Designing and evaluating the children's developmental motor disorders system: an experience from a developing country.

BACKGROUND: Developmental disorders are a prevalent problem in the health sector of low- and middle-income countries (LMICs), and children in these countries are at greater risk. A registry system is helpful and vital to monitoring and managing this disease. OBJECTIVE: The present study aims to develop an electronic registry system for children's developmental motor disorders. METHODS: The study was conducted between 2019 and 2020 in three phases. First, the requirements of the system were identified. Second, UML diagrams were first drawn using Microsoft Visio software. Then, the system was designed using the ASP.NET framework in Visual Studio 2018, and the C# programming language was used in the NET 4.5 technology platform. In the third phase, system usability was evaluated from the users' viewpoint. RESULTS: The findings of this research included system requirements, a conceptual model, and a web-based system. The client and system server connection was established through the IP/TCP communication protocol in a university physical network. End users approved the system with an agreement rate of 87.14%. CONCLUSION: The study's results can be used as a model for designing and developing systems related to children's developmental movement disorders in other countries. It is also suggested as a valuable platform for research and improving the management of this disease.

An evaluation of the quality of COVID-19 websites in terms of HON principles and using DISCERN tool.

BACKGROUND: As many people relied on information from the Internet for official scientific or academically affiliated information during the COVID-19 pandemic, the quality of information on those websites should be good. OBJECTIVE: The main purpose of this study was to evaluate a selection of COVID-19-related websites for the quality of health information provided. METHOD: Using Google and Yahoo, 36 English language websites were selected, in accordance with the inclusion criteria. The two tools were selected for evaluation were the Health on the Net (HON) Code and the 16-item DISCERN tool. RESULTS: Most websites (39%) were related to information for the public, and a small number of them (3%) concerned screening websites in which people could be informed of their possible condition by entering their symptoms. The result of the evaluation by the HON tool showed that most websites were reliable (53%), and 44% of them were very reliable. Based on the assessment results of the Likert-based 16-item DISCERN tool, the maximum and minimum values for the average scores of each website were calculated as 2.44 and 4.25, respectively. CONCLUSION: Evaluation using two widely accepted tools shows that most websites related to COVID-19 are reliable and useful for physicians, researchers and the public.

Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review.

BACKGROUND: Machine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation. METHOD: A systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet. RESULTS: Searches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models. CONCLUSION: The outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

Suggesting a framework for preparedness against the pandemic outbreak based on medical informatics solutions: a thematic analysis.

BACKGROUND: When an outbreak emerged, each country needs a coherent and preventive plan to deal with epidemics. In the era of technology, adopting informatics-based solutions is essential. The main objective of this study is to propose a conceptual framework to provide a rapid and responsive surveillance system against pandemics. METHODS: A three-step approach was employed in this research to develop a conceptual framework. These three steps comprise (1) literature review, (2) extracting and coding concepts, and determining main themes based on thematic analysis using ATLAS.ti® software, and (3) mapping concepts. Later, all of the results synthesized under expert consultation to design a conceptual framework based on the main themes and identified strategies related to medical informatics. RESULTS: In the literature review phase, 65 articles were identified as eligible studies for analysis. Through line by line coding in thematic analysis, more than 46 themes were extracted as potential foremost themes. Based on the key themes and strategies were employed by studies, the proposed framework designed in three main components. The most appropriate strategies that can be used in each section were identified based on the demands of each part and the available solutions. These solutions were employed in the final framework. CONCLUSION: The presented model in this study can be the first step for a better understanding of the potential of medical informatics solutions in promoting epidemic disease management. It can be applied as a reference model for designing intelligent surveillance systems to prepare for probable future pandemics.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Home-based telerehabilitation software systems for remote supervising: a systematic review.

