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Nursing unit characteristics and patient outcomes.

PUBLIC, PAYER, and economic forces have converged to create an unprecedented amount of change within hospitals. Such change inevitably filters down into the work environment of nursing units, affecting the work of nurses at the unit level.

Organizational characteristics have been studied primarily at the hospital level with scant attention paid to nursing unit work environment and the relationship between nurses' work and patient outcomes. Hospital-level outcome data mask unit-level patterns. Yet the nursing unit is the locus of care and of clinical and operational management.

Since little is known about the relationship between unit organizational characteristics and patient outcomes, efforts to construct optimal unit work environments continue to be hampered by lack of empirical evidence. Adverse event data have traditionally served as quality care indicators in the hospital setting although few researchers have examined the indicators as a means to assess patient care and understand nursing practice at the unit level. The gap may be attributed in large part to the difficulties researchers encounter in securing unit-level data from large hospital, state, or national data sets (Egglefield Beaudoin & Edgar, 2003; Needleman, Buerhaus, Mattke, Stewart, & Zelevinsky, 2001).

Patient safety and quality care are compelling factors in the unit work environment. Early anticipation and prevention of adverse events are part of the care plan for every hospitalized patient given the shortened length of stay (LOS) and potential risk for complications. The current hospital practice of discharging acutely ill patients after abbreviated lengths of stay has intensified the burden of care for nurses and risk for adverse events. If nurses in a positive work environment can more effectively rescue patients from adverse events, this would have major implications for work design in the context of safety and quality management.

Review of Literature

It is inevitable that changes in the hospital environment affect nursing operations at the unit level, and alter care processes and the occurrence of adverse events. To address the effects of hospital change, Aiken, Smith, and Lake (1994) transformed the original Magnet hospital work into a program of research that examined the relationship between Magnet hospital characteristics and patient outcomes. Aiken et al. (1994) constructed a body of research on a set of hospitals with reputations as good places for nurses to work. The inquiry led to investigation of the relationship between nursing and outcomes measured at the hospital level and evolved into an organizational change framework. Magnet hospitals were conceptualized as a specific organizational form with organizational characteristics of autonomy, practice control, and collaboration. Nursing practice was defined as the operant mechanism and outcomes were measured for nurses and patients.

The Nursing Work Index-Revised (NWI-R) was used to measure organizational characteristics for comparisons of Magnet and non-Magnet hospitals with mortality as the patient outcome measure (Aiken et al., 1994). Nurse autonomy, control of practice, and collaboration with physicians were identified as Magnet characteristics that affected patient outcomes through the organizational form of Magnet hospital environments. This body of research suggests Magnet hospital characteristics affect the quality of patient care. However, questions about whether such characteristics translate to the nursing unit level and affect patient outcomes differentially have not been addressed.

Magnet Hospitals

Magnet hospitals have been recognized for excellent patient care, supporting strong nursing practice environments, and the ability to attract and retain nurses (American Nurses Association, 1997; Kramer & Hafner, 1989; Kramer, Schmalenbergh, & Hafner, 1987). The term Magnet hospital was derived from a policy study commissioned in 1982 by the American Academy of Nursing. The study examined the organizational characteristics of United States hospitals successful in recruiting and retaining nurses during a national nursing shortage, hence the name (McClure, Poulin, Sovie, & Wandelt, 1982). Forty-one hospitals became the focus of extensive research efforts.

Aiken and colleagues expanded the initial Magnet hospital work into a program of research congruent with quality of care and organizational effectiveness through study of the links between hospital organizational characteristics and care outcomes. Aiken et al. (1994) examined mortality rates in 39 Magnet hospitals and 195 control hospitals with multivariate matched control sampling. Magnet hospitals had a significantly lower mortality rate (4.6% lower) for Medicare patients than did control hospitals. The study demonstrated that although Magnet hospitals employed a greater proportion of registered nurses in their skill mix, Magnet hospitals provided higher levels of autonomy, control of practice, and fostered strong professional relationships between nurses and physicians. Multivariate matched sampling allowed for control of hospital characteristics. Registered nurses as a percentage of total nursing personnel did not emerge as a predictor in regression models. Aiken and colleagues (1994) concluded that the mortality effect was a consequence of levels of autonomy and control of practice experienced by nurses at Magnet hospitals, rather than an increased number of nurses. The findings suggested that specific organizational characteristics of autonomy, control of practice, and collaboration were present in Magnet hospitals and could be reproduced in other hospital settings.

