Primary care asthma surveillance: a review of knowledge translation tools and strategies for quality improvement.

Background: Viable knowledge translation (KT) strategies are increasingly sought to improve asthma diagnosis, particularly in primary care. Despite this understanding, practical KT tools to support primary care practitioners are not widely available. Electronic medical records (EMRs) offer an opportunity to optimize the diagnosis and surveillance of chronic diseases such as asthma, and support quality improvement initiatives that increase adherence to guideline-recommended care. This review aims to describe the current state of electronic KT electronic tools (eTools) and surveillance systems for asthma and identify opportunities to increase adherence to asthma diagnostic guidelines by implementing digital KT eTools.

Methods: Systematic literature searches were conducted on Ovid MEDLINE that included the search terms: asthma, asthma diagnosis, asthma surveillance, electronic health records, translational medical research, quality improvement, professional practice gaps, and primary health care published in the previous 10 years. In total, the searches returned 971 articles, 163 of which were considered relevant and read in full. An additional 28 articles were considered after reviewing the references from selected articles. 75 articles were included in this narrative review.

Results: Established KT eTools for asthma such as electronic questionnaires, computerized clinical decision support systems (CDSS), chronic disease surveillance networks, and asthma registries have been effective in improving the quality of asthma diagnosis and care. As well, chronic disease surveillance systems, severe asthma registries, and workplace asthma surveillance systems have demonstrated success in monitoring asthma outcomes. However, lack of use and/or documentation of objective measures of lung function, challenges in identifying asthma cases in EMRs, and limitations of data sources have created barriers in the development of KT eTools. Existing digital KT eTools that overcome these data quality limitations could provide an opportunity to improve adherence to best-practice guidelines for asthma diagnosis and management.

Conclusion: Future initiatives in the development of KT eTools for asthma care should focus on strategies that assist healthcare providers in accurately diagnosing and documenting cases of asthma. A digital asthma surveillance system could support adherence to best-practice guidelines of asthma diagnosis and surveillance by prompting use of objective methods of confirmation to confirm an asthma diagnosis within the EMR.