Health informatics

Health informatics applies information science and technology to collect, manage, analyze, and use health data. It connects computing, clinical practice, public health, and health-system management.

Electronic health record: A digital system that stores and organizes patient health information for authorized clinical use. It is a central information system through which clinicians document and retrieve patient data.
Clinical decision support system: A computer-based system that provides clinicians with patient-specific information or recommendations to support decisions. It turns clinical data and medical knowledge into prompts at the point of care.
Health Level Seven: An organization and family of standards for exchanging electronic health information. Its messaging and document standards enable data exchange among clinical information systems.
Medical informatics: The application of information science and computing to medicine and medical practice. The term often emphasizes clinical medicine, while health informatics also spans public health and health systems.
Medical error: A preventable failure in the process of care that can lead to inappropriate treatment or patient harm. Poorly designed information systems can introduce errors even as they aim to prevent them.
Health information exchange: The electronic sharing of health information among organizations and systems. It addresses how clinical data moves between otherwise separate care providers.
Telemedicine: The delivery of clinical services across distance using communication technologies. It depends on informatics infrastructure for remote consultations, records, and monitoring.
Fast Healthcare Interoperability Resources: A standard from Health Level Seven for exchanging healthcare information through modular resources and web technologies. FHIR provides a widely adopted technical approach to accessing and sharing health data.
Biomedical informatics: The broad study of using data, information, and knowledge to improve human health and biomedical research. It is a wider umbrella that can include health informatics alongside bioinformatics and related areas.
Algorithmic bias: Systematic and unfair differences in an algorithm’s outputs across groups or individuals. Health algorithms can reproduce inequities when their data or design reflects existing disparities.

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