Predicting symptom complexity: Using autoregressive integrated moving average (ARIMA) models to create responsive clinic scheduling (original) (raw)

Journal of Clinical Oncology, 2021

Abstract

e13529 Background: Increasing cancer incidence, coupled with a trend in treating patients for longer periods of time, presents challenges in addressing all patients’ symptoms/concerns within the allotted time for ambulatory clinic appointments. Consequently, the ability to forecast and monitor the percentage of cancer patients with different symptom complexity levels is extremely valuable. Symptom complexity is a summary score that weighs the severity of all patient reported symptom scores at one time point. If a clinic could predict how many patients may need more time due to complex symptom management needs, clinic-scheduling templates could be adjusted to include a set number of longer appointments. Methods: Auto Regressive Integrated Moving Average (ARIMA) models were utilized to forecast the percentage of patients with a high symptom complexity level within one cancer clinic in Alberta, Canada. Goodness-of-fit measures such as Bayesian information criterion (BIC) and Ljung-Box ...

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