Patterns in nursing home medication errors: disproportionality analysis as a novel method to identify quality improvement opportunities

Authors

  • Richard A. Hansen,

    Corresponding author
    1. Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
    • University of North Carolina at Chapel Hill, UNC Eshelman School of Pharmacy, Division of Pharmaceutical Outcomes and Policy, 2205 Kerr Hall; CB 7573, Chapel Hill, NC 27599-7573, USA.
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  • Portia Y. Cornell,

    1. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
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  • Patrick B. Ryan,

    1. Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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  • Charlotte E. Williams,

    1. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA
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  • Stephanie Pierson,

    1. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA
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  • Sandra B. Greene

    1. Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
    2. Cecil G. Sheps Center for Health Services Research, University of North Carolina, Chapel Hill, NC, USA
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Abstract

Purpose

To explore the use of disproportionality analysis of medication error data as a novel method to identify relationships that might not be obvious through traditional analyses. This approach can supplement descriptive data and target quality improvement efforts.

Methods

Data came from the Medication Error Quality Initiative (MEQI) individual event reporting system. Participants were North Carolina nursing homes who submitted incident reports to the Web-based MEQI data repository during the 2006 and 2007 reporting years. Data from 206 nursing homes were summarized descriptively and then disproportionality analysis was applied. Associations between medication type and possible causes at the state level were explored. A single nursing home was selected to illustrate how the method might inform quality improvement at the facility level. Disproportionality analysis of drug errors in this home was compared with benchmarking.

Results

Statewide, 59 drug-cause pairs met the disproportionality signal and 11 occurred in 10 or more reports. Among these, warfarin was co-reported with communication errors; esomeprazole, risperidone, and nitrofurantoin were disproportionately associated with transcription error; and oxycodone and morphine were disproportionately reported with name confusion. Facility-level analyses illustrate how descriptive frequencies and disproportionality analysis are complementary, but also identify different safety targets.

Conclusions

Exploratory analysis tools can help identify medication error types that occur at disproportionate rates. Candidate associations might be used to target patient safety work, although further evaluation is needed to determine the value of this information. Copyright © 2010 John Wiley & Sons, Ltd.

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