Author: Melstrom, Laleh G; Rodin, Andrei S; Rossi, Lorenzo A; Fu, Paul; Fong, Yuman; Sun, Virginia
Title: Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning. Cord-id: 0y5bph34 Document date: 2020_9_24
ID: 0y5bph34
Snippet: 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.
Document: 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.
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