Scott, Sochalski, and Aiken (1999) conducted a review of Magnet hospital research and argued that Magnet hospital characteristics remained relevant in the nursing practice environment. Their literature review validated the presence of a relationship between nursing and outcomes and identified a method for quantifying contributions that nurses make within the hospital organization.

Havens and Aiken (1999) suggested that organization of nurses' work has the power to determine patient and staff welfare. Magnet hospital research identified specific organizational characteristics that enabled nurses to deploy fully their knowledge and experience in providing high-quality patient care as measured by fewer complications and lower mortality rates.

Aiken, Havens, and Sloane (2000) investigated how hospitals currently recognized as Magnet hospitals fared when compared with original Magnet hospitals. The 20-hospital study employed a comparative multi-site observational design. Contemporary Magnet hospitals exhibited evidence of the original organizational characteristics of autonomy, control over practice, effective collaboration, and improved patient outcomes. Of particular note was the finding that organizational support enabled nurses to use their professional knowledge, skills, and judgment to initiate interventions to rescue patients from adverse events.

Buchan (1999) reported similar findings in a contemporary case study approach that assessed whether or not the concept of a Magnet hospital had endured over time as a relevant organizational concept for nurses in 14 Magnet hospitals. Every hospital reported significant change and internal reorganization. Each of the Magnet hospitals had opted to make organizational change by relying on the knowledge of internal resources, thus limiting use of external management consultants. The consistent theme reiterated Magnet hospital organizational reliance on the knowledge and expertise of their own nursing staff and illustrated Magnet hospital characteristics at work in the current environment.

Aiken and colleagues conducted seminal research that established a link between nursing work environment in Magnet hospitals and patient outcomes. A number of researchers have provided evidence about organizational characteristics present in Magnet hospitals that enable nurses to use fully their knowledge and expertise to provide quality patient care (Aiken et al., 2000; Aiken et al., 1994; Laschinger Spence, Amlmost, & Tuer-Hodes, 2003; Laschinger Spence, Shamian, & Thomson, 2001; McClure et al., 1982). The Magnet hospital structure may be described as an organizational form that promotes nursing practice and results in better patient outcomes when compared to other hospitals. The entire causal chain remains unclear; however, these studies have provided empirical evidence that suggests nurses in Magnet hospitals produced better patient outcomes. Furthermore, the findings confirm the link between hospital work environment, nursing practice, quality care, and safety. The question to be addressed is whether or not such findings hold true at the level of the actual nursing unit operation.

Nurses and Practice

The essence of excellence in nursing practice exists in the employment of risk assessment, anticipation, intervention, and judgment. Knowing the patient suggests that the nurse has the knowledge, expertise, and competence to coordinate care and maintain patient surveillance for subtle signs of deviation from normal.

Nursing care is provided around the clock on hospital nursing units, positioning nurses as the individuals most likely to detect early complications and initiate rescue measures. Early detection of potential adverse events is important not only for the critically ill patient, but for any hospitalized patient.

Norrish and Rundall (2001) reasoned that the work of nurses has changed as nursing models such as primary nursing and patient-focused care were destabilized through hospital cost reductions. Nurse work roles, workload, and control of work were viewed as key characteristics that defined nursing practice in a hospital and affected patient outcomes. Change coupled with unit activity fluctuations and scarce or unpredictable staff resources was thought to thwart opportunities for continuity of care and evolution of a cohesive unit work group. Norrish and Rundall reiterated the urgent need to appreciate how nursing work characteristics mediate patient outcomes echoing recommendations of Aiken, Sochalski, and Lake (1997), Mitchell and Shortell (1997), Seago (2001), Shortell et al. (1994), and Sovie et al. (2000). Clifford (1998) contends organizational change has diverted nurse administrators from the professional domain of nursing effectively marginalizing nurses, practice, and control of the work environment.

Adverse Events

Mitchell and Shortell (1997) examined 81 studies to understand the state of the science of the relationship between organizational structures or processes, mortality, and adverse events. Physician-nurse collaboration and nurse surveillance emerged as the two variables consistently related to lower mortality. Adverse events were more closely aligned with organizational characteristics than mortality and surfaced as more sensitive markers for differences in quality (Mitchell & Shortell, 1997). The authors speculated that adverse events might serve as sentinels when care conditions on a nursing unit become problematic.

