Design and usability evaluation of a mobile application for self-care among Iranian adolescents.

BACKGROUND: Mobile phones can be an ideal platform to engage adolescents to maintain, improve, and promote self-care. Therefore, the current study aims to design and evaluate the usability of a mobile application for self-care in adolescents with a user-centered approach. METHODS: The current applied developmental study was done in four steps. The first step, polling and examining opinions was conducted through in-depth semi-structured interviews, with the aim of user-centered mobile application design with the involvement of 30 participants. The second step, extracting and compiling the educational content related to the main themes of the self-care app, was obtained from national and international guidelines and instructions, including the World Health Organization, the Center for Disease Control and Prevention, the Ministry of Health and Medical Education, etc. In the third step, the initial version of the mobile application was developed. In the fourth step, app usability was evaluated by 30 participants from the target group, 2 weeks after using the app, using the MAUQ questionnaire. RESULTS: In the first step, 789 codes, 12 sub-categories, and 3 categories were extracted. These codes were used in the design of the mobile application. In the second step, educational information was prepared and arranged in 5 sections (physical activity, nutrition, personal hygiene, risky behaviors and safety and events) in the form of text, images and short videos. In the third step, the mobile application was designed based on step 1 and 2. This application operates in online mode and under the Android operating system. the initial version of the mobile application was developed using JavaScript and Typescript programming languages in a Visual Studio Code environment. In the fourth step, the participants the overall level of usability of the application as very good with an average of 6.28 ± 0.55. The highest average score was given to the user interface and satisfaction with an average score of 6.43 ± 0.58. CONCLUSIONS: The "My-Care" app is a collaboratively designed smartphone app for adolescents that targets 5 dimensions of physical self-care. This app has the potential to teach, assess, and promote self-care among adolescents.

Developing public health surveillance dashboards: a scoping review on the design principles.

BACKGROUND: Public Health Dashboards (PHDs) facilitate the monitoring and prediction of disease outbreaks by continuously monitoring the health status of the community. This study aimed to identify design principles and determinants for developing public health surveillance dashboards. METHODOLOGY: This scoping review is based on Arksey and O'Malley's framework as included in JBI guidance. Four databases were used to review and present the proposed principles of designing PHDs: IEEE, PubMed, Web of Science, and Scopus. We considered articles published between January 1, 2010 and November 30, 2022. The final search of articles was done on November 30, 2022. Only articles in the English language were included. Qualitative synthesis and trend analysis were conducted. RESULTS: Findings from sixty-seven articles out of 543 retrieved articles, which were eligible for analysis, indicate that most of the dashboards designed from 2020 onwards were at the national level for managing and monitoring COVID-19. Design principles for the public health dashboard were presented in five groups, i.e., considering aim and target users, appropriate content, interface, data analysis and presentation types, and infrastructure. CONCLUSION: Effective and efficient use of dashboards in public health surveillance requires implementing design principles to improve the functionality of these systems in monitoring and decision-making. Considering user requirements, developing a robust infrastructure for improving data accessibility, developing, and applying Key Performance Indicators (KPIs) for data processing and reporting purposes, and designing interactive and intuitive interfaces are key for successful design and development.

Do blue light filter applications improve sleep outcomes? A study of smartphone users' sleep quality in an observational setting.

