Intro to Health Informatics

• Lecture 1 serves as an introduction to health informatics.
• The course instructor is Bobaker madi, and the contact email is [email protected].

Learning Objectives

• The lecture will help participants define core informatics terms.
• Participants will be able to define terminology related to biomedical and health informatics.
• Participants will be able to describe how biomedical and health informatics is an informatics science.

Historical Background

• In the pre-digital era (1900s–1950s), health information systems were manual and relied on paper records.
• In 1928, the American College of Surgeons established standards for hospital records, marking progress toward organized medical record-keeping.
• In the 1940s, advancements in epidemiology and public health standardized health data collection for disease surveillance and improved healthcare delivery.
• The advent of computers in healthcare (1950s–1970s) led to early attempts to digitize patient records and medical data.
• By the 1960s, healthcare facilities began using computers for administrative tasks like billing and appointment scheduling.
• In the 1960s, the first hospital information systems (HIS) integrated clinical, administrative, and financial data.
• The birth of health informatics as a discipline occurred in the 1980s–1990s.
• The term "health informatics" began to gain recognition as a distinct field of study, combining medicine, computer science, and information science.
• In the 1980s, universities such as the University of Utah and Stanford University started offering formal courses and degrees in medical informatics.

Overview of Health Informatics

• The course will provide a practical overview of biomedical and health informatics (BMHI), sometimes called health informatics (HI).
• Informatics is derived from the French word informatique, coined by Philippe Dreyfus in 1962, combining information and automatique (automation), and referred to the science of automated information processing.
• Health informatics is the discipline concerned with the management of healthcare data and information through the application of computers.
• The original term "medical informatics" has been replaced by other terms, including health informatics.
• The American Medical Informatics Association (AMIA) defines health informatics as “an interdisciplinary field of study that utilizes technology to organize, analyze, manage, and use information to improve healthcare.”
• Informatics is the science of information and the blending of people, biomedicine, and technology.
• Individuals who practice informatics are known as informaticians.
• A health informatician helps patients, staff, and citizens through the use of technology.

Shift from "Medical Informatics" to "Health Informatics"

• "Health informatics" reflects the broader application of informatics to all areas of health and wellness, including public health, nursing, patient care, and health policy.
• The rise of electronic health records (EHRs), telemedicine, and health information exchanges (HIEs) required a broader term.
• As the field became more interdisciplinary, incorporating fields like public health, computer science, and engineering, "health informatics" became the preferred term to represent its holistic nature.

Areas within Biomedical and Health Informatics

• Bioinformatics applies informatics in cellular and molecular biology, often focusing on genomics.
• Imaging informatics focuses on imaging, including the use of systems to store and retrieve images.
• Clinical informatics applies informatics in healthcare settings such as nursing, dentistry, and pathology.
• Consumer health informatics is devoted to informatics from a consumer perspective, often focusing on mobile health.
• Clinical research informatics facilitates clinical research, emphasizing translational research to accelerate findings into clinical practice.
• Public health informatics applies informatics in areas of public health, including surveillance, reporting, and health promotion.

Related Terminology

• Information technology (IT) generally refers to computers and related technology.
• Computer science (CS) focuses on the scientific aspects of computing and IT.
• Management information systems is another field underlying IT and is usually taught in business schools.
• Health information technology (HIT), or health IT, refers to the health-related application of IT.
• Digital health emanates from information and communications technology (ICT).
• eHealth is the use of ICT for health.
• mHealth involves the use of mobile devices for health.
• Telemedicine and Telehealth provide healthcare when participants are separated by time and/or distance.
• "Tele" can be added to a clinical discipline, e.g., telepathology or teleradiology.
• Health systems science (HSS) is defined as a new science for medicine, different from basic and clinical science.
• Evidence-based medicine (EBM) is the application of the best scientific evidence in medical decision-making.
• Evidence-based practice (EBP) is the application of EBM in patient care.
• Comparative effectiveness research (CER) compares diagnostic or treatment options to evaluate effectiveness, safety, or outcomes (also called patient-centered outcomes research).
• The electronic health record (EHR) refers to the patient's health record in digital form and has mostly supplanted the older term, electronic medical record (EMR).
• The personal health record (PHR) refers to the personally controlled health record.
• The health information exchange (HIE) refers to the exchange of health information across traditional business and other boundaries.
• Translational research translates basic research into clinical applicability.
• Translational bioinformatics applies bioinformatics to health-related problems.
• Clinical research informatics (CRI) is the area of informatics applied to clinical research.
• Data science studies the methods involved in the analysis and processing of data and proposes technology to improve methods in an evidence-based manner.
• Artificial intelligence (AI) refers to information systems and algorithms capable of performing tasks associated with human intelligence.
• Machine learning (ML) enables computer programs to learn without being explicitly programmed.
• The current most successful approach to machine learning involves the use of neural networks; when using multiple layers, it is called deep learning.

