Biomedical Informatics: Science & Pragmatics | Presentation PDF

Ch 1: Biomedical Informatics: The Science and the Pragmatics Biomedical informatics is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve hum...

Ch 1: Biomedical Informatics: The Science and the Pragmatics Biomedical informatics is the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving, and decision making, driven by efforts to improve human health. This presentation will explore the key concepts, applications, and future directions of this rapidly evolving field. by Ayman Khalifa The Information Revolution in Medicine 1 1940s-1950s Development of first digital computers and speculation about their potential use in medicine 2 1960s-1970s Early hospital information systems and medical decision support research 3 1980s-1990s Introduction of personal computers and the Internet, leading to widespread adoption in healthcare 4 2000s-Present Rapid growth of electronic health records, mobile health technologies, and big data analytics Electronic Health Records: The Core of Modern Healthcare Benefits Challenges Improved access to patient information Data standardization and interoperability Enhanced communication among providers Privacy and security concerns Support for clinical decision-making User interface design and workflow integration Facilitation of research and quality improvement Implementation costs and organizational change The Learning Health System Data Analysis Data Collection Application of advanced analytics 2 Capture of clinical data from EHRs 1 to identify patterns and insights and other sources Knowledge Generation Development of new clinical 3 guidelines and best practices Evaluation 5 Continuous assessment of Implementation outcomes and system 4 Integration of new knowledge into performance clinical practice and decision support Biomedical Informatics: A Multidisciplinary Field Computer Science Biology Medicine Algorithms, data structures, Molecular biology, genetics, Clinical practice, public and software engineering and systems biology health, and healthcare delivery Statistics Data analysis, machine learning, and predictive modeling Biomedical informatics draws on and contributes to a wide range of disciplines, integrating diverse knowledge and methodologies to address complex healthcare challenges. Core Areas of Biomedical Informatics Bioinformatics 1 Molecular and cellular level Imaging Informatics 2 Tissue and organ level Clinical Informatics 3 Individual patient level Public Health Informatics 4 Population and societal level Bioinformatics: Unlocking the Secrets of Life Key Applications Challenges Genome sequencing and analysis Managing and analyzing big data Protein structure prediction Integrating diverse data types Drug discovery and development Developing accurate predictive models Systems biology modeling Translating findings to clinical applications Imaging Informatics: Seeing Inside the Body Imaging informatics encompasses the development and application of computational tools for acquiring, processing, analyzing, and visualizing medical images across various modalities and scales. Clinical Informatics: Enhancing Patient Care Electronic Health Records Comprehensive digital patient records supporting clinical documentation, order entry, and decision support Clinical Decision Support Intelligent systems providing evidence-based recommendations and alerts to clinicians Telemedicine Remote patient care and monitoring using telecommunications technology Patient Engagement Tools for patient education, self-management, and shared decision-making Public Health Informatics: Protecting Populations Data Collection Gathering health data from various sources, including EHRs, registries, and surveys Surveillance Monitoring disease trends, outbreaks, and health behaviors Analysis Applying statistical and machine learning techniques to identify patterns and risk factors Intervention Developing and implementing targeted public health programs and policies Translational Bioinformatics: Bridging Bench and Bedside Basic Research 1 Molecular and cellular studies Preclinical Studies 2 Animal models and drug screening Clinical Trials 3 Testing in human subjects Clinical Practice 4 Implementation in patient care Translational bioinformatics aims to accelerate the process of moving scientific discoveries from the laboratory to clinical applications, leveraging informatics tools and methods at each stage of the translational pipeline. Precision Medicine: Tailoring Treatment to the Individual Key Components Informatics Challenges Genomic profiling Integration of multi-omics data Molecular diagnostics Clinical interpretation of genomic variants Targeted therapies Development of predictive models Pharmacogenomics Privacy and ethical considerations Artificial Intelligence in Healthcare Diagnosis Treatment Planning Predictive Analytics Robotic Surgery AI-assisted image analysis Personalized therapy Forecasting disease AI-enhanced surgical and pattern recognition for recommendations based on progression and treatment planning and execution disease detection patient data and outcomes responses Artificial intelligence and machine learning are revolutionizing healthcare by augmenting human expertise and enabling more precise, efficient, and personalized care. Ethical and Social Implications 1 Privacy and Data Security Protecting sensitive health information while enabling beneficial data sharing and analysis 2 Algorithmic Bias Ensuring fairness and equity in AI-driven healthcare decisions 3 Digital Divide Addressing disparities in access to health technologies and information 4 Human-AI Interaction Balancing automation with human judgment and empathy in healthcare The Future of Biomedical Informatics Virtual and Augmented Reality Internet of Medical Things Quantum Computing Immersive technologies for medical Ubiquitous health monitoring and Next-generation computational education, surgical planning, and data collection through connected power for solving complex biological patient care devices problems The future of biomedical informatics holds immense potential for transforming healthcare through emerging technologies, continued integration of diverse data sources, and increasingly sophisticated analytical approaches.