Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning. Issue 1 (24th September 2020)

• Record Type: Journal Article
• Authors: Melstrom, Laleh G.; Rodin, Andrei S.; Rossi, Lorenzo A.; Fu, Paul; Fong, Yuman; Sun, Virginia
• Is Part Of: Journal of surgical oncology. Volume 123:Issue 1(2021)
• Page Start: 52
• Page End: 60
• Publication Date: 2020-09-24
• Subjects: artificial intelligence -- biometrics -- machine learning in surgery -- patient‐reported outcomes -- telehealth; Cancer -- Surgery -- Periodicals; Neoplasms -- Periodicals; 616
• DOI: 10.1002/jso.26232
• Languages: English
• ISSNs: 0022-4790

Abstract

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.