Feasibility of Using Algorithm-Based Clinical Decision Support for Symptom Assessment and Management in Lung Cancer

Abstract

ContextDistressing symptoms interfere with the quality of life in patients with lung cancer. Algorithm-based clinical decision support (CDS) to improve evidence-based management of isolated symptoms seems promising, but no reports yet address multiple symptoms. ObjectivesThis study examined the feasibility of CDS for a Symptom Assessment and Management Intervention targeting common symptoms in patients with lung cancer (SAMI-L) in ambulatory oncology. The study objectives were to evaluate completion and delivery rates of the SAMI-L report and clinician adherence to the algorithm-based recommendations. MethodsPatients completed a web-based symptom assessment and SAMI-L created tailored recommendations for symptom management. Completion of assessments and delivery of reports were recorded. Medical record review assessed clinician adherence to recommendations. Feasibility was defined as 75% or higher report completion and delivery rates and 80% or higher clinician adherence to recommendations. Descriptive statistics and generalized estimating equations were used for data analyses. ResultsSymptom assessment completion was 84% (95% CI=81–87%). Delivery of completed reports was 90% (95% CI=86–93%). Depression (36%), pain (30%), and fatigue (18%) occurred most frequently, followed by anxiety (11%) and dyspnea (6%). On average, overall recommendation adherence was 57% (95% CI=52–62%) and was not dependent on the number of recommendations (P=0.45). Adherence was higher for anxiety (66%; 95% CI=55–77%), depression (64%; 95% CI=56–71%), pain (62%; 95% CI=52–72%), and dyspnea (51%; 95% CI=38–64%) than for fatigue (38%; 95% CI=28–47%). ConclusionThe CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.