ArticleInternational journal of environmental research and public health2021
Using Autoregressive Integrated Moving Average (ARIMA) Modelling to Forecast Symptom Complexity in an Ambulatory Oncology Clinic: Harnessing Predictive Analytics and Patient-Reported Outcomes.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Trends and projections in cause-specific mortality among patients with colorectal cancer in the United States, 1999-2040.Frontiers in public health · 2026Article
- Declining Myocarditis Mortality in the United States and the Impact of the COVID-19 Pandemic.Journal of clinical medicine · 2025Article
- Trends and Disparities in Parkinson's Disease Mortality in the United States with Predictions Using Machine Learning.NeuroSci · 2025Article
- A 25-Year Retrospective of Health IT Infrastructure Building: The Example of the Catalonia Region.Journal of medical Internet research · 2024Article
- Predicting which patients with cancer will see a psychiatrist or counsellor from their initial oncology consultation document using natural language processing.Communications medicine · 2024Article
- Article
- Disparities in the Place of Death for Patients With Malignant Neoplasms of the Thyroid Gland.Cureus · 2024Article
- Prediction of early-onset colorectal cancer mortality rates in the United States using machine learning.Cancer medicine · 2024Article
- Clinical Integration of Digital Patient-Reported Outcome Measures in Primary Health Care for Chronic Disease Management: Protocol for a Systematic Review.JMIR research protocols · 2023Article
- Understanding the Symptoms and Concerns of Adolescents and Young Adults with Cancer in Alberta: A Comparative Cohort Study Using Patient-Reported Outcomes.Journal of adolescent and young adult oncology · 2023Article
- Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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