The Importance of Data

• One outcome of EHRs, radiology information systems (RISs), mobile technologies, and other systems is the voluminous amount of healthcare data.
• "Big Data" is a term that encompasses key characteristics that define the complexity and challenges of managing and analyzing large datasets.
• Volume: Refers to the enormous size or quantity of data being generated, stored, and processed.
• Velocity: Refers to the speed at which data is generated, processed, and analyzed.
• Variety: Represents the different types and formats of data.
• Veracity: Refers to the quality and accuracy of data.
• Value: Focuses on the usefulness of the data, which must be processed and analyzed effectively to extract meaningful insights and drive decision-making.

Data, Information, Knowledge, and Wisdom Hierarchy

• There is an information hierarchy that is important in the information sciences.
• Data: Raw facts, signals, or observations with no inherent meaning
• Examples: Numbers, symbols, or text like "30°C", "Ali", or "160/100"
• Data: Plural of datum (singular), the lowest level of representation
• Thus a number in a database
• Information: Meaningful data or facts from which conclusions can be drawn by humans or computers
• Example: The temperature outside is 30°C"
• Knowledge: The application of information, showing patterns and relationships
• Example: The average temperature in summer is 30°C, so it's a warm day
• Wisdom: The critical use of knowledge to make intelligent decisions
• Example: Since it's 30°C, wearing light clothing would be a good choice
• Health information Technology (HIT): Provides the tools to generate information from data that humans can turn into knowledge and wisdom

Health Data Sources

• Personal Health Record (PHR)
• Electronic Health Records (EHR)
• Claims data (known as administrative data)
• Warehouse
• Health information exchange (HIE)
• Home monitoring

Scope of Health Informatics

• It is broad and multidisciplinary, encompassing various domains within healthcare, technology, and data science.
• EHRs and health information systems are a core area involving implementation, management, and optimization to ensure accurate, accessible, and standardized patient information across healthcare settings. Includes working with systems such as Electronic Medical Records (EMRs), Personal Health Records (PHRs), and Health Information Exchange (HIE).
• Clinical informatics focuses on improving the quality of patient care by applying informatics principles to clinical practice through designing and evaluating clinical decision support systems (CDSS), and the integration of clinical data systems with other health technologies.
• Public health informatics involves the use of informatics to improve public health surveillance, research, and policy, including disease outbreak tracking, data collection, and strategies for improving population health. Health data analytics is applied to epidemiological studies and health trend monitoring.
• Biomedical informatics bridges the gap between health informatics and biomedical research and involves the development of computational tools and data systems to analyze biological data, supporting drug discovery, genetic research, and the application of artificial intelligence (AI) to personalized medicine.
• Health data analytics involves the extraction, transformation, and analysis of large healthcare datasets for decision-making, predictive modeling, and trend analysis.
• Big data is used in analyzing patient data, treatment outcomes, and population health to identify insights that can inform healthcare policies and practices.
• Telemedicine and remote health monitoring uses telehealth technologies for remote consultations, diagnostics, and management of patients, particularly in underserved or rural areas. Remote patient monitoring (RPM) devices transmit real-time health data to healthcare providers, enabling continuous care.
• Artificial Intelligence(AI) and Machine Learning(ML) play an increasingly important role in healthcare informatics, with applications ranging from automated diagnostics to predicting patient outcomes based on historical data. The scope extends to the development of intelligent systems that can assist in clinical decision-making, medical imaging analysis, and personalized treatment strategies.

Benefits of Health Informatics

• Improved Patient Care:
  • Increased accuracy with electronic health records (EHRs) through centralization and digitization of information.
  • Tailored treatments based on individual needs due to access to comprehensive patient data.
  • Real-time remote monitoring allows healthcare providers to track conditions and improves response times.
• Increased Efficiency:
  • Streamlined workflows due to automating administrative tasks such as scheduling, billing, and documentation reducing time spent on manual processes.
  • Quick retrieval of patient records which improve decision-making and reduce delays in treatment with faster data access.
• Cost savings:
  • Reduced administrative costs with automation and electronic systems require less paper and reduced administrative overhead.
  • Minimized duplicate testing sharing electronic records prevents unnecessary repeat tests, saving time and resources.
• Enhanced Communication:
  • Interoperability from Health informatics which enables different systems and providers to seamlessly share data, ensuring continuity of care.
• Improved collaboration allowing providers across various specialties can access shared records to coordinate treatment plans effectively.
• Data-Driven Insights:
• Health systems use artificial intelligence and machine learning to predict patient outcomes and identify at-risk populations for predictive analytics.
• Tools provide recommendations for aiding diagnosis- and evidence-based recommendations to treat patients using clinical decision support.

Acknowledgement

• The majority of the lectures' content comes from two books:
  • "Health Informatics: Practical Guide" (8th Edition, 2022) by William R Hersh
  • "Guide to Health Informatics" (3rd Edition, 2015) by Enrico Coiera

Assessment

• Assessment methods include:
  • Midterm Exam (May, 20%)
  • Lab Exam ( June, 10%)
  • Research-based essay + Presentation (July, 5%)
  • Attendance (All year round, 5%)
  • Final Exam (August, 60%)