Numerous other researchers have investigated adverse events and reached similar conclusions (Aiken, Clarke, & Sloane, 2000; Aiken et al., 1997; Needleman et al., 2001; Seago, 2001; Shortell et al., 1994; Sovie & Jawad, 2001). In general, recommendations advocate further research on the influence of organizational characteristics of nursing care particularly at the smaller aggregate level of the nursing unit. The powerful role of nurses as the operant mechanism in recognition and detection of change in patient status, collaborative communication with physicians, and safety has received little attention in patient safety and quality management literature.

The Nursing Leadership Academy (2002) recognized the value of understanding unit-level performance and devoted an entire module to evaluating the health of the nursing unit through continuous quantitative and qualitative assessment. Use of scorecards to monitor performance at the unit level has become standard practice. Curran (2003) questioned whether benchmarking at the organizational level makes sense and suggested that the focus on measurement must shift to the "right" metrics and include communication with staff at the nursing unit level.

Research Design

An exploratory cross-sectional design extended Aiken's line of inquiry into how organizational characteristics influence adverse events and failure to rescue to the level of the nursing unit. A 944-bed teaching hospital in the Northeast was the study site while the 21 medical and surgical nursing units in the organization served as the unit of analysis. The sample comprised registered nurses (N=390) and 6 months of patient discharges (N=11,496). Registered nurse survey responses on the NWI-R generated organizational characteristic scores that were aggregated to the unit level for the independent variable. Nurses were eligible to participate provided they had a consistent work assignment of 1 month or more to one of the units.

The patient sample comprised patients discharged from the 21 medical or surgical units between March 1, 2001 and August 31, 2001. Descriptive, administrative, and patient adverse event data were extracted for 3 months prior to and 3 months post NWI-R survey.

Patient outcomes defined as nurse-sensitive adverse events included falls, nosocomial pressure ulcers, urinary tract infections (UTIs), pneumonia, cardiac arrest, mortality, LOS, and failure to rescue (see Table 1). Failure to rescue was defined as death following any of the listed adverse events.

The research question asked: What is the relationship between specific organizational unit characteristics and adverse events? Because unit-level analysis is a new area of inquiry, exploration of unit characteristics and particular patient outcomes was also performed.


Following human subjects research review committee approval the NWI-R survey was administered to nurses from the 21 units (N=390). The investigator administered the NWI-R and collected survey data using a consistent process and script for each unit. Data on adverse events were obtained from standard hospital databases as ICD codes or as entries in freestanding databases.


Principal component factor analysis was conducted to validate the NWI-R instrument and compare results with three characteristic clusters reported by Aiken and Sloane (1997). The complete NWIR (57 items) was tested and 14 factors emerged. Factor analysis proceeded with the 15 items used by the Aiken research team. Four factors emerged. The three clusters of autonomy, practice control, and collaboration conceptually developed by Aiken and Sloane (1997) were not confirmed. Practice control was the only factor congruent with that work while nurse/physician collaboration and autonomy merged into a single complementary factor. The third factor was called nurse manager support and the fourth factor was named continuity/specialization. The four factor version was called NWI-R(B) to distinguish it from the original three-cluster version. Cronbach alpha of 0.95 was found for the four-factor version. Aiken and Sloane (1997) developed the three NWI-R clusters from conceptualizations about organizational characteristics rather than statistical analysis which may explain the variation in findings for this sample. NWI-R (B) factor scores were computed for every nurse on each factor and aggregated to unit-level data. A factor score is based on the strength of the correlation of each item with the factor and the relative importance of each item to the factor as indicated by that correlation (Dixon, 1997). Factor scores ranged from -1.84 to 6.19 around a mean of 0.

Data Analysis

All survey, nurse, and patient data were extracted into Microsoft Access[C] to form a single data set. Descriptive analysis was performed to profile the total and individual nurse sample as well as the patient population sample by unit. Statistical analysis was performed with Statistical Package for Social Sciences, Version 10.

The analysis sequence included descriptive examination of data, correlation tests, and multiple regression tests for the principal variables of NWI-R(B) factor scores (organizational characteristics), adverse events, nurse staff variables, and unit-level findings. All data were aggregated into representative mean scores at the unit level creating a sample size of 21 units.


