Introduction:management challenges of clinical laboratories are more complicated for educational hospital clinical laboratories. Managers can use tools of business intelligence (BI), such as information dashboards that provide the possibility of intelligent decision-making and problem solving about increasing income, reducing spending, utilization management and even improving quality. Critical phase of dashboard design is setting indicators and modeling causal relations between them. The paper describes the process of creating a map for laboratory dashboard.Methods:the study is one part of an action research that begins from 2012 by innovation initiative for implementing laboratory intelligent dashboard. Laboratories management problems were determined in educational hospitals by the brainstorming sessions. Then, with regard to the problems key performance indicators (KPIs) specified.Results:the map of indicators designed in form of three layered. They have a causal relationship so that issues measured in the subsequent layers affect issues measured in the prime layers.Conclusion:the proposed indicator map can be the base of performance monitoring. However, these indicators can be modified to improve during iterations of dashboard designing process.

Study design Descriptive study. Objectives The aim of this manuscript is to describe the development process of the data set for the National Spinal Cord Injury Registry of Iran (NSCIR-IR). Setting SCI community in Iran. Methods The NSCIR-IR data set was developed in 8 months, from March 2015 to October 2015. An expert panel of 14 members was formed. After a review of data sets of similar registries in developed countries, the selection and modification of the basic framework were performed over 16 meetings, based on the objectives and feasibility of the registry. Results The final version of the data set was composed of 376 data elements including sociodemographic, hospital admission, injury incidence, prehospital procedures, emergency department visit, medical history, vertebral injury, spinal cord injury details, interventions, complications, and discharge data. It also includes 163 components of the International Standards for the Neurologic Classification of Spinal Cord Injury (ISNCSCI) and 65 data elements related to quality of life, pressure ulcers, pain, and spasticity. Conclusion The NSCIR-IR data set was developed in order to meet the quality improvement objectives of the registry. The process was centered around choosing the data elements assessing care provided to individuals in the acute and chronic phases of SCI in hospital settings. The International Spinal Cord Injury Data Set was selected as a basic framework, helped by comparison with data from other countries. Expert panel modifications facilitated the implementation of the registry process with the current clinical workflow in hospitals.

The National Spinal Cord Injury Registry of Iran (NSCIR-IR) is a not-for-profit, hospital-based, and prospective observational registry that appraises the quality of care, long-term outcomes and the personal and psychological burden of traumatic spinal cord injury in Iran. Benchmarking validity in every registry includes rigorous attention to data quality. Data quality assurance is essential for any registry to make sure that correct patients are being enrolled and that the data being collected are valid. We reviewed strengths and weaknesses of the NSCIR-IR while considering the methodological guidelines and recommendations for efficient and rational governance of patient registries. In summary, the steering committee, funded and maintained by the Ministry of Health and Medical Education of Iran, the international collaborations, continued staff training, suitable data quality, and the ethical approval are considered to be the strengths of the registry, while limited human and financial resources, poor interoperability with other health systems, and time-consuming processes are among its main weaknesses.

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

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