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Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare; claims and cost data, pharmaceutical and research and development (R&D) data, clinical data (collected from electronic medical records (EHRs)), and patient behavior and sentiment data (patient behaviors and preferences, (retail purchases e.g. data ...
Health data. Health data is any data "related to health conditions, reproductive outcomes, causes of death, and quality of life " [ 1] for an individual or population. Health data includes clinical metrics along with environmental, socioeconomic, and behavioral information pertinent to health and wellness.
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [ 1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...
Health informatics. Medical informatics introduces information processing concepts and machinery to the domain of medicine. Health informatics is the study and implementation of computer structures and algorithms to improve communication, understanding, and management of medical information. [ 1] It can be viewed as a branch of engineering and ...
The Healthcare Effectiveness Data and Information Set ( HEDIS) is a widely used set of performance measures in the managed care industry, developed and maintained by the National Committee for Quality Assurance (NCQA). HEDIS was designed to allow consumers to compare health plan performance to other plans and to national or regional benchmarks.
Clinical data management ensures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to ensure that conclusions drawn from ...
Artificial intelligence. Artificial intelligence in healthcare is the application of artificial intelligence (AI) to copy human cognition in the analysis, presentation, and understanding of complex medical and health care data. It can also augment and exceed human capabilities by providing faster or new ways to diagnose, treat, or prevent ...
Due to the complexity and variability of public health data, like health care data generally, the issue of data modeling presents a particular challenge. While a generation ago flat data sets for statistical analysis were the norm, today's requirements of interoperability and integrated sets of data across the public health enterprise require ...
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