Oncologists are guardedly optimistic about AI. But will it drive real improvements in cancer care?

Over the course of my 25-year career as an oncologist, I’ve witnessed a lot of great ideas that improved the quality of cancer care delivery along with many more that didn’t materialize or were promises unfulfilled. I keep wondering which of those camps artificial intelligence will fall into.

Hardly a day goes by when I don’t read of some new AI-based tool in development to advance the diagnosis or treatment of disease. Will AI be just another flash in the pan or will it drive real improvements in the quality and cost of care? And how are health care providers viewing this technological development in light of previous disappointments?

To get a better handle on the collective “take” on artificial intelligence for cancer care, my colleagues and I at Cardinal Health Specialty Solutions fielded a survey of more than 180 oncologists. The results, published in our June 2019 Oncology Insights report, reveal valuable insights on how oncologists view the potential opportunities to leverage AI in their practices.

Limited familiarity tinged with optimism. Although only 5% of responding oncologists describe themselves as being “very familiar” with the use of artificial intelligence and machine learning in health care, 36% said they believe it will have a significant impact in cancer care over the next few years, with a considerable number of practices likely to adopt artificial intelligence tools.

The survey also suggests a strong sense of optimism about the impact that AI tools may have on the future: 53% of respondents said that such tools are likely or very likely to improve the quality of care in three years or more, 58% said they are likely or very likely to drive operational efficiencies, and 57% said they are likely or very likely to improve clinical outcomes. In addition, 53% described themselves as “excited” to see what role AI will play in supporting care.

An age gap on costs. The oncologists surveyed were somewhat skeptical that AI will help reduce overall health care costs: 47% said it is likely or very likely to lower costs, while 23% said it was unlikely or very unlikely to do so. Younger providers were more optimistic on this issue than their older peers. Fifty-eight percent of those under age 40 indicated that AI was likely to lower costs versus 44% of providers over the age of 60. This may be a reflection of the disappointments that older physicians have experienced with other technologies that promised cost savings but failed to deliver.

Hopes that artificial intelligence will reduce administrative work. At a time when physicians spend nearly half of their practice time on electronic medical records, we were not surprised to see that, when asked about the most valuable benefit that AI could deliver to their practice, the top response (37%) was “automating administrative tasks so I can focus on patients.” This response aligns with research we conducted last year showing that oncologists need extra hours to complete work in the electronic medical record on a weekly basis and the EMR is one of the top factors contributing to stress at work. Clearly there is pent-up demand for tools that can reduce the administrative burdens on providers. If AI can deliver effective solutions, it could be widely embraced.

Need for decision-support tools. Oncologists have historically been reluctant to relinquish control over patient treatment decisions to tools like clinical pathways that have been developed to improve outcomes and lower costs. Yet, with 63 new cancer drugs launched in the past five years and hundreds more in the pipeline, the complexity surrounding treatment decisions has reached a tipping point. Oncologists are beginning to acknowledge that more point-of-care decision support tools will be needed to deliver the best patient outcomes. This was reflected in our survey, with 26% of respondents saying that artificial intelligence could most improve cancer care by helping determine the best treatment paths for patients.

AI-based tools that enable providers to remain in control of care while also providing better insights may be among the first to be adopted, especially those that can help quickly identify patients at risk of poor outcomes so physicians can intervene sooner. But technology developers will need to be prepared with clinical data demonstrating the effectiveness of these tools — 27% of survey respondents said the lack of clinical evidence is one of their top concerns about AI.

Challenges to adoption. While optimistic about the potential benefits of AI tools, oncologists also acknowledge they don’t fully understand AI yet. Fifty-three percent of those surveyed described themselves as “not very familiar” with the use of AI in health care and, when asked to cite their top concerns, 27% indicated that they don’t know enough to implement it effectively. Provider education and training on AI-based tools will be keys to their successful uptake.

The main take-home lesson for health care technology developers from our survey is to develop and launch artificial intelligence tools thoughtfully after taking steps to understand the needs of health care providers and investing time in their education and training. Without those steps, AI may become just another here today, gone tomorrow health care technology story.

How Can Bioinformatics Help Advance Precision Cancer Care?

Chris Sander PhD

Chris Sander, PhD, director of the cBio Center at Dana-Farber. Sander is co-founder of the computational biology field and a leader in applying its methods to cancer research.

Scattered amongst the letters of genetic code in a tumor cell are telltale mutations and DNA alterations that spur its malignant activity. But there are billions of letters of code and each patient’s cancer is different, with its own particular genetic changes. These changes may dictate how it behaves, how aggressively the cancer progresses, and it may spell out which molecular weaknesses might be successfully attacked with treatments.

Technology like the OncoPanel platform of Dana-Farber’s Profile research program can rapidly decipher the DNA code of 400 cancer-related genes in an individual’s tumors, detecting mutations, missing or extra copies of genes, and other changes. But it’s a process that generates massive volumes of digital data – data which mean nothing until processed and analyzed. Only then, may researchers and oncologists be able to prescribe a precision treatment to target the specific mutation. This is the goal of precision cancer medicine, but it’s often a needle in a haystack search.

Dana-Farber’s Chris Sander, PhD, founded and directs the new cBio Center at DFCI, along with Ethan Cerami, PhD, who leads the Knowledge Systems Group in the Center, to help researchers mine the genomic data, using a user-friendly web-based tool they developed called cBioPortal for 

The cBio Portal software digests the data and presents it in diverse visual formats that help investigators detect patterns of abnormalities across groups of patients and cancer types.

With cBioPortal, a researcher can tap into the Profile tumor base of nearly 8,000 genomic tumor profiles and rapidly compare the mutation pattern in one patient’s tumor to those of hundreds of other patients. Some cancers with certain mutations might have an approved drug targeting those changes; finding the same mutations in other tumor types may lead to new trials of that drug. “We will be able to use cancer genomics to define groups of patients who might be eligible for new kinds of genomically informed clinical trials,” says Sander.

The cBioPortal analysis tools can also help scientists sift genomic data for clues to why a few patients in a clinical trial had dramatic responses to a drug that had little benefit for the majority of other patients. The cause of this “exceptional response” may be a previously undiscovered mutation that made the tumor vulnerable to the drug – and cBioPortal analysis of genomic profile data may help scientists discover it.

The Center’s Knowledge Systems Group has also devised a clinically oriented tool called MatchMiner. “Clinicians use it to recruit patients for clinical trials,” Cerami explains. “They look in the Profile database for patients whose tumors’ mutations match targeted drugs in available clinical trials, and they can have MatchMiner notify them when appropriate new patients are identified.”

The scientists are now working on tools to enable oncologists and patients to sit down together and use MatchMiner to search for clinical trials of drugs targeted to their cancer’s specific mutation pattern.