Some 74% of the nurse sample reported level of education as bachelor's degree or higher. Many (46%) of the nurses had worked on their current unit less than 2 years and an additional 16% for less than 5 years. This means greater than 60% of the nurses in the sample had relatively limited experience in caring for a high volume of diverse and acutely ill patients. Study hospital nurses dealt continually with a high volume of emergency patient admissions (51%). Patients often moved from unit to unit. Some 762 patients were not included in analysis because they had not spent 50% or more of their hospital LOS on one unit. The mean LOS (5.93 days) for patients in the study suggested patients moved rather quickly through the hospital which burdened nurses with increased patient assessments, planning, care delivery, and the need to manage patient flow effectively to support unit operations. Sixty percent to 100% of nurses on each unit completed the survey, supporting aggregation of individual data into representative unit characteristic scores for NWI-R factor scores (Verran, Gerber, & Milton, 1995).

Patient discharges accounted for a total of 73,075 patient days for the medical and surgical study units. General medical and surgical units experienced wide variation in number of patient DRGs per unit (range = 2 to 184). Overall casemix index was 2.13 (range = 1.05 to 5.41).

Adverse event rates for nosocomial pressure ulcer prevalence, falls, cardiac arrest, nosocomial pneumonia, and UTIs are reported per thousand patient days while death and failure to rescue rates are percentages. Detailed unit-level rates have not been described in large-scale studies (see Table 2).

Note that some units had no adverse events; four units experienced no failure to rescue vet another general unit had a 100% failure to rescue rate meaning that all patients who died on that unit had a preceding adverse event during the study time. Differences for the medical and surgical populations are visible in unit-level adverse rates which were higher than those reported in large-scale studies. National level studies generally report rates reflective of all hospital patients including obstetrics and newborns yielding lower adverse event rates since certain high-volume populations are at low risk.

Unit Characteristics and Adverse Events

Bivariate correlation with Pearson's r was used to detect linear relationships between variables and to glean information about the strength and nature of the relationship between variables. The correlation matrix revealed a number of significant associations between NWI-R(B) factors and adverse events. Three statistically significant relationships and a number of associations between unit characteristics and adverse events were found. Autonomy/collaboration had a statistically significant inverse relationship with failure to rescue while the significant association with pressure ulcer prevalence was positive. Continuity and specialization had a statistically significant association with death. Practice control and nurse manager support did not have any statistically significant correlations with adverse events.

Autonomy/collaboration had an inverse association with UTI (r = -0.29) (high autonomy/collaboration scores were associated with low rates for nosocomial UTIs). Practice control had a positive association with UTI (r = 0.40) suggesting that high practice control was related to high incidence of UTIs. Nurse manager support was correlated inversely with pressure ulcer prevalence (r = -0.31) and death (r = -0.30), yet had a positive correlation with failure to rescue (r = 0.28). A high nurse manager support score was associated with low rates for pressure ulcer prevalence and death. Continuity/specialization had inverse associations with pneumonia (r = -0.33), cardiac arrest (r = -0.31) and LOS (r = -0.30). High continuity/specialization was linked with low rates for pneumonia, cardiac arrest, LOS, and age. Interestingly, age was not strongly associated with adverse events or LOS, dispelling the notion that older patients are the ones who have adverse events and long lengths of stay in hospitals.

Linear regression was performed with NWI-R(B) factor scores and adverse events. Each of the four NWI-R(B) factor scores was regressed on pressure ulcers, falls, cardiac arrests, pneumonia, UTIs, death, failure to rescue, casemix, age, admit source, payer, and LOS for the 21-unit sample. As expected, relationships between factors and adverse events identified in correlations were validated in linear regressions. Autonomy/collaboration explained failure to rescue (24% variance) and pressure ulcer prevalence (18%). Practice control explained 11% of variance in UTIs. Continuity/specialization accounted for 19% of the variance in mortality. Three characteristics were associated with casemix index: autonomy/collaboration explained 17%, practice control explained 26%, and continuity/specialization accounted for 15% of variance in casemix index.

