Nursing care plays a critical role in determining patient safety and clinical outcomes in high-demand hospital environments. Decades of research have consistently demonstrated a direct correlation between adequate nurse staffing and improved patient outcomes, including reduced mortality, fewer complications, and lower readmission rates.1 These findings underscore the importance of sufficient human resources in maintaining the quality and continuity of care, particularly in acute care settings where patient needs are complex and time-sensitive. However, beyond staffing levels alone, attention has increasingly shifted toward the processes of care delivery at the bedside, where variations in workload and resource allocation directly influence the consistency and completeness of nursing interventions.
A growing body of evidence identifies “missed nursing care”—defined as required patient care that is left undone, delayed, or incomplete—as a pervasive and measurable issue in hospital settings.2 This phenomenon reflects the operational consequences of constrained staffing environments, where nurses must prioritize tasks under time and workload pressures, often leading to the omission of essential but less urgent aspects of care. Staffing constraints and heavy workloads are primary drivers of these care omissions, establishing a direct link between structural resource limitations and the day-to-day realities of clinical practice.3 As a result, missed nursing care has emerged as a critical intermediary concept that connects organizational capacity with patient-level outcomes.
While the association between staffing levels and patient outcomes is well-documented, the underlying mechanism through which these effects occur remains underexplored. Specifically, there is a notable research gap characterized by limited empirical studies that explicitly model missed nursing care as a mediating variable within this relationship.4,5 Existing studies often examine staffing and outcomes independently or assess missed care descriptively, without integrating these elements into a unified analytical framework. Addressing this gap is essential for advancing both theoretical understanding and practical interventions. Therefore, the objective of this study is to examine whether missed nursing care mediates the relationship between nurse staffing levels and patient outcomes, providing a more comprehensive explanation of how structural constraints translate into measurable clinical effects.
An extensive body of research demonstrates a robust and consistent relationship between nurse staffing levels and patient outcomes. Favorable patient-to-nurse ratios and higher proportions of registered nurses have been associated with lower 30-day mortality rates, fewer hospital-acquired complications, and reduced unplanned readmissions.1 These findings reinforce the importance of adequate staffing as a foundational determinant of care quality and patient safety. However, while these studies establish a strong association, they often treat staffing levels as an isolated structural variable, without fully examining the care processes through which these effects are operationalized at the bedside. As a result, the mechanisms translating staffing capacity into measurable clinical outcomes remain only partially understood.
Concurrently, a growing body of evidence identifies missed nursing care as a measurable and widespread phenomenon across hospital settings globally.5 Studies consistently show that staffing shortages are associated with increased incidences of care omissions, particularly in high-demand environments where nurses must prioritize tasks under time constraints.3 In such contexts, care is frequently rationed, with priority given to immediate clinical interventions over essential supportive activities such as patient education, mobility assistance, and emotional support. This pattern highlights a critical operational dynamic: as workloads increase, the likelihood of incomplete or delayed care also rises, suggesting that missed nursing care may serve as a key intermediary linking structural constraints to patient-level outcomes.
Conceptually, missed nursing care functions as a pathway through which staffing influences outcomes. Delayed interventions, reduced patient monitoring, and incomplete treatments have been directly associated with adverse events, including pressure ulcers, medication errors, and healthcare-associated infections.6 To quantify this phenomenon, researchers have developed various measurement approaches, most notably survey-based instruments such as the MISSCARE Survey, which captures self-reported care omissions, as well as observational methods that assess care delivery in real time.2 Despite these methodological advances, important limitations persist within the existing literature. Most notably, there is a lack of formal mediation analysis that explicitly models missed nursing care as an intermediary variable, alongside inconsistent integration of staffing, care processes, and outcomes within a single, cohesive statistical framework.4 This gap underscores the need for more comprehensive analytical models that move beyond correlation to better explain the causal pathways underlying observed relationships.
