Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial

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This is a review for R Tamblyn’s Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial. [1]


Asthma is a chronic condition that causes substantial mobility. The cost of treating chronic lung diseases, including asthma, is high and could most likely be lowered using a computerized clinical decision support system (CDS). Primary care physicians provide the majority of asthma care and new more efficient approaches to helping these physicians incorporate evidence-based guidelines into practice are needed. CDS systems have been shown to improve preventative care and drug management through theh use of reminders, but have been less successful in evidence-based chronic disease management. Prior research suggests that future asthma CDS systems need to facilitate asthma monitoring and follow-up patients with out-of-control asthma and also offer physicians patient-specific recommendations. In this study, the researchers developed a patient-specific asthma CDS management system that incorporated asthma surveillance through real-time monitoring, guideline-based treatment recommendations customized to asthma status, current medication, and follow-up management through and asthma home-care program.


Design overview and study population

A single-blind, cluster-randomized controlled trial was conducted to test the hypothesized benefits of CDS support for asthma management. The benefit of the intervention was assessed by comparing asthma patients of physicians who received asthma decision support with asthma patients of physicians who did not. The sample size was expected to demonstrate a reduction in the proportion of patients with poorly controlled asthma to 9% in the intervention group, assuming 48 physician clusters, 120 patients per physician, an intra-cluster correlation of 0.03, and Types I and II errors of 5% and 20%, respectively.

Intervention and control group

The asthma decision support (ADS) system uses Canadian consensus guidelines to address problems in asthma management. The three components of the ADS system are integrated into the MOXXI EHR. The components are the dashboard alert, decision support for evidence-based asthma management, and asthma home care monitoring program provides physicians with the option to refer.

Randomization and blinding

Physicians were randomized to either: 1) MOXXI with ADS or 2) MOXXI alone. Patients, physicians, and research assistants involved in data collection and analysis were blinded to the study outcomes.

Outcomes and follow-up

Primary outcome: rate of out-of-control asthma episodes

This was defined as a patient’s excessive use of fast-acting bronchodilator [1], an ER visit, or hospitalization for asthma or closely related respiratory condition..

Secondary outcome: quality of asthma management

The inhaled corticosteroids to fast-acting beta-2 agonist ratio is a commonly used measure of quality of asthma care.

Statistical analysis

To test the hypothesis that ADS would reduce the rate of poor asthma control, we used Poisson regression within a generalized estimating equation framework to estimate the difference in out-of-control asthma event rates between the intervention and control groups. The numerator was the number of 3-month periods where the patient’s asthma was out of control. The denominator was the number of patient-months of follow-up, defined, for each patient, starting from the date of the first visit to the study physician post-randomization to the end of follow-up. A binary variable was used to represent the patient’s intervention group assignment, and the control group was used as the reference in the regression model. “Patient” was the unit of analysis, “physician” was the cluster, and an independent correlation structure and robust standard errors were used to account for dependence in outcomes among patients who had the same physician.


Overall, 81 physicians were randomized to the intervention and control groups. A total of 4447 patients in the practices of study physicians had a diagnosis of asthma, were covered by the provincial drug plan, and consented to participate. During 33 months of follow-up, eight physicians retired, moved, or dropped out of the study, along with their asthma patients (n = 166), a slightly higher proportion in the control compared to the intervention group. In 39.5% of visits for out-of-control asthma, compared to 5.3% of visits for in-control asthma, the physicians accessed the ADS system. For patients with out-of-control asthma, an increase in treatment was recommended in 69.8% of visits and referral to a specialist in 10.1%. In 20.1% of visits for out-of-control asthma, no recommendation was possible given the particular combination of medications used. The most frequent recommendations generated for patients with out-of-control asthma were to add an inhaled corticosteroid, a leukotriene inhibitor, or to increase the dose of the existing therapy. The mean ratio of doses of inhaled corticosteroid use to FABA use was significantly higher in the intervention group (mean: 0.93) compared to the control group, indicating that there was a greater use of inhaled corticosteroids relative to FABAs among patients in the intervention group.


This study evaluated the effectiveness of a novel computer-assisted ADS system that facilitates systematic monitoring of asthma control status, follow-up of patients with out of control asthma, and evidence-based, patient-specific treatment recommendations. We found that physicians were more likely to use ADS for patients with out-of-control asthma; that in the majority of these patients, physicians were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient’s treatment regimen; and that this intervention significantly increased the mean ratio of inhaled corticosteroid use to FABA use during follow-up. It also reduced the rate of out-of-control asthma episodes during follow-up among patients whose asthma was out of control at the time of study entry.


  1. Tamblyn et al. Evaluating the impact of an integrated computer-based decision support with person-centered analytics for the management of asthma in primary care: a randomized controlled trial. J Am Med Inform Assoc. 2015: Feb. http://jamia.oxfordjournals.org.ezproxyhost.library.tmc.edu/content/early/2015/02/09/jamia.ocu009