Introduction: Standard data processing plays an important role in patient care. Nursing data forms the first level of nursing informatics. These are essential tools for documentation of nursing process by methodology of assessment, diagnosis, interventions, outcomes, evaluation and documentation of patient care. Nursing minimum data set (NMDS) is the first action for standardization of gathering unified and essential nursing data for using in multiple sets and patient groups. The objective of this research was comparison of data elements of nursing minimum data set in selected countries and recommendation for Iran.Methods: This was a descriptive-comparative study and was done at 2009. Nursing minimum data set in the US, Thailand, Belgium, Finland, Canada, Netherlands, Swiss surveyed. Data collection was performed through internet search, books and journals and was collected in the form of a checklist. Results presented in statistical tables.Results: Findings showed that all countries had a national NMDS. In all NMDSs, Nursing data elements divided to three groups: Nursing care, patient and service elements. There is a nursing minimum data set for nursing management in US. There is no NMDS in Iran.Conclusion: Since every countries of this study have a domestic NMDS and also there is no standard in Iran for which data elements must include in Electronic Health Records, then creating an Iranian Nursing Minimum Data Set (IrNMDS) is essential. For identifying Iranian NMDS, we recommend that a professional and legal organization administer to creating of a NMDS.

Introduction: Prenatal care documentation is crucial for maintaining a safe pregnancy and desired outcomes for the mother, baby, and family. This study was conducted to determine the national minimum data set of pregnancy status to plan for preventing maternal mortality and reducing of delivery complications outstanding lack of information about pregnancy conditions during childbirth. Methods: This descriptive-comparative study was conducted through qualitative approach in the academic year 2021 in four steps as follows: exploring minimum data set in selected countries, determining information needs of experts, compiling initial minimum data sets, and confirming their validity through Delphi technique. Results: Investigating in IFRC, WHO, EBCOG, and South Africa, Tanzania, Newziland, Australia, England, India and Iran as well as interviewing midwives, 177 data components were identified and validated. Finally, 86 data components were agreed upon in seven areas: referral information, maternal information, medical history, previous pregnancy, and delivery history, current pregnancy description, ultrasound results, and test results using the Delphi technique Conclusion: Determining the minimum pregnancy status data set provides a structured framework for collecting and reporting important maternal status data. Health Information system (HIS) in caregiving will be fed by important maternal status data, awareness of vital situations during delivery which in turn leads to timely action, and directly prevents many deaths.

Introduction: Access to quality information plays an important role in disasters. Due to lack of minimum dataset necessary for health management in disasters, this study aimed to design a minimum dataset for health management in disasters in Iran. Methods: This descriptive study was conducted in 2016 using Delphi technique. 18 individuals were selected and enrolled the study, using heterogeneous sampling method. The Delphi technique was performed in three steps, and mean calculation was used to analyze the data. Results: The minimum health management dataset of before, during, and after the disaster included 9 data classes and 84 data elements, 9 data classes and 45 data elements, and 6 data classes and 54 data elements, respectively. Conclusion: Disaster managers and policymakers can use the results of this study to collect and process the correct health information, which facilitates health management during the disasters.

Introduction: Biochemistry laboratories provide important information about the health status of people for clinical interventions and decisions. The purpose of this study was to evaluate the data elements of biochemistry laboratory information systems, measure their values, and determine the local minimum dataset (MDS) for designing these systems.Methods: A descriptive-analytical survey was used in this study. The statistical population of this research was hospital and non-hospital biochemistry laboratories in Isfahan city, Iran, and sampling was carried out using the convenient sampling method. The research tools were a checklist and a questionnaire with confirmed validity. Data were analyzed using descriptive statistics and one-sample t, independent t, Friedman and Leven tests via SPSS21 software.Results: The average utilization of the standard data elements in non-hospital and hospital biochemistry laboratory information systems were 61.7% and 61.2, respectively. The minimum utilization of the standard elements was in the identity information section, physicians and referring centers information sections; and the maximum utilization of the standards was in the accounting section. Some data elements had high value, but had not been considered in the laboratory information systems and some of the current data elements used in the laboratory information systems had low values.Conclusion: Despite the widespread use of the biochemistry laboratory information systems in Iran, the required minimum dataset and international standards have not been considered in their design. Because of the different applications of these systems, using a local minimum dataset for designing these systems is very important.

