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Electronic Health Records (EHRs) are digital versions of patients' paper charts, providing real-time, patient-centered records that make information available instantly and securely to authorized users. They enhance the quality of care by facilitating better coordination among healthcare providers and improving patient outcomes through comprehensive data analysis.
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Predictive analytics involves using historical data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It is a powerful tool for businesses to forecast trends, understand customer behavior, and make data-driven decisions to improve efficiency and competitiveness.
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Data mining is the process of discovering patterns and insights from large datasets by using machine learning, statistics, and database systems. It enables organizations to transform raw data into meaningful information, aiding in decision-making and predictive analysis.
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Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to enable computers to improve their performance on a task through experience. It leverages data to train models that can make predictions or decisions without being explicitly programmed for specific tasks.
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Big data refers to the vast volumes of structured and unstructured data generated at high velocity from various sources, necessitating advanced methods for storage, processing, and analysis to extract meaningful insights. It is crucial for making informed decisions in fields like business, healthcare, and technology, driving innovation and competitive advantage.
Clinical Decision Support Systems (CDSS) are computer-based tools designed to assist healthcare professionals in making informed clinical decisions by providing evidence-based knowledge and patient-specific information. These systems aim to enhance healthcare quality, reduce errors, and improve patient outcomes by integrating data from various sources to offer recommendations and alerts tailored to individual patient needs.
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Health Informatics is an interdisciplinary field that leverages technology to improve healthcare delivery by managing and analyzing health information. It plays a crucial role in enhancing patient care, optimizing healthcare processes, and facilitating research through the integration of data from various sources.
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Data visualization is the graphical representation of information and data, which leverages visual elements like charts, graphs, and maps to provide an accessible way to see and understand trends, outliers, and patterns in data. It is a crucial step in data analysis and decision-making, enabling stakeholders to grasp complex data insights quickly and effectively.
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Population Health Management (PHM) is a strategic approach that aims to improve the health outcomes of a group by monitoring and identifying individual patients within that group. It focuses on leveraging data analytics to enhance care delivery, reduce costs, and improve the overall health of populations by addressing social determinants and preventive care measures.
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Data privacy and security are critical aspects of managing personal and organizational information, ensuring that data is protected from unauthorized access and breaches while complying with legal standards. This involves implementing robust security measures, understanding regulatory requirements, and fostering a culture of privacy within organizations to safeguard sensitive information.
The International Classification of Diseases (ICD) is a globally recognized system used to categorize and code all known diseases and health conditions, facilitating international health data comparison and policy-making. It is maintained by the World Health Organization and is regularly updated to reflect advances in medical research and changes in health trends.
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