OBJECTIVES: In the past decade, with the ever-increasing growth of information and communication technologies, telerehabilitation, especially home-based rehabilitation (HBR), has been widely considered by researchers. Many software systems are developed to address HBR programs, which includes various functionalities. The aim of this study is to review the functional features of these systems designed for remote supervising of HBR programs. METHODS: Scopus, PubMed, EMBASE, ISI Web of Science, Cochrane Library, IEEE Xplore Digital Library, and ProQuest databases were searched for English-language articles published between January 2008 and February 2018 to retrieve studies reported an home-based telerehabilitation software system aiming to remotely supervise HBR program. RESULTS: A total of fifty studies that reported twenty-two unique systems met the inclusion criteria. Various functional features were identified including but not limited to exercise plan management, report/statistics generating, patient education, and task scheduling. Disorders or diseases addressed by these systems could mainly be grouped into five categories: musculoskeletal, neurological, respiratory, cardiovascular, and other health-related problems. Usability and acceptability, and clinical/patient outcomes were the most reported outcomes and data analysis was used by the majority of included studies to measure the outcomes. CONCLUSIONS: Systems developed for supervising of HBR program are diverse. However, preliminary results of this review revealed that these systems share more or less common functionalities. However, further research is needed to determine the requirements, structure, and effectiveness of these systems in real-life settings.

Better monitoring of abused children by designing a child abuse surveillance system: Determining national child abuse minimum data set.

BACKGROUND: Monitoring the trend of child abuse can significantly help in measuring the magnitude of the problem and understanding its recurrence. The minimum data set (MDS) is a set of elements of each domain that provides the basis for decision-making. This study was conducted to determine the comprehensive national minimum data set for child abuse surveillance system (CASS) in Iran. METHODS: This is a cross-sectional descriptive study. Data were gathered from the selected countries and child abuse registry and surveillance systems. The MDS questionnaire was designed based on a review of the publications and experts' opinions. The final data elements of the CASS were determined using the Delphi technique by visiting pediatricians. RESULTS: In total, 147 data elements were included in the Delphi survey. The data elements of the CASS were classified into seven categories as follows: demographic data, incident related data, medical history, diagnostic tests, incident nature, therapeutic measures, and other required data. CONCLUSION: The existence of national MDS as the core of the child abuse surveillance program is essential and leads to appropriate decisions in this regard. The MDS can meet the needs of professionals, decision makers, researchers, and policymakers who decide on reducing the incidence of child abuse.

Current approaches in identification and isolation of cancer stem cells.

Cancer stem cells (CSCs) are tumor cells with initiating ability, self-renewal potential, and intrinsic resistance to conventional therapeutics. Efficient isolation and characterization of CSCs pave the way for more comprehensive knowledge about tumorigenesis, heterogeneity, and chemoresistance. Also a better understanding of CSCs will lead to novel era of both basic and clinical cancer research, reclassification of human tumors, and development of innovative therapeutic strategies. Finding novel diagnostic and effective therapeutic strategies also enhance the success of treatment in cancer patients. There are various methods based on the characteristics of the CSCs to detect and isolate these cells, some of which have recently developed. This review summarized current techniques for effective isolation and characterization of CSCs with a focus on advantages and limitations of each method with clinical applications.

Developing a new intelligent system for the diagnosis of oral medicine with case-based reasoning approach.

OBJECTIVE: Since the clinical manifestations of many oral diseases can be quite similar despite the wide variety in etiology and pathology, the differential diagnosis of oral diseases is a complex and challenging process. Intelligent system for differential diagnosis of oral medicine using the artificial intelligence (AI) capabilities helps specialists in achieving differential diagnosis in a wide range of oral diseases. MATERIALS AND METHODS: First, the essential data elements to design and develop an intelligent system were identified in a cross-sectional descriptive study. The case-based reasoning method was selected to design and implement the system, which consists of three stages: collect the clinical data, construct the cases database, and case-based reasoning cycle. The problem is solved by CBR method in a cycle consisting of four main stages of retrieval, reuse, review, and retention. The evaluation process was conducted in a pilot-based way through the evaluation of the system's performance in the clinical setting and also using the usability assessment questionnaire. RESULTS: The output of the present project is a web-based intelligent information system, which is developed using the Visual Studio 2015 software. The database of this system is the Microsoft SQL Server version 2012, which has been programmed based on Net framework (version 4.5 or higher) using Visual Basic language. The results of the system evaluation by specialists in clinical settings showed that the system's diagnosis power in different aspects of the disease is influenced by their prevalence and incidence. CONCLUSIONS: System development using the artificial intelligence capabilities and through the clinical data analysis has potential to help specialists to determine the best diagnostic strategy to achieve a differential diagnosis of a wide range of oral diseases. The results of evaluation present the potential of the system to improve the quality and efficiency of patient care.

