Promotion of prehospital emergency care through clinical decision support systems: opportunities and challenges

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced

Applying data mining techniques to determine important parameters in chronic kidney disease and the relations of these parameters to each other.

Introduction: Chronic kidney disease (CKD) includes a wide range of pathophysiological processes which will be observed along with abnormal function of kidneys and progressive decrease in glomerular filtration rate (GFR). According to the definition decreasing GFR must have been present for at least three months. CKD will eventually result in end-stage kidney disease. In this process different factors play role and finding the relations between effective parameters in this regard can help to prevent or slow progression of this disease. There are always a lot of data being collected from the patients' medical records. This huge array of data can be considered a valuable source for analyzing, exploring and discovering information. Objectives: Using the data mining techniques, the present study tries to specify the effective parameters and also aims to determine their relations with each other in Iranian patients with CKD. Material and Methods: The study population includes 31996 patients with CKD. First, all of the data is registered in the database. Then data mining tools were used to find the hidden rules and relationships between parameters in collected data. Results: After data cleaning based on CRISP-DM (Cross Industry Standard Process for Data Mining) methodology and running mining algorithms on the data in the database the relationships between the effective parameters was specified. Conclusion: This study was done using the data mining method pertaining to the effective factors on patients with CKD

Developing a Clinical Decision Support System for Prediction Postoperative Coronary Artery Bypass Grafting Infection in Diabetic Patients

Background: Postoperative infection in Coronary Artery Bypass Graft (CABG) is one of the most common complications for diabetic patients, due to an increase in the hospitalization and cost. To address these issues, it is necessary to apply some solutions. Objective: The study aimed to the development of a Clinical Decision Support System (CDSS) for predicting the CABG postoperative infection in diabetic patients.Material and Methods: This developmental study is conducted on a private hospital in Tehran in 2016. From 1061 CABG surgery medical records, we selected 210 cases randomly. After data gathering, we used statistical tests for selecting related features. Then an Artificial Neural Network (ANN), which was a one-layer perceptron network model and a supervised training algorithm with gradient descent, was constructed using MATLAB software. The software was then developed and tested using the receiver operating characteristic (ROC) diagram and the confusion matrix. Results: Based on the correlation analysis, from 28 variables in the data, 20 variables had a significant relationship with infection after CABG (P<0.05). The results of the confusion matrix showed that the sensitivity of the system was 69%, and the specificity and the accuracy were 97% and 84%, respectively. The Receiver Operating Characteristic (ROC) diagram shows the appropriate performance of the CDSS.  Conclusion: The use of CDSS can play an important role in predicting infection after CABG in patients with diabetes. The designed software can be used as a supporting tool for physicians to predict infections caused by CABG in diabetic patients as a susceptible group. However, other factors affecting infection must also be considered for accurate prediction

Clinical decision support system, a potential solution for diagnostic accuracy improvement in oral squamous cell carcinoma: A systematic review

BACKGROUND AND AIM: Oral squamous cell carcinoma (OSCC) is a rapidly progressive disease and despite the progress in the treatment of cancer, remains a life-threatening illness with a poor prognosis. Diagnostic techniques of the oral cavity are not painful, non-invasive, simple and inexpensive methods. Clinical decision support systems (CDSSs) are the most important diagnostic technologies used to help health professionals to analyze patients’ data and make decisions. This paper, by studying CDSS applications in the process of providing care for the cancer patients, has looked into the CDSS potentials in OSCC diagnosis. METHODS: We retrieved relevant articles indexed in MEDLINE/PubMed database using high-quality keywords. First, the title and then the abstract of the related articles were reviewed in the step of screening. Only research articles which had designed clinical decision support system in different stages of providing care for the cancer patient were retained in this study according to the input criteria. RESULTS: Various studies have been conducted about the important roles of CDSS in health processes related to different types of cancer. According to the aim of studies, we categorized them into several groups including treatment, diagnosis, risk assessment, screening, and survival estimation. CONCLUSION: Successful experiences in the field of CDSS applications in different types of cancer have indicated that machine learning methods have a high potential to manage the data and diagnostic improvement in OSCC intelligently and accurately. KEYWORDS: Squamous Cell Carcinoma; Clinical Decision Support System; Neoplasm; Dental Informatic

Computational Modeling and Analysis to Predict Intracellular Parasite Epitope Characteristics Using Random Forest Technique

Background: In a new approach, computational methods are used to design and evaluate the vaccine. The aim of the current study was to develop a computational tool to predict epitope candidate vaccines to be tested in experimental models. Methods: This study was conducted in the School of Allied Medical Sciences, and Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran in 2018. The random forest which is a classifier method was used to design computer-based tool to predict immunogenic peptides. Data was used to check the collected information from the IEDB, UniProt, and AAindex database. Overall, 1,264 collected data were used and divided into three parts; 70% of the data was used to train, 15% to validate and 15% to test the model. Five-fold cross-validation was used to find optimal hyper parameters of the model. Common performance metrics were used to evaluate the developed model. Results: Twenty seven features were identified as more important using RF predictor model and were used to predict the class of peptides. The RF model improves the performance of predictor model in comparison with the other predictor models (AUC±SE: 0.925±0.029). Using the developed RF model helps to identify the most likely epitopes for further experimental studies. Conclusion: The current developed random forest model is able to more accurately predict the immunogenic peptides of intracellular parasites

