research institute, three types of data analysis have been performed. The algorithms used to predict ascites were KNN, cross-validation (CV), and multilayer perceptron neural networks (MLPNN), which achieved an average accuracy of 94, 91, and 90%, respectively. Also, in the average accuracy of the algorithms, KNN had the highest accuracy of 94%.

CONCLUSIONS

We applied well-known ML approaches to predict ascites. The findings showed a strong performance compared to the classical statistical approaches. This ML-based approach can help to avoid unnecessary risks and costs for patients with acute stages of the disease.

Reliability of trauma coding with ICD-10

PURPOSE

The reliability of trauma coding is essential in establishing the reliable trauma data and adopting efficient control and monitoring policies. The present study aimed to determine the reliability of trauma coding in educational hospitals affiliated to Shahid Beheshti University of Medical Sciences, Iran.

METHODS

In this descriptive cross-sectional study, 591 coded medical records with a trauma diagnosis in 2018 were selected and recoded by two coders. The reliability of trauma coding was calculated using Cohen's kappa. The data were recorded in a checklist, in which the validity of the content had been confirmed by experts.

RESULTS

The reliability of the coding related to the nature of trauma in research units was 0.75-0.77, indicating moderate reliability. Also, the reliability of the coding of external causes of trauma was 0.57-0.58, suggesting poor reliability.

CONCLUSION

The reliability of trauma coding both in terms of the nature of trauma and the external causes of trauma does not have a good status in the research units. This can be due to the complex coding of trauma, poor documentation of the cases, and not studying the entire case. Therefore, holding training courses for coders, offering training on the accurate documentation to other service providers, and periodically auditing the medical coding are recommended.

Applications, features and key indicators for the development of Covid-19 dashboards: A systematic review study

Introduction

Interactive dashboards can collect data from various information sources and be used nationally and internationally. These information systems have played an important role in managing and controlling epidemic diseases, especially Covid-19. This study aimed to identify the applications, features, and key indicators of advanced dashboards in Covid-19.

Method

The present article is a systematic review study that searched the PubMed, Scopus, and ISI web of sciences databases in 2021 by combining the relevant keywords. After applying the inclusion and exclusion criteria and selecting articles, data collection was prepared using a data collection form. Data analysis was performed using the content analysis method.

Results

Out of 171 articles retrieved, 19 were included in the study for review by applying inclusion and exclusion criteria in the first stage. The most important data sources for the studied dashboards included general online, national, and hospital databases. Monitoring and tracking in the target community and resource management (hospital and public) are the most important issues in Covid-19 dashboards. The study showed that KPIs in 5 main categories of indicators related to hospital beds, clinical data in the hospital, diagnostic and therapeutic measures of hospitals, epidemiological data at the level community, and follow-up indicators of Covid-19 studies were worldwide.

Conclusion

Considering the technological advances at the world level and the large amount of data produced, one of the effective solutions for managing and controlling epidemic and pandemic conditions and diseases is the rapid development of interactive dashboards; Therefore, it is suggested that health officials and policymakers, in addition to developing and updating the existing dashboards in the field of Covid-19, developing the dashboard immediately in case of similar conditions.

Efficacy of Telemedicine for the Management of Asthma: A Systematic Review

Background

Considering the increased prevalence of asthma and its consequences for individuals and society, its effective management and close monitoring is essential. Awareness of the effects of telemedicine can improve asthma management. The present study aimed to systematically review articles examining the effect of telemedicine on the management of asthma, including control of the symptom, patients' quality of life, costs, and adherence to treatment programs.

Materials and Methods

A systematic search was performed on four databases: PubMed, Web of Science, Embase, and Scopus. English language clinical trials investigating the effectiveness of telemedicine in asthma management published from 2005 to 2018 were selected and retrieved. The present study was designed and conducted based on the PRISMA guidelines.

Results

Out of 33 articles included in this research, telemedicine was employed by 23 studies for the promotion of patient adherence to treatment in the form of reminders and feedback, by 18 for telemonitoring and communicating with healthcare providers, by six for offering remote patient education, and by five for counseling. The most frequently used telemedicine approach was asynchronous (used in 21 articles), and the most commonly utilized tool was Web-based (utilized in 11 articles).

Conclusion

Telemedicine can improve symptom control, patients' quality of life, and adherence to treatment programs. However, little evidence exists confirming the effectiveness of telemedicine in decreasing costs.

mHealth Self-Management System to Supporting Children with a Acute Lymphocytic Leukemia (ALL) and their caregivers in low-middle income country: Qualitative Co-Design Study (Preprint)

Background

The unique features of smartphones have extended their use in different fields, especially in the health care domain. These features offer new opportunities to support patients with chronic conditions by providing them with information, education, and self-management skills. We developed a digital self-management system to support children with cancer and their caregivers in Iran (low- and middle-income country).