Bivariate unit analysis was added to the original plan since the sample size of 21 units limited statistical analysis. The intent was to see if grouping units by high or low NWI-R(B) factor scores might reveal evidence of patterns for unit characteristics and adverse event relationships. Units for the two groups moved from factor to factor. For example, a particular unit might place in the high group for autonomy/collaboration, yet move to the low group for practice control, the high group for nurses manager support, and low for continuity/specialization suggesting other dynamics influence nurses' work at the unit level. Two units ranked consistently high across all four NWI-R(B) factors. ICUs had more high scores for factors than did other units.

Units clustered in the high group for autonomy/collaboration had lower rates for pressure ulcer, fall, pneumonia, death, and shorter LOS than did the low group of units. The high-practice control group had lower rates for fall and UTI than did the low group. The high nurse manager support group had lower rates for fall, cardiac arrest, pneumonia, and failure to rescue than did the low group. Units with high continuity/specialization had lower pressure ulcer prevalence and UTI rates. Patterns for the unit factor scores illustrated the complexities and unique characteristics of each unit work environment.

Results suggest an association exists between unit characteristics and adverse events. The four NWI-R(B) characteristics constitute elements of a professional practice environment for nurses. Certain NWI-R(B) characteristics had consistent associations with specific adverse events: autonomy/collaboration with failure to rescue and pressure ulcer prevalence; practice control, UTI, and continuity/specialization with death. Nurse manager support had no significant findings. Exploratory study of 21 units in one hospital produced an indepth view of NWI-R(B) factors and adverse events at the nursing unit level.


This study validated the NWI-R(B) as a reliable instrument for use at the unit level. Factor analysis of the NWI-R resulted in a new four-factor instrument called the NWI-R(B) for this study. Factors identified were autonomy/collaboration, practice control, nurse manager support, and continuity/specialization. Variation in factor items between the present study and Aiken et el. may be related to their use of a conceptual method to define Magnet characteristics, rather than formal factor analysis.

Bivariate analysis revealed a number of statistically significant and some interesting relationships (r>0.25) between unit characteristics and adverse events. Autonomy/collaboration had an inverse relationship with failure to rescue (r = -0.53) and UTI (r = -0.29), and a positive association with pressure ulcer prevalence (r = 0.47). Perhaps a unit where nurses perceive a high level of autonomy/collaboration has improved communication linked to early detection of change in clinical condition or need for intervention so fewer adverse events and incidents of failure to rescue occur. Positive association of autonomy and collaboration with pressure ulcer prevalence might be attributed to unit practice standards where nurses are expected to routinely assess skin so that more pressure ulcers are detected at an early stage.

Practice control had an inverse association with death (r = -0.29), suggesting a unit with strong practice control has the nurse staff resources needed for patient care resulting in fewer deaths. The finding may relate to some of the Magnet hospital research findings. Aiken et el. (1994) reported fewer deaths in a multivariate-matched control sample at the hospital level and concluded that the mortality effect was a consequence of level of autonomy, control of practice, and collaboration present in the Magnet hospital, rather than an increased number of nurses. It would be interesting to understand such an effect from the nursing unit perspective; that is, does every NWI-R(B) factor need to be present in order to exert a positive effect on outcomes?

High nurse manager support was associated with lower death rates (r = -0.48) and lower pressure ulcer prevalence (r = -0.31) emphasizing the importance of nurse manager leadership in establishing unit practice expectations for staff that affect patient outcomes.

High continuity/specialization was associated with statistically significantly lower death rates (r = -0.48) and lower rates for pneumonia (r = -0.33), cardiac arrest (r = -0.31), and shorter LOS (r = -0.44). Aiken and Sloane (1997) contend that such specialization fosters nurse development, improved communication with physicians, and nurse dominance in important aspects of care in specialty units. AIDS specialty units were organizational work environments characterized by greater nurse autonomy and practice control that allowed nurses to practice fully. These findings and present study continuity/specialization results challenge prevalent health care consultant recommendations that suggest hospitals can reduce costs by filling every unit bed regardless of patient diagnosis (Rimer, 2002).

Linear regression results confirmed that autonomy/collaboration explained variance for failure to rescue (24%) and pressure ulcer prevalence (18%). A nursing unit where nurses perceive the presence of autonomy/collaboration would seem to be a plausible explanation for low failure to rescue rates and pressure ulcers.

Practice control explained 11% of variance in UTI. This characteristic reflected NWI-R(B) items that addressed adequate staff and resources to care for patients and provide quality care. Preventing nosocomial UTIs would be dependent upon having enough nursing staff able to devote attention to details such as timely removal of indwelling Foley catheters and scheduled times to toilet patients.

