Accurately predicting nurse staffing levels in infusion centers is essential for delivering safe, timely, and high-quality care. However, staffing needs in these settings are difficult to determine due to variations in treatment complexity, high patient throughput, and the lack of a standardized nurse-to-patient ratio.
Acuity-based staffing is recognized as a best practice, but traditional methods to gather acuity information rely heavily on nursing teams and can be time-consuming and inconsistent. As outpatient oncology treatments grow in complexity and volume, streamlined and consistent methods are needed to provide timely acuity-based staffing insights.
To address this challenge, The US Oncology Network, supported by McKesson, initiated a collaborative effort between nurses and data specialists to modernize acuity-based staffing. The Patient Acuity Model (PAM) uses predictive analytics and machine learning to automate acuity-based staffing insights, enabling faster, more consistent staffing decisions that align with patient needs and support safer, more effective oncology care.
The PAM was developed through collaboration between nursing experts and data specialists to ensure practical nursing insights informed its design. Nurses identified potential acuity drivers based on established models, literature, and clinical experience. Nurses from nine care sites across four practices documented acuity ratings in clinical notes, which were then extracted using natural language processing. Statistical analyses identified the most impactful features influencing acuity.
Key factors prioritized in the model include the number of drugs administered, routes of administration, drug hypersensitivity and emetic risks, appointment duration, number of antineoplastic drugs, cancer diagnosis, treatment cycle phase (Cycle 1, Day 1), IV access difficulty, performance status (e.g., ECOG score), depression screening results, primary language, and mobility status. The PAM was trained to predict patient acuity ratings using these features.
The PAM dashboard offers an interactive tool designed for nursing and operational leaders. It provides daily updated acuity predictions, historical staffing data, smart visualizations, and filters that enable quick assessment of staffing needs, trend comparisons across sites, and efficient, consistent decision-making. The dashboard includes key patient information to aid assignment creation and infusion room preparation, fostering proactive planning for safer, more effective care.
Currently, the PAM supports 50 infusion centers across five practices within The Network. Users report significant improvements in infusion center operations, including increased efficiency and consistency in staffing decisions.
By automating acuity predictions and data compilation, the PAM reduces the burden on nursing teams. One practice estimates the model saves over 400 hours weekly by replacing manual processes previously performed by nurses and nurse leaders. These time savings are redirected toward initiatives such as improved infusion room preparation and patient safety checks.
In another practice, the PAM enabled a licensed practical nurse to balance infusion room assignments, a task formerly handled by registered nurse leads, saving 16 hours per week of RN lead time. The model’s consistent approach and user-friendly dashboard allow leaders to assess staffing needs across multiple sites instantly, which is especially valuable for resource allocation and delivering safe care.
Practices utilize the PAM’s seven-day prospective staffing recommendations to support anticipatory planning, including float staff allocation. This facilitates acuity-based staffing decisions that were previously impractical due to time constraints. Users highlight enhanced consistency, objectivity, and transparency in staffing decisions, fostering collaboration between sites and equitable resource distribution.
Historical data trends provided by the PAM help leaders understand baseline staffing needs and justify hiring additional nurses. One practice leader successfully secured approval for two new positions to address increasing patient volume and treatment complexity.
Shifting from volume-based to acuity-driven staffing is vital in oncology infusion to ensure appropriate staffing levels and skill mix for patient preparation, safety checks, administration monitoring, and early escalation when needed, as recommended by the Oncology Nursing Society. The PAM supports this shift by enabling staffing and nursing assignments tailored to individual patient acuity.
Oncology nurses report improved workflows, optimized staffing, and more personalized patient care with PAM use, enhancing professional satisfaction for nursing teams.
The development of the PAM demonstrates the power of collaboration between nursing professionals and data experts to address complex healthcare challenges. Engaging nurses from the start ensured the model met real-world needs. Today, the PAM equips nurses with actionable data, enabling informed decisions to deliver high-quality care confidently.
By leveraging tools like the PAM, The US Oncology Network advances its mission to provide safe, timely, and quality cancer care. As oncology care continues to evolve, ongoing collaboration and innovation are essential to develop smarter, safer, and more efficient approaches to meet the growing demands of this field.
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