Context Distressing symptoms interfere with quality of life in patients with lung cancer. Algorithm-based clinical decision support (CDS) to improve evidence-based management of isolated symptoms appears promising but no reports yet address multiple symptoms. Objectives This 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. Methods Patients 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% report completion and delivery rates and ≥ 80% clinician adherence to recommendations. Descriptive statistics and generalized estimating equations were used for data analyses. Results Symptom 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%). Conclusion CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.

Context Adequate symptom management is essential to ensure quality cancer care, but symptom management is not always evidence based. Adapting and automating national guidelines for use at the point of care may enhance use by clinicians. Objectives This article reports on a process of adapting research evidence for use in a clinical decision support system that provided individualized symptom management recommendations to clinicians at the point of care. Methods Using a modified ADAPTE process, panels of local experts adapted national guidelines and integrated research evidence to create computable algorithms with explicit recommendations for management of the most common symptoms (pain, fatigue, dyspnea, depression, and anxiety) associated with lung cancer. Results Small multidisciplinary groups and a consensus panel, using a nominal group technique, modified and subsequently approved computable algorithms for fatigue, dyspnea, moderate pain, severe pain, depression, and anxiety. The approved algorithms represented the consensus of multidisciplinary clinicians on pharmacological and behavioral interventions tailored to the patient’s age, comorbidities, laboratory values, current medications, and patient-reported symptom severity. Algorithms also were reconciled with one another to enable simultaneous management of several symptoms. Conclusion A modified ADAPTE process and nominal group technique enabled the development and approval of locally adapted computable algorithms for individualized symptom management in patients with lung cancer. The process was more complex and required more time and resources than initially anticipated, but it resulted in computable algorithms that represented the consensus of many experts.

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1 Background: Integration of palliative care into oncology is recommended for quality care. Clinicians may benefit from assistance in assessing and managing multiple symptoms. Palliative care clinicians have the expertise but may not be available or are not consulted early in the course of a patient’s disease. Clinical decision support (CDS) offers an innovative way to deliver symptom management and trigger palliative care referrals at the point-of-care. Methods: Twenty clinicians and their patients were randomized to usual care (UC) or CDS using the symptom assessment and management intervention (SAMI), which provided tailored suggestions for pain, fatigue, depression, anxiety and/or dyspnea. One-hundred seventy-nine patients completed a Web-based symptom assessment prior to each visit for 6 months. A tailored report provided a longitudinal symptom report and suggestions for management were provided to clinicians in the SAMI arm prior to the visit. Standardized questionnaires were administered to patients at baseline, 2, 4 and 6 months later to measure communication about symptoms and health-related quality of life (HR-QOL). The treatment outcome index (TOI) was the primary outcome for HR-QOL. Management of the target symptoms was assessed through chart review. Linear mixed models and logistic regression were used for analyses. Results: Patient characteristics were: mean age of 63 years, 58% female, 88% white, and 32% had < HS education. No differences were noted in communication between patients and their clinicians. Significant differences were noted in physical well-being (p = 0.007, 0.08 adjusted for baseline) and a clinically significant difference in the TOI (62 vs. 68) at 4 months in SAMI as compared to UC. The odds of managing depression (1.6, 90% CI, 1.0-2.5), anxiety (1.7, 90% CI, 1.0-3.0) and fatigue (1.6, 90% CI, 1.1-2.5) were higher in SAMI as compared to UC. The odds of palliative care consults for pain (3.2, 90% CI, 0.7-13.4) appear to be higher in SAMI as compared to UC. Conclusions: Enhanced HR-QOL was noted among patients in the SAMI arm at 4 months. SAMI increased management of depression, fatigue and anxiety and appeared to increase palliative care consults for pain. Clinical trial information: NCT00852462.