Artificial Intelligence Is Still a Long Way From Understanding the Subtlety of a Texas Metaphor

Tony Hagen @oncobiz
Published: Sunday, Aug 27, 2017
Debra Patt, MD, MPH, MBA
Debra Patt, MD, MPH, MBA
It is not far-fetched to believe that, one day soon, automated systems will be able to obtain payer approval for therapies without any human involvement, says Debra Patt, MD, a member of the The US Oncology Network breast cancer research committee. “I think that’s coming and will be incredibly useful to patient care.”

It’s something that payers want as well as oncologists. The bottom line is that payers want improved resource management, and it is not their intention to deny claims and preauthorization requests simply for the sake of saving money, Patt says. “Payers aren’t trying to put inappropriate barriers between patients and the care that they need. Those delays are the only way they know how to make sure that utilization is appropriate. If we find a way to make sure that utilization is appropriate by getting payers the data faster, that serves everyone, and so I think there’s a willingness to participate in those kinds of collaborative solutions.”

Patt, who practices in Austin, Texas, recently discussed some of the limitations and advances in electronic health reporting with Oncology Business Management™. Much of the initiative these days is focused on drawing information from electronic health records (EHRs) without human involvement. This relies on the data being in a structured format that computer systems can recognize and make sense of. The advances in developing and implementing these systems have been uneven.

Patt says that The US Oncology Network has been vigilant about using technical teams to design systems that allow for critical clinical data to be carefully structured in the EHR so that it is accessible to retrieval programs. But that isn’t the case everywhere. Many oncology practices don’t have the luxury of large technical support teams to develop this software. Companies like Flatiron that mine oncology information for clues to better treatment must employ human chart reviewers to pore through clinical information that is unstructured, she notes.

“We need to have consistency in the structured data we are using. In my practice, the doctor interacts with the health information systems systematically, but given our robust number of structured data elements, it’s a unique scenario,” Patt said. Even so, one of the problems encountered is that physicians have individual styles of reporting data, which can confuse even the strongest forms of artificial intelligence (AI).

Patt gave an example of how she and another doctor from her own practice might differ in the way they explain things to a hypothetical patient. “I might say to a woman, ‘You have stage II breast cancer, but you’re 80 and you’ve had a heart attack, so although chemotherapy might reduce your risk of recurrence, it might increase your risk of a heart attack and other morbid occurrences, and so I would not recommend chemotherapy.’” Conversely, a practitioner known for his Texas-style metaphorical way of speaking might tell the same patient the same thing, but in a way that no computer could recognize as identical to the opinion voiced by Patt. “He might say, ‘Mrs Smith, your breast cancer is not good, and we could give you chemotherapy, but that dog is not going to hunt.’ There’s no language processing system in the world that would see those 2 reports as the same, but because we oncologists have so much variability today in how we report various elements that influence our treatment and treatment choices, right now that remains a large barrier.” The only solution is to standardize systems and data reporting styles to cause that barrier to diminish, Patt said.

Being able to tap clinical systems for data is vital to the success of using real-world evidence (RWE) for oncology solutions. The potential inherent in RWE is exciting because there are questions about treatment efficacy that can be answered only by examination of clinical feedback on large populations of patients who have been treated. “Ultimately, when we feel comfortable with that data, I think it should be used to support FDA approval, because once we feel really comfortable with the information that we’re getting, we should make the most of that data and learn from all patients and not just the 35 that we may enroll in a particular clinical trial,” Patt said.

Efforts have been under way for a long time to peel away the barriers to putting RWE to good use. Kaiser Permanente and Emcore have been very active in this sphere. “Within The US Oncology Network, too, we’ve been publishing and using our data on RWE for a long time,” Patt said.

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