Predicting Survivorship Appointment Nonattendance in a Community Cancer Center: A Machine-Learning Approach. (July 2023)

• Record Type: Journal Article
• Authors: Schlairet, Maura C.; Heddon, Mary Ann; Randolph, Justus
• Is Part Of: Western journal of nursing research. Volume 45:Number 7(2023)
• Page Start: 607
• Page End: 617
• Publication Date: 2023-07
• Subjects: cancer survivorship -- health care utilization -- machine-learning -- appointment nonattendance -- survivorship models -- community cancer care -- consultative appointment; Nursing -- Research -- Periodicals; Nursing -- Periodicals; Nursing -- Research -- West (U.S.) -- Periodicals; Nursing -- West (U.S.) -- Periodicals; Nursing -- Periodicals; Nursing Research -- Periodicals; Soins infirmiers -- Recherche -- Périodiques; Soins infirmiers -- Périodiques; Soins infirmiers -- Recherche -- États-Unis (Ouest) -- Périodiques; Soins infirmiers -- États-Unis (Ouest) -- Périodiques; Soins infirmiers; Recherche; États-Unis (Ouest); Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 610.730702
• DOI: 10.1177/01939459231165749
• Languages: English
• ISSNs: 0193-9459

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.