National Minimum Data Set for Antimicrobial Resistance Management: Toward Global Surveillance System.

BACKGROUND: Success of infection treatment depends on the availability of accurate, reliable, and comprehensive data, information, and knowledge at the point of therapeutic decision-making. The identification of a national minimum data set will support the development and implementation of an effective surveillance system. The goal of this study was to develop a national antimicrobial resistance surveillance minimum data set. METHODS: In this cross-sectional and descriptive study, data were collected from selected pioneering countries and organizations which have national or international antimicrobial resistance surveillance systems. A minimum data set checklist was extracted and validated. The ultimate data elements of the minimum data set were determined by applying the Delphi technique. RESULTS: Through the Delphi technique, we obtained 80 data elements in 8 axes. The resistance data categories comprised basic, clinical, electronic reporting, infection control, microbiology, pharmacy, World Health Organization-derived, and expert-recommended data. Relevance coding was extracted based on the Iranian electronic health record coding system. CONCLUSION: This study provides a set of data elements and a schematic framework for the implementation of an antimicrobial resistance surveillance system. A uniform minimum data set was created based on key informants' opinions to cover essential needs in the early implementation of a global antimicrobial resistance surveillance system in Iran.

Highlighting a valuable dimension in health care librarianship: A systematic review.

Background: Educational role is one of the most important roles of librarians, which has taken on wider dimensions. A review of the literature on the training programs provided by librarians revealed an evolution of the ideas and trends in this area. This systematic review aimed at providing a clear image of the available educational programs, their target groups, and the way they are performed. Methods: This systematic review was done to identify different aspects of the educational role of medical librarians. It was conducted on the studies published in PubMed database during 2005 and 2015. All the studies that described the educational activities of medical librarians were considered for inclusion. All the studies were evaluated by 2 researchers using a checklist, which was developed as an assessment tool. Variables that were considered were as follow: skills taught by librarians, target group, providing training on information resource, teaching method, and session location. After data extraction process and appraisal, the mentioned variables were classified into main categories. Results: A total of 24 studies met the inclusion criteria. The training skills taught by librarians were classified into 3 main groups: information literacy, evidence-based practice, and health literacy. The target groups were library users, patients, and health professionals. Group training was provided, and if necessary, personal training was also offered. Recently, synchronous online training has also been added to the training methods. Most of the training programs are held in classrooms. Conclusion: By categorizing different aspects of training programs, this study aimed at providing a basis for designing a framework to identify the tasks of educational librarians in health sciences.

Computational prediction of drug-drug interactions based on drugs functional similarities.

Therapeutic activities of drugs are often influenced by co-administration of drugs that may cause inevitable drug-drug interactions (DDIs) and inadvertent side effects. Prediction and identification of DDIs are extremely vital for the patient safety and success of treatment modalities. A number of computational methods have been employed for the prediction of DDIs based on drugs structures and/or functions. Here, we report on a computational method for DDIs prediction based on functional similarity of drugs. The model was set based on key biological elements including carriers, transporters, enzymes and targets (CTET). The model was applied for 2189 approved drugs. For each drug, all the associated CTETs were collected, and the corresponding binary vectors were constructed to determine the DDIs. Various similarity measures were conducted to detect DDIs. Of the examined similarity methods, the inner product-based similarity measures (IPSMs) were found to provide improved prediction values. Altogether, 2,394,766 potential drug pairs interactions were studied. The model was able to predict over 250,000 unknown potential DDIs. Upon our findings, we propose the current method as a robust, yet simple and fast, universal in silico approach for identification of DDIs. We envision that this proposed method can be used as a practical technique for the detection of possible DDIs based on the functional similarities of drugs.

Knowledge Sharing as a Powerful Base for Management: Barriers and Solutions.

