Health informatics and clinical data analysis focus on the systematic collection, management, and computational interpretation of healthcare data to improve patient outcomes, clinical decision-making, and public health strategies. This field integrates electronic health records, medical imaging, laboratory results, wearable device data, and population-level health statistics into unified analytical frameworks. Using methods such as machine learning, statistical modeling, natural language processing, and predictive analytics, researchers and clinicians can identify disease trends, stratify patient risk, optimize treatment pathways, and monitor population health in real time. Health informatics also supports the development of clinical decision-support systems, interoperability standards, and digital health platforms that enhance the accuracy and efficiency of healthcare delivery. As healthcare data continue to expand rapidly, maintaining robust data governance, ensuring patient privacy, and implementing scalable computational infrastructures remain essential for translating clinical data insights into evidence-based medical practice and improved health outcomes.