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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.
Health data are classified as either structured or unstructured. Structured health data is standardized and easily transferable between health information systems. [4] For example, a patient's name, date of birth, or a blood-test result can be recorded in a structured data format.
A volunteer-run database of testing and medical stats in the United States Sentiment Analysis of users reviews on COVID-19 contact tracing mobile applications [10] [11] March 2021 This dataset is intended to support sentiment analysis of users' reviews on COVID-19 contact tracing mobile applications.
Health care analytics is the health care analysis activities that can be undertaken as a result of data collected from four areas within healthcare: (1) claims and cost data, (2) pharmaceutical and research and development (R&D) data, (3) clinical data (such as collected from electronic medical records (EHRs)), and (4) patient behaviors and preferences data (e.g. patient satisfaction or retail ...
Examples include—MODIFY and—DECOD, which are equivalent terms for a --TRT or—TERM topic variable, --TEST and—LOINC which are equivalent terms for a --TESTCD. Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record).
The Healthcare Cost and Utilization Project (HCUP, pronounced "H-Cup") is a family of healthcare databases and related software tools and products from the United States that is developed through a Federal-State-Industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ).
Clinical data standards are used to store and communicate information related to healthcare so that its meaning is unambiguous. They are used in clinical practice, in activity analysis and finding, and in research and development. There are many existing and proposed standards and many bodies working in this field.
Biomedical data science is a multidisciplinary field which leverages large volumes of data to promote biomedical innovation and discovery. Biomedical data science draws from various fields including Biostatistics, Biomedical informatics, and machine learning, with the goal of understanding biological and medical data.