Abstract: Increased mediation of the practice of medicine through digital data capture systems such as the electronic health record creates opportunities for increased efficiency in conducting data-intensive research and faster turnaround in delivering knowledge to impact patient care. This is particularly relevant in cancer, where the complexity and volume of patient-specific clinical and molecular data and a plethora of ever-changing treatment options make rapid, data-driven adaptation of clinical practice essential for optimal patient outcomes. This talk will describe our group's work toward developing technical and human infrastructure for a Cancer Learning Healthcare System (LHS) within the national VA healthcare system. These efforts include: (A) creation of clinical knowledge bases to integrate and manage a growing volume of diverse data types, (B) efficient generation of actionable knowledge using biostatistical methods and coordinated group efforts, and (C) rapid delivery, application and iterative adaptation of these newly discovered insights (knowledge) to improve patient care. By integrating data, knowledge, and action, LHSs can accelerate the pace of innovation and improve the quality of care for patients.
About the speaker: Nathanael (Nate) Fillmore, PhD, is the Associate Director of Data Science at the CSP Informatics Center, MAVERIC, VA Boston Healthcare System, and an Assistant Professor at Harvard Medical School. He leads a group that utilizes clinical, genomic, and imaging data, along with machine learning and statistical methods, to advance research aimed at improving patient care. His work also includes software and methodological advancements to support these efforts. His research has been published in JCO, JAMA Oncology, Blood, PNAS, Nature Medicine, and other high-impact journals.
.png){kind=logo}
