Data Mining Medication Administration Incident Data to Identify Opportunities for Improving Patient Safety

Abstract

This research analyzed historical data related to medication administration errors at a 340 bed regional medical center. The objective was to determine if data mining techniques could identify relationships within the error data that point to processes and circumstances that enable medication administration errors. The Cross Industry Standard Process for Data Mining (CRISP-DM) was used to determine if data mining techniques applied to medication administration error data could yield information that could improve the systems and processes supporting medication administration at a regional medical center. Data sources from the point of medication dispensing to the patient’s response were investigated. Base data over a one year period were queried to obtain all available information relating to acknowledged medication administration errors. These data were analyzed using Microsoft SQL Server 2005 - Clustering Algorithm. The clustering algorithm results confirm the limitations of self reporting as a means of medication administration error measurement. Further, the research identifies cultural, process, and policy inconsistencies that drive self reporting behavior and subsequently lead to marginalized error event knowledge capture. These findings contribute to the development of recommendations for design improvements for medication error reporting systems. Additionally, the difficulty of deriving information from multiple Healthcare IT systems that are not integrated is demonstrated. The results provide practical guidance for organizations evaluating Clinical Decision Support Systems designed to support the medication use process.