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Nursing Diagnoses as Predictors of Hospital Length of Stay: A Prospective Observational Study
Author(s) -
D'Agostino Fabio,
Vellone Ercole,
Cocchieri Antonello,
Welton John,
Maurici Massimo,
Polistena Barbara,
Spandonaro, Federico,
Zega Maurizio,
Alvaro Rosaria,
Sanson Gianfranco
Publication year - 2019
Publication title -
journal of nursing scholarship
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.009
H-Index - 80
eISSN - 1547-5069
pISSN - 1527-6546
DOI - 10.1111/jnu.12444
Subject(s) - medicine , observational study , medical diagnosis , emergency medicine , medical record , prospective cohort study , nursing care , nursing , pathology
Purpose To investigate whether the number of nursing diagnoses on hospital admission is an independent predictor of the hospital length of stay. Design A prospective observational study was carried out. A sample of 2,190 patients consecutively admitted (from July to December 2014) in four inpatient units (two medical, two surgical) of a 1,547‐bed university hospital were enrolled for the study. Methods Data were collected from a clinical nursing information system and the hospital discharge register. Two regression analyses were performed to investigate if the number of nursing diagnoses on hospital admission was an independent predictor of length of stay and length of stay deviation after controlling for patients’ sociodemographic characteristics (age, gender), clinical variables (disease groupers, disease severity morbidity indexes), and organizational hospital variables (admitting inpatient unit, modality of admission). Findings The number of nursing diagnoses was shown to be an independent predictor of both the length of stay (β = .15; p < .001) and the length of stay deviation (β = .19; p < .001). Conclusions The number of nursing diagnoses is a strong independent predictor of an effective hospital length of stay and of a length of stay longer than expected. Clinical Relevance The systematic inclusion of standard nursing care data in electronic health records can improve the predictive ability on hospital outcomes and describe the patient complexity more comprehensively, improving hospital management efficiency.