Creating Computable Algorithms for Symptom Management in an Outpatient Thoracic Oncology Setting

• Published in: Journal of pain and symptom management 2013-12, Vol.46 (6), p.911-924.e1
• Main Authors: Cooley, Mary E., PhD, RN, FAAN, Lobach, David F., MD, PhD, Johns, Ellis, MD, MS, Halpenny, Barbara, MA, Saunders, Toni-Ann, MPH, Del Fiol, Guilherme, MD, PhD, Rabin, Michael S., MD, Calarese, Pamela, NP, MS, CS, Berenbaum, Isidore L., MD, Zaner, Ken, MD, PhD, Finn, Kathleen, MS, Berry, Donna L., PhD, RN, FAAN, Abrahm, Janet L., MD
• Format: Article
• Language: English
• Subjects: Algorithms; Anesthesia & Perioperative Care; Anxiety-Depression; Biological and medical sciences; consensus methods; decision making; decision support systems; Decision Support Systems, Clinical; Evidence-Based Medicine; Experts; Fatigue - diagnosis; Fatigue - etiology; Fatigue - prevention & control; guideline implementation; Humans; Individualized; Lung cancer and symptom management algorithms; Medical Oncology - methods; Medical sciences; Mental Disorders - diagnosis; Mental Disorders - etiology; Mental Disorders - prevention & control; Multiple tumors. Solid tumors. Tumors in childhood (general aspects); Pain; Pain - etiology; Pain Management - methods; Pain Measurement - methods; Pain Medicine; Pharmacology. Drug treatments; Pneumology; Symptom Assessment - methods; Symptoms; Thoracic Neoplasms - complications; Thoracic Neoplasms - diagnosis; Thoracic Neoplasms - therapy; Tumors; Tumors of the respiratory system and mediastinum
• ISSN: 0885-3924; 1873-6513
• DOI: 10.1016/j.jpainsymman.2013.01.016

Abstract 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.