Background and Aim: The main challenge in each organization, in particular HEALTHCARE ORGANIZATIONS deal is effective utilization of information stored in their various information systems. Large amounts of data will have organizational value as they convert to a KNOWLEDGE which help the ORGANIZATIONS in cost controlling, profit increasing as well as maintaining the quality of patient care. The aim of this study was to determine initiatives and methods for effective utilization of data and information stored in databases of HEALTHCARE ORGANIZATIONS in developed countries. Materials and Methods: Through systematic review, the articles regarding KDD published since 1990 were reviewed. After studying books and journals related to data and databases in the libraries of Iran University of medical sciences, central library of Amir Kabir University, library of Science and Industry University, articles published by databases such as ScienceDirect, Pubmed, Proquest, Blakwell as well as the sites of international ORGANIZATIONS working on KNOWLEDGE discovery and data mining were also reviewed. Results: Necessity for effective utilization of large amount of data has been the subject of a field called KNOWLEDGE Discovery in Databases (KDD). Extensive initiatives have been done in developed countries and distinctive steps have been determined for KNOWLEDGE discovery since 1989. Fraud detection, marketing and customer retention are some examples of KDD applications in several industries. Similar applications regarding patient-oriented and quality improvement in HEALTHCARE are examples of KDD applications in the HEALTHCARE industry. Using capabilities of various sciences, several approaches have been developed by which current soft wares are extracting hidden KNOWLEDGE from large databases. Quantitative approaches such as Probabilistic and statistical approaches, classification approaches such as Bayesian, decision tree and other methods including deviation and Trend Analysis, generic algorithms, neural networks and hybrid methods which use two or more techniques are used in KDD tools. Conclusion: HEALTHCARE ORGANIZATIONS can discover hidden KNOWLEDGE in databases by using software programs developed based on one or more KDD techniques, and along with cost reduction, they can use its interesting results for decision making in their activities.

Clinical coding is a translation of medical documents to diagnostic, procedure and financial codes. Health care data are displayed with codes. Clinical coding is a KNOWLEDGE process in health care and include concepts of KNOWLEDGE management such as codification of KNOWLEDGE, storage of KNOWLEDGE, packaging of KNOWLEDGE with its details and delivery to users and coordinating of intellectual resources.Conversion process of data into KNOWLEDGE is a continual process in health care industry and KNOWLEDGE created in any phase is a data for upper phase too. So clinical coding performs organization of KNOWLEDGE is related to diseases. The databases taken from clinical coding process display diagnostic data and codes in relational table formats of data mining and On-Line Analysis Processing (OLAP) can discover KNOWLEDGE from it. Furthermore creation of On-Line Discussion Group can play important role in KNOWLEDGE sharing and improvement in process of Health care information management particularly clinical coding.Health care information management professions need to identify real situation of clinical coding and impact of their functions on KNOWLEDGE management in HEALTHCARE and health decisions and thus improve their practice with KNOWLEDGE management tools.

Introduction: KNOWLEDGE Management Maturity [KMM] determines the capacity and stages of the organization’, s growth in processes and the readiness and identifies the necessary element for the successful implementation of KNOWLEDGE Management strategies, goals and plans. This study was conducted with the aim of designing a model for assessing KMM in HEALTHCARE ORGANIZATIONS. Methods: This was an applied research that has been done by mixed method. At the qualitative phase, snowball sampling was used. Semi structured interview was done with 12 experts in HEALTHCARE ORGANIZATIONS in Tehran. The interviews were analyzed by qualitative content analysis. Two methods were used for reliability, reviewing articles after implementation by expert peoples and agreement of two coders. According to extracted factors, close questionnaire was prepared and distributed to 120 expert peoples by purposive sampling method and requested them to prioritize the choices according to Likert scale. For data analysis, SPSS 22 and exploratory factor analysis was used. Results: In extracted model, five main layers were identified, including strategy, processes, enablers, outcomes and external factors. The KMM levels include of initial, awareness defined, managed, optimized, entrepreneur and professional. The maximum load factor was related to the managed level [0. 964]. Each level of maturity was correlated with next level, indicating continuity of KMM levels [p<0. 05]. Conclusion: The results showed that in KMM and learning ORGANIZATIONS, the social dimension in HEALTHCARE service was more important than technical aspect as relates to the health of community and peoples. The role of KNOWLEDGE culture and leadership in KMM and learning organization were more prominent than other factors.

