Introduction: Considering the growth of cancer in the world and also in Iran, the oncology ward is considered as one of the most significant wards in hospitals. Information quality plays a key role in improving the performance of this ward. Dashboards can track the key indicators and improve the performance of oncology wards through transferring information to managers. Therefore, the current study aimed to identify the performance indicators for oncology wards, based on the the needs of decision makers, and then design and evaluate an appropriate dashboard. Method: In the first stage, the key performance indicators were determined using a questionnaire which was completed by all 30 oncology administrators in private hospitals as well as hospitals affiliated with Medical Universities in Tehran. In the second stage, QLIKVIEW software was used to design a dashboard which was evaluated by hospital directors, and directors of the oncology wards in Shohada Haftom Tir and Firouzgar Hospitals. Results: Seven key performance indicators, including the number of patients, days off, hours of training, personnel, complaints, as well as income and expense were identified. The satisfaction mean scores of hospital directors, the technical staff and the internal director of the oncology ward were 7. 5± 0. 32, 7. 4± 0. 39, and 7. 3± 0. 37, respectively. Conclusion: Dashboards can collect data scattered between various depatments, summarize them, and provide key performance indicators. The information available in dashboards in oncology wards can provide a basis for informed decision making to improve the performance of the ward.

Supply chain management helps partners in the chain to justify internal costs. The pursuit of cost information through the supply chain is very important for controlling costs, which helps to establish and optimize activities in institutions in the value chain. In this research, we have tried to review the literature on Supply Chain Costing in order to predict a model of management dashboard design for supply chain cost estimation, focusing on quantitative models and prediction. For this purpose, after reviewing the supply chain cost literature and examining the scope and purpose of the dashboard design, using the mathematical models and applying actual production and sales data in Tehran Province in the Zamzam Iran Company and Zamzam Tehran Company 22 data courses From March 2016 to December 2017, the subject of this study, the design of the Supply Chain Cost Chain (CHCDM) model and the use of specialized software to prepare reports in the field of business intelligence, predicted the cost estimation dashboard model Supply chain and analysis reports were sensitive. Findings of the research are the best way to predict the combination method and regression method.

Supply chain management helps partners in the chain to justify internal costs. The pursuit of cost information through the supply chain is very important for controlling costs, which helps to establish and optimize activities in institutions in the value chain. In this research, we have tried to review the literature on Supply Chain Costing in order to predict a model of management dashboard design for supply chain cost estimation, focusing on quantitative models and prediction. For this purpose, after reviewing the supply chain cost literature and examining the scope and purpose of the dashboard design, using the mathematical models and applying actual production and sales data in Tehran Province in the Zamzam Iran Company and Zamzam Tehran Company 22 data courses From March 2016 to December 2017, the subject of this study, the design of the Supply Chain Cost Chain (CHCDM) model and the use of specialized software to prepare reports in the field of business intelligence, predicted the cost estimation dashboard model Supply chain and analysis reports were sensitive. Findings of the research are the best way to predict the combination method and regression method.

Intelligent oilfield technology is one of the newest technologies in the oil/gas industry, which is increasingly regarded by international oil companies as a highly competitive advantage in production. The intelligent oilfield has various dimensions and components such as human resources, processes and equipment, but the cornerstone of intelligent oilfield is the effective use and management of field’ s big data in order to make strategic and timely decisions on production. In order to achieve the goal of optimal management and utilization of oilfield’ s data, in this study, by using PPDM standard and Azadegan oilfield as a case study, the oilfield’ s data model is designed in seven specialized subject areas, 70 tables and 2700 columns. Then, the field’ s data warehouse model is developed based on Kimball’ s methodology. In the next step, both data and data warehouse models were validated by experts. Consequently, in order to facilitate strategic and timely decisions on production, based on the designed data and data warehouse models in the previous stage, about 40 “ key performance indicators” were obtained and validated by the experts of oil and gas industry in three intervals of short, medium and long term, and the importance of each in the decision-making about the field’ s production was determined. The “ equipment type” , “ gas to oil ratio” and “ real production amount” indicators were evaluated with the highest weight and therefore the highest importance in the field’ s strategic decisions. At the end, in order to facilitate the production of real-time managerial reports about oilfield’ s production, the Azadegan oilfield’ s management dashboard is designed and presented.

