Lessons learned from hemolytic uremic syndrome registries: recommendations for implementation

Background: Hemolytic uremic syndrome (HUS) is a rare condition which diagnosed with the triad of thrombocytopenia, microangiopathic hemolytic anemia, and acute renal injury. There is a high requirement for research to discover treatments. HUS registries can be used as an important information infrastructure. In this study, we identified and compared the different features of HUS registries to present a guide for the development and implementation of HUS registries. Results: The purposes of registries were classified as clinical (9 registries), research (7 registries), and epidemiological (5 registries), and only 3 registries pursued all three types of purposes. The data set included demographic data, medical and family history, para-clinical and diagnostic measures, treatment and pharmacological data, complications, and outcomes. The assessment strategies of data quality included monthly evaluation and data audit, the participation of physicians to collect data, editing and correcting data errors, increasing the rate of data completion, following guidelines and data quality training, using specific data quality indicators, and real-time evaluation of data at the time of data entry. 8 registries include atypical HUS patients, and 7 registries include all patients regardless of age. Only two registries focused on children. 4 registries apply prospective and 4 applied both prospective, and retrospective data collection. Finally, specialized hospitals were the main data source for these registries. Conclusion: Based on the findings, we suggested a learning framework for developing and implementing an HUS registry. This framework includes lessons learned and suggestions for HUS registry purposes, minimum data set, data quality assurance, data collection methods, inclusion and exclusion criteria as well as data sources. This framework can help researchers develop HUS registries. © 2021, The Author(s)

Information needs of managers and expert panels in the office of disaster management and emergency medical services in Iran�s ministry of health and medical education

Background and purpose: The office of disaster management and emergency medical service is one of the most important subdivisions of the Ministry of health. Analyzing the tasks and functions of this office is critical to its evaluation. This study aimed at analyzing the information needs of this office to develop statistical indicators required. Materials and methods: This qualitative-quantitative study was carried out during 2015. The study population included the managers and expert panels in disaster management and emergency medical service in the Ministry of Health and Medical Education of Iran. We interviewed 14 individuals in different departments within the office and reviewed the administrative tasks and the available documents. After analyzing the data, different information needs of all departments were identified and classified. Results: According to the administrative tasks and practices, 69 groups of information needs were identified of which 17.4 are not met. 45.3 of the information needs did not have any standard sources or forms to collect the data required. Conclusion: Lack of standard sources for the most identified information needs, decentralized information systems, and out-of-date information are the major problems of managers and expert panels. So, designing national standard forms to collect data, designing a comprehensive statistics and information system and reviewing current paper forms and databases seem to be essential. © 2016, Mazandaran University of Medical Sciences. All rights reserved

Designing an artificial neural network for prediction of pregnancy outcomes in women with systemic lupus erythematosus in Iran

Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9, 80.0, and 94.1 respectively and for the total data were 97.3, 93.5, and 99.0 respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables. © 2015, Tehran University of Medical Sciences. All rights reserved

Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation

Disease registry systems provide a strong information infrastructure for decision-making and research. The purpose of this study is to describe the implementation method and protocol of the COVID-19 registry in Khuzestan province, Iran. We established a steering committee and formulated the purposes of the registry. Then, based on reviewing the literature, and expert panels, the minimum data set, the data collection forms and the web-based software were developed. Data collection is done retrospectively through Hospital Information Systems, Medical Care Monitoring Center system (MCMC), Management of Communicable Disease Prevention and Control system (MCDPC) as well as, patients' records. For prospective data collection, the data collection forms are compiled with patients' medical records by the medical staff and are then entered into the registry system. We collect patients' administrative and demographic data, history and physical examinations, test and imaging results, disease progression, treatment, outcomes, and follow-ups of the confirmed and suspected inpatients and outpatients. From April 20 to December 5, 2020, the data of 4,812 confirmed cases and 7,113 suspected cases were collected from two COVID-19 referral hospitals. Based on our experience, recording information along with providing care for patients and putting patients' data registration in the medical staff's routine, structuring data, having a flexible technical team and rapid software development for multiple and continuous updates, automating data collection by connecting the registry to existing information systems and having different incentives, the registration process can be strengthened. © 2021 The Author

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�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. � 2016,Tehran University of Medical Sciences. All Rights Reserved

Physicians� perspectives on causes of health care errors and preventive strategies: A study in a developing country

