title: Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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% 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.
  
  
  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%).
  
  
  CONCLUSIONS
  The CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA. Electronic address: mary_cooley@dfci.harvard.edu.
authors.forename: Mary E
authors.initials: ME
authors.lastname: Cooley
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Traci M
authors.initials: TM
authors.lastname: Blonquist
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Paul J
authors.initials: PJ
authors.lastname: Catalano
authors.affiliation: Religent Health, Durham, North Carolina, USA.
authors.forename: David F
authors.initials: DF
authors.lastname: Lobach
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Barbara
authors.initials: B
authors.lastname: Halpenny
authors.affiliation: Yale University, New Haven, Connecticut, USA.
authors.forename: Ruth
authors.initials: R
authors.lastname: McCorkle
authors.affiliation: Virginia Commonwealth University Shenandoah Valley, Front Royal, Virginia, USA.
authors.forename: Ellis B
authors.initials: EB
authors.lastname: Johns
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Ilana M
authors.initials: IM
authors.lastname: Braun
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Rabin
authors.affiliation: University of Massachusetts, Boston, Massachusetts, USA.
authors.forename: Fatma Zohra
authors.initials: FZ
authors.lastname: Mataoui
authors.affiliation: Boston Medical Center, Boston, Massachusetts, USA.
authors.forename: Kathleen
authors.initials: K
authors.lastname: Finn
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Donna L
authors.initials: DL
authors.lastname: Berry
authors.affiliation: Dana-Farber Cancer Institute, Boston, Massachusetts, USA.
authors.forename: Janet L
authors.initials: JL
authors.lastname: Abrahm
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D020407:Internet; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000523:psychology / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D010166:Palliative Care / Q000379:methods; D010818:Practice Patterns, Physicians'; D011788:Quality of Life; D063189:Symptom Assessment / Q000379:methods*
keywords: Palliative care; clinical decision support; clinical practice guidelines; lung cancer; symptom management