Objective

This study is aimed at the development and preliminary evaluation of a cancer self-management system (CanSelfMan) tailored to the needs of children with cancer and their parents or caregivers.

Methods

This study was conducted in collaboration with a multidisciplinary team between January and February 2020 at MAHAK’s Pediatric Cancer Treatment and Research Center. We developed a self-management system in six stages: requirement analysis, conformity assessment, preparation of educational content, app prototyping, preliminary evaluation, and developing the final version.

Results

A total of 35 people (n=24, 69% parents and n=11, 31% children) volunteered to participate in the study. However, only 63% (15/24) of parents and 73% (8/11) of children were eligible to participate. By adopting a user-centered design approach, we developed a mobile app, CanSelfMan, that includes five main modules (knowledge base, self-management tips, self-assessment report, ask a question, and reminders) that provide access to reliable information about acute lymphocytic leukemia and the self-management skills required for side effect measurement and reporting. A web-based dashboard was also developed for oncologists and included a dashboard to monitor users’ symptoms and answer their questions.

Conclusions

The CanSelfMan app can support these groups by providing access to reliable information about cancer, facilitating communication between children or parents and health care providers, and helping promote medication adherence through a reminder function. The active participation of the target group can help identify their needs. Therefore, through the involvement of stakeholders such as patients, caregivers, and oncologists in the design process, we improved usability and ensured that the final product was useful. This app is now ready to proceed with feasibility studies.

Eye Injury Registries: A Review on Key Registry Processes

Background:

Data management related to eye injuries is vital in improving care process, improving treatment and implementing preventive programs. Implementation of a registry to manage data is an integral part of this process. This systematic review aimed to identify processes related to eye injury registries.

Methods:

Databases such as PubMed, Web of Science, Embase and Scopus were used in searching for articles from 2010 to Oct 2020 using the keywords “eye injuries” and” registry”. The identified processes related to eye injuries registry such as case finding, data collection, abstracting, reporting, follow-up and data quality control are presented in this review.

Results:

Of 1493 articles retrieved, 30 articles were selected for this study based on the inclusion and exclusion criteria. Majority of these studies were conducted in the United States. All registries had case finding and the most common resources for case finding included medical documents, reports and screening results. Moreover, majority of registries collected data electronically. However, few registries used data quality attributes to improve the data collected.

Conclusion:

Eye injury registry plays an important role in the management of eye injury data and as a result, better management of these data will be established. Taking into consideration that the quality of collected data has a vital role in adopting prevention strategies, it is essential to use high-quality data and quality control methods in planning and designing eye injury registries.

Telehealth in Management of COVID-19 Pandemic: A Scoping Review

COVID-19 has created major health-related, economic, and social challenges in societies, and its high contagion has dramatically altered access to healthcare. COVID-19 management can be improved by the use of telehealth. This study aimed to examine different telehealth technologies in the management of COVID-19 disease in the domains of surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up and investigate the challenges to the application of telehealth in COVID-19 management. This scoping review was conducted based on Arksey and O’Malley's framework. Searches were performed in Web of Science, PubMed, and Scopus databases to examine the evidence on the effectiveness of telehealth in COVID-19 management. Eventually, 36 articles were selected based on the inclusion criteria. The majority of these studies (33%) were conducted in China. Most services offered via telehealth focused on surveillance, tracking, and follow-up, in that order. Moreover, the most frequently used technologies were social networks, web-based apps, and mobile apps, respectively. The use of telehealth in COVID-19 disease management plays a key role in surveillance, diagnosis, screening, treatment, monitoring, tracking, and follow-up.

COVID-19 pandemic and management on hospital length of stay: A review

COVID-19 virus is a serious threat to public health everywhere on the planet. The World Health Organization (WHO) declared the disease epidemic in December 2019 because of its rapid prevalence around the world. The disease is transferred by inhalation or contact with contaminated droplets, and the incubation period varies from 2 to 14 days. COVID-19 has led to unprecedented pressures as demand for healthcare in hospitals and intensive care units around the world increases. As the epidemic intensifies, determining the resulting needs for health care resources (beds, staff, equipment) has become a priority for many countries. Predicting future demand requires estimating how long COVID-19 patients must have access to different levels of hospital care. The length of hospitalization for these patients is one of the management priorities. It is possible to pass through the crisis only with careful planning and comprehensive cooperation.

