A protocol for capturing daily variability in nursing care.
ABSTRACT To meet current and future patient safety and quality requirements, traditional analyses based on data aggregated to the hospital or unit level over months or years may need to change. Nine customized databases were developed, five with patient data (e.g., age, illness severity, perceptions of nursing care quality, desired health outcomes) and four with nurse data (e.g., education, experience). These were merged to create a Patient-Nurse database. Nurse managers, clinicians, and researchers could use the protocol to conduct a more robust analysis of the relationships between nursing system characteristics and patient care processes and outcomes. The protocol described here could be used by nurse managers, clinicians, and researchers to better understand temporal phenomena and patient level data. Through information derived from applying this protocol, staffing decisions can be made to assure the right mix of nurses is available, not just the right number.
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ABSTRACT: The objective of the study was to measure the variability of direct nursing cost for similar patients and to examine the characteristics of nurses assigned to different types of patients. There is no standard method for measuring direct nursing cost by patient. Deidentified data were collected from 3 databases for patients admitted from January 2010 through December 2012 on 1 medical/surgical unit in a large Magnet hospital. Direct nursing care time and costs were calculated from the nurse-patient assignment. Variability in nursing intensity (0.36-13 hours) and cost per patient day ($132-$1,455) was significant for similar patients. Higher cost nurses were not assigned sicker patients (F3, 3029 = 87.09, P < .001, R = 0.124). Mean (SD) nursing direct cost per day was $96.48 ($55.73). Standard measurement of nursing cost per patient could be benchmarked across hospitals and inform nursing administration care delivery decisions.The Journal of nursing administration 05/2014; 44(5):257-62. · 1.37 Impact Factor