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
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.
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
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
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.