Biomedical Data: Acquisition, Storage, and Use - Presentation PDF

Biomedical Data: Acquisition, Storage, and Use This presentation explores the nature of clinical data, its uses in healthcare, the transition from paper to electronic records, and the structure and coding of medical information. We'll examine how biomedical data is collected, stored, and utilized to...

Biomedical Data: Acquisition, Storage, and Use This presentation explores the nature of clinical data, its uses in healthcare, the transition from paper to electronic records, and the structure and coding of medical information. We'll examine how biomedical data is collected, stored, and utilized to improve patient care and advance medical knowledge. by Ayman Khalifa What Are Clinical Data? 1 Definition 2 Types Clinical data are Clinical data can be observations about patients, narrative text, numerical including symptoms, measurements, genetic physical signs, lab results, information, recorded imaging studies, and more. signals, drawings, or Each datum typically images. They range from includes the patient, patient histories to lab parameter observed, value, results to radiologic images. time, and method of observation. 3 Collectors Various healthcare professionals collect data, including physicians, nurses, lab technicians, and imaging specialists. Technological devices also generate data directly. Uses of Health Data Communication Historical Record The medical record facilitates Data create a detailed communication among healthcare compilation of patient providers, supporting coordinated 2 information, serving as a record 1 care over time. of observations, interventions, and reasoning behind Risk Assessment management decisions. Data help anticipate future 3 health problems and identify 5 patients at high risk for specific Legal Documentation conditions. The medical record serves as a Preventive Care 4 legal document, justifying Records track preventive diagnoses and treatment choices. measures like immunizations and health education interventions. Transition from Paper to Electronic Records Limitations of Paper Records Advantages of Electronic Health Records (EHRs) Unavailability when needed Improved accessibility Difficulty finding specific information Enhanced searchability Illegible handwriting Legible, standardized entries Physical storage challenges Efficient storage and retrieval Challenges in Data Management Data Volume The increasing amount of data generated in healthcare, including genomic information, creates challenges in storage and analysis. Standardization Lack of standardized terminology and nomenclature in medicine complicates data interpretation and aggregation. Integration Combining data from various sources and formats presents technical and semantic challenges. Privacy and Security Protecting sensitive patient information while allowing appropriate access for care and research is an ongoing concern. Structure of Clinical Data Knowledge 1 Interpreted information, including formal studies and heuristics Information 2 Analyzed and organized data with meaning Data 3 Individual observations or measurements Understanding the structure of clinical data is crucial for effective storage, retrieval, and analysis. The data-to- knowledge spectrum illustrates how raw data is transformed into actionable knowledge in healthcare. Coding Systems in Healthcare 1 ICD-10-CM 2 SNOMED-CT International Systematized Classification of Nomenclature of Diseases, 10th revision, Medicine - Clinical Clinical Modification. Terms. A comprehensive Used for diagnostic clinical terminology for coding in hospitals and electronic health records. insurance claims. 3 CPT Current Procedural Terminology. Used for coding medical services and procedures for billing purposes. Benefits of Standardized Coding Consistency Standardized codes ensure consistent reporting of diagnoses and procedures across different healthcare providers and settings. Analysis Coded data facilitates statistical analysis, epidemiological studies, and health trend monitoring. Interoperability Common coding systems enable data exchange between different healthcare systems and organizations. Reimbursement Standardized codes are essential for accurate billing and insurance reimbursement processes. Challenges in Medical Coding Granularity Issues Balancing Precision and Usability Coding systems may be too detailed in some areas and There's a tension between the need for precise, unique not detailed enough in others, depending on the terms and the desire for a system that's general specific clinical context. For example, a hematologist enough to cover many patients without unduly might need more specific codes for constraining physicians' descriptions. Finding this hemoglobinopathies than what ICD-10-CM provides. balance is an ongoing challenge in medical coding. The Role of Databases in Healthcare Electronic Health Records (EHRs) EHRs serve as databases, storing individual patient observations and clinical data. Research Databases Specialized databases collect and organize data for clinical research and epidemiological studies. Public Health Databases These databases aggregate population-level health data to monitor trends and identify public health issues. Genomic Databases Specialized databases store and manage vast amounts of genetic and genomic data. Knowledge Bases in Healthcare Definition Applications A knowledge base is a collection of facts, heuristics, Clinical decision support systems and models used for problem-solving and analysis of Diagnostic aids organized data. Unlike databases, knowledge bases Treatment planning tools contain interpreted information and can support Drug interaction checkers decision-making processes. The Impact of Big Data in Healthcare 1 Precision Medicine Big data analytics enable personalized treatment plans based on individual patient characteristics and genetic profiles. 2 Population Health Large-scale data analysis helps identify health trends and risk factors across populations. 3 Clinical Research Big data facilitates more comprehensive and efficient clinical trials and observational studies. 4 Operational Efficiency Data-driven insights can improve hospital operations and resource allocation. Emerging Trends in Biomedical Data Genomic Data AI and Machine Internet of Medical Integration Learning Things Incorporating genomic data Using advanced algorithms Integrating data from into EHRs to support to analyze complex medical wearable devices and home precision medicine data and support clinical health monitors into clinical initiatives. decision-making. care. Blockchain in Healthcare Exploring blockchain technology for secure, decentralized health data management. Ethical Considerations in Biomedical Data Consent Privacy Ensuring informed consent for data collection and use, especially Protecting patient confidentiality 2 in research contexts. while enabling necessary data sharing for care and research. 1 Equity Addressing potential biases in 3 data collection and analysis that could lead to healthcare Data Ownership 5 disparities. Clarifying rights and Transparency responsibilities regarding 4 Maintaining clear communication ownership and control of health about how patient data is used data. and shared. The Future of Biomedical Data Interoperability 1 Seamless data exchange between different healthcare systems and organizations. Real-time Analytics 2 Instant analysis of patient data to support immediate clinical decision-making. Predictive Medicine 3 Using big data and AI to predict and prevent health issues before they occur. Global Health Insights 4 Leveraging worldwide health data to address global health challenges. As we look to the future, the integration of diverse data sources, advanced analytics, and ethical data practices will continue to transform healthcare, leading to more personalized, efficient, and effective medical care.