Improving the management of dyspnea in the community using rapid learning approaches

Abstract

Patients with chronic pulmonary disease often suffer from breathlessness or dyspnea. Traditional evidence generation techniques to expand upon current treatment paradigms are limited by the significant delay between study initiation and clinical implementation of findings. Rapid learning health care is a novel approach to health care delivery that relies on intelligent and continuous integration of clinical and research data sets to deliver personalized medicine using the most current evidence available. Results of important studies in the management of chronic respiratory disease are presented in brief; however, the focus of this review is on evidence supporting the implementation of a rapid learning model for symptom management. Recent findings suggest that a rapid learning system is feasible and acceptable to patients with advanced illness, helps monitor symptoms overtime, facilitates study of the impact of novel interventions, and can identify unrecognized needs and concerns. A rapid learning model improves comprehensive assessment, timeliness of intervention, and accrual of contemporaneous data to support best practice that tailors care specific to the needs of patients as their disease and lifestyle change overtime. Using the rapid learning health care model, data collected in the process of routine care can simultaneously function both as clinical information and as a resource for research on patient-centered experiences and outcomes.