Knowledge or information sharing (KIS) is an important aspect of organizational knowledge management (KM). Several factors affect the KIS process. This study investigates KM from a managerial viewpoint using a theoretical model that was developed based on a literature review conducted to evaluate KIS. This model was then tested for validity and reliability using an expert panel. Finally, an analysis was conducted on data collected from a survey of top and midlevel managers at Iran's Ministry of Health and Medical Education covering knowledge sharing, its barriers, and solutions. More than 90% of managers believe that the most important barriers to implementing a KIS system are limitations brought about by specialized knowledge, a culture of independent working, nonstandard processes, and a lack of a proper system for KIS. The results suggest that a successful implementation of KM initiatives depends on a careful consideration of the organizational culture, employee education levels, and previous knowledge of the benefits of KM.

Identifying training and informational components to develop a psoriasis self- management application.

Background: Psoriasis is a complex disease with lifelong emotional and social consequences for affected patients. It also reduces the patients' quality of life and requires a long-term management. Therefore, in addition to appropriate treatment of the disease, selfmanagement strategies to improve patient health and quality of life are essential. On the other hand, smartphone-based applications alter the way people interact with health care and public health systems. This study aimed at identifying training and informational components to develop a psoriasis self- management application. Methods: This descriptive-analytic study was conducted on 100 patients with psoriasis and 26 dermatologists who were selected randomly, using Morgan table. The data were collected using a researcher- made questionnaire, which included demographic and clinical information, lifestyle training and management, and application capabilities in psoriasis self-management. A group of experts and a test-retest method were used to confirm the validity and reliability of the questionnaire, respectively. Results: The mean scores for demographic and clinical information, lifestyle training and management, and application capabilities in self-management were 80.55%, 85.7%, and 88.8% from the patients' perspective, and 83.7%, 71%, and 75% from the specialists' viewpoint, respectively. Conclusion: Determining self-management components by patients as persons who are suffering from the disease and physicians as specialists in the field will be helpful in efficient psoriasis self-management. It is more likely that self-reliant patients, who are aware of the benefits and risks of their disease management application, will follow their treatment plan and pursue the management of their disease more seriously.

The simulation model of teleradiology in telemedicine project.

Telemedicine projects are aimed at offering medical services to people who do not have access to direct diagnosis and treatment services. As a powerful tool for analyzing the performance of complex systems and taking probable events into consideration, systemic simulation can facilitate the analysis of implementation processes of telemedicine projects in real-life-like situations. The aim of the present study was to propose a model for planning resource capacities and allocating human and operational resources to promote the efficiency of telemedicine project by investigating the process of teleradiology. In this article, after verification of the conceptual model by the experts of this field, the computerized simulation model is developed using simulation software Arena. After specifying the required data, different improvement scenarios are run using the computerized model by feeding the data into the software and validation and verification of the model. Fixing input data of the system such as the number of patients, their waiting time, and process time of each function, for example, magnetic resonance imaging or scan, has been compared with the current radiology process. Implementing the teleradiology model resulted in reduction of time of patients in the system (current: 1.84 ± 0.00, tele: 0.81 ± 0.00). Furthermore, through this process, they can allocate the lower resources to perform better functions of staff. The use of computerized simulation is essential for designing processes, optimal allocation of resources, planning, and making appropriate decisions for providing timely services to patients.

Development of balanced key performance indicators for emergency departments strategic dashboards following analytic hierarchical process.

Dynamic reporting tools, such as dashboards, should be developed to measure emergency department (ED) performance. However, choosing an effective balanced set of performance measures and key performance indicators (KPIs) is a main challenge to accomplish this. The aim of this study was to develop a balanced set of KPIs for use in ED strategic dashboards following an analytic hierarchical process. The study was carried out in 2 phases: constructing ED performance measures based on balanced scorecard perspectives and incorporating them into analytic hierarchical process framework to select the final KPIs. The respondents placed most importance on ED internal processes perspective especially on measures related to timeliness and accessibility of care in ED. Some measures from financial, customer, and learning and growth perspectives were also selected as other top KPIs. Measures of care effectiveness and care safety were placed as the next priorities too. The respondents placed least importance on disease-/condition-specific "time to" measures. The methodology can be presented as a reference model for development of KPIs in various performance related areas based on a consistent and fair approach. Dashboards that are designed based on such a balanced set of KPIs will help to establish comprehensive performance measurements and fair benchmarks and comparisons.

