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Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes

Author

Listed:
  • Linda Watson

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Siwei Qi

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Andrea DeIure

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Claire Link

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Lindsi Chmielewski

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • April Hildebrand

    (Applied Research and Patient Experience, Cancer Research and Analytics, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Krista Rawson

    (Quality, Safety & Practice Integration, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada)

  • Dean Ruether

    (Medical Oncology, Cancer Care Alberta—Alberta Health Services, Calgary, AB T2S 3C3, Canada
    Department of Oncology, Cumming School of Medicine, Foothills Campus, University of Calgary, Calgary, AB T2N 4N1, Canada)

Abstract

An increasing incidence of cancer has led to high patient volumes and time challenges in ambulatory oncology clinics. By knowing how many patients are experiencing complex care needs in advance, clinic scheduling and staff allocation adjustments could be made to provide patients with longer or shorter timeslots to address symptom complexity. In this study, we used predictive analytics to forecast the percentage of patients with high symptom complexity in one clinic population in a given time period. Autoregressive integrated moving average (ARIMA) modelling was utilized with patient-reported outcome (PRO) data and patient demographic information collected over 24 weeks. Eight additional weeks of symptom complexity data were collected and compared to assess the accuracy of the forecasting model. The predicted symptom complexity levels were compared with observation data and a mean absolute predicting error of 5.9% was determined, indicating the model’s satisfactory accuracy for forecasting symptom complexity levels among patients in this clinic population. By using a larger sample and additional predictors, this model could be applied to other clinics to allow for tailored scheduling and staff allocation based on symptom complexity forecasting and inform system level models of care to improve outcomes and provide higher quality patient care.

Suggested Citation

  • Linda Watson & Siwei Qi & Andrea DeIure & Claire Link & Lindsi Chmielewski & April Hildebrand & Krista Rawson & Dean Ruether, 2021. "Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes," IJERPH, MDPI, vol. 18(16), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:16:p:8365-:d:610146
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    References listed on IDEAS

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    2. Yan-Ling Zheng & Li-Ping Zhang & Xue-Liang Zhang & Kai Wang & Yu-Jian Zheng, 2015. "Forecast Model Analysis for the Morbidity of Tuberculosis in Xinjiang, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-13, March.
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