Identifying the knowledge requirements of medical data management using a systematic review method

Objective : Knowledge management in the health sector as an effective management system can help this system to use its knowledge resources optimally. Understanding the clinical workflow , the information needs of different users ( system designers , doctors , nurses , researchers , managers ) and the ability to transform data into practical knowledge to improve decision-making and patient care are essential requirements. This study was conducted with the aim of identifying the knowledge requirements of medical data management.

Methodology : This study is an applied study in terms of purpose and is a qualitative - documentary research in terms of method that follows the Cochrane systematic review and 53 articles were entered into the final systematic analysis by using the PRISMA approach amoung various stages. To ensure the quality of the articles in terms of alignment with the research objective, research up – to – dateness , research design , sampling method , data collection method , and generalizability , the CASP standard checklist was used. In this study , data analysis including open coding to identify primary concepts , axial coding using MAXQDA software to group similar themes and finally selective coding was used.

Findings : The results of the study showed that the knowledge requirements of medical data management include knowledge-based data acquisition ( identification , collection and integration ) ; Data processing and analysis are aimed at knowledge generation ( valuation, labeling and visualization of knowledge , classification and analysis of knowledge - based data , ranking and prioritization of knowledge information, data analysis ) ; storage of knowledge-based data ( formatting and separation of data format types and creation of thematic and semantic relationships ) ; selection of knowledge -based data ( relationship with knowledge goals, knowledge production potential, quality and reliability for knowledge production, context and meaning and needs of knowledge users ); sharing and dissemination of knowledge extracted from data (creation of knowledge sharing platforms, documentation and dissemination of knowledge of communities of practice and knowledge exchange); evaluation and governance of knowledge-based data (definition of key performance evaluation indicators, monitoring and evaluation, knowledge-based data governance); security of knowledge-based data (role-based access control (RBAC) and knowledge, strong authentication and authorization, data encryption, infrastructure protection, physical security and secure data deletion).

Conclusion: Based on the research findings, the knowledge requirements of medical data management are interconnected and intertwined with each other. Meanwhile, knowledge-based data acquisition in the health sector is interconnected with other elements such as data processing and analysis, and other elements. Optimal management of medical data requires standardization, integration, and accurate data analysis to show its true value in saving human lives. Integrating heterogeneous medical data and converting them into operational knowledge requires the use of knowledge management principles in medical sciences, and the results of this study indicate the existence of high heterogeneity in medical data. Data processing in medical sciences with a knowledge management approach is an essential process for converting raw data into valuable information and usable knowledge. This approach, by utilizing organizational expertise and knowledge, enables more effective organization, understanding, and use of complex health data. Knowledge requirements in health data management are a vital process for organizing, sharing, and effectively using existing information and experiences in health organizations, which aims to improve the quality of patient care, promote clinical and management decisions, facilitate learning and innovation, and increase operational efficiency. It requires creating appropriate infrastructure, promoting a culture of learning and collaboration, and utilizing information technologies to transform data into valuable and accessible knowledge for all stakeholders, including system designers, physicians, nurses, and management and executive staff in the health sector.