Empirical Data Mining: Conservation of Nursing Energy and Care Capacity in Medical-Surgical Hospital Work Environments
Ann L. Hendrich Loyola University Chicago EMPIRICAL DATA MINING: CONSERVATION OF NURSING ENERGY AND CARE CAPACITY IN MEDICAL-SURGICAL HOSPITAL WORK ENVIRONMENTS Nurses are a critical component of the hospital care delivery system and provide essential observation and surveillance of inpatients. Mounting evidence describes an association between nurse staffing, the nurse work environment, and patient and nurse outcomes. In particular, more registered nurse hours per patient day have been linked to reduced patient mortality. However, recent studies indicate that only a minority of nursing time is dedicated to patient observation and assessment of vital signs. Therefore, increasing the proportion of nursing time available for direct patient care is an imperative and is hypothesized to lead to improved patient and nurse outcomes. A novel conceptual model of nurse care capacity derived from conservation of energy theory is proposed. This model identifies specific variables that consume nurse time and reduce care capacity and forms the basis for an empirical analysis of data collected in a nurse time and motion study. How medical-surgical nurses spend their time has been identified as a key driver of transformative changes in the hospital work environment; to date, however, only very limited data have been published describing the specific patterns of movement and activities of hospital nurses. The goal of this study was to identify key drivers of inefficiency in the nurse work environment. Cluster analysis identified a group of nurses across units who outperformed their peers with regard to trips to and time spent in the patient room. These results have implications for nurse workflow and hospital systems redesign.
energy theory, Medical surgical nursing, nursing care capacity, nursing energy, Nursing time, patient safety, Nursing
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