“You will never know it all, but you can know where to look.” 

This is a phrase, or some iteration of it, that I find myself saying often to novice oncology nurses as I meet with them during orientation, especially during that week 4 one-on-one when the scope of information oncology has to offer feels like a daunting hurdle for them to conquer in 12 weeks. 

When applied to machine learning models and other artificial intelligence (AI), the Gerchberg-Saxton algorithm more equitably and uniformly distributes a dataset’s racial and ethnic biomedical information, researchers reported in study findings published in the Journal of Healthcare Informatics Research. They demonstrated that the algorithm created parity in a group of patients spanning a broad range of population backgrounds.

More than 85% of oncology healthcare professionals say that they need to be able to explain clinical use of artificial intelligence (AI) to their patients, but less than half report being familiar with AI in health care or receiving education on the technology, researchers reported in JAMA Network Open. In response, the National Cancer Institute’s (NCI’s) Center for Biomedical Informatics and Information Technology created a top-5 tip sheet for clinicians and researchers to use when evaluating AI tools for health care.

Oncology nurses are frontrunners in identifying and managing symptoms experienced by people with cancer. From communication approaches and apps to integrative interventions, today’s tools can help nurses more efficiently assess and address side effects, thereby improving patient care.

Amid the growing number of patients with cancer, ongoing nursing shortages, high staff turnover rates, and nurses’ general desire for more flexible and balanced work, innovative oncology nurse managers are turning to technology and data as creative—even critical—tools for effective staff scheduling.