The biology of a complex healthy (homeostatic), injured, or diseased human is phenomenally complex. The amount of known information about human bio-medicine doubles every ten years. Much of what a physician learns in medical school is out of date, or known to be in error (or forgotten) within a few years of completing residency training and entering in regular clinical practice. It has long been established, and is gaining acceptance, that human cognition, both from a recall of facts, and capacity for error free judgment is a limiting factor in optimal care. Medical informatics is reasonably novel hybrid discipline which employs methodology from cognitive science, human factors engineering, computer science, epistemology, evidence based medicine, statistics, molecular biology, research method logy, systems analytics, project management, information theory and data visualization to help physicians (as well as other health care providers in a primary clinical diagnosis and treatment role) improve the care they can provide. As a consequence, it is largely believed (based on well done studies at a handful of academic medical centers) that costs will be reduced (largely by avoiding costly complications and to some extent avoiding wasteful behaviors) with a subsequent narrowing of the gap between those receiving the most expensive health-care (per capital) in the world, and the tremendous (up to 1/3 of the United States population in some estimates) with substandard or no access to modern health care.
Medical Informatics is an approximately 50 year-old discipline closely related to Clinical or Health Informatics, and many users interchange the terms freely. However, as a discipline chiefly concerned with semantics, it warrants are precise explanation. It is a discipline which is a predominantly applied science, sharing some features with engineering, as well as having some purely exploratory and theoretical aspects, similar to the usual conception of a science. It draws heavily from the structure, meaning, representation, transmission, and comprehension of information in the biomedical domain, predominantly allopathic human clinical medicine. It is closely related to studies of (and provides means to study and interventions for) public health informatics, evidence based medicine, clinical research, health information technology, medical library science, and artificial intelligence.
Medical Informatics attempts to study, understand and augment the management of the complexity surrounding the decision making and automation of clinical care, principally via the use of electronic health record systems (EHRS), creation of clinical decisions support systems and augmentation of human decision support through better understanding of the cognitive science surrounding expert decision making and medical errors.
Some of the key concepts in medical (shared with clinical/health informatics, and as well as with public health informatics, nursing informatics, and many other closely related disciplines) include:
- Semiotics and the conveyance of information, knowledge, data, such that it has (formal) computable semantics.
- Analytics of biomedical information, using methods from machine learning and statistical analysis, leading to knowledge discovery, and better recognition of patterns of disease (either via optimizing display or retrieval and / or comparison of prior results).
- Creation of clinical decision support systems, using algorithms, predictive instruments (such as decision trees), and rules based expert systems and implementing them within the workflow of an electronic health record system.