The proposed study is a two-phase study that seeks to determine the usability of a coordinated symptom assessment and management intervention (SAMI) system in phase I and to evaluate the feasibility of SAMI in Phase II. Over 80% of lung cancer patients experience multiple symptoms at presentation. To date, most studies have addressed the treatment of single symptoms. Because patients with lung cancer experience multiple symptoms, they represent an ideal group to develop and test new approaches to simultaneous management of multiple distressing symptoms. Standardized symptom and health-related quality of life (HRQOL) assessment has been identified as a potentially useful strategy to decrease distressing symptoms and improve HR-QOL. Only 3 studies have been conducted in lung cancer patients and these studies have had methodological issues. The intervention in all of the studies consisted of providing feedback to the health care provider (HCP) about results of the assessment. This study extends previous studies by adding clinical decision support to augment the effect of only providing feedback to HCPs about patient symptoms. During phase I, computer-processable algorithms for managing fatigue, pain, dyspnea, and depression in lung cancer patients will be derived from national clinical practice guidelines by a multidisciplinary team. These algorithms will be implemented on a standards-based clinical decision support system to generate patient-specific care recommendations based on data collected through a computerized symptom assessment tool. A usability evaluation using formative evaluation methods will be conducted with patients and HCPs to develop a final prototype. A pilot study using a group randomized trial (GRT) design will be conducted during phase II to evaluate the feasibility of SAMI regarding completion of the computerized symptom assessment tool and adherence to the algorithms as the primary aim and to evaluate differences in HR-QOL, patient-HCP communication, and clinical management of symptoms within two groups (SAMI and usual care) over time (baseline, 2, 4, and 6 months later) to estimate the effect size of the intervention and to estimate the within- HCP correlation of these measures among patients randomized to SAMI and treated by the same HCP as the secondary aims. We will provide HCPs with a summary of patient-reported symptoms and patient-specific symptom management recommendations based on established clinical guidelines at each clinic visit for six months. Fatigue, pain, dyspnea, and depression were chosen as the target symptoms because they are common in lung cancer patients and national guidelines are available for their management. One-hundred eighty patients will be recruited from two clinical sites;one with a racial and socioeconomic diverse population. The intervention, if feasible, will be tested in a larger GRT in the future.

Public Health Relevance

Symptom management interventions are important for patients with lung cancer because over 80% of patients have disease-related symptoms as well as high degrees of psychological distress. This two-part study seeks to determine the usability of a computerized symptom assessment and management system and then to pilot test the effect of the intervention in improving patient-provider communication, clinical management of symptoms, and health-related quality of life in patients receiving treatment for lung cancer.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Nursing Science: Adults and Older Adults Study Section (NSAA)
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O'Mara, Ann M
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Dana-Farber Cancer Institute
United States
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Lobach, David F; Johns, Ellis B; Halpenny, Barbara et al. (2016) Increasing Complexity in Rule-Based Clinical Decision Support: The Symptom Assessment and Management Intervention. JMIR Med Inform 4:e36
Cooley, Mary E; Blonquist, Traci M; Catalano, Paul J et al. (2015) Feasibility of using algorithm-based clinical decision support for symptom assessment and management in lung cancer. J Pain Symptom Manage 49:13-26
Cooley, Mary E; Lobach, David F; Johns, Ellis et al. (2013) Creating computable algorithms for symptom management in an outpatient thoracic oncology setting. J Pain Symptom Manage 46:911-924.e1