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. OBJECTIVES: Clinical decision support offers an innovative approach to integrate guideline-based symptom management into oncology care. This study evaluated the effect of clinical decision support-based recommendations on clinical management of symptoms and health-related quality of life (HR-QOL) among outpatients with lung cancer. METHODS: Twenty providers and 179 patients were allotted in group randomization to attention control (AC) or Symptom Assessment and Management Intervention (SAMI) arms. SAMI entailed patient-report of symptoms and delivery of recommendations to manage pain, fatigue, dyspnea, depression, and anxiety; AC entailed symptom reporting prior to the visit. Outcomes were collected at baseline, two, four and six-months. Adherence to recommendations was assessed through masked chart review. HR-QOL was measured by the Functional Assessment of Cancer Therapy-Lung questionnaire. Descriptive statistics with linear and logistic regression accounting for the clustering structure of the design and a modified chi-square test were used for analyses. RESULTS: Median age of patients was 63 years, 58% female, 88% white, and 32% ≤high school education. Significant differences in clinical management were evident in SAMI vs. AC for all target symptoms that passed threshold. Patients in SAMI were more likely to receive sustained-release opioids for constant pain, adjuvant medications for neuropathic pain, opioids for dyspnea, stimulants for fatigue and mental health referrals for anxiety. However, there were no statistically significant differences in HR-QOL at any time point. CONCLUSION: SAMI improved clinical management for all target symptoms but did not improve patient outcomes. A larger study is warranted to evaluate effectiveness.

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. OBJECTIVE: The objective of this study was to develop and evaluate a rule-based CDS system to enable management of multiple symptoms in lung cancer patients at the point-of-care. METHODS: This study was conducted in three phases involving a formative evaluation, a system evaluation, and a contextual evaluation of clinical use. In Phase 1, we conducted iterative usability testing of user interface prototypes with patients and health care providers (HCPs) in two thoracic oncology clinics. In Phase 2, we programmed complex algorithms derived from clinical practice guidelines into a rules engine that used Web services to communicate with the end-user application. Unit testing of algorithms was conducted using a stack-traversal tree-spanning methodology to identify all possible permutations of pathways through each algorithm, to validate accuracy. In Phase 3, we evaluated clinical use of the system among patients and HCPs in the two clinics via observations, structured interviews, and questionnaires. RESULTS: In Phase 1, 13 patients and 5 HCPs engaged in two rounds of formative testing, and suggested improvements leading to revisions until overall usability scores met a priori benchmarks. In Phase 2, symptom management algorithms contained between 29 and 1425 decision nodes, resulting in 19 to 3194 unique pathways per algorithm. Unit testing required 240 person-hours, and integration testing required 40 person-hours. In Phase 3, both patients and HCPs found the system usable and acceptable, and offered suggestions for improvements. CONCLUSIONS: A rule-based CDS system for complex symptom management was systematically developed and tested. The complexity of the algorithms required extensive development and innovative testing. The Web service-based approach allowed remote access to CDS knowledge, and could enable scaling and sharing of this knowledge to accelerate availability, and reduce duplication of effort. Patients and HCPs found the system to be usable and useful.

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. The study objectives were to evaluate completion and delivery rates of the SAMI-L report and clinician adherence to the algorithm-based recommendations. METHODS: Patients completed a web-based symptom assessment and SAMI-L created tailored recommendations for symptom management. Completion of assessments and delivery of reports were recorded. Medical record review assessed clinician adherence to recommendations. Feasibility was defined as 75% or higher report completion and delivery rates and 80% or higher clinician adherence to recommendations. Descriptive statistics and generalized estimating equations were used for data analyses. RESULTS: Symptom assessment completion was 84% (95% CI=81-87%). Delivery of completed reports was 90% (95% CI=86-93%). Depression (36%), pain (30%), and fatigue (18%) occurred most frequently, followed by anxiety (11%) and dyspnea (6%). On average, overall recommendation adherence was 57% (95% CI=52-62%) and was not dependent on the number of recommendations (P=0.45). Adherence was higher for anxiety (66%; 95% CI=55-77%), depression (64%; 95% CI=56-71%), pain (62%; 95% CI=52-72%), and dyspnea (51%; 95% CI=38-64%) than for fatigue (38%; 95% CI=28-47%). CONCLUSION: The CDS systems, such as SAMI-L, have the potential to fill a gap in promoting evidence-based care.

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. 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.