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Using Time-Referenced Data to Assess Medication Administration Performance and Quality.

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This study shows that analyzing medication administration times from large datasets can identify patterns in late and as-needed (PRN) medication dispensing by registered nurses (RNs). This data helps assess clinical processes and performance.

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Area of Science:

  • Health Informatics
  • Nursing Practice
  • Clinical Data Analysis

Background:

  • Accurate medication administration is crucial for patient safety in inpatient settings.
  • Identifying patterns in medication administration, such as delays or as-needed (PRN) use, can highlight areas for process improvement.
  • Large clinical datasets offer potential for retrospective analysis of nursing practices.

Purpose of the Study:

  • To evaluate the feasibility of using a large clinical dataset to extract time-referenced medication administration data.
  • To identify delayed medication doses and as-needed (PRN) administration patterns by registered nurses (RNs).
  • To assess the utility of time-stamped data for analyzing inpatient medication administration.

Main Methods:

  • Secondary analysis of bar-code medication administration time stamps (n=3,043,812) from 50,883 patients.
  • Data collected from a single, urban, 525-bed hospital over a two-year period (April 2013 - March 2015).
  • Involved 714 nurses across 11 inpatient units.

Main Results:

  • The majority of scheduled medications (43.3%) were administered between 9-10 AM and 9-10 PM, contributing to the most delayed doses.
  • Patients received an average of 8.9 medications daily.
  • Nurses administered an average of 19.7 medications per shift, with full-time nurses administering 3414 annually.

Conclusions:

  • Time-referenced medication administration data is feasible for analysis.
  • This data can effectively identify clinical processes and performance related to scheduled and PRN medication administration.
  • Findings support the use of data analytics to monitor and improve nursing medication practices.