Information and its increasing effective use in decision making have caused the emergence of KM. The core reasons leading to emergence of KM are: (1) The shift from the dominancy of tangible assets era to intellectual capital era, (2) The dramatic increases in the volume of information, its electronic storage and increased access to information in general, and (3) The demographical changes accompanied with the danger of losing people through retirements. Initially, information technology departments were the focus of KM initiatives, however, as the focus moved to people's expertise, other departments became increasingly involved. Six strategies have been offered as KM strategies. These are the KNOWLEDGE Strategy as Business Strategy, the Intellectual Asset Management Strategy, the Personal KNOWLEDGE Asset Responsibility Strategy, the KNOWLEDGE Creation Strategy, the KNOWLEDGE Transfer Strategy, and the Customer-Focused KNOWLEDGE Strategy. To find theoretical concepts of KM and its applicability in HEALTHCARE ORGANIZATIONS, different KNOWLEDGE management-associated journals such as the Information Research and the KNOWLEDGE Management Practice journals, published since 1990, were examined. Search engines such as the Science Direct and the Emerald were also used for hunting internet sources.

Damage prediction of buried pipeline under earthquake environments is the first stage for the seismic risk analysis. In this paper, we use a KNOWLEDGE Discovery in Database (KDD) method for the pipeline damage prediction even though many studies have been performed so far with the aid of empirical, statistical, and/or theoretical methods. By employing the KDD method, much higher accurate damage prediction could be done for better understanding of pipeline damage distribution. Related factors were analyzed by a GIS based model of the Kobe water buried pipelines in the 1995 Kobe Earthquake, and a decision tree of pipeline damage classification was developed based on the Classification and Regression Tree (CART) method. A verification of the method was focused to the modeled area, and accuracy of the proposed prediction method was confirmed in comparison with an actual damage as well as predicted ones by commonly used formula of damage estimation. Results of the developed KDD model showed that the model could predict correctly the number of damage in pipeline network. The proposed method by KDD turned out the distribution of damage better than other damageestimation methods.

Background. Effective and efficient sharing of KNOWLEDGE in health care can lead to the clarification of health data and dissemination of up-to-date health information among employees, decision-makers, and other stakeholders, thereby improving the probability of occurrence of desirable health outcomes. The current study aimed to identify the determinants of effective KNOWLEDGE management and sharing in HEALTHCARE. Methods. This study was conducted to identify the facilitators and barriers of KNOWLEDGE sharing in HEALTHCARE and also identify successful strategies in this field using the approach of review studies in 2023. A thorough literature review was conducted using relevant keywords in PubMed, Scopus, and SID databases and the Google Scholar search engine. Data extraction was done by two researchers using the data extraction table. The content analysis method was used to analyze the data. Results. Reviewing 21 studies revealed 176 determinants of KNOWLEDGE management and sharing in HEALTHCARE ORGANIZATIONS. Among the determining factors, 96 were facilitators, and 80 were barriers to KNOWLEDGE sharing. After removing duplicate factors and merging similar cases, 54 facilitators and 55 barriers were obtained uniquely. The determining factors were classified into four categories: organizational factors, infrastructure factors, individual factors, and factors related to the information system. Also, 35 effective strategies for KNOWLEDGE management in HEALTHCARE ORGANIZATIONS were identified, the most important of which was related to the empowerment of managers in information technology (IT). Conclusion. The success of KNOWLEDGE sharing in HEALTHCARE requires a strong IT infrastructure, supportive leadership, an organizational culture that governs KNOWLEDGE development, commitment and cooperation among HEALTHCARE team members, and effective KNOWLEDGE management.

This short literature review argues that the Resource-Based View (RBV) school of strategic management has recently become of increased interest to scholars of HEALTHCARE ORGANIZATIONS. RBV links well to the broader interest in more effective KNOWLEDGE Mobilization (KM) in HEALTHCARE. The paper outlines and discusses key concepts, texts and authors from the RBV tradition and gives recent examples of how RBV concepts have been applied fruitfully to HEALTHCARE settings. It concludes by setting out a future research agenda.