An information retrieval system tries to retrieve documents related to a question/query. The retrieval is done from a large collection of documents, and the size of this collection can be from a few thousand documents to millions of documents. In recent years, a lot of research has been done to develop information retrieval systems using language models. However, in this research field, no research has been done for the Persian language. One of its main reasons is the lack of a suitable Persian dataset for training language models. In this research, first, a Persian dataset for information retrieval is presented. After that, methods for enriching this data set are investigated. This enrichment is done by defining multi-level relationships between a document and a question. In this regard, the new dataset can show the relationship between question and document in four levels (unrelated - related - highly related - completely related) instead of two levels (completely unrelated - completely related). The name of the generated dataset is PersianMLIR. Experiments show that by using multi-level relationships, the performance of the system improves for both Persian and English languages, where the improvement is 1.87% for the Persian language.

Background: Orthopedic injuries are the most common types of injuries. To identify the main causes of injuries, collecting data in the standard manner at the national level are needed, which justifies necessity of making a minimum data set. The aim of this study was designing a minimum data set for orthopedic injuries.Methods: This research was applied and performed in a descriptive and cross-sectional manner in 2013. Data collected from hospitals affiliated to Tehran University of Medical Sciences that had orthopedic department, insurance, medico-legal and emergency centers, and so internet and library. Research documents were orthopedic injury records in 2012 and documents that retrieved from the internet. Records with Random sampling by S22-S99 categories from ICD_10 were selected, and the related Internet sourced data was evaluated entirely. Data collected using a checklist. In order to make a consensus about the data elements the Delphi technique was applied, using a questionnaire. The content validity of questionnaire was assessed using expert’s opinions and reliability by test-retest method.Results: A minimum data set of orthopedic injuries divided into two categories: administrative with six classes including 142 data elements, and clinical with 17 classes including 250 data elements.Conclusions: This study showed that some of the essential data elements that were in other country’s MDS or required for organizations and health care providers was not included. So a complete list of a minimum set of data elements was created. Existence of comprehensive data about the causes and mechanisms of injuries enables public health policy-makers to be informed about injuries occurrence, and taking rationale measures to deal with these problems.

Objective To collect, store, and distribute the electronic health records of patients with amblyopia, it is important to determine the data elements. The present study aims to determine the minimum data set (MDS) for electronic health record of patients with amblyopia in Iran. Methods This is an applied study that was conducted in 2020. To identify data elements, a search was conducted in PubMed, Scopus, Google Scholar, Web of Science, Scientific Information Database (SID), Magiran and Barakat knowledge system and the records of patients with amblyopia were examined and consultation with ophthalmologists in Farabi Hospital was done. Then, a questionnaire with two sections of “, demographic information”,and “, suggested MDS for amblyopia”,was prepared based on a five-point Likert scale. After confirming the validity and reliability of the questionnaire, it was completed by 20 experts from the amblyopia clinic of Farabi Hospital who were selected using a convenience sampling method. Data analysis was done using descriptive statistics in SPSS software. The data elements with an agreement score >3 were selected for the MDS of amblyopia. Results Out of 98 proposed data elements, 92 were included in the MDS for amblyopia and were classified into 6 categories of demographic data, clinical data, type of strabismus, sensory tests, doctors’,prescriptions, and treatment plan. Conclusion It seems that the integration of information for patients with amblyopia in Iran and the improvement of their information management can be possible by determining the MDS for their electronic health record. By storing and retrieving standard electronic health information based on a minimum data set, it is possible to compare information, obtain high-quality information, and increase the effectiveness of medical services.

Aim: To identify the main causes of malnutrition in childhood, it is necessary to collect quality and coherent data at the national level, which requires the creation of a minimum data set. Therefore, the present study aimed to design a minimum set of malnutrition data for children under five years of age. Methods: This quantitative applied study was conducted in two stages in 2019. In the first step, the descriptive-comparative method was used to compare the data elements needed for malnutrition in children under five at the national and international levels. In the second stage, the initial MDS was designed and then validated by experts in two rounds using the Delphi method. The study population was composed of 30 experts in nutritionists and pediatrics who were selected by non-random purposive sampling (criterion Sampling) finally, the cases reached a collective agreement of more than 75% were included in the MDS. The items with a score below 50% were excluded from the proposed MDS. The collected data were analyzed by using descriptive statistics and Excel software. Results: Approved MDS for malnutrition in children under five had 56 data elements and seven subsets, including child development, demographic and management, clinical and diagnostic examinations, nutritional status, medical history, and tests. Conclusion: The development of MDS plays an important role in improving the quality of information management and early detection of childhood malnutrition. In order to gather uniform data, achieve an integrated information system, and facilitate the sharing of malnutrition data, it is recommended to use this MDS.