Exposure to blue light at bedtime, suppresses melatonin secretion, postponing the sleep onset and interrupting the sleep process. Some smartphone manufacturers have introduced night-mode functions, which have been claimed to aid in improving sleep quality. In this study, we evaluate the impact of blue light filter application on decreasing blue light emissions and improving sleep quality. Participants in this study recorded the pattern of using their mobile phones through a questionnaire. In order to evaluate sleep quality, we used a PSQI questionnaire. Blue light filters were used by 9.7% of respondents, 9.7% occasionally, and 80% never. The mean score of PSQI was more than 5 in 54.10% of the participants and less than 5 in 45.90%. ANOVA test was performed to assess the relationship between using blue light filter applications and sleep quality (p-value = 0.925). The findings of this study indicate a connection between the use of blue light filter apps and habitual sleep efficiency in the 31-40 age group. However, our results align only to some extent with prior research, as we did not observe sustained positive effects on all parameters of sleep quality from the long-term use of blue light filtering apps. Several studies have found that blue light exposure can suppress melatonin secretion, exacerbating sleep problems. Some studies have reported that physical blue light filters, such as lenses, can affect melatonin secretion and improve sleep quality. However, the impact of blue light filtering applications remains unclear and debatable.

Using smartphones before bedtime and being exposed to its blue light can make it harder to fall asleep and disrupt your sleep. Some smartphone makers have introduced a night mode feature claiming it can help improve your sleep. In this study, we wanted to find out if using these blue light filters on smartphones really makes a difference. We asked people how often they used blue light filters on their phones and also had them fill out a questionnaire about their sleep quality. Only about 10% of people said they used blue light filters regularly, another 10% used them occasionally, and the majority, around 80%, never used them. When we looked at the results, more than half of the participants had sleep scores higher than 5, indicating they might have sleep problems. Less than half had sleep scores lower than 5, suggesting better sleep quality. We used some statistical tests to see if using blue light filters had any link to sleep quality, and the results showed that there was only a connection between the use of blue light filter apps and habitual sleep efficiency in the 31­40 age group. Our findings matched what other studies have found before, that using blue light filters on smartphones may not significantly help improve sleep. So, while it might be a good idea to limit smartphone use before bed, using a blue light filter app may not be the magic solution for better sleep.

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.

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.

Requirements and challenges of hospital dashboards: a systematic literature review.

BACKGROUND: Today, the use of data in administrative and clinical processes is quite challenging due to the large volume of data, data collection from various sources, and lack of data structure. As a data management tool, dashboards play an important role in timely visual display of critical information on key performances. OBJECTIVES: This systematic review aimed to identify functional and non-functional requirements, as well as challenges of using dashboards in hospitals. METHODS: In this systematic review, four databases, including the Web of Science, PubMed, EMBASE, and Scopus, were searched to find relevant articles from 2000 until May 30, 2020. The final search was conducted on May 30, 2020. Data collection was performed using a data extraction form and reviewing the content of relevant studies on the potentials and challenges of dashboard implementation. RESULTS: Fifty-four out of 1254 retrieved articles were selected for this study based on the inclusion and exclusion criteria. The functional requirements for dashboards included reporting, reminders, customization, tracking, alert creation, and assessment of performance indicators. On the other hand, the non-functional requirements included the dashboard speed, security, ease of use, installation on different devices (e.g., PCs and laptops), integration with other systems, web-based design, inclusion of a data warehouse, being up-to-data, and use of data visualization elements based on the user's needs. Moreover, the identified challenges were categorized into four groups: data sources, dashboard content, dashboard design, implementation, and integration in other systems at the hospital level. CONCLUSION: Dashboards, by providing information in an appropriate manner, can lead to the proper use of information by users. In order for a dashboard to be effective in clinical and managerial processes, particular attention must be paid to its capabilities, and the challenges of its implementation need to be addressed.

Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.

This study aimed to design an end-to-end deep learning model for estimating the value of fractional flow reserve (FFR) using angiography images to classify left anterior descending (LAD) branch angiography images with average stenosis between 50 and 70% into two categories: FFR > 80 and FFR ≤ 80. In this study 3625 images were extracted from 41 patients' angiography films. Nine pre-trained convolutional neural networks (CNN), including DenseNet121, InceptionResNetV2, VGG16, VGG19, ResNet50V2, Xception, MobileNetV3Large, DenseNet201, and DenseNet169, were used to extract the features of images. DenseNet169 indicated higher performance compared to other networks. AUC, Accuracy, Sensitivity, Specificity, Precision, and F1-score of the proposed DenseNet169 network were 0.81, 0.81, 0.86, 0.75, 0.82, and 0.84, respectively. The deep learning-based method proposed in this study can non-invasively and consistently estimate FFR from angiographic images, offering significant clinical potential for diagnosing and treating coronary artery disease by combining anatomical and physiological parameters.

