Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

Journal: Journal of surgical oncology
Published Date: Jan 1, 2021

Abstract

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Authors

  • Laleh G Melstrom
    Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.
  • Andrei S Rodin
    Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope National Medical Center, Duarte, California, USA.
  • Lorenzo A Rossi
    Applied AI and Data Science Department, City of Hope National Medical Center, Duarte, California, USA.
  • Paul Fu
    Department of Pediatrics, City of Hope National Medical Center, Duarte, California, USA.
  • Yuman Fong
    Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY.
  • Virginia Sun
    Department of Surgery, City of Hope National Medical Center, Duarte, California, USA.

Keywords

External Resources

Popular Topics
Recent Journals