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Healthcare quality and safety require that the right information be available at the right time to support patient care and health system management decisions. Gaining consensus on essential data content and documentation standards is a necessary prerequisite for high-quality data in the interconnected healthcare system of the future.
Medical data, including patients' identity information, health status, disease diagnosis and treatment, and biogenetic information, not only involve patients' privacy but also have a special sensitivity and important value, which may bring physical and mental distress and property loss to patients and even negatively affect social stability and national security once leaked.
Some of the problems tackled by CRI are: creation of data warehouses of health care data that can be used for research, support of data collection in clinical trials by the use of electronic data capture systems, streamlining ethical approvals and renewals (in US the responsible entity is the local institutional review board), maintenance of ...
Big data in health research is particularly promising in terms of exploratory biomedical research, as data-driven analysis can move forward more quickly than hypothesis-driven research. [88] Then, trends seen in data analysis can be tested in traditional, hypothesis-driven follow up biological research and eventually clinical research.
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 ...
The data management plan describes the activities to be conducted in the course of processing data. Key topics to cover include the SOPs to be followed, the clinical data management system (CDMS) to be used, description of data sources, data handling processes, data transfer formats and process, and quality control procedure
Data from the PCEHR is to be predominantly used in patient healthcare, but other uses are possible, for policy, research, audit and public health purposes. The concern is that in the case of research, what is allowed goes beyond existing privacy legislation.
The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.” [7] Other journals have a more general scope than biomedical data science, but regularly publish biomedical data science research such as Health Data Science [8] and Nature Machine Intelligence. [9]