Background: To prevent health care errors, the main causes and preventive strategies should be identified. The purpose of this study was to identify the causes and preventive strategies of health care errors from the perspectives of physicians. Methods: We surveyed 250 randomly selected physicians in five teaching hospitals in Tehran, Iran, in 2015. We used a questionnaire with 29 questions regarding causes and 17 ones regarding the preventive strategies. The participants were asked to answer the questions based on Likert�s five-point score (1=very low to 5= very high). The data was analyzed using descriptive (frequency, and mean scores) and inferential statistics in SPSS. Results: Managerial factors (3.6±0.7), personal factors of providers (3.5±0.6), factors related to the patients (3.4±0.71), and the factors pertinent to laboratory and pharmacy (3.2±0.8) were the main causes respectively. The most important preventive strategies were improvement of academic education, better taking past medical history, implementing electronic prescription and increasing healthcare budget. Conclusion: Heavy workloads, long work shifts, failure to do thorough examination and to collect detailed history information, providers� fatigue, patients� reluctance to follow orders or to give their complete information, failure to give detailed instruction to patients about the medications, lack or insufficient monitoring and supervising systems, and lack of enough budget were some of the most important causes of errors. Using IT to access patients� information, improving patients� adherence, reducing workload, developing efficient methods for collecting patients� information, dedicating adequate budget for improvement programs are recommended. © 2018, Iranian Journal of Public Health. All rights reserved

Physicians� perspectives on causes of health care errors and preventive strategies: A study in a developing country

Background: To prevent health care errors, the main causes and preventive strategies should be identified. The purpose of this study was to identify the causes and preventive strategies of health care errors from the perspectives of physicians. Methods: We surveyed 250 randomly selected physicians in five teaching hospitals in Tehran, Iran, in 2015. We used a questionnaire with 29 questions regarding causes and 17 ones regarding the preventive strategies. The participants were asked to answer the questions based on Likert’s five-point score (1=very low to 5= very high). The data was analyzed using descriptive (frequency, and mean scores) and inferential statistics in SPSS. Results: Managerial factors (3.6±0.7), personal factors of providers (3.5±0.6), factors related to the patients (3.4±0.71), and the factors pertinent to laboratory and pharmacy (3.2±0.8) were the main causes respectively. The most important preventive strategies were improvement of academic education, better taking past medical history, implementing electronic prescription and increasing healthcare budget. Conclusion: Heavy workloads, long work shifts, failure to do thorough examination and to collect detailed history information, providers’ fatigue, patients’ reluctance to follow orders or to give their complete information, failure to give detailed instruction to patients about the medications, lack or insufficient monitoring and supervising systems, and lack of enough budget were some of the most important causes of errors. Using IT to access patients’ information, improving patients’ adherence, reducing workload, developing efficient methods for collecting patients’ information, dedicating adequate budget for improvement programs are recommended. Keywords: Medical error, Healthcare error, Patient safety, Physician

Physicians� perspectives on causes of health care errors and preventive strategies: A study in a developing country

Background: To prevent health care errors, the main causes and preventive strategies should be identified. The purpose of this study was to identify the causes and preventive strategies of health care errors from the perspectives of physicians. Methods: We surveyed 250 randomly selected physicians in five teaching hospitals in Tehran, Iran, in 2015. We used a questionnaire with 29 questions regarding causes and 17 ones regarding the preventive strategies. The participants were asked to answer the questions based on Likert�s five-point score (1=very low to 5= very high). The data was analyzed using descriptive (frequency, and mean scores) and inferential statistics in SPSS. Results: Managerial factors (3.6±0.7), personal factors of providers (3.5±0.6), factors related to the patients (3.4±0.71), and the factors pertinent to laboratory and pharmacy (3.2±0.8) were the main causes respectively. The most important preventive strategies were improvement of academic education, better taking past medical history, implementing electronic prescription and increasing healthcare budget. Conclusion: Heavy workloads, long work shifts, failure to do thorough examination and to collect detailed history information, providers� fatigue, patients� reluctance to follow orders or to give their complete information, failure to give detailed instruction to patients about the medications, lack or insufficient monitoring and supervising systems, and lack of enough budget were some of the most important causes of errors. Using IT to access patients� information, improving patients� adherence, reducing workload, developing efficient methods for collecting patients� information, dedicating adequate budget for improvement programs are recommended. © 2018, Iranian Journal of Public Health. All rights reserved

Prediction of neonatal deaths in NICUs: development and validation of machine learning models

Background: Prediction of neonatal deaths in NICUs is important for benchmarking and evaluating healthcare services in NICUs. Application of machine learning techniques can improve physicians� ability to predict the neonatal deaths. The aim of this study was to present a neonatal death risk prediction model using machine learning techniques. Methods: This study was conducted in Tehran, Iran in two phases. Initially, important risk factors in neonatal death were identified and then several machine learning models including Artificial Neural Network (ANN), decision tree (Random Forest (RF), C5.0 and CHART tree), Support Vector Machine (SVM), Bayesian Network and Ensemble models were developed. Finally, we prospectively applied these models to predict neonatal death in a NICU and followed up the neonates to compare the outcomes of these neonates with real outcomes. Results: 17 factors were considered important in neonatal mortality prediction. The highest Area Under the Curve (AUC) was achieved for the SVM and Ensemble models with 0.98. The best precision and specificity were 0.98 and 0.94, respectively for the RF model. The highest accuracy, sensitivity and F-score were achieved for the SVM model with 0.94, 0.95 and 0.96, respectively. The best performance of models in prospective evaluation was for the ANN, C5.0 and CHAID tree models. Conclusion: Using the developed machine learning models can help physicians predict the neonatal deaths in NICUs. © 2021, The Author(s)