Patient monitoring in mobile health: opportunities and challenges.

BACKGROUND: In most countries chronic diseases lead to high health care costs and reduced productivity of people in society. The best way to reduce costs of health sector and increase the empowerment of people is prevention of chronic diseases and appropriate health activities management through monitoring of patients. To enjoy the full benefits of E-health, making use of methods and modern technologies is very important. METHODS: This literature review articles were searched with keywords like Patient monitoring, Mobile Health, and Chronic Disease in Science Direct, Google Scholar and Pub Med databases without regard to the year of publications. RESULTS: Applying remote medical diagnosis and monitoring system based on mobile health systems can help significantly to reduce health care costs, correct performance management particularly in chronic disease management. Also some challenges are in patient monitoring in general and specific aspects like threats to confidentiality and privacy, technology acceptance in general and lack of system interoperability with electronic health records and other IT tools, decrease in face to face communication between doctor and patient, sudden interruptions of telecommunication networks, and device and sensor type in specific aspect. CONCLUSIONS: It is obvious identifying the opportunities and challenges of mobile technology and reducing barriers, strengthening the positive points will have a significant role in the appropriate planning and promoting the achievements of the health care systems based on mobile and helps to design a roadmap for improvement of mobile health.

Electronic Health Record (EHR) As a Vehicle for Successful Health Care Best Practice.

OBJECTIVE: application of EHR in journey toward the development and adaptation of best practice approach in health care has particular importance. The aim of this review article is survey of successful best practice through EHR. METHODS: In this literature review articles were searched with keywords like Electronic Health Record, Best Practice in Science Direct, Google Scholar and Pub Med databases since 1999. RESULTS: best practice in health care through some services like utilization management, case management, and information technology tools can perform. Utilization management in combination with evidence based medicine facilitate determine best decision. Health records are based on evidence medicine and be the richest source of health information. Definitely use of EHR has play pivotal role in journey toward the development and adaptation of best practice approach. CONCLUSION: Because of potential capabilities, EHR can be regarded as a main core and fundamental element in best practice approach. Success implementation of EHR relies on many factors that should be considered. Some critical success factors for EHR implementation that should be noted are change management, Physicians, nurses and key stakeholders involvement, leadership, provide reliable information technology infrastructure, system design, privacy and security, right budget, support high level management, clear communicate, determine goals and user needs, and define roles and responsibilities, interoperability standards.

A Framework for Neonatal Prematurity Information System Development Based on a Systematic Review on Current Registries: An Original Research.

Background: Registries are regarded as a just valuable fount of data on determining neonates suffering prematurity or low birth weight (LBW), ameliorating provided care, and developing studies. Objective: This study aimed to probe the studies, including premature infants' registries, adapt the needed minimum data set, and provide an offered framework for premature infants' registries. Material and Methods: For this descriptive study, electronic databases including PubMed, Scopus, Web of Science, ProQuest, and Embase/Medline were searched. In addition, a review of gray literature was undertaken to identify relevant studies in English on current registries and databases. Screening of titles, abstracts, and full texts was conducted independently based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of the registry, and important variables were extracted and analyzed. Results: Fifty-six papers were qualified and contained in the process that presented 51 systems and databases linked in prematurity at the popular and government levels in 34 countries from 1963 to 2017. As a central model of the information management system and knowledge management, a prematurity registry framework was offered based on data, information, and knowledge structure. Conclusion: To the best of our knowledge, this is a comprehensive study that has systematically reviewed prematurity-related registries. Since there are international standards to develop new registries, the proposed framework in this article can be beneficial too. This framework is essential not only to facilitate the prematurity registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

Effective radiology dashboards: key research findings.

Innovative organizations have access to information for business intelligence through the objectives displayed in dashboards. In healthcare organizations, where the goal is to improve quality of care along with reducing costs, the radiology department is important from both financial and clinical aspects. Therefore, how to manage this department has critical impact on the effectiveness and efficiency of the organization. Today, since the information in this department not only has different data structure but also is gathered from different data sources, a well defined, comprehensive dashboard can be an effective tool to enhance performance.