Unit continuity/specialization explained 19% of variance for death. This finding is of particular interest since nurses have not been well recognized as contributors in preventing death. The high characteristic score suggests organizational characteristics that operate in specialty units as described by Aiken et el. (1994) allow nurses to deliver better patient care. Benner's (1984) work on expert practice and knowing the patient supports this finding.

Overall NWI-R(B) factor scores were consistently high for intensive care units suggesting that nurses perceived ICU environments as inherently able to provide characteristics required to sustain nursing practice. Several other specialty units with local reputations for excellent nursing care and known as good places to work generally had high NWI-R(B) scores as well. The real challenge lies in identifying ways to use knowledge about those units to create positive practice environments in other general medical or surgical units. Intensive care units are recognized as demanding work environments, yet the data imply medical or surgical units may be equally challenging due to the complex demands of caring for a large volume of patients with many different diagnoses (DRGs).

Unit-level analysis provided a detailed picture of practice and patient care that simply cannot be achieved through hospital-level study. Large-scale studies generally rely on state or national sources for sample data about the overall performance of different hospitals. Hospital-level adverse event rates cannot provide information about unit patterns or differentiate units. Unit-level data are able to depict patterns and variations at the care delivery site where the real nursing operations take place.

Adverse events represent patient-level data and outcomes of nursing care, but do not necessarily reflect nursing incompetence or indifference. Unit work environment characteristics exerted an effect upon nurses and adverse events as described in this study. Scarce comparison data exist for adverse events; the lowest level data available has been reported as medical or surgical service line (Needleman et el., 2001; Sovie et el., 2000). Unit-level adverse event data can inform nurse administrators about organizational characteristics, structure of nurses' work, and offers a way to detect and diagnose patient care trends and unit patterns. Indeed, unit-level adverse data are recommended for inclusion as elements in performance dashboards (Nursing Leadership Academy, 2002).

Research supports the fact that patient LOS is influenced by collaboration between nurse and physician (Diers & Potter, 1997; Shortell et el., 1994). Benner, Tanner, and Chesla (1996) described how expertise is fostered through nurse-to-nurse collaboration and sharing of subtle warning signs and symptoms related to clinical events. Communication and interaction promote recognition of potential adverse events.

Benner's work on expert practice and knowing the patient also helps to inform this finding. Benner (1984) explained the profound effect that experience has on the decision-making process for nurses. The expert nurse operates at a higher reasoning level and internalizes basic principles through experience. In contrast, the novice nurse with limited clinical experience is unable to integrate knowledge in the same way. For example, subtle antecedent signs of an impending adverse event might prove difficult for a novice nurse to analyze, synthesize, and rapidly formulate a plan for intervention.


This study relied on one hospital as the data source. The limitation was counterbalanced by the control and reliability within a single organization in consistent definition of dependent variables and data collection methods, coding, and data management. Sample size was reduced to the number of medical or surgical units (21) in the organization limiting statistical analysis. It was not possible to increase the units since all medical or surgical nursing units in the study hospital were included. Extending the study time frame would not have made any difference and, in fact, would have compromised the deliberate time selected for the NWI-R(B) survey administration and adverse event occurrence. Threat of cyclical and seasonal influence upon adverse events was minimized by this time frame. Addition of units from other hospitals would have threatened validity of data because nurse staffing data and adverse events are defined, measured, and calculated differently in each hospital. The study sample and adverse events cannot be viewed as representative of the entire hospital patient population since only medical or surgical patients composed the sample.

Lack of equivalent reported adverse event rates hindered discussion about contrast or comparison. Dependence on incident reports for falls, nurse reports for pressure ulcers, and cardiac arrests raises the potential issue of underreporting and suggest that findings are conservative. Use of existing hospital data meant that the investigator had no control over accuracy of data entry. Clerical error in coding secondary diagnoses is based on chart documentation or lack thereof; data entry or alterations in data procedures may have contributed to unknown reliability issues

The method used to attribute adverse events to the unit where patients spent 50% of their LOS could have underestimated ICU adverse events since that is generally the shortest portion of hospitalization for most patients. The criterion for 50% LOS on one unit may have been too low; however, there was no better method to connect a patient to a specific unit for analysis. It is extremely difficult to get patient data chronologically linked to the day of stay, occurrence of adverse events, or secondary diagnosis assignment from the computer-based information systems in hospitals. Furthermore, these data are not available in large public available data sets.

