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The process of how to best determine nurse staffing has challenged nurse leaders for decade. Research has demonstrated that appropriate allocation of staff favorably impacts patient outcomes, patient safety, financial outcomes, and staff satisfaction (Myner et al., 2012; Shekelle, 2013). Nurse leaders are faced with higher patient acuities and unanticipated events that are not accounted for in traditional staffing models. Dynamic patient events (DPEs) have been defined in this study as rapid, unanticipated clinical situations that result in sudden shifts in nursing workload and the need to carry out rapid staffing adjustments. DPEs require vigilant attention to nurse staffing, and currently are not incorporated into staffing models at most hospitals. Increasingly, hospitals are leveraging new technologies to efficiently and effectively evaluate workload and determine staffing solutions. These new technological advances offer opportunities to measure nursing workload and determine optimal staffing. This study aims seeks to: 1). describe nurses’ perception of DPEs and their impact on workflow and patient care; and 2). examine how DPEs such as code blues, emergency response needs, bedside procedures, monitored patient travel time and requirements for patient safety attendants can be incorporated into staffing plans.
Methods:
For aim 1, a qualitative approach was utilized. Five–60 minute focus groups were conducted on 3 nursing units (1 general medical surgical, 1 general medicine and 1 general cardiac). A semi-structured interview format was used to guide the discussion. The interview began with providing the subjects a definition of DPEs. Interviews were audio-recorded and transcribed. A constant comparative method was used for the analyses. Codes were agreed upon by a consensus of 5 research team members and we identified patterns, trends and themes.
A cross-sectional quantitative approach was used for aim 2. Our institution has used an outcome driven acuity system, The Cerner ClairviaTM Workforce Management, since 2009. This system is designed to track patient acuities and predict nursing workload. To measure the impact of DPEs on nurse staffing and workload a cross-sectional approach was used. A random selection of 24 shifts across 3 units in three hospitals at one Academic Medical Center was selected (n=72 shifts). Units included one general cardiac unit, one general medical surgical unit and one medical unit. At the end of each selected shift, the RNs and PCAs were interviewed (n=511) and asked to describe their involvement in a DPE (type and length of time) during the shift, and to describe care that was missed using an adopted version of the MissCare Survey (Kalisch, Landstrom & Hinshaw., 2009). The data collected following each shift regarding the DPE was entered into the Cerner ClairviaTM Workforce Management system, specifically the Dynamic Event Workload module. Upon entering the DPEs into the system, the workload demand for that particular shift was recalculated. Differences between the staffing predicted before DPE entry and after DPE entry were compared to examine the changes in target staffing in workload following a DPE. Descriptive analyses were used (frequencies) to examine the amount of missed nursing care reported. Other variables examined included admissions, discharges and transfers (ADTs), and unit census. Descriptive analyses, correlational analyses and regression modeling were used to describe the number of DPEs, length of DPEs and their impact on staffing and missed care.
Results:
Findings from aim 1 identified five major themes: 1). types of DPEs, 2). impact of DPEs on patients, 3). impact of DPES on nursing workload, 4). missed or delayed nursing care, and 5). impact on nurses. Nurse participants articulated and categorized types of DPEs which included frequent travel off unit with patients, code blues, rapid emergency response events and unplanned one-on-one safety attendants. Nurse participants described the impact of DPEs on patients that included concerns for patient safety and patient satisfaction. Other themes included impact on nursing workload in which nurses had other staff members cover their patient assignment and the reprioritization of the nursing workload. Nurse participants identified issues related to missed or delayed care. Participants felt that there were many delays in care which potentially delayed discharge time. Care missed was often passed on to the next shift. DPEs were viewed as having a tremendous impact on nurses. This impact included feelings about job satisfaction and their perception of personal job performance. Nurse participants expressed feelings of stress and burnout.
The findings from aim 2 demonstrated that the average number of DPEs varied considerably across units and shifts (day vs night). The number of DPEs for Unit A, B, and C was 2.7 (47% of census), 4.8 (65% of census) and 6.6 (81% of census) respectively for day shift. This dropped considerably at night to 1.2, 2.1 and 4.5 events for each respective unit. Length of DPE also varied considerably by shift and unit. During day shift, the time ranged from a mean of 91 minutes (unit A) to 184 minutes (Unit C). Length of time for DPEs on night shift ranged from 30 minutes (unit A) to 192 minutes (Unit C). The most frequent DPE reported was transporting monitored patients to other areas of the hospital. Staff reported frequently missed care. The nursing care most frequently reported as being delayed or/missed included documentation, late medication administration, and delayed response to call lights. A negative moderate correlation was found between missed patient hygiene (r= -.48, p=.05) missed patient turns (r= -.53, p=.03) missed hourly rounds (r= -.51, p=.03), missed patient assessments (r= -.46, p.03), missed opportunities to provide emotional support (r= -.44, p=.01), and missed documentation (r = -.56, p=.02) and dynamic patient events on the night shift. A low negative correlation was found between missed patient assessments (r=-.42, p=.005) and dynamic patient events on day shift. There were three significant predictors of the average total missed nursing care; the change in staffing secondary to the DPE (B=.-400), the change in unit census (B= -.337), and overall staff concerns about patient safety (B=-.556).
Conclusion:
DPEs significantly impact nursing workload and should be incorporated into staffing models. Although DPEs are unanticipated, nurse administrators can use newer robust technological nurse staffing support systems to assure a safer patient care environment. Staffing models that do not account for potential DPEs can result in missed nursing care. This study validates the unpredictable nature of DPEs in today’s fast-paced acute care practice environments. It is also consistent with the growing body of research that describes missed care in nursing (Kalisch, et al, 2009; Kalisch, Gosselin & Choi, 2012) . It is incumbent upon nursing leaders to understand and anticipate trends and patterns affecting nursing workload and staffing. By exploring innovations in technology to capture workload changes resulting from DPEs, nurse leaders have additional tools to provide the right staff at the right time