Application of ICT in effective crisis management: A systematic review

INTRODUCTION

Effective crisis management can reduce the costs and consequences of a crisis and has a significant impact on saving human lives in critical situations. Proper use of information and communication technologies (ICTs) can improve all crisis management phases and crisis communication cycles according to the needs of stakeholders. The purpose of this review article is to identify which ICTs have been used in effective crisis management and what managerial tasks they support.

METHOD

A systematic review was conducted based on PRISMA protocol. The investigated articles that have been published in English were all indexed in PubMed, Science Direct, IEEE, Web of Science, and Google Scholar databases from 2005 to 2019. The keywords searched were "Crisis Management," "Emergency Management," "Information and Communication Technology," and their synonyms.

RESULTS

A total of 1,703 articles were retrieved, and 81 articles that met the inclusion criteria were retained. In terms of content, there were 54 case studies/review articles, 38 proposals, and seven prototypes among which 18 case studies and proposals were the same. According to surveys, 18 ICT tools and technologies have been used in effective crisis management with the purpose of supporting managerial tasks such as situation assessment, decision-making, coordination/command and control, communication with the public, and supply of basic services in order to enable crisis management and logistics.

CONCLUSION

This study showed that proper use of ICT can help crisis managers optimize their performance that will consequently result in effective crisis management and the reduction of casualties. In the crisis management cycle, several tools and technologies have been used for various purposes, however; some crisis managers' tasks were still not taken into consideration sufficiently, and thus, some recommendations for further research in this field were provided.

Development of Inflammatory Bowel Diseases Registry Software

BACKGROUND With an increase in the prevalence and incidence of inflammatory bowel diseases (IBDs), they have become a global challenge. The IBD registry provides complete and timely data, thereby greatly contributing to the estimation of the burden of these diseases and development of control and prevention programs. We aimed to develop an IBD registry software. METHODS The present applied-developmental study had two main stages: determining user requirements, and developing the IBD registry software. The software was created using a Web-based software development technology called ASP.NET Core 2. The programming language in this framework was C#, and the SQL Server 2017 was employed to create a strong and integrated software databank in the relational form. RESULTS When determining user requirements, the data elements were classified into two main categories of patient information and visits and tests. Moreover, in this stage, registry functions, including case ascertainment, abstracting, follow-up, quality control, and reporting were identified. In the registry software development stage, the object-oriented conceptual model was designed with five use case diagrams and 59 classes. The user interface comprised the following main sections: add patient, find patient, complete source report, report, staff, and drugs. Precise user authentication and authorization were also employed to enhance the security of the developed software. CONCLUSION Development of an IBD registry which can precisely record patients and estimate the incidence, prevalence, and socioeconomic burden of these diseases can assist in planning for the control and prevention of IBD in healthcare systems.

Evaluating the effect of an in-service training workshop on ICD-10 coding instructions of pregnancy, childbirth and the puerperium for clinical coders

The quality of the data coded based on the 10^th revision of the International Classification of Diseases (ICD-10) can be improved by providing continuous education and promoting the clinical coders’ knowledge and skills. Due to the significance of maternal health in promoting the health of society, the present study evaluated the effects of an in-service training workshop on ICD-10 coding instructions of pregnancy, childbirth, and the puerperium for clinical coders. This applied evaluation study was conducted to evaluate the effects of a coding instructions training course focusing on the 15^th chapter of the ICD-10. The statistical population comprised 45 clinical coders working in the hospitals. The data were collected by a researcher-made questionnaire scored on a five-point Likert scale at the reaction level and by pretest and posttest questionnaires at the learning level. The data were then analyzed by descriptive statistics at the reaction level and by a paired-samples t-test at the learning level. The participants’ satisfaction with the training course was 94.7% on average at the reaction level. At the learning level, the results of the paired-samples t-test showed a significant difference between the means of scores before and after the training course (p=0.000). The training course led to satisfaction and enhanced the capabilities of the clinical coders with regard to coding the 15^th chapter of ICD-10. Clinical Coders must receive training on the new changes and guidelines in the other chapters of ICD-10 based on its most recent revision and employ them in the workplace.

