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Appl Clin Inform 2023; 14(01): 076-090
DOI: 10.1055/a-1993-7627
Research Article

A Novel Use of Bar Code Medication Administration Data to Assess Nurse Staffing and Workload

Melissa K. Knox
1   Michael E. DeBakey VA Medical Center, Houston, Texas, United States
2   Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
3   Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
,
Paras D. Mehta
4   Department of Medicine, University of Houston, Houston, Texas, United States
,
Lynette E. Dorsey
1   Michael E. DeBakey VA Medical Center, Houston, Texas, United States
,
Christine Yang
1   Michael E. DeBakey VA Medical Center, Houston, Texas, United States
2   Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
3   Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
,
Laura A. Petersen
1   Michael E. DeBakey VA Medical Center, Houston, Texas, United States
2   Center for Innovations in Quality, Effectiveness, and Safety, Houston, Texas, United States
3   Department of Medicine, Baylor College of Medicine, Houston, Texas, United States
› Author Affiliations Funding This material is based upon work supported in part by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, and the Center for Innovations in Quality, Effectiveness, and Safety (grant no.: CIN 13-413), IIR 15-438, and C19 20-212 (PI: L.A.P.). Support for VA nursing unit data is provided by the VA Office of Nursing Services.
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Abstract

Objective The aim of the study is to introduce an innovative use of bar code medication administration (BCMA) data, medication pass analysis, that allows for the examination of nurse staffing and workload using data generated during regular nursing workflow.

Methods Using 1 year (October 1, 2014–September 30, 2015) of BCMA data for 11 acute care units in one Veterans Affairs Medical Center, we determined the peak time for scheduled medications and included medications scheduled for and administered within 2 hours of that time in analyses. We established for each staff member their daily peak-time medication pass characteristics (number of patients, number of peak-time scheduled medications, duration, start time), generated unit-level descriptive statistics, examined staffing trends, and estimated linear mixed-effects models of duration and start time.

Results As the most frequent (39.7%) scheduled medication time, 9:00 was the peak-time medication pass; 98.3% of patients (87.3% of patient-days) had a 9:00 medication. Use of nursing roles and number of patients per staff varied across units and over time. Number of patients, number of medications, and unit-level factors explained significant variability in registered nurse (RN) medication pass duration (conditional R2  = 0.237; marginal R2  = 0.199; intraclass correlation = 0.05). On average, an RN and a licensed practical nurse (LPN) with four patients, each with six medications, would be expected to take 70 and 74 minutes, respectively, to complete the medication pass. On a unit with median 10 patients per LPN, the median duration (127 minutes) represents untimely medication administration on more than half of staff days. With each additional patient assigned to a nurse, average start time was earlier by 4.2 minutes for RNs and 1.4 minutes for LPNs.

Conclusion Medication pass analysis of BCMA data can provide health systems a means for assessing variations in staffing, workload, and nursing practice using data generated during routine patient care activities.

Keywords

nursing care - bar code medication administration - assessment - registered nurse - licensed practical nurse - workload

Protection of Human and Animal Subjects

This research was approved by the Baylor College of Medicine Institutional Review Board.




Publication History

Received: 14 September 2022

Accepted: 02 December 2022

Accepted Manuscript online:
06 December 2022

Article published online:
01 February 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

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