Conclusions and Recommendations

Study findings suggest a relationship exists between some NWI-R(B) characteristics and certain patient adverse events including failure to rescue at the nursing unit level. The influence of staff nurses on adverse events can be measured and quantified in patient outcomes at the nursing unit level. Quantification of the costs for adverse events would add an important financial dimension to results. Study replication in other hospitals is recommended. It is important to note that cross-hospital comparison of nursing units has rarely been done and the effect of different mixes of patients on both the work environment and patient outcomes is simply unknown. Administrative data provide a readily available and cost-effective method to examine nursing unit environment effect upon the work of nurses and quality care.

The instrument, NWI-R(B), should be tested further at the unit level as a measurement for organizational characteristics and nurse perception of unit environment. The use of factor scores would seem to provide the best method for reporting scores. Additional factor analysis should be conducted to identify and compare factor findings. A longitudinal study that examined units over time with repeated NWI-R(B) survey measurement may add key information about the stability and sustainability of unit organizational characteristics.

Consistent definition and methods for measuring and calculating adverse event rates is an urgent need. Other nurse-sensitive patient outcomes must be identified and tested. The adverse events used in this study, Sovie et al. (2000) and Needleman et al. (2001), confirmed that nurse-sensitive patient outcomes exist and can be measured. Further analysis might include interventions intended to improve unit work environment characteristics with pre and post measures of NWI-R(B) and adverse events. Research may help explain how organizational change affects nursing work at the unit level and the impact upon patient adverse events. Patterns for NWI-R(B) factor scores indicated nursing staff and patient populations require unique unit practice models that fit the work and population needs.

Organizational support and effective nursing leadership provide the foundation for success in structuring a healthy work environment (McManis & Monsalve Associates, 2003). Clear demonstration of collective commitment to support empowerment of frontline staff nurses with the autonomy and practice control needed to deliver safe quality care is a critical factor.

Nurse administrators and managers are linchpins for the strategies and change processes needed to improve unit-level practice. Access to timely administrative data can support the work of rapid cycle unit teams who may design change and work processes to meet unique unit and patient population needs.

Study results explicate the pivotal role of the nurse manager in establishing a work environment where nurses understand their accountability for professional practice and patient outcomes. Staff nurse participation in team rounds, unit-level decision making, and administrator visibility on nursing units are concrete actionable items that could enhance unit work environment. Techniques to capitalize on ways to empower nurses, enhance communication with staff nurses, improve nurse-physician relationships, and collaborative communication would help build a Magnet unit.

Unit structure change affects patient outcomes by changing the process of how nurses on the frontline work. Staff nurse ability to practice surveillance, thus preventing failure to rescue, has been undermined by stark realities of staffing to demand coupled with a workforce shortage. As a result, patients experience less consistency and nurse continuity, limiting development of a committed cohesive unit team and potentially affecting safety and quality of care. Unit organizational characteristics may determine response speed for intervention once the nurse has identified a potential adverse event.

Adverse events impose substantial economic costs and inflict financial and devastating harm to patients and families. Although adverse events serve as a headliner, the real issue is change in nurses' work and an untenable unit work environment with little practice control. Study findings for adverse event rates suggest a compelling need for nursing unit and work process change. Results highlight the importance of creating unit environments that motivate nurses to practice in ways that influence positive patient outcomes. Findings described how unit organizational characteristics seem to affect nurses and patients in this sample.

Study findings strengthen the premise that effects of nursing interventions are mediated by organizational form at the unit level. The challenge is to define and measure nursing unit characteristics that support and transform nursing process into patient outcomes within the context of the nursing unit. An immense reservoir of professionalism and talent exists among nurses that deserves to be effectively mobilized in the form of quality care. This study focused on the nursing unit environment and provided a microscopic view of unit environments where nurses practice and have the potential to rescue patients from certain adverse events.
Table 1.
Nurse-Sensitive Adverse Events

           Adverse Event
       Operational Definition                         Rationale

Fall defined as any unintentional      Fall risk assessed by nurses.
movement to the floor by a patient.    Prevention of fall and injury is
                                       domain of nurses.