Minimum Data Set for a Poisoning Registry: A Systematic Review

Poisoning, as a well-known medical condition, puts everyone at risk. As a data management tool, a registry plays an important role in monitoring the poisoned patients. Having a poisoning minimum data set is a major requirement for creating a poisoning registry. Therefore, the present systematic review was conducted in 2019 to identify the minimum data set for a poisoning registry. Searches were performed in four scientific databases, i.e., PubMed, Scopus, Web of Science, and Embase. The keywords used in the searches included minimum data set, "poison", and "registry". Two researchers independently evaluated the titles, abstracts, and texts of the papers. The data were collected from the related papers. Ultimately, the minimum data set was identified for the poisoning registry. Data elements extracted from the sources were classified into two general categories: administrative data and clinical data. Ninety-eight data elements in the administrative data category were subdivided into three sections: general data, admission data, and discharge data. One-hundred and thirty-one data elements in the clinical data category were subdivided into five sections: clinical observation data, clinical assessment data, past medical history data, diagnosis data, and treatment plan data. The minimum data set is a prerequisite for creating and using a poisoning registry and data system. It is suggested to evaluate and use the poisoning minimum data set in accordance with the national laws, needs, and standards based on the opinion of the local experts.

Diagnostic Point-of-Care Tests with an Approach to Data Management

Introduction: Diagnostic point- of- care (POC) tests are considered as an approach to ease the diagnosis of diseases, deliver quicker patient care, and improve patient safety. The aim of this study was to review the diagnostic POC tests with an approach to data management.Material and Methods: In this review study, PubMed, Science Direct, Google Scholar, Scopus, and Wolters Kluwer databases were searched from 2000 to 2020 using a combination of related keywords. A total of 96 articles were retrieved of which 48 articles considered as relevant.  The content of these articles were then analyzed with respect to the aim of the study. The inclusion criteria for the articles were: 1) they focused the POC test; 2) addressed data management aspects; 3) written in English.  Articles that only addressed the POC tests from a clinical or technical perspective and with no indication of data management were excluded.Results: Rapid and timely collection and processing of test results, the capability of exchanging test results, and capabilities such as documentation and data quality control play a significant role in reducing the average length of stay in hospital, planning, decision-making, organizing, controlling clinical and managerial activities, and achieving the efficiency of services provided.Conclusion: In addition to applying diagnostic POC tests technologies, medical settings should have necessary approaches for managing data generated by these technologies to improve better use of data in service delivery.

Developing A General Framework for National Health Information Network for Developing Countries

Background

The National Health Information Network (NHIN) is one of the key issues in health information systems in any country. However, the development of this network should be based on an appropriate framework. Unfortunately, the conducted projects of health information systems in the Ministry of Health of Iran do not fully comply with the concept of NHIN. The present study was aimed to develop a general framework for NHIN in Iran.

Materials and Methods

In this study, in the first stage, the required information about the concept of the NHIN framework and related NHIN documents in the USA and the UK were collected based on a literature review. Then, according to the results of the first stage and with regards to the structure of the Iranian health system, a general framework for Iranian NHIN was proposed. The Delphi technique was conducted to verify the framework.

Results

The proposed framework for Iranian NHIN includes three dimensions; components, principles, and architecture. Over 80% of experts have evaluated all three aspects of the framework at an acceptable scale. In total, the proposed framework has been evaluated by 83.8% of the experts at an acceptable scale.

Conclusion

The proposed framework was expected to serve as the starting point for moving towards the design and creation of Iranian NHIN. At any rate, the framework could be criticized, and it could only be used for the countries whose health system is similar to the structure of the health system in Iran.

The Features and Processes of Poisoning Registries: A Scoping Review

Background: Poisoning is a common condition worldwide that requires precise evaluation of the quality and rapid management. Registry plays an essential role in the management of toxins. This study aimed to examine the features and processes of poisoning registries. Methods: This review study was conducted in 2020. Several searches were conducted in the following scientific databases: PubMed, Scopus, Embase, and Web of Science using a combination of keywords, such as “data management, registry, poison, and toxic”. The review of titles, abstracts, and full-text of the selected articles was independently performed by two researchers. Besides, the obtained data were analyzed based on the research objectives by the content analysis method. Results: Some critical features of registries were considered the confidentiality of patients’ information, i.e., equipped with various technologies, such as Geographical Information System (GIS), warning systems, searches, and text retrieval tools. The most common sources of case findings were self-reported contacts by individuals and healthcare professionals to poison control centers. Moreover, the main tool for data collection was electronic forms. The major indices of data quality were the accuracy, completeness, and consistency of the data. Phone calls were usually made at follow-ups. Conclusion: The registry’s features and processes are an essential and fundamental step to achieve the registry goals, as well as designing and developing these systems. It is recommended that the registries be equipped with various technologies to better manage the exposure cases. It is recommended to use educational, incentive, competitive, participatory, and motivational mechanisms among all organizations and individuals involved in poisoning registry programs.

