title: Creating computable algorithms for symptom management in an outpatient thoracic oncology setting.
abstract: BACKGROUND
  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.
  
  
  OBJECTIVE
  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.
  
  
  CONCLUSIONS
  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.
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.forename: David F
authors.initials: DF
authors.lastname: Lobach
authors.forename: Ellis
authors.initials: E
authors.lastname: Johns
authors.forename: Barbara
authors.initials: B
authors.lastname: Halpenny
authors.forename: Toni-Ann
authors.initials: TA
authors.lastname: Saunders
authors.forename: Guilherme
authors.initials: G
authors.lastname: Del Fiol
authors.forename: Michael S
authors.initials: MS
authors.lastname: Rabin
authors.forename: Pamela
authors.initials: P
authors.lastname: Calarese
authors.forename: Isidore L
authors.initials: IL
authors.lastname: Berenbaum
authors.forename: Ken
authors.initials: K
authors.lastname: Zaner
authors.forename: Kathleen
authors.initials: K
authors.lastname: Finn
authors.forename: Donna L
authors.initials: DL
authors.lastname: Berry
authors.forename: Janet L
authors.initials: JL
authors.lastname: Abrahm
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D019317:Evidence-Based Medicine; D005221:Fatigue / Q000175:diagnosis / Q000209:etiology / Q000517:prevention & control*; D006801:Humans; D008495:Medical Oncology / Q000379:methods; D001523:Mental Disorders / Q000175:diagnosis / Q000209:etiology / Q000517:prevention & control*; D010146:Pain / Q000209:etiology; D059408:Pain Management / Q000379:methods*; D010147:Pain Measurement / Q000379:methods; D063189:Symptom Assessment / Q000379:methods; D013899:Thoracic Neoplasms / Q000150:complications / Q000175:diagnosis / Q000628:therapy*
keywords: Lung cancer and symptom management algorithms; consensus methods; decision making; decision support systems; guideline implementation