Pneumonia defined as infection         Nosocomial pneumonia results
post hospital admission.               from immobility; inadequate
                                       ventilation of lobes of lungs,
                                       inadequate pulmonary toileting

Urinary tract infection defined as     UTI associated with indwelling
infection post hospital admission.     urinary catheters. Infection
                                       results from catheter use, lack
                                       of sterile technique during
                                       placement, delay in securing MD
                                       order to remove catheter

Pressure ulcer defined as reddened     Pressure ulcer created by
skin at a pressure point not present   pressure on bony prominences,
on admission.                          inadequate nutrition,
Classified as Stage I-IV or            incontinence, shearing action,
unstageable.                           immobility, lack of positioning.
                                       Prevention is domain of nurses.

Cardiac arrest defined as cessation    Respiratory and cardiac failures
of cardiac or respiratory function     are antecedent signs of change
requiring documented hospital          in patient clinical status.
code team response.                    Surveillance is role of nurses.

Mortality defined as death during      Anticipation for risk and need
hospitalization.                       for intervention.

Failure to rescue is death following   Nurses intervene to prevent
adverse event.                         adverse events and/or subsequent

Table 2.
Patient Adverse Event Data Aggregated by Unit (N=21)

Unit                   Pressure            Cardiac
ID     Service          Ulcer      Fall    Arrest

1      Surgery           6.4       3.1       0.8
2      Medicine          0.0       4.2       1.7
3      Medicine          2.4       3.7       1.1
4      Medicine/ICU      4.6       1.2       6.4
5      Surgery/ICU      20.5       3.1       1.2
6      Medicine          9.6       3.9       2.0
7      Medicine         12.7       5.3       0.5
8      Medicine          8.3       1.1       1.9
9      Medicine         12.1       2.6       0.8
10     Medicine/ICU     11.5       0.4       6.4
11     Medicine          6.0       4.2       1.8
12     Surgery           5.6       2.4       0.3
13     Surgery           2.1       6.6       0.3
14     Surgery           3.9       0.3       0.5
15     Medicine          4.6       4.1       0.0
16     Surgery/ICU      29.2       1.3       2.1
17     Surgery          18.4       0.5       0.3
18     Surgery           5.3       2.0       0.3
19     Surgery           4.4       1.0       0.3
20     Surgery           9.2       3.4       0.8
21     Surgery/ICU      32.1       0.0       0.0

Unit                                                   Fail to
ID     Service         Pneumonia   UTI      Death     Rescue %

1      Surgery           5.0        2.7       0.4          0
2      Medicine          7.2       12.1       1.4         54
3      Medicine          7.4       11.2       0.5        100
4      Medicine/ICU      8.7        6.4      19.8         31
5      Surgery/ICU       7.5        1.2      18.8         37
6      Medicine          5.2       13.4       4.7         43
7      Medicine          7.0       18.5       3.3         29
8      Medicine          6.9        9.4       2.8         40
9      Medicine          8.5       15.8       2.3         53
10     Medicine/ICU     12.4        6.9      33.1         45
11     Medicine          7.3        6.4       5.7         59
12     Surgery           5.9       10.5       0.4         60
13     Surgery           1.3       12.8       0.6         50
14     Surgery           2.3        7.9       0.3         33
15     Medicine          2.5       12.0       0.0          0
16     Surgery/ICU       9.6        4.8      27.1         19
17     Surgery           5.9        4.6       0.5          0
18     Surgery           4.2        6.8       0.8         40
19     Surgery           3.0        5.7       0.5          0
20     Surgery           2.6        7.7       1.3         57
21     Surgery/ICU       6.1        1.8      28.9          7

NOTE: Death and Failure to Rescue convert to percents; other measures
are rates per 1,000 patients days.

ACKNOWLEDGMENT: The author wishes to acknowledge the expert advice and support from Donna Diers, PhD, RN, FAAN, and Annie W. Goodrich Professor of Nursing, Yale University, New Haven, CT.


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SUZANNE MULVEY BOYLE, DNSc, RN, is Director, Center for Professional Practice Excellence, Yale-New Haven Hospital, New Haven, CT. She was the recipient of Nursing Economic$ Foundation Scholarship Award in 2000.
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Author:Boyle, Suzanne Mulvey
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Date:May 1, 2004
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