X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic

PURPOSE

Due to the excessive use of raw materials in diagnostic tools and equipment during the COVID-19 pandemic, there is a dire need for cheaper and more effective methods in the healthcare system. With the development of artificial intelligence (AI) methods in medical sciences as low-cost and safer diagnostic methods, researchers have turned their attention to the use of imaging tools with AI that have fewer complications for patients and reduce the consumption of healthcare resources. Despite its limitations, X-ray is suggested as the first-line diagnostic modality for detecting and screening COVID-19 cases.

METHOD

This systematic review assessed the current state of AI applications and the performance of algorithms in X-ray image analysis. The search strategy yielded 322 results from four databases and google scholar, 60 of which met the inclusion criteria. The performance statistics included the area under the receiver operating characteristics (AUC) curve, accuracy, sensitivity, and specificity.

RESULT

The average sensitivity and specificity of CXR equipped with AI algorithms for COVID-19 diagnosis were >96% (83%-100%) and 92% (80%-100%), respectively. For common X-ray methods in COVID-19 detection, these values were 0.56 (95% CI 0.51-0.60) and 0.60 (95% CI 0.54-0.65), respectively. AI has substantially improved the diagnostic performance of X-rays in COVID-19.

CONCLUSION

X-rays equipped with AI can serve as a tool to screen the cases requiring CT scans. The use of this tool does not waste time or impose extra costs, has minimal complications, and can thus decrease or remove unnecessary CT slices and other healthcare resources.

An Intelligent Clinical Decision Support System for Predicting Acute Graft-versus-host Disease (aGvHD) following Allogeneic Hematopoietic Stem Cell Transplantation

Background:

Acute graft-versus-host disease (aGvHD) is a complex and often multisystem disease that causes morbidity and mortality in 35% of patients receiving allogeneic hematopoietic stem cell transplantation (AHSCT).

Objective:

This study aimed to implement a Clinical Decision Support System (CDSS) for predicting aGvHD following AHSCT on the transplantation day.

Material and Methods:

In this developmental study, the data of 182 patients with 31 attributes, which referred to Taleghani Hospital Tehran, Iran during 2009–2017, were analyzed by machine learning (ML) algorithms which included XGBClassifier, HistGradientBoostingClassifier, AdaBoostClassifier, and RandomForestClassifier. The criteria measurement used to evaluate these algorithms included accuracy, sensitivity, and specificity. Using the machine learning developed model, a CDSS was implemented. The performance of the CDSS was evaluated by Cohen’s Kappa coefficient.

Results:

Of the 31 included variables, albumin, uric acid, C-reactive protein, donor age, platelet, lactate Dehydrogenase, and Hemoglobin were identified as the most important predictors. The two algorithms XGBClassifier and HistGradientBoostingClassifier with an average accuracy of 90.70%, sensitivity of 92.5%, and specificity of 89.13% were selected as the most appropriate ML models for predicting aGvHD. The agreement between CDSS prediction and patient outcome was 92%.

Conclusion:

ML methods can reliably predict the likelihood of aGvHD at the time of transplantation. These methods can help us to limit the number of risk factors to those that have significant effects on the outcome. However, their performance is heavily dependent on selecting the appropriate methods and algorithms. The next generations of CDSS may use more and more machine learning approaches.

Deep Convolutional Neural Network-Based Computer-Aided Detection System for COVID-19 Using Multiple Lung Scans: Design and Implementation Study

BACKGROUND

Owing to the COVID-19 pandemic and the imminent collapse of health care systems following the exhaustion of financial, hospital, and medicinal resources, the World Health Organization changed the alert level of the COVID-19 pandemic from high to very high. Meanwhile, more cost-effective and precise COVID-19 detection methods are being preferred worldwide.

OBJECTIVE

Machine vision-based COVID-19 detection methods, especially deep learning as a diagnostic method in the early stages of the pandemic, have been assigned great importance during the pandemic. This study aimed to design a highly efficient computer-aided detection (CAD) system for COVID-19 by using a neural search architecture network (NASNet)-based algorithm.

METHODS

NASNet, a state-of-the-art pretrained convolutional neural network for image feature extraction, was adopted to identify patients with COVID-19 in their early stages of the disease. A local data set, comprising 10,153 computed tomography scans of 190 patients with and 59 without COVID-19 was used.

