Predicting Survivorship Appointment Nonattendance in a Community Cancer Center: A Machine-Learning Approach.
West J Nurs Res 2023:1939459231165749. [PMID:
37085980 DOI:
10.1177/01939459231165749]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023]
Abstract
Understanding and predicting cancer survivors' health care utilization is critical to promote quality care. The consultative system of survivorship care uses a onetime consultative appointment to transition patients from active treatment into survivorship follow-up care. Knowledge of attributes associated with nonattendance at this essential appointment is needed. An ability to predict patients with a likelihood of nonattendance would be of value to practitioners. Unfortunately, traditional data modeling techniques may not be useful in working with large numbers of variables from electronic medical record platforms. A variety of machine-learning algorithms were used to develop a model for predicting 843 survivors' nonattendance at a comprehensive community cancer center in the southeastern United States. A parsimonious model resulted in a k-fold classification accuracy of 67.3% and included three variables. Practitioners may be able to increase utilization of follow-up care among survivors by knowing which patient groups are more likely to be survivorship appointment nonattenders.
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