Design and evaluation of mobile application for adolescents' self-care: protocol for a randomized controlled field trial.

BACKGROUND: Adolescence is a critical stage for the development of self-care. Many adolescents use mobile apps to monitor and improve their health. Health information technology plays a significant role in the field of self-care. This article provides a protocol for a study to design and evaluate mobile applications for adolescent self-care. METHODS: The present research is a combination of applied development study, methodological, and intervention experimental. This study will be done in three stages: The first stage is the design and evaluation of a mobile application for adolescent self-care. The second stage is, designing and evaluating the psychometric properties of the "Questionnaire for Measuring Adolescent Self-Care Performance." This questionnaire will be used before and after using the application in the third stage. The third stage is determining the effectiveness of self-care interventions based on mobile applications among adolescents. The target group will be adolescents aged 10-19 from the schools of Amol City. In the first stage, the opinions of 30 people adolescents, parents, and experts will be used. In the second stage, the number of samples will be 10 times the number of items in the questionnaire. In the third stage, 50 people will be in the intervention group and 50 people will be in the control group. Descriptive statistics will be used for data analysis. Between-group and intra-group comparisons will be calculated about quantitative variables, independent t-test and paired t-test, and analysis of variance. The chi-square test and Fisher's exact test will be used in SPSS 16 software to test the homogeneity of qualitative variables between the two groups. DISCUSSION: In the first stage, based on the opinions received from the target group, a user-centered educational application for self-care of adolescents will be designed. In the second stage, after determining the validity and reliability, a questionnaire will be designed to measure the self-care performance of adolescents. In the third stage, using an intervention study for 3 months, the effectiveness of the training will be determined through the designed application. Our findings are scheduled for a full analysis, with expectations that analyses will be completed by September 2023.

Requirements and Key Features of a Mobile Application for Adolescent Self-Care From a Stakeholders Perspective: A Qualitative Study.

Adolescence is characterized by many changes and these changes differentiate adolescents' self-care needs. The use of smartphones and tablets to provide healthcare services has expanded, and the user-centered design could help to create mobile applications based on users' needs. Therefore, the present study aimed to identify the data requirements and key features of mobile application for adolescent self-care from a stakeholder perspective. This study was conducted with a qualitative approach to identify the key features of mobile application for adolescent's self-care as well as educational content axes for five component of self-care using conventional and directed content analysis respectively. From 3 sub-groups 30 participants were selected based on purposive sampling with maximum variety and sampling was performed until data saturation. Data were collected through in-depth semi-structured interviews. Participants' informed consent was obtained before the interview. The interview lasted 20-40 min and MAXQDA software version 10 was used for data analysis. In this study, four criteria of acceptability, reliability, transferability, and validity proposed by Guba and Lincoln were used to evaluate and validate the data. After conducting the interviews, 789 initial codes, 12 sub-categories, and 3 categories (app view, app content architecture, app self-care content) were emerged, which reflects the key features of a mobile application and the necessary educational content. The research findings could provide a guide for future mobile application development considering the viewpoints of health professionals, content, and software experts. Addressing the features and requirements in practice could lead to designing efficient and effective mobile applications.

Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation. This systematic review assesses the application of AI to diagnosis CME, specifically focusing on disorders like postoperative CME (Irvine Gass syndrome) and retinitis pigmentosa without obvious vasculopathy, using OCT imaging. A comprehensive search was conducted across 6 databases (PubMed, Scopus, Web of Science, Wiley, ScienceDirect, and IEEE) from 2018 to November, 2023. Twenty-three articles met the inclusion criteria and were selected for in-depth analysis. We evaluate AI's role in CME diagnosis and its performance in "detection", "classification", and "segmentation" of OCT retinal images. We found that convolutional neural network (CNN)-based methods consistently outperformed other machine learning techniques, achieving an average accuracy of over 96 % in detecting and identifying CME from OCT images. Despite certain limitations such as dataset size and ethical concerns, the synergy between AI and OCT, particularly through CNNs, holds promise for significantly advancing CME diagnostics.

Features and functions of decision support systems for appropriate diagnostic imaging: a scoping review.

BACKGROUND: Diagnostic imaging decision support (DI-DS) systems could be effective tools for reducing inappropriate diagnostic imaging examinations. Since effective design and evaluation of these systems requires in-depth understanding of their features and functions, the present study aims to map the existing literature on DI-DS systems to identify features and functions of these systems. METHODS: The search was performed using Scopus, Embase, PubMed, Web of Science, and Cochrane Central Registry of Controlled Trials (CENTRAL) and was limited to 2000 to 2021. Analytical studies, descriptive studies, reviews and book chapters that explicitly addressed the functions or features of DI-DS systems were included. RESULTS: A total of 6,046 studies were identified. Out of these, 55 studies met the inclusion criteria. From these, 22 functions and 22 features were identified. Some of the identified features were: visibility, content chunking/grouping, deployed as a multidisciplinary program, clinically valid and relevant feedback, embedding current evidence, and targeted recommendations. And, some of the identified functions were: displaying an appropriateness score, recommending alternative or more appropriate imaging examination(s), providing recommendations for next diagnostic steps, and providing safety alerts. CONCLUSIONS: The set of features and functions obtained in the present study can provide a basis for developing well-designed DI-DS systems, which could help to improve adherence to diagnostic imaging guidelines, minimize unnecessary costs, and improve the outcome of care through appropriate diagnosis and on-time care delivery.

Insertion profiles of 4 headless compression screws.

PURPOSE: In practice, the surgeon must rely on screw position (insertion depth) and tactile feedback from the screwdriver (insertion torque) to gauge compression. In this study, we identified the relationship between interfragmentary compression and these 2 factors. METHODS: The Acutrak Standard, Acutrak Mini, Synthes 3.0, and Herbert-Whipple implants were tested using a polyurethane foam scaphoid model. A specialized testing jig simultaneously measured compression force, insertion torque, and insertion depth at half-screw-turn intervals until failure occurred. RESULTS: The peak compression occurs at an insertion depth of -3.1 mm, -2.8 mm, 0.9 mm, and 1.5 mm for the Acutrak Mini, Acutrak Standard, Herbert-Whipple, and Synthes screws respectively (insertion depth is positive when the screw is proud above the bone and negative when buried). The compression and insertion torque at a depth of -2 mm were found to be 113 ± 18 N and 0.348 ± 0.052 Nm for the Acutrak Standard, 104 ± 15 N and 0.175 ± 0.008 Nm for the Acutrak Mini, 78 ± 9 N and 0.245 ± 0.006 Nm for the Herbert-Whipple, and 67 ± 2N, 0.233 ± 0.010 Nm for the Synthes headless compression screws. CONCLUSIONS: All 4 screws generated a sizable amount of compression (> 60 N) over a wide range of insertion depths. The compression at the commonly recommended insertion depth of -2 mm was not significantly different between screws; thus, implant selection should not be based on compression profile alone. Conically shaped screws (Acutrak) generated their peak compression when they were fully buried in the foam whereas the shanked screws (Synthes and Herbert-Whipple) reached peak compression before they were fully inserted. Because insertion torque correlated poorly with compression, surgeons should avoid using tactile judgment of torque as a proxy for compression. CLINICAL RELEVANCE: Knowledge of the insertion profile may improve our understanding of the implants, provide a better basis for comparing screws, and enable the surgeon to optimize compression.

