Background: Patients with cancer in Canada are often effectively managed in ambulatory settings; however, patients with unmanaged or complex symptoms may turn to the emergency department (ED) for additional support. These unplanned visits can be costly to the healthcare system and distressing for patients. This study used a novel patient-reported outcomes (PROs)–derived symptom complexity algorithm to understand characteristics of patients who use acute care, which may help clinicians identify patients who would benefit from additional support. Patients and Methods: This retrospective observational cohort study used population-based linked administrative healthcare data. All patients with cancer in Alberta, Canada, who completed at least one PRO symptom-reporting questionnaire between October 1, 2019, and April 1, 2020, were included. The algorithm used ratings of 9 symptoms to assign a complexity score of low, medium, or high. Multivariable binary logistic regressions were used to evaluate factors associated with a higher likelihood of having an ED visit or hospital admission (HA) within 7 days of completing a PRO questionnaire. Results: Of the 29,133 patients in the cohort, 738 had an ED visit and 452 had an HA within 7 days of completing the PRO questionnaire. Patients with high symptom complexity had significantly higher odds of having an ED visit (OR, 3.10; 95% CI, 2.59–3.70) or HA (OR, 4.20; 95% CI, 3.36–5.26) compared with low complexity patients, controlling for demographic covariates. Conclusions: Given that patients with higher symptom complexity scores were more likely to use acute care, clinicians should monitor these more complex patients closely, because they may benefit from additional support or symptom management in ambulatory settings. A symptom complexity algorithm can help clinicians easily identify patients who may require additional support. Using an algorithm to guide care can enhance patient experiences, while reducing use of acute care services and the accompanying cost and burden.
Submitted April 29, 2022; final revision received September 21, 2022; accepted for publication October 13, 2022.
Author contributions: Conceptualization: Watson, Qi, Afzal, Barbera. Data curation: Qi, Afzal. Formal analysis: Qi, Afzal. Funding acquisition: Watson. Investigation: All authors. Methodology: All authors. Project administration: Link. Resources: Watson. Software: Qi. Supervision: Watson, Barbera. Validation: Watson, Qi. Visualization: Qi, Link. Writing—original draft preparation: Watson, Qi, Link, DeIure. Writing—review and editing: All authors.
Disclosures: The authors have disclosed that they have not received any financial considerations from any person or organization to support the preparation, results, or discussion of this article.