Predicting Caregiver Communications in a Geriatric Clinic.

Abstract

The current study evaluated the use of a machine learning model to determine benefit of medical record variables in predicting geriatric clinic communication requirements. Patient behavioral symptoms and global cognition, medical information, and caregiver intake assessments were extracted from 557 patient records. Two independent raters reviewed the subsequent 12 months for documented (1) incoming caregiver contacts, (2) outgoing clinic contacts, and (3) clinic communications. Random forest models' average explained variance in training sets for incoming, outgoing, and clinic communications were 7.42%, 3.65%, and 6.23%, respectively. Permutation importances revealed the strongest predictors across outcomes were patient neuropsychiatric symptoms, global cognition, and body mass, caregiver burden, and age (caregiver and patient). Average explained variance in out-of-sample test sets for incoming, outgoing, clinic communications were 6.17%, 2.78%, and 4.28%, respectively. Findings suggest patient neuropsychiatric symptoms, caregiver burden, caregiver and patient age, patient body mass index, and global cognition may be useful predictors of communication requirements for patient care in a geriatric clinic. Future studies should consider additional caregiver variables, such as personality characteristics, and explore modifiable factors longitudinally.