A model for measuring e-health status across the world.

OBJECTIVE AND BACKGROUND: Today, e-health plays a significant role in enhancing the quality of healthcare services. However, noticeable differences still exist in developing and applying e-health across the world. The aim of the current study is to present a model for assessing e-health status in different countries. MATERIALS AND METHODS: This study was conducted using a cross-sectional design. Five indicators were determined, and the combination of the indicators was considered as "e-health status indicators." At the next stage, a weight coefficient was assigned to each indicator. Then the identified information and communication technology by the International Telecommunication Union (ITU) composed of four groups was assigned as a basis for sampling. One-third of each group of the ranking was selected randomly, and ultimately scores of the indicators were calculated for each country. Then the status of e-health was determined for countries under study, in particular for Iran. The validity of the tool was evaluated through content validity, and the test-retest method was used to check the reliability. RESULTS: The current study resulted in developing a model for measuring e-health status. The model helped to measure the e-health status score for Iran and other countries under study. CONCLUSIONS: The model designed in this study can be used by organizations such as the ITU to grade countries according to their e-health status. According to the model, countries with a higher e-health grade could have a higher chance of success in e-health development and deployment.

Design and Development of a Hospital-Based Coronary Artery Disease (CAD) Registry in Iran.

Background: The incidence of coronary artery disease (CAD), the leading cause of mortality in most developed and developing countries, is increasing. The adoption of hospital registries can improve care delivery and facilitate the management of CAD through better planning, as well as help with outcome assessment through more effective data management. Objectives: The present study is aimed at designing a hospital-based CAD registry for managing CAD data. Methods: This developmental study was conducted in three phases. Initially, sources related to CAD registries were reviewed, the results of which were published in two studies. In the next phase, the prerequisites and requisites of the software were determined through a qualitative study. In this phase, the registry dataset was determined by using a questionnaire. Finally, the developed conceptual model of the software was validated. The software was then developed based on the validated conceptual model. Results: The registry data elements were classified into 13 main categories, including identification data, medical history, and risk factors. The dataset included 171 data elements, including data related to surgical and nonsurgical procedures. The conceptual model was approved by field experts, and the software was developed accordingly. Conclusion: The steps followed in the present study for developing the CAD registry can be used as an appropriate approach for designing similar hospital-based registries. Considering the pivotal role of the registry in the management of CAD, the routine and systemic use of the registry is suggested in all healthcare centers.

Predicting the mortality of patients with Covid-19: A machine learning approach.

Background and Aims: Infection with Covid-19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid-19 using machine learning techniques can be effective in reducing mortality rate in Covid-19. The aim of this study is to compare four machine-learning algorithm for predicting mortality in Covid-19 disease. Methods: The data of this study were collected from hospitalized patients with COVID-19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid-19. Each record contained 38 variables. Four machine-learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results: GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion: Considering the combination of multiple influential factors affecting death Covid-19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

Usability Evaluation of Dashboards: A Systematic Literature Review of Tools.