RESULTS

After fitting on the training data set, hyperparameter tuning, and topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test data set and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS

The proposed model achieved acceptable results in the categorization of 2 data classes. Therefore, a CAD system was designed on the basis of this model for COVID-19 detection using multiple lung computed tomography scans. The system differentiated all COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly help radiologists detect COVID-19 in its early stages. During the COVID-19 pandemic, the use of a CAD system as a screening tool would accelerate disease detection and prevent the loss of health care resources.

Functions and Outcomes of Personal Health Records for Patients with Chronic Diseases: A Systematic Review

INTRODUCTION

The personal health record (PHR) makes it possible for patients to access, manage, track, and share their health information. By engaging patients in chronic disease care, they will be active members in decision-making and healthcare management.

OBJECTIVES

This study aimed to identify the functions and outcomes of PHR for patients with four major groups of chronic diseases (cardiovascular diseases, cancers, diabetes, and chronic respiratory diseases).

METHOD

A systematic review was conducted on studies published in PubMed, Scopus, Web of Science, and Embase. Searching and screening were performed using the keyword of "Personal Health Record" without time limitation, and ended in August 2018.

RESULTS

In total, 3742 studies were retrieved, 35 of which met the inclusion criteria. Out of these 35, 18 studies were conducted in the United States, 24 studies were related to patients with diabetes, and 32 studies focused on tethered PHRs. Moreover, in 25 studies, the function of viewing and reading medical records and personal health information was provided for three groups of chronic patients. Results showed that the use of PHRs helps the management and control of chronic diseases (10 studies).

CONCLUSION

It is recommended that integrated PHRs with comprehensive functions and features were designed in order to support patient independence and empowerment in self-management, decrease the number of referrals to health centers, and reduce the costs imposed on families and society.

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review

Introduction

The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population.

Result

This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values.

Conclusion

The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

Developing a Model for National Health Information Governance Program in Iran

With regard to the importance of health Information Governance (IG) programs in improving the quality and reducing the cost of healthcare services and the lack of a coherent health IG program in Iran’s health system, this study aimed to develop a model for national health information governance program in Iran.

The present research was an applied, cross-sectional descriptive study that was done in three steps, including literature review, development of a model for national health IG program in Iran, and model validation. In the third step, we used a questioner to validate the model through the Delphi method. Data analysis was done by descriptive statistics.

The model for the national IG program in Iran was developed in 3 main sections consisting of 13 components, 12 principles, natural and judicial authorities of the health IG program, and their job description. Findings from the validation of the initial model showed that most experts (93%) confirmed the components and sub-components, principles, and natural and legal bodies supervising the national health IG program and their job description in the proposed model.

Considering the structure of the Iranian health system, it was recommended to establish a health IG council in the Ministry of Health and Medical Education in order to develop guidelines and give advice to health care providers. Based on the proposed model, directors and staff of different departments of health care centers, especially those involved in health IG, are also responsible for the better implementation of the national health IG program.

Usability Evaluation of the Information System Used in Neuroscience Research Centres

Introduction: Information systems are tools for facilitating information management in research centres that improve quality by reducing errors and increasing speed and precision. Accordingly, their usability is of utmost importance. Usability problems can lead to user errors, may threaten patient safety, and negatively impact the quality of care. Aim: To evaluate the usability of the information system used in Neuroscience Research centres of hospitals affiliated with Shahid Beheshti University of Medical Sciences (SBUMS), Tehran, Iran. Materials and Methods: This was a descriptive study conducted in July 2020 at SBUMS. Before starting the study, ethical considerations such as obtaining informed consent, anonymity, confidentiality, and the participants’ freedom to withdraw from the study were taken into account. The data collection instrument was a questionnaire adapted from the Usefulness, Satisfaction, and Ease of Use (USE) and ISO Metrics questionnaires. Samples were information system used in Neuroscience Research Centres of hospitals affiliated with SBUMS. Therefore, from the centres affiliated with SBUMS, only two hospitals met this requirement, which were Educational hospitals affiliated with SBMUS. The content validity of the questionnaire was examined, and its reliability was checked by Cronbach’s alpha. Results: The information systems of the mentioned centres had a usefulness of 5.93, learnability of 5.79, memorability of 5.22, user satisfaction of 4.89, and ease of use of 4.76, based on a 7-point Likert scale. Overall, the usability of the designed systems had an acceptable and favourable state based on all the criteria. Conclusion: Of the examined criteria, usefulness and learnability achieved a higher score, indicating the good design of the system in terms of these dimensions. However, the ease of use had the lowest score, showing the poor user design of the information system in this dimension. To achieve an excellent level of information system usability in these centres, attention should be paid to all the dimensions of information system usability.

