Guideline based clinical decision support systems provide patient-specific medical guidance to physicians, often at the point-of-care. A large body of research shows that these systems have the potential to reduce practice variation and human error. However, there is also evidence suggesting that these systems may introduce unintended risk into the medical-decision making process. The poor quality of data in medical records and databases poses one such risk. As a result, appropriately assessing the magnitude of the risk posed by data quality is an important, but difficult problem. The nature of this risk depends on several complex and interrelated factors. This paper provides a novel framework that explicitly models the nature of data, errors, and how guideline based clinical decisions support systems process information and produce guidance. The framework gives the decision-maker the ability to assess how uncertainty about data quality translates into the risk of negative medical consequences and determine which data elements are most critical for minimizing this risk. The results of the framework can inform both efficient data-quality improvement and risk minimization strategies.
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