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The intersection of security risk and laws that set standards of care is where data liability are defined. A handful of databases are emerging to help risk managers research laws that define liability at the country, province/state, and local levels. In these control sets, compliance with relevant laws are the actual risk mitigators.
For example, data produced during human subject research might be de-identified to preserve the privacy of research participants. Biological data may be de-identified in order to comply with HIPAA regulations that define and stipulate patient privacy laws. [1] When applied to metadata or general data about identification, the process is also ...
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
The standard provides examples of conditions that may be identified during the audit that might indicate fraud. One example is management denying the auditors access to key IT operations staff including security, operations, and systems development personnel. The auditors must determine whether the results of their tests affect their assessment.
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.
Risk Evaluation and Mitigation Strategies (REMS) is a program of the US Food and Drug Administration for the monitoring of medications with a high potential for serious adverse effects. REMS applies only to specific prescription drugs, but can apply to brand name or generic drugs. [1] The REMS program was formalized in 2007.
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 ...
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 ...