Dr Elizabeth Kwo on Using Predictive Analytics to Curb Avoidable Hospitalizations During Cancer Therapy

Anemia, fevers, dehydration, and other side effects from cancer treatment are all candidates for targeting with predictive analytics in an effort to care for patients are home instead of the hospital, said Elizabeth Kwo, MD, MBA, MPH, the deputy chief clinical officer at Anthem BCBS, and a speaker at the 10th anniversary of Patient-Centered Oncology Care® conference.

Transcript

What are some innovative ways payers are looking to use predictive analytics to keep patients out of the hospital as they go through cancer treatment?

Within the space of how predictive analytics has been used, we are, as a payer system, looking at preventing all sorts of things with predictions: preventing readmissions, or even admissions, within the first 30 days of starting chemotherapy or other types of treatment. The way we do that is, we think that there's preventable admissions, whether it's anemia, or fevers, or things such as dehydration, where someone is not doing well because they’re not eating as much and they're not drinking as much. And when you do that, there are all sorts of ways we can look at it, from the electronic medical records to monitoring patients in the home.

We also can see sort of patterns that are emerging, if patients have filled their medication or if they haven't. There's a really big goal right now: to look at the person [through] whole person care. So leveraging things such as a practice care team, looking at member-level data, information sharing across partners. There's also a resource team that we're building out to really try to predict, are there food shortages, are there social determinants of health data that we can collect to help support a patient while they're going through some difficult times. There's also community resources that are very important to leverage. And there's a lot of data right now on patients and how they're responding also to apps.

For example, Anthem even has an app that allows members to engage with us, connect with a care manager, look at their deductibles. And then, of course, the data that we're looking at for predictions can consist of both claims data combined with EMR data combined with outside external data, and, like, we mentioned social determinants of health.