This study adopts a retrospective cross-sectional research design to examine the relationship between nurse staffing levels, missed nursing care, and patient outcomes within a formal mediation analysis framework. The design enables the integration of multiple data sources to capture both structural and process-level variables, allowing for a more comprehensive evaluation of how staffing conditions translate into clinical outcomes. By explicitly modeling missed nursing care as a mediating variable, the study moves beyond traditional association-based approaches and seeks to quantify both direct and indirect effects within a unified analytical structure.4
The methodology employs a hybrid data approach that links institutional records with primary survey data. Electronic Health Records (EHRs) and hospital administrative datasets provide objective measures of staffing levels and patient outcomes, ensuring consistency and reliability in key variables. To accurately capture the mediating construct, nurse-reported data are collected using the MISSCARE Survey, a validated instrument designed to quantify omissions in nursing care.2 The study is conducted across multiple acute care hospital settings, with defined study periods to ensure temporal alignment between survey responses and administrative discharge records. This alignment is critical for establishing meaningful relationships between staffing conditions, care delivery processes, and observed outcomes.
The study population consists of adult inpatients admitted to medical-surgical and intensive care units, where nursing care demands are both intensive and measurable. Inclusion criteria require a minimum hospital stay of 24 hours to ensure sufficient exposure to the nursing care environment, while exclusion criteria remove pediatric and psychiatric units due to differences in care delivery models and outcome measures. This focused population enhances internal validity by maintaining consistency in clinical context and care expectations.
Key study variables are structured to reflect the mediation framework, as summarized below:
Table 1. Summary of Study Variables and Measurements
| Variable Type | Definition | Measurement |
|---|---|---|
| Independent Variable | Nurse staffing levels | Nurse-to-patient ratios; Nursing Hours Per Patient Day (NHPPD)7 |
| Mediator | Missed nursing care | MISSCARE Survey + EHR-derived proxy indicators2 |
| Dependent Variables | Patient outcomes | 30-day mortality, adverse events, length of stay1,8 |
| Covariates | Control variables | Demographics, clinical severity, hospital characteristics |
The statistical analysis is designed to estimate the relationships between these variables within a mediation framework. The Baron and Kenny approach is applied to evaluate direct, indirect, and total effects of staffing on patient outcomes through missed nursing care.4 To enhance analytical rigor, structural equation modeling (SEM) is used to estimate these pathways simultaneously, allowing for a more robust representation of complex relationships between variables. In addition, sensitivity analyses are conducted to assess the stability of results under varying assumptions and to account for potential confounders and measurement biases.
For clarity, the analytical workflow can be summarized as follows:
The results provide a comprehensive overview of patient characteristics, staffing conditions, and the prevalence of missed nursing care across the study population. Descriptive statistics indicate that omissions in fundamental aspects of care—such as patient education, ambulation, and emotional support—are consistently reported, particularly during shifts characterized by high patient turnover and lower staffing levels.8 These findings suggest that missed nursing care is systematically associated with operational strain rather than occurring randomly, reflecting the impact of workload intensity on the prioritization and completion of nursing tasks. Variations observed across units further highlight how differences in staffing allocation and patient demand can influence the consistency and completeness of care delivery.
Association analyses demonstrate a clear and consistent relationship between staffing levels, missed nursing care, and patient outcomes. As nurse-to-patient ratios increase, indicating a higher patient load per nurse, the frequency of missed care events rises significantly.9 This inverse relationship underscores the direct impact of staffing constraints on the likelihood of care omissions. In turn, increased levels of missed nursing care are associated with adverse clinical outcomes, including higher rates of hospital-acquired complications and prolonged lengths of stay.6 These findings reinforce the conceptual linkage between staffing and outcomes by highlighting how care processes deteriorate under constrained conditions.
The mediation analysis further clarifies these relationships by confirming the presence of an indirect effect of staffing on patient outcomes through missed nursing care. Results indicate that missed nursing care functions as a partial mediator, accounting for a substantial proportion of the variance in outcomes associated with inadequate staffing.10 In particular, outcomes such as hospital-associated infections and pressure ulcers are influenced by the frequency of omitted or delayed care activities. This mediated relationship suggests that the adverse effects of staffing deficits are primarily driven by the specific clinical tasks that are delayed or left incomplete, providing empirical support for a process-based explanation of how structural constraints translate into measurable patient harm.
This section interprets the study findings and examines their clinical and policy implications within the context of nursing care delivery and healthcare system performance.
The findings provide a clear and structured explanation of missed nursing care as a key mechanism linking staffing constraints to patient outcomes. Rather than attributing adverse events solely to reduced staffing levels, the results indicate that the impact of understaffing is mediated through specific, measurable omissions in care delivery. Activities such as missed vital sign monitoring, incomplete discharge teaching, and delayed interventions represent tangible breakdowns in the care process that contribute directly to clinical deterioration.3 This reinforces a process-based understanding of patient safety, where the consistency and completeness of care delivery serve as the primary pathways through which structural conditions influence outcomes.