Introduction: In recent years, the use of dashboards in healthcare has been considered an effective approach for the visual presentation of information to support clinical and administrative decisions. Effective and efficient use of dashboards in clinical and managerial processes requires a framework for the design and development of tools based on usability principles. Objectives: The present study is aimed at investigating the existing questionnaires used for the usability evaluation framework of dashboards and at presenting more specific usability criteria for evaluating dashboards. Methods: This systematic review was conducted using PubMed, Web of Science, and Scopus, without any time restrictions. The final search of articles was performed on September 2, 2022. Data collection was performed using a data extraction form, and the content of selected studies was analyzed based on the dashboard usability criteria. Results: After reviewing the full text of relevant articles, a total of 29 studies were selected according to the inclusion criteria. Regarding the questionnaires used in the selected studies, researcher-made questionnaires were used in five studies, while 25 studies applied previously used questionnaires. The most widely used questionnaires were the System Usability Scale (SUS), Technology Acceptance Model (TAM), Situation Awareness Rating Technique (SART), Questionnaire for User Interaction Satisfaction (QUIS), Unified Theory of Acceptance and Use of Technology (UTAUT), and Health Information Technology Usability Evaluation Scale (Health-ITUES), respectively. Finally, dashboard evaluation criteria, including usefulness, operability, learnability, ease of use, suitability for tasks, improvement of situational awareness, satisfaction, user interface, content, and system capabilities, were suggested. Conclusion: General questionnaires that were not specifically designed for dashboard evaluation were mainly used in reviewed studies. The current study suggested specific criteria for measuring the usability of dashboards. When selecting the usability evaluation criteria for dashboards, it is important to pay attention to the evaluation objectives, dashboard features and capabilities, and context of use.

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.

The attributes of hospital-based coronary artery diseases registries with a focus on key registry processes: A systematic review.

BACKGROUND: The management of data on coronary artery disease (CAD) plays a significant role in controlling the disease and reducing the mortality of patients. The diseases registries facilitate the management of data. OBJECTIVE: This study aimed to identify the attributes of hospital-based CAD registries with a focus on key registry processes. METHOD: In this systematic review, we searched for studies published between 2000 and 2019 in PubMed, Scopus, EMBASE and ISI Web of Knowledge. The search terms included coronary artery disease, registry and data management (MeSH terms) at November 2019. Data gathering was conducted using a data extraction form, and the content of selected studies was analysed with respect to key registry processes, including case finding, data gathering, data abstracting, data quality control, reporting and patient follow-up. RESULTS: A total of 17,604 studies were identified in the search, 55 of which were relevant studies that addressed the 21 registries and were selected for the analysis. Results showed that the most common resources for case finding included admission and discharge documents, physician's reports and screening results. Patient follow-up was mainly performed through direct visits or via telephone calls. The key attributes used for checking the data quality included data accuracy, completeness and definition. CONCLUSION: CAD registries aim to facilitate the assessment of health services provided to patients. Putting the key registry processes in place is crucial for developing and implementing the CAD registry. The data quality control, as a CAD registry process, requires developing standard tools and applying appropriate data quality attributes. IMPLICATIONS: The findings of the current study could lay the foundation for successful design and development of CAD registries based on the key registry processes for effective data management.

Prediction of Breast Cancer using Machine Learning Approaches.

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data. Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Effects of Guideline-based Computerized Provider Order Entry Systems on the Chemotherapy Order Process: a Systematic Review.

Background: Computerized Provider Order Entry (CPOE) systems developed based on clinical guidelines are believed to greatly reduce chemotherapy medication prescription errors. Objective: The present study reviewed the effects of guideline-based CPOEs on the chemotherapy order process. Methods: PubMed, Scopus, Embase, Web of Science, and IEEE Xplore databases published up to 1 June 2020 were systematically searched for studies investigating the effect of guideline-based CPOEs on the chemotherapy order process. Moreover, the bibliography of relevant retrieved publications was also checked. Results: Nineteen articles from the five databases met the eligibility criteria and were reviewed. Eleven out of 19 (58%) articles investigated the effect of CPOEs on medication errors, and other studies examined other aspects of CPOE efficacy, including time required for chemotherapy prescriptions; Safety, policy compliance and communication between health care providers; physicians prescribing behavior; quality and safety of treatment; workflow; direct patient care time; and adherence to guidelines. In addition, 15 out of 19 mentioned the use of specific clinical guidelines. Conclusion: Evidence indicates CPOEs can positively affect the quality of healthcare service delivery for cancer patients, but there is still a dearth of clinical outcome evaluation data about the effects of these systems on patients undergoing chemotherapy. Moreover, there is limited information about guideline compliance errors, which highlights the needs for further research in this area.