Designing a National Haemodialysis Registry Model for Iran

Introduction: The global prevalence of End-Stage Renal Disease (ESRD) requiring therapeutic dialysis is on the rise. Haemodialysis is the main therapeutic dialysis method. Evaluating its effectiveness and planning to promote the quality of care and epidemiological research necessitate the development of registries as the main management tool. Aim: To design a national haemodialysis registry model for Iran. Materials and Methods: This was an applied descriptive study. Based on a review of articles and information sources, and a comparative study of national hemodialysis registries in developed countries, a national haemodialysis registry model was designed for Iran. After confirming the reliability and validity of the questionnaire, the designed model was given to nephrologists in a two-stage Delphi technique, and their comments were applied to the final model. Results: The presented national haemodialysis registry model main components consist of: goals, structure, data sources, minimum dataset, standards, and processes, all of which received 100% expert consensus. Conclusion: This registry is a powerful database for the progress of treatment, understanding changes in the treatment and outcomes, examining the factors affecting prognosis and quality of life.

Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis

The diagnosis of multiple sclerosis (MS) is difficult considering its complexity, variety in signs and symptoms, and its similarity to the signs and symptoms of other neurological diseases. The purpose of this study is to design and develop a clinical decision support system (CDSS) to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS software was developed in four stages: requirement analysis, system design, system development, and system evaluation. The Rational Rose and SQL Server were used to design the object-oriented conceptual model and develop the database. The C sharp programming language and the Visual Studio programming environment were used to develop the software. To evaluate the efficiency and applicability of the software, the data of 130 medical records of patients aged 20 to 40 between 2017 and 2019 were used along with the Nilsson standard questionnaire. SPSS Statistics was also used to analyze the data. For MS diagnosis, CDSS had a sensitivity, specificity and accuracy of 1, 0.97 and 0.99, respectively, and the area under the ROC curve was 0.98. The agreement rate of kappa coefficient (κ) between software diagnosis and physician's diagnosis was 0.98. The average score of software users was 98.33%, 96.65%, and 96.9% regarding the ease of learning, memorability, and satisfaction, respectively. Therefore, the applicability of the CDSS for MS diagnosis was confirmed by the neurologists. The evaluation findings show that CDSS can help physicians in the accurate and timely diagnosis of MS by using the rule-based method.

Proposing a Model for the National Hemovigilance Information System in Iran

The present study aimed to propose a model for the national hemovigilance information system with a database approach, considering the importance and necessity of developing an information system for such a network.

This is an applied, descriptive, and cross-sectional study, which was conducted in 2018. The research population comprised hemovigilance information systems in advanced countries, including the USA, UK, Australia, and France. Data were collected from library sources and the Internet from 2000 to 2018. The proposed model for the national hemovigilance information system was introduced using comparative tables and based on the similarities and differences of systems in the studied countries. The proposed model was then validated using the two-step Delphi technique through a researcher-made questionnaire whose validity was confirmed, and reliability was approved by a Cronbach’s alpha of 94%.

The final model of the national hemovigilance information system comprised five main components: goals, organizations involved in the blood transfusion process, databases of blood transfusion organizations, data transfer flow between the databases of blood transfusion organizations, and transferable datasets, and hemovigilance-related committees. This model was approved by experts with an >85% agreement coefficient.

The national hemovigilance information system with a database approach can improve blood transfusion health by providing access to reliable sources on blood transfusion complications to everyone, especially the medical community. Thus, it is essential to implement this standard accurately and precisely control the practical methods of this process based on international guidelines.

Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches

BACKGROUND

About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques are tools that can improve the analysis and interpretation or extraction of knowledge from the data. These techniques may enhance the prognosis and diagnosis associated with reducing diseases such as T2DM. We applied four classification models, including K-nearest neighbor (KNN), support vector machine (SVM), logistic regression, and artificial neural networks (ANN) for diagnosing T2DM, and we compared the diagnostic power of these algorithms with each other. We performed the algorithms on six LncRNA variables (LINC00523, LINC00995, HCG27_201, TPT1-AS1, LY86-AS1, DKFZP) and demographic data.

