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assemble patients' health information including medical history, symptoms, examination results, diagnostic tests, treatment methods, and all other healthcare provider services. Technicians organize and manage health information data by ensuring its quality, accuracy, accessibility, and security.
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 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.
Quality of health information on the internet becomes important in this context as both misdiagnosis and inaccurate recommendations for triage are possible. Additionally, most online diagnostic tools fail to account for the user's familial and personal medical history, including current diagnoses and conditions they possess.
The trend of large health companies merging allows for greater health data accessibility. Greater health data lays the groundwork for the implementation of AI algorithms. A large part of industry focus of implementation of AI in the healthcare sector is in the clinical decision support systems. As more data is collected, machine learning ...
The terms EHR, electronic patient record (EPR), and electronic medical record (EMR) have often been used interchangeably, but "subtle" differences exist. [6] The electronic health record (EHR) is a more longitudinal collection of the electronic health information of individual patients or populations.
Data quality assurance is the process of data profiling to discover inconsistencies and other anomalies in the data, as well as performing data cleansing [17] [18] activities (e.g. removing outliers, missing data interpolation) to improve the data quality.
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 ...