The findings of this study add to the substantial body of work on the impact of nurse staffing on inpatient care outcomes by (1) documenting the impact of hospital nursing care beyond hospital discharge; (2) linking unit-level staffing to quality of a nursing care process (discharge teaching), and to outcomes at discharge (readiness) and postdischarge (utilization); (3) estimating the costs and savings associated with investment in nurse staffing. The use of unit-level staffing data measured within-unit over time and inclusion of structure and process measures provides stronger evidence of a link to patient outcomes and cost-benefit than in prior cross-sectional studies that used between-unit or between-hospital comparisons.
In this study sample, there was a direct, negative association between RN hours to which patients were exposed during hospitalization and the odds of subsequent readmission. Among patients hospitalized on the same nursing unit, those who were discharged when RN nonovertime staffing was higher were less likely to subsequently be readmitted. When RN overtime staffing was higher, the odds of postdischarge occurrence of ED visits increased, consistent with previous findings that nurses' performance may be suboptimal in extended work hour situations (Rogers et al. 2004
). Variation in amount of non-RN staffing did not explain postdischarge utilization, pointing to the importance of the amount of RN-level care provider time to patient outcomes (Aiken et al. 2002
; Needleman et al. 2002
; Cho et al. 2003
To understand the mechanism through which nurse staffing levels could affect postdischarge outcomes, the relationship to a nursing process measure (quality of discharge teaching) was evaluated. As anticipated, when RN staffing hours were higher, patients reported higher quality discharge teaching. With more hours allocated per-patient-day, nurses have more time available for core functions, particularly time-consuming functions like discharge teaching. Effective discharge preparation goes beyond basic information-giving to planning and problem solving for self-care management in the home after discharge. Patient engagement in self-management is an important part of successful transition to home-based care (Glasgow et al. 2002
; Hibbard et al. 2004
; Ryan, Aloe, and Mason-Johnson 2009
The study documented a path of influence from RN nonovertime staffing through discharge teaching and patient perception of discharge readiness to ED use, but not readmission. Posthospitalization ED use occurs due to concerns about symptoms, complications (that may arise from failure to follow home instructions or inadequate knowledge about recovery), or lack of access to other care sources (Burt, McCaig, and Simon 2008
). Effective discharge teaching and the subsequent increase in discharge readiness may have prevented ED use associated with self-care deficiencies but not postdischarge complications unrelated to self-care abilities that require readmission.
Explanations of nursing process mediators of the relationship between nurse staffing and readmission are needed. Although others have documented beneficial effects of programmed discharge transition activities on readmission and the role of discharge planning on patient outcomes (Coleman et al. 2008
; Jack et al. 2009
; Popejoy, Moylan, and Galambos 2009
), the unique role and contribution of the hospital staff nurse, who is often responsible for carrying out discharge preparation functions, is still unclear. With direct interaction and indirect coordination time for discharge processes approaching 1.5 hours per patient (Jack et al. 2009
), differentiation of discharge processes requiring RN-level skill could support planning for adequate staffing to achieve critical patient outcomes.
Three recommendations for health care policy and practice emerge from the study findings: (1) manage nurse staffing levels to achieve optimal patient outcomes; (2) implement assessment of quality of discharge teaching and discharge readiness as standard predischarge practices; and (3) align payment models to encourage nurse staffing levels supportive of postdischarge outcomes. These recommendations contribute to the arsenal of strategies addressing health care quality and cost reforms.
Management of within-unit variation in nurse staffing holds the potential to improve postdischarge outcomes and costs of care. Staffing management is both a hospital and unit-level function. Strategic decisions to increase nurse staffing and recruitment/retention efforts to sustain optimal staffing levels are hospital-level management actions; control strategies to avoid understaffing are the role of unit-based managers.
Assessment of quality of discharge teaching and discharge readiness are not standard predischarge practices either for quality measurement purposes or as opportunities for anticipatory correction. Implementation of these assessments within discharge protocols will promote early identification of patients without adequate discharge knowledge and skills for self-management after discharge. The assessments could trigger anticipatory interventions for reinforcement of discharge preparation and for transitional support services during the posthospitalization period. The QDTS and RHDS tools have been used in research with adults of all ages (Bobay et al. 2010
). Shortened forms are currently being tested.
The cost-benefit analysis revealed a substantial potential economic benefit to increasing nurse staffing. Costs of improved hospital RN staffing could be offset by costs avoided through averting postdischarge utilization. However, there is no business case for increasing nurse staffing when the financial beneficiaries of reduced postdischarge utilization are the payer and patient (Needleman 2008
). Implementation of payment reforms such as gain-sharing, bundling of payments for hospital and posthospital care, and creation of structures accountable for continuum of care services will incentivize optimal staffing to improve discharge processes, and achieve desired patient outcomes and cost savings (Guterman and Drake 2010
There are several limitations to the study design and methods. The patient sample included patients at least 18 years of age who were discharged home. Patients discharged to long-term care were not included. Postdischarge encounters outside the four study hospitals were not accessible. The Magnet-recognized health system that served as the study site may not represent the staffing patterns and quality of care, including discharge preparation process, within non-Magnet facilities. Therefore, the relationships of nurse staffing to discharge preparation and postdischarge utilization may be different in other patient and health system samples.
The focus of the study was the impact of within-unit variation of direct RN and non-RN hours of care. Within-unit variation was sampled over 7 monthly intervals. The availability of monthly staffing averages rather than daily staffing assigned to individual patients precluded linking patient-specific data directly to the actual days of hospitalization. Unit-level staffing aggregated within the month of discharge is the best routinely available approximation of care delivered to individual patients and offers better explanatory support than hospital-level aggregate data. As information technology systems evolve, direct linking of nurse staffing data to the individual patient's hospital stay will be possible. The precision with which staffing estimates and patient-level data are linked should be a consideration in planning future studies.
The study model did not include variables related to models of nursing and interdisciplinary care coordination and delivery, other measures of staffing such as FTEs or nurse-patient ratios, or direct observation of discharge teaching and other preparatory activities. Nurse staffing variables that have been previously linked to hospitalization outcomes, such as RN education and experience (Blegen, Vaughn, and Goode 2001
; Aiken et al. 2003
), were reported annually by the study units and therefore were insufficient for within-unit panel analysis. Nonnurse factors that could impact readmission, such as physician practices, were not investigated.
The cost model used regional nurse staffing cost estimates. While this was done to increase generalizability of the study findings, it may be different than the health care system's actual staffing costs. Projections for costs and savings included hospital-reported costs only and did not include payments for physician services.