In practical terms, the results suggest that staffing levels influence outcomes not only through workforce availability, but through how that availability shapes care prioritization under pressure. When nurses are required to manage higher patient loads, essential but non-urgent tasks are more likely to be delayed or omitted. This creates a cascade effect in which seemingly minor omissions accumulate and increase the risk of adverse events. The identification of missed nursing care as a partial mediator strengthens the argument that improving patient outcomes requires attention to both staffing adequacy and the reliability of care processes.
These findings highlight the critical importance of addressing care omissions alongside efforts to improve staffing levels. While increasing nurse staffing remains a foundational goal, it may not fully resolve outcome disparities if inefficiencies in workflow and task prioritization persist. Clinical leaders must therefore adopt operational strategies that directly target the reduction of missed nursing care.11 Such strategies should focus on improving both the organization of work and the support systems available to frontline staff.
Key clinical strategies include:
Together, these approaches can help reduce variability in care delivery and improve the likelihood that essential nursing activities are completed consistently, even in high-demand environments.
The study carries important implications for healthcare policy, particularly in the design of staffing regulations and performance measurement systems. Traditional staffing metrics, such as fixed nurse-to-patient ratios, may not fully capture the operational realities that lead to adverse outcomes. By contrast, incorporating missed nursing care as a measurable indicator provides a more direct assessment of how system strain affects care delivery. Policymakers should therefore consider integrating missed care metrics into hospital performance evaluation frameworks as early indicators of quality degradation.
In addition, there is a need to move toward more dynamic staffing models that account for patient acuity and workload complexity. Acuity-based staffing approaches, which consider both the cognitive and physical demands placed on nurses, offer a more accurate alignment between staffing resources and patient needs.1,11 These models can help prevent the widespread rationing of care by ensuring that staffing decisions are responsive to real-time clinical demands. Ultimately, aligning policy frameworks with both structural and process-level indicators can support more effective interventions aimed at improving patient outcomes.
This study has several limitations that should be considered when interpreting the findings. The measurement of missed nursing care relies heavily on self-reported survey data, which introduces the potential for both self-report and recall bias. Although the use of EHR-derived proxy indicators helps mitigate this limitation, not all instances of omitted or delayed care are formally documented, making it difficult to fully capture the scope of missed nursing care in practice. Additionally, variations in how respondents perceive and report care omissions may introduce inconsistencies in measurement, particularly across different units and institutional settings.
Further limitations are associated with the cross-sectional and observational nature of the study design, which restricts the ability to establish definitive causal relationships between staffing levels, missed nursing care, and patient outcomes. While the mediation analysis framework provides a structured approach to examining these relationships, the findings should be interpreted as indicative rather than conclusive evidence of causality.4 Additional considerations include:
Future research should prioritize longitudinal and interventional study designs to better assess causal pathways and temporal relationships. In particular, studies that evaluate the impact of targeted staffing interventions on the reduction of missed nursing care would provide valuable insight into the effectiveness of system-level changes. Expanding measurement approaches to incorporate more objective and real-time indicators of care delivery may also enhance the accuracy of future analyses.4
This study highlights the critical mediating role of missed nursing care in the relationship between nurse staffing levels and patient outcomes, offering a more nuanced understanding of how structural constraints translate into measurable clinical effects. By demonstrating that the impact of staffing shortages is largely transmitted through specific omissions in care delivery, the findings shift the focus from staffing levels alone to the reliability and completeness of care processes at the bedside. This perspective contributes to a more comprehensive framework for evaluating patient safety, emphasizing the importance of both resource allocation and care execution.
The results reinforce the need for healthcare organizations to adopt a dual approach to improving patient outcomes. Ensuring adequate nurse staffing remains essential, but must be complemented by targeted efforts to redesign workflows, strengthen care coordination, and reduce the likelihood of missed nursing care. Addressing both structural and operational dimensions of care delivery can help mitigate the downstream effects of workload pressures and enhance the consistency of essential nursing interventions. Ultimately, integrating these insights into clinical practice and policy development can support more effective strategies for improving patient safety and overall healthcare system performance.
