Specialties across the healthcare spectrum are increasingly looking to AI to help them take their practices to a new level, and oncology is certainly no exception.

Pointing to an array of developing AI tools, for example, a recent article at OncLive, an oncology community webzine, notes that although tools such as predictive analytics and “evidence-based mechanisms” are just beginning to be felt in oncology, “tools that support routine clinical decisions and genomic risk stratification are likely to be adopted more broadly within the next several years. Experts say this is a trend that promises to dramatically change the way care is practiced and delivered.”

In particular, the writer discusses the potential for AI to help predict the risk of adverse effects (AEs) of chemotherapy on patients, as well as “the likely duration of response from chemotherapy, recurrence risk, and overall life expectancy. Systems that incorporate evidenced-based mechanisms or predictive analytics can also help deliver value-based care and reduce the risk of hospitalization due to AEs, proponents say.”

According to James Hamrick, MD, senior medical director at Flatiron Health, an oncology technology and research company, the rise of alternative, value-focused payment models could help facilitate the spread of AI.

“There is some appetite to pay for [these tools],” Hamrick suggested, “but I think practices and hospitals are appropriately conservative. These are sophisticated tools so they have to work and deliver value.”

Specific motivations for adoption aside, Hamrick said predictive analytics is expected to allow clinicians to understand up front the patients who are more likely to experience AEs. 

“As oncologists, we live in a reactive world,” Hamrick said. “The only time I have insight into what is going on with a patient is when they are in my office, or perhaps when I hear that they landed in the emergency room or hospital. For the patient, the journey is every day. They are dealing with the physical, financial, and emotional toxicity of their cancer. …If we can understand at the beginning of chemotherapy what are the factors that make it likely that this patient may wind up in the emergency [department], then we can shift resources and interventions so we can move from reactive to proactive.”

Beyond predicting AEs, the article notes, oncologists are increasingly turning to AI to find “more accurate answers about which treatment is most effective for a specific patient with certain characteristics and the likely short- and long-term risks and benefits of a given therapy.^”

Moving forward, providers are also looking to advanced analytics tools to enable point-of-care decisions that are backed by guidelines and published evidence, but the necessary data still isn’t available via most EHR systems.