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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 clinical methods used to help patients clarify and achieve their health-related goals are different for each goal type though the categories are inter-related. [13] The uniting factor of this conceptual framework is that the goal is formed in a discussion involving both the patient and the health care providers prior to the development of a plan of care that is based upon the patient's ...
Health Services Management Research; Human Resources for Health; Journal for Healthcare Quality; Journal of Healthcare Management; Journal of Innovation in Health Informatics; Journal of Medical Marketing
Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains the data that has already been collected by other sources, such as data disseminated in a scientific journal). Data analysis methodologies vary and include data triangulation and data ...
S.M.A.R.T. (or SMART) is an acronym used as a mnemonic device to establish criteria for effective goal-setting and objective development. This framework is commonly applied in various fields, including project management, employee performance management, and personal development.
Data-driven models encompass a wide range of techniques and methodologies that aim to intelligently process and analyse large datasets. Examples include fuzzy logic, fuzzy and rough sets for handling uncertainty, [3] neural networks for approximating functions, [4] global optimization and evolutionary computing, [5] statistical learning theory, [6] and Bayesian methods. [7]
Scholarship has explored the potential barriers to collecting community-based participatory research data. The CBPR approach is in line with the body of sociological work that advocates for "protagonist driven ethnography". [22] The approach provides for and demands that researchers collaborate with communities throughout the research process.