The Impact of Nursing Staffing Levels on Patient Outcomes: A Systematic Review with Implications for Healthcare Policy and Practice
Yashvi Gupta
AIIMS Neuroscience Centre Delhi, India.
Desly Philip
Corporate Infection Preventionist, WellSpan Health Hospital, PA, USA.
Anusha Raj
Tipperary University Hospital, Ireland.
Manju Rajput *
GNIOT Institute of Medical Sciences and Research, Greater Noida, Uttar Pradesh, India.
Veena Salilkumar Chaudhary
SGT University, Gurugram, Haryana, India.
Shikha Gupta
GNIOT Institute of Medical Sciences and Research, Greater Noida, Uttar Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Nursing staffing levels play a critical role in shaping patient outcomes across healthcare settings. This review synthesizes literature to explore the relationship between nursing staffing and patient safety, satisfaction, and clinical outcomes. Specific improvements associated with adequate staffing include reduced infection rates, lower mortality, and shorter recovery times. The review focuses on healthcare settings such as hospitals and long-term care facilities within regions like North America and Europe, providing a comprehensive understanding of global staffing practices. Key factors influencing staffing levels include financial constraints, regulatory requirements, and hospital policies. Evidence-based staffing guidelines, such as nurse-to-patient ratios and skill-mix models, and increased funding are identified as essential strategies for mitigating adverse events and enhancing patient care quality. Policy recommendations emphasize the adoption of safe staffing standards and targeted investments to support nursing workforce sustainability. Future research should investigate the effects of staffing on diverse patient populations and evaluate innovative staffing models to address the dynamic needs of healthcare systems.
Keywords: Adverse events, clinical outcomes, financial constraints, hospital policies, nurse-to-patient ratios, patient satisfaction, regulatory requirements, staffing models