AI Model Supports Workflow of Cancer Patient Navigators
New research describes the capabilities of an AI model in distributing work evenly among care navigators working with cancer patients.
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By - A new predictor-informed distribution model that leverages artificial intelligence, developed by researchers at OSF Healthcare and the OSF Innovation group, eased the workflow of cancer patient navigators while limiting burnout and improving work-life balance.
Given the severity of the condition, a cancer diagnosis can be very taxing. In response, cancer patient navigators must dedicate a great deal of time and attention to patients throughout the care process. The demands of the job, however, can be complex as they must provide education, advocacy, and support. These, combined with the growing number of cancer patients, often lead to burnout among navigators.
At the largest hospital within OSF HealthCare, Saint Francis Medical Center in Peoria, Illinois, 15 cancer patient navigators aim to support patients based on their cancer type. However, the workflows often result in certain employees taking on more work than others, according to the press release. Due to the goal of creating and maintaining integral patient relationships, the work is often non-transferable.
In pursuit of higher efficiency and reduced burnout, OSF’s Senior Fellow for Innovation, Jonathan Handler, MD, led a team in creating an AI-based solution. This project gained financial support from Jump ARCHES, a strategic partnership between OSF HealthCare, the University of Illinois College of Medicine at Peoria, and the University of Illinois Urbana-Champaign.
The AI-based solution aims to predict the work week ahead for cancer patient navigators for existing and new patients. To limit differences in workload, a secondary model assists with distributing new patients to navigators within a specific specialty. The models leverage EHR data to make predictions and distribute workloads.
To ensure the efficacy of the solution, researchers compared its abilities to those of a random distribution process. This allowed them to confirm that the AI predictor-informed models displayed higher workload fairness.
“Our cancer patient nurse navigators are highly dedicated, and their workload can sometimes be overwhelming. They never want to shortchange the patient, so they shortchange themselves, working extra hours and sacrificing their own well-being to help patients. We hope our system can even out those workloads and improve their work-life balance,” said Handler in the press release.
AI capabilities have supported numerous healthcare operations, including staff workflow.
In April, OSF Healthcare announced plans to use CORTEX, an XSOLIS AI solution, to maximize staff efficiency.
XSOLIS aims to increase efficiency levels within healthcare systems through new solutions.
OSF Healthcare indicated that this technology was comprehensive and allowed the organization to measure performance and improve outcomes. According to the health system plans to use the technology to move away from manual care management processes and optimize data insights to evaluate performance.
The CORTEX solution enables users to determine lengths of stay and update patient conditions, allowing providers to improve case prioritization.