RESULTS

To select the best performance, we considered the AUC, sensitivity, specificity, plotted the ROC curve, and showed the average curve and range. The mean AUC for the KNN algorithm was 91% with 0.09 standard deviation (SD); the mean sensitivity and specificity were 96 and 85%, respectively. After applying the SVM algorithm, the mean AUC obtained 95% after stratified 10-fold cross-validation, and the SD obtained 0.05. The mean sensitivity and specificity were 95 and 86%, respectively. The mean AUC for ANN and the SD were 93% and 0.03, also the mean sensitivity and specificity were 78 and 85%. At last, for the logistic regression algorithm, our results showed 95% of mean AUC, and the SD of 0.05, the mean sensitivity and specificity were 92 and 85%, respectively. According to the ROCs, the Logistic Regression and SVM had a better area under the curve compared to the others.

CONCLUSION

We aimed to find the best data mining approach for the prediction of T2DM using six lncRNA expression. According to the finding, the maximum AUC dedicated to SVM and logistic regression, among others, KNN and ANN also had the high mean AUC and small standard deviations of AUC scores among the approaches, KNN had the highest mean sensitivity and the highest specificity belonged to SVM. This study's result could improve our knowledge about the early detection and diagnosis of T2DM using the lncRNAs as biomarkers.

The Effect of Information Technology on the Information Exchange between Laboratories and Ambulatory Care Centers: A Systematic Review

Laboratory services form an integral part of medical care in the decision-making of physicians, including those working at ambulatory care centers. Information exchange is essential between ambulatory care centers and laboratories. Inevitable errors have always existed in the exchange of such information on paper, which can be to some extent avoided by developing appropriate computer-based interfaces. Therefore, this review aimed to examine studies conducted to determine the effect of electronic communication between ambulatory care centers and laboratories. This systematic review was conducted on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Studies were searched in the PubMed, Embase, Cochrane, and Web of Science, and those written in English and published between 2000 and February 2019 with full texts available were selected. From a total of 3898 papers retrieved from the studied databases, 24 papers were eligible for entering this study after removing similar and nonrelated studies. Electronic exchanges between ambulatory care centers and laboratories can have numerous benefits in terms of financial, organizational, and quality. This evidence for the value of electronic communications is an important factor contributing to its local investment and adoption.

Machine Learning Classification Algorithms to Predict aGvHD following Allo-HSCT: A Systematic Review

BACKGROUND

 The acute graft-versus-host disease (aGvHD) is the most important cause of mortality in patients receiving allogeneic hematopoietic stem cell transplantation. Given that it occurs at the stage of severe tissue damage, its diagnosis is late. With the advancement of machine learning (ML), promising real-time models to predict aGvHD have emerged.

OBJECTIVE

 This article aims to synthesize the literature on ML classification algorithms for predicting aGvHD, highlighting algorithms and important predictor variables used.

METHODS

 A systemic review of ML classification algorithms used to predict aGvHD was performed using a search of the PubMed, Embase, Web of Science, Scopus, Springer, and IEEE Xplore databases undertaken up to April 2019 based on Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statements. The studies with a focus on using the ML classification algorithms in the process of predicting of aGvHD were considered.

RESULTS

 After applying the inclusion and exclusion criteria, 14 studies were selected for evaluation. The results of the current analysis showed that the algorithms used were Artificial Neural Network (79%), Support Vector Machine (50%), Naive Bayes (43%), k-Nearest Neighbors (29%), Regression (29%), and Decision Trees (14%), respectively. Also, many predictor variables have been used in these studies so that we have divided them into more abstract categories, including biomarkers, demographics, infections, clinical, genes, transplants, drugs, and other variables.

CONCLUSION

 Each of these ML algorithms has a particular characteristic and different proposed predictors. Therefore, it seems these ML algorithms have a high potential for predicting aGvHD if the process of modeling is performed correctly.

Informational Needs in Patients With Breast Cancer With Lymphedema: Is It Important?

Introduction

Lymphedema is one of the complications of breast cancer treatment. It has no cure yet and can affect the quality of life. This study aimed to identify and investigate informational needs, preferred delivery methods, and time of receiving information about lymphedema for these patients.

Methods

One hundred participants were recruited through Lymphedema Clinic in Motamed Cancer Institute in Tehran, Iran, through convenience sampling and were asked to complete a self-administered survey. Data collection took place on all opening days between October 2018 and mid-March 2019.

Results

Most of the participants were above 40 years, have a diploma, homemaker, and the average income of most of the participants (57.2%) was low. The importance of having lymphedema information was very high for them. Most of them wanted detailed information at diagnosis of breast cancer. The preferred information of delivery methods were private sessions and social networks.

Conclusions

Patients with breast cancer who have lymphedema have high needs as regards concise lymphedema information. Private sessions with physicians and social networks can provide detailed information for them.