Publications by authors named "Lynette E Dorsey"
Background: Studies of nurse staffing frequently use data aggregated at the hospital level that do not provide the appropriate context to inform unit-level decisions, such as nurse staffing.
Objectives: Describe a method to link patient data collected during the provision of routine care and recorded in the electronic health record (EHR) to the nursing units where care occurred in a national dataset.
Research Design: We identified all Veterans Health Administration acute care hospitalizations in the calendar year 2019 nationwide.
Article Synopsis
- The study explores how nurse leaders in the Veterans Health Administration utilize data to make staffing decisions, highlighting the need for effective evidence-based practices in healthcare.* -
- Interviews with 27 nurse leaders revealed that they primarily use data for quality improvement and monitoring, but face challenges such as data fragmentation, lack of access, and insufficient knowledge about available data.* -
- Emphasizing the importance of understanding nurse leaders' experiences, the research suggests improving data governance and continuous involvement of nurse leaders to enhance nursing care quality and support informed decision-making.*
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.