Using Information Technology to Improve Asthma Adherence

In 2001 an estimated 31.1 million people in the United States reported ever having had an asthma diagnosis. Asthma is a leading cause of preventable hospitalizations, and it accounts for an estimated 14 million days of missed school and 100 million days of restricted activity yearly. The routine use of anti-inflammatory medications, particularly inhaled corticosteroids (ICS), in the treatment of asthma can markedly improve symptoms and reduce complications. Yet, evidence suggests that these medications are under-prescribed by physicians and poorly taken by patients. For example, in one study of asthma patients enrolled in a large, California HMO only 71.7% of patients with severe symptoms reported having a steroid inhaler, and only 53.6% of those reported using it daily in the preceding month. Using electronic monitoring devices to record inhaler use, researchers have estimated that patients use their ICS as directed 20 to 73% of the time. Using claims-based measures of adherence we have shown that adherence to inhaled steroids is inversely correlated with the frequency of oral steroid use and asthma-related emergency room visits. In addition, these measures suggest that non-adherence to ICS is an independent predictor of asthma-related hospitalizations. In our study population, we estimated that 60% of asthma-related hospitalizations were attributable to poor adherence to ICS. Together these findings suggest that increasing ICS use may improve asthma outcomes. Studies employing health-behavioral models of adherence suggest that medication adherence is associated with treatment-related concerns regarding complications, efficacy, and benefits. Unfortunately, the results of behavioral and educational interventions to improve adherence have been disappointing. Despite, in some cases, considerable time spent with patients, these interventions have at best resulted in modest adherence improvements. Given the time, training, and resources required to implement these interventions, they are unlikely to be widely adopted in the clinical setting. Some recent studies, however, suggest that providing adherence data to clinicians may improve patient adherence. In one, clinicians gave repeated feedback to patients regarding ICS adherence; this resulted in sustained improvements over the study period. Unfortunately, this small study did not find differences in asthma outcomes. In this proposal we seek to test an asthma adherence intervention specifically designed for use in the clinical setting. Adherence measures will be generated by linking currently available electronic data. In this cluster-randomized trial, primary care physician-practice groups will be randomized to receive asthma medication adherence information electronically for patients with asthma associated with these practices. In addition to ICS adherence information, clinicians in intervention practices will have data on patient beta-agonist use to better tailor ICS therapy to disease severity. Eligible patients with asthma will be identified prior to randomizing practices and will be invited to participate. Patient surveys will be sent in the pre-intervention survey and in the post-intervention period. Patient-level outcomes will be assessed through the medical record, patient surveys, and claims data. We will perform an intention-to-treat analysis with all eligible patients identified pre-randomization included in the analysis (the primary analysis). This study is powered to allow for only 60% of the eligible patient population being seen within the first 6-months (i.e., no effect in 40% of the patient population). As a secondary analysis, we will perform a modified intention-to-treat (or per protocol) analysis, whereby we will analyze the results of only those patients in both study arms seen within the first 6-months of the intervention.