Objective Considering the impact of service quality in the hemodialysis departments on the survival rate and quality of life (QoL) of patients, it is important to monitor the performance of this department. In this regard, identifying key performance indicators (KPIs) can be useful. This study aims to determine the k KPIs in the dialysis management dashboard. Methods This is a descriptive cross-sectional study that was conducted in 2022. Participants were 346 nephrology specialists in Iran which were selected using the census method. The data collection tool was a researcher-made questionnaire with acceptable validity and reliability. Data were analyzed using SPSS software, version 16. Results A total of 40 KPIs were identified in three main categories: Structure (n=16), process (n=11), and outcome (n=13). The most important structure-related KPIs were: Underlying cause of kidney failure, comorbidity diseases, blood type, history of viral diseases, dry weight, and history of kidney transplantation. The most important process-related KPIs were: Type of filtration, type of vascular access, and duration of dialysis. The most important outcome-related KPIs were: Kt/V, urea reduction ratio, normalized protein catabolic rate, and fistula infection. Conclusion Accurate identification of KPIs and their use in continuous monitoring of dialysis department performance can help improve service quality and patient safety.

Background and Aim: Access to integrated information, up-to-date statistics, and timely diagnosis of the disease will help managers and health care providers to manage the crisis caused by the COVID-19 epidemic. In the present study, a management dashboard was designed by determining the key performance indicators of COVID-19. Methods: In the first step, using scientific articles published between 2019 and 2020, the most important symptoms of COVID-19 patients were identified, and demographic characteristics were considered as key performance indicators. In the next step, the data entry method was determined in such a way that after entering the patient data in the dashboard, it is possible to make a decision about his/her infection with Covid-19 and also to examine the statistics of patients by age, sex and time of infection. Finally, using Excel 2016 and QlikView software, the proposed dashboard was designed in two parts: monitoring and diagnostics. Results: Four COVID-19 diagnostic methods including epidemiological history, clinical signs, chest CT images and laboratory diagnosis were considered as components of the diagnostic dashboard and for each of them, related key indicators were identified. In the monitoring dashboard, statistics on patients, age, gender and time of onset were included as key performance indicators. Conclusion: Due to the diagnostic dashboard's ability to integrate data about COVID-19, medical staff is able to make decisions in less time and with fewer mistakes. The management and leadership team can also manage the crisis through the suitable allocation of resources by receiving up-to-date statistics and making the right decisions.

Background and Aim: The speed of service delivery in medical centers, especially emergencies, is of particular importance in reducing mortality and disability. Time management in providing vital and short-term health care services can endanger patients' lives. One of the most important indicators used in the evaluation of emergency centers is the waiting time for patients to receive diagnostic and treatment services. This study was conducted in 1397 in order to study the timing indicators and design of the management dashboard in a specialized heart center in Tehran. Materials and Methods: This type of research is a section in which the emergency time indicators of the Ministry of Health were examined. All the relevant data in the emergency department was collected in a hospital in Tehran in 1397. Then, the design and creation of the dashboard was loaded based on the data of the indicators specified in the emergency department using QuickView software and the performance of this section was presented. Results: In the present study, six emergency timing indicators were examined. Then, according to the information obtained, the relevant dashboard was designed. Conclusion: Reducing the length of stay in the emergency department leads to increased patient satisfaction and potentially cost savings. The main mission of the emergency is to providing high quality services in the shortest possible time. Prolonged emergency stops reduced the possibility of providing services to other patients in need of emergency medical care, which can lead to patient dissatisfaction and increased casualties.

In today's world, organizations have to deal with a large amount of data, which has forced them to use administrative automation systems. In this regard, the use of management dashboards for quick access to classified data and their analysis has been used by many organizations. The purpose of writing the present research is to present a model for measuring the management dashboard in the office automation system of Tejarat Bank. The present research was conducted using a qualitative method. The statistical population of this research included the experts of Tejarat Bank (10 people). In this research, library studies were used to collect the models available worldwide. The data collection method included a semi-structured interview of experts. The method of data analysis is coding. In this research, using the opinions of the experts of this organization and during the coding process, the initial research model includes three main dimensions of factors related to customers (including components of operational features, personalization of customer information and dashboard content), technical factors (including The components of objective features and ease) and organizational factors (including the components of structural factors, managers' decision making and monitoring and control) were obtained.