Q&A: Informaticist Philip Payne on

Washington U.’s Precision Medicine Journey Founding director of university’s Institute for Informatics describes ‘deep phenotyping’ to better understand the environments in which patients live by David Raths


hilip Payne, Ph.D., wears a lot of hats at the Washington University School of Medicine in St. Louis. In addition to leading the Institute for Informatics, he is associate dean of Health Information and Data Science and chief data scientist. He also has been involved in informatics efforts related to the COVID-19 pandemic. In a recent e-mail Q&A with Healthcare Innovation, Payne described some of Washington University’s work in the area of personalized medicine.

Healthcare Innovation: Can you describe the ARCH Personalized Medicine Initiative, a joint venture between the Washington University School of Medicine and Centene? What are some of its goals? Payne: Washington University School of Medicine has a strategic focus in both research and clinical practice that will advance precision medicine. That means we need to better understand at a biomo- lecular and a clinical and a population

level the features of our patients that both contribute to wellness but also disease and how patients respond to therapy so that we can use that increased understanding to make better decisions at an individual patient level that optimize quality, safety and outcomes of care. We have a variety of collaborators that we work with that help support this research, including tradi- tional funding sources such as the National Institutes of Health. But in equal measure, we also have collaborations with organiza- tions such as Centene and others that are investing in precision medicine research in order to improve the health and wellness of patient communities.

What are some of the elements of informatics infrastructure that underpin this personalized medicine approach? Fundamentally, the challenge that we have with personalized medicine or precision medicine is that rather than treating patients as a function of how the average

18 | NOVEMBER/DECEMBER 2021 Philip Payne, Ph.D.

patient presents or the average patient may respond to therapy, we instead want to understand the individual features of each unique patient that contribute to both wellness

and disease, and response to therapy. That means we need a lot more data to understand the patients that we have seen historically or that might be participating in clinical studies now, so that we can build the evidence base that informs that very tailored approach. A lot of the work that we do in informat- ics is in the context of what we refer to as deep phenotyping. For example, how do we extract all of this critical information from the electronic health record from a variety of biomolecular instruments such as those that we use to genotype or sequence patients, not to mention patient-generated

Photo 169444901 © Transversospinales |

Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32