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

Robyn Tamblyn, Pierre Ernst, Nancy Winslade, Allen Huang, Roland Grad, Robert W Platt, Sara Ahmed, Teresa Moraga, Tewodros Eguale, Robyn Tamblyn, Pierre Ernst, Nancy Winslade, Allen Huang, Roland Grad, Robert W Platt, Sara Ahmed, Teresa Moraga, Tewodros Eguale

Abstract

Background: Computer-based decision support has been effective in providing alerts for preventive care. Our objective was to determine whether a personalized asthma management computer-based decision support increases the quality of asthma management and reduces the rate of out-of-control episodes.

Methods: A cluster-randomized trial was conducted in Quebec, Canada among 81 primary care physicians and 4447 of their asthmatic patients. Patients were followed from the first visit for 3-33 months. The physician control group used the Medical Office of the 21st century (MOXXI) system, an integrated electronic health record. A custom-developed asthma decision support system was integrated within MOXXI and was activated for physicians in the intervention group.

Results: At the first visit, 9.8% (intervention) to 12.9% (control) of patients had out-of-control asthma, which was defined as a patient having had an emergency room visit or hospitalization for respiratory-related problems and/or more than 250 doses of fast-acting β-agonist (FABA) dispensed in the past 3 months. By the end of the trial, there was a significant increase in the ratio of doses of inhaled corticosteroid use to fast-acting β-agonist (0.93 vs. 0.69: difference: 0.27; 95% CI: 0.02-0.51; P = 0.03) in the intervention group. The overall out-of-control asthma rate was 54.7 (control) and 46.2 (intervention) per 100 patients per year (100 PY), a non-significant rate difference of -8.7 (95% CI: -24.7, 7.3; P = 0.29). The intervention's effect was greater for patients with out-of-control asthma at the beginning of the study, a group who accounted for 44.7% of the 5597 out-of-control asthma events during follow-up, as there was a reduction in the event rate of -28.4 per 100 PY (95% CI: -55.6, -1.2; P = 0.04) compared to patients with in-control asthma at the beginning of the study (-0.08 [95% CI: -10.3, 8.6; P = 0.86]).

Discussion: 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 out-of-control patients, that in the majority of these patients, they were advised to add an inhaled corticosteroid or a leukotriene inhibitor to the patient s treatment regimen, and the intervention significantly increased the mean ratio of inhaled corticosteroids to FABA during follow-up. It also reduced the rate of out-of-control episodes during follow up among patients whose asthma was out-of-control at the time of study entry. Future research should assess whether coupling patient-specific treatment recommendations, automated follow-up, and home care with comparative feedback on quality and outcomes of care can improve guideline adoption and care outcomes.

Conclusions: A primary care-personalized asthma management system reduced the rate of out-of-control asthma episodes among patients whose asthma was poorly controlled at the study's onset.

Trial registration: ClinicalTrials.gov NCT00170248.

Keywords: MOXXI; RCT; asthma; computer decision support; out-of-control; personalized medicine.

© The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

Figures

Figure 1:
Figure 1:
The dashboard alert. An out-of-control alert based on ER visits for asthma and overuse of fast-acting β-agonists.
Figure 2:
Figure 2:
Decision support for evidence-based asthma management. Individualized treatment recommendations for out-of-control asthma based on a patient’s current medication profile.
Figure 3:
Figure 3:
Consort diagram of physicians and patients eligible for the study. Physicians and patients were included in this study if they met the criteria outlined in the “Methods” section. Physicians were stratified by practice size and then, along with their patients, were randomly assigned to either the intervention or control group.
Figure 4:
Figure 4:
Flow chart of the breakdown of visits of patients in the intervention group. Patients in the intervention group were categorized as having in-control or out-of-control asthma. Physicians accessed the asthma decision support more often in patients with out-of-control asthma than patients with in-control asthma.

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