Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer.

Published

Journal Article

Distressing 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.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.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% 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.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%).The CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.

Full Text

Duke Authors

Cited Authors

  • Cooley, ME; Blonquist, TM; Catalano, PJ; Lobach, DF; Halpenny, B; McCorkle, R; Johns, EB; Braun, IM; Rabin, MS; Mataoui, FZ; Finn, K; Berry, DL; Abrahm, JL

Published Date

  • January 2015

Published In

Volume / Issue

  • 49 / 1

Start / End Page

  • 13 - 26

PubMed ID

  • 24880002

Pubmed Central ID

  • 24880002

Electronic International Standard Serial Number (EISSN)

  • 1873-6513

International Standard Serial Number (ISSN)

  • 0885-3924

Digital Object Identifier (DOI)

  • 10.1016/j.jpainsymman.2014.05.003

Language

  • eng