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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: (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 ...
Health data can be used to benefit individuals, public health, and medical research and development. [14] The uses of health data are classified as either primary or secondary. Primary use is when health data is used to deliver health care to the individual from whom it was collected. [15]
The General Practice Data for Planning and Research system was set up by the British National Health Service as a replacement for the General Practice Extraction Service as a means of transmitting data intended for use beyond that of providing individual health care. This might include healthcare planning, or research.
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3] Exploratory data ...
The patient health record is the primary legal record documenting the health care services provided to a person in any aspect of the health care system. The term includes routine clinical or office records, records of care in any health related setting, preventive care, lifestyle evaluation, research protocols and various clinical databases.
A Clinical Decision Support System (CDSS) is a computerized tool designed to assist healthcare providers in making clinical decisions by integrating medical knowledge with patient data. These systems utilize algorithms, databases, and patient information to provide tailored recommendations, alerts, and reminders to healthcare professionals at ...
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. [1] [2] Exploratory causal analysis (ECA), also known as data causality or causal discovery [3] is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions.
Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. [1]