Automating Patient-Nurse Assignments: A Game-Changer for Oncology Nurse Wellbeing
Oncology nurses face a challenging environment, with staffing levels and patient acuity significantly impacting their burnout, job satisfaction, and retention. Inadequate staffing can lead to compromised care, resulting in higher infection rates, increased pain, and less frequent patient education completion. Yet, providing nurse managers with the necessary resources to manage staffing effectively remains a rarity. This crisis is further exacerbated by staff shortages and rising patient acuity. The decision-making process for nurse assignments is often complex, influenced by multiple patient, workforce, and hospital policy considerations, with inconsistent equity and information usage.
A groundbreaking study led by Sharon Catherine Le Roux, DNP, RN, OCN®, at the University of Texas Southwestern Medical Center, introduces an automated patient-nurse assignment tool to address these challenges. The project, published in the Clinical Journal of Oncology Nursing, focused on improving oncology nurse staffing and enhancing nurses' well-being. The tool utilizes dynamic nursing documentation within the electronic medical record, such as patient medications, assessments, discharge orders, and activities of daily living, to calculate a workload score. This score predicts a nurse's upcoming workload intensity and provides real-time information, summarizing patient needs.
The study involved 41 nurses, most with bachelor's degrees and 1-5 years of oncology nursing experience. The research team trained charge nurses in the technology and tracked patient-nurse assignments. The results were impressive, showing that the automated assignments created a balanced workload, with 75% of nurses expressing satisfaction. Interestingly, daytime nurses experienced slightly higher workload scores than night-shift nurses. Moreover, the tool's impact on nurses' well-being was significant. Before implementation, nurses reported higher levels of physical health issues interfering with work. However, after using the automated assignments, their satisfaction with assignments increased, and the perception of physical health issues impacting work decreased.
The study also revealed that factoring in nurses' years of oncology experience led to more equitable assignments, accurate acuity levels, higher satisfaction, and a sense of meaningful work. This automated tool holds promise for improving oncology nursing workload management, potentially reducing burnout, stress, and emotional strain associated with cancer patient care. However, the research team emphasizes the importance of thorough pilot assessments and stakeholder evaluations to refine the tool, ensuring it meets the needs of nurses and the hospital while avoiding ethical and bias pitfalls common in AI-based tools.
The project's success can be attributed to the nurses' active involvement in all tool development stages, fostering trust and a sense of ownership. This approach ensures that the tool addresses their pain points and incorporates their feedback for continuous improvement.
Further research and collaboration are essential to building upon these findings and creating a more sustainable and supportive environment for oncology nurses, ultimately benefiting patient care.
