Melstrom LG, Rodin AS, Rossi LA, Fu P, Fong Y, Sun V. Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.
J Surg Oncol 2020;
123:52-60. [PMID:
32974930 DOI:
10.1002/jso.26232]
[Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Accepted: 09/11/2020] [Indexed: 12/16/2022]
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.
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