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Implementing a business intelligence system signals a hospital's readiness to embrace the future of data analysis for performance improvement, but the tool's true power is lost if the data entered are not reliable and decision makers use traditional practices for data analysis.
Hospitals can use business intelligence (BI) systems to improve quality of care, margins, employee and patient satisfaction, and operational and clinical efficiencies. Business intelligence tools give users the ability to correlate data elements for multidimensional macro- and microanalysis of information for effective strategic decision making. But a BI system is only as good as the data it contains and the skill of the analysts using it. To realize the full benefits of a BI tool, a hospital must first optimize the entering, integration, and analysis of data.
Data Reliability, Lineage, and Integration
Information from BI systems is only as good as the core data. Frequently, data are incorrectly entered because employees are using processes that have not evolved from legacy/sunsetted systems or the data have been extracted from systems with different data structures. In some instances, employees do not understand the importance of consistent data entry because they have not been instructed on how BI tools can impact the way data are used to make management decisions. Often, in such instances, the lack of instruction can lead to clerical staff populating the fields with default data when they do not really understand certain information.
Consider, for example, the case of a 350 -bed community hospital that is experiencing operating room (OR) staff turnover. Current staff instruct new hires on the surgical documentation system use based on historical practices from use of an outdated, replaced system that required a work-around because of missing functionality. Drop - down selection lists have been added to over the years without maintaining standardization of the data sets.
As a result, add-on surgical cases performed are being misclassified and entered with scheduled start times instead of being entered as converted add-on cases. This data entry skews analysis of late starts and case types making it impossible to accurately evaluate associated revenue accurately.
By taking steps to ensure that case types are correctly assigned during data entry, hospital administrators can reliably determine lost revenue associated with add-on cases not...