Clinical Data Value Engine

Identify the value of data as it relates to expected payback in terms of reduced medical costs

Healthcare Insurance providers face proliferation of clinical and health related data from many sources and must decide what is the most valuable data to aggregate by data element or type and what is the value of getting data in near real-­-time versus batch processing systems. Organizations need to identify the value of data as it relates to expected payback in terms of reduced medical costs. Having good data cannot guarantee that effective analytic’s tools will be utilized effectively, and result in the quality and performance improvements desired. Bad data, however, will most certainly mean that efforts to use information will be hindered due to a lack of trust and belief in the analytic’s and/or results.

XtLytics Clinical Data Value Engine is built on the following data quality dimensions:

Accuracy – reflects how well information within data reflects the actual reality

Timeliness – reflects how recent and up to date data is at the time it is available for use in analytics

Comparability -­- refer to the extent to which the data is uniform over time and uses standard conventions

Usability – reflects how easy is to access use, and understand the data

Relevance – reflects how well the data meets the current and future needs of the healthcare organization

Completeness – refers to how much of all potential electronic data (for example, from electronic health records, claims data, and    other sources) is available for analytics.

Conformity – reflects how well the available data conforms to expected formats

Consistency – measures how well values agree across data sets and the extent of agreement exhibited by different data sets that are  describing the same thing.