Identification of Effective Computerized Cognitive Rehabilitation Programs in Improving Attention in the Children and Adolescents with Attention Deficit Hyperactivity Disorder

Introduction: Computerized programs for brain empowerment with regular, intensive, and continuous cognitive exercises improve cognitive functions such as attention in attention deficit hyperactivity disorder. This review study aimed to identify effective computerized programs for improving attention in children and adolescents with attention deficit hyperactivity disorder (ADHD). Method: This descriptive narrative review was done in 2018 through library review and searching keywords including "attention deficit hyperactivity disorder", "computerized cognitive rehabilitation programs", and "play therapy" in databases such as PubMed, Scopus, and Google Scholar. The results of this review were investigated through the semi-structured interview and classified based on the attention areas of children and adolescents with ADHD. Results: Based on the review, Caption Log, Pay Attention, Cogmed Working Memory, Memory GYM, Attention GYM, Brainware Safari, 8 Sciences' ACTIVATET, Lumosity, My Happy Neuron, Smart Driver, and educational packages such as Maghzine were effective programs to improve different types of attention (focused, divided, intermittent, continuous, and selective) in the ADHD children aged four years old and older. Conclusion: Considering the significant effects of computerized cognitive rehabilitation programs in empowering cognitive skills such as attention, their use as a complementary treatment in improving the quality of life of children and adolescents with ADHD is promising. However, it is suggested to evaluate such programs with larger samples, transfer their educational effects to daily life, and improve their quality by providing more components of such programs

Design and implementation of a children vaccination reminder system based on short message service

Background: Most problems related to quality of care and patient safety are related to human negligence. One of the causes of these problems is forgetting to do something. This problem can be avoided with information technology in many cases. Some forgotten are very important. Among these is failure to comply with vaccination schedule by parents that can result in inappropriate outcomes. In this study, we developed and evaluated a SMS reminder system for regular and timely vaccination of children. Methods: In this developmental-applied research, firstly, a child vaccination reminder system was designed and implemented to help parents reduce the forgetfulness. This system based on the child's vaccination history and the date of birth, offer time and type of future vaccines. Then the parents of 27 children, that their vaccination was between 22 June and 21 August 2015, referred to Children's Medical Center, were sent text messages by using this system. We evaluated the accuracy of the system logic by using some scenarios. In addition, we evaluated parents' satisfaction with the system using a questionnaire. Results: In all cases but one, the system proposed the type and date of future children vaccines correctly. All the parents who have received text messages had good perception and satisfaction on the majority of questions (total mean score of 4.15 out of 5). Most parents (4.92 out of 5) stated that using the system to remind their visit for child immunization was helpful and willing to offer the system to their friends and other families. Conclusion: Using the short message system is beneficial for parents to remind their children&rsquo;s vaccination time and increases their satisfaction. So, it can be considered as an important and essential tool in providing healthcare services. SMS is an easy, cheap and effective way to improve the quality of care services

Designing a Minimum Data Set for Major Thalassemia Patients: Towards Electronic Personal Health Record

Introduction: In advanced medicine, large amounts of data are produced. However, there is always a gap between their collection and their understanding and interpretation. Thus, the minimum data sets are prepared. Thalassemia major is a chronic genetic blood disorder and the most common genetic disorder in the world. So, the aim of this study was to determine the data set for personal health record of patients with major thalassemia. Methods: This present applied research was done descriptively by Delphi method. To determine the dataset, the manual health records of thalassemia major patients were first evaluated based on the standard paper forms of the Ministry of Health, and the data required were collected according to the checklist. The questionnaire w first reviewed by a 6-expert team. Its content validity was determined by the team and its reliability by Cronbach's alpha as 96%. Then, a researcher-made questionnaire was prepared and surveyed among 113 experts on blood and oncology specialists around the country (Iran). Results: from the 126 information element data surveyed, 117 IEDs were identified as the main elements with the agreement of more than 75% in the range of high and very high, while nine elements with the agreement of less than 50% in the range of low and very low were excluded from the elements list of personal health records of thalassemia patients. The information element data with the agreement of 50 to 75 in the range of moderate to high was not found in the survey. Finally, the minimum data set of individual health records of patients with thalassemia major was provided in 9 groups of demographic information, health history information, assessment information, laboratory data, drug information, blood transfusion, physical examinations, immunization (vaccination) and dental care. Conclusion: In this study, the data elements were defined for personal health record of thalassemia patients. These data elements are considered as an appropriate data set for inclusion in the manual systems and electronic medical records and based on the patients' needs can be changed to be used as a national document

Additional file 1 of Exploring the geospatial epidemiology of breast cancer in Iran: identifying significant risk factors and spatial patterns for evidence-based prevention strategies

Additional file 1