Clinical Decision Support for Symptom Management in Lung Cancer Patients: A Group RCT.

Context: Clinical guidelines are available to enhance symptom management during cancer treatment but often are not used in the practice setting. Clinical decision support can facilitate the implementation and adherence to clinical guidelines. and improve the quality of cancer care.

Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention.

Background: Management of uncontrolled symptoms is an important component of quality cancer care. Clinical guidelines are available for optimal symptom management, but are not often integrated into the front lines of care. The use of clinical decision support (CDS) at the point-of-care is an innovative way to incorporate guideline-based symptom management into routine cancer care.

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

Context: 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.

Objectives: 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.

Creating computable algorithms for symptom management in an outpatient thoracic oncology setting.

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.