Background
Globally, the number of people diagnosed with asthma is over 340 million and has continually increased over a 10-year period [
1]. Asthma is diagnosed based on a combination of patient history, physical examination, and objective tests. Asthma poses a significant burden on individuals and the health care system at-large. As the prevalence of asthma increases, the burden of asthma on healthcare systems around the world will also increase given that individuals with asthma use significantly greater health care resources than those without asthma, have a poorer quality of life, and have a higher chance of suffering from mental illness [
2‐
5].
A major contributor to the burden of asthma on individuals and the healthcare system is that gaps exist between the published guidelines for asthma diagnosis and actual strategies for diagnosis used in primary care [
4,
6]. Although standards for asthma diagnosis are well established, less than half of individuals diagnosed with asthma have a confirmed diagnosis through the use of objective measurements of pulmonary function within two years of their original diagnosis [
7]. Other challenges in the diagnosis of asthma include differentiating asthma from other respiratory conditions given the vast differential diagnosis for characteristic asthma symptoms [
8]. These issues are compounded by a limited number of validated knowledge translation (KT) initiatives that could potentially support practitioners in the diagnosis and surveillance of asthma patients in primary care [
9]. Incorporation of data element standards outlined in the Pan-Canadian Respiratory Standards Initiative for Electronic Health Records (PRESTINE) and specific indicators for asthma in primary care have created the possibility for improved asthma KT eTools in primary care by adopting these data standards [
10,
11].
KT is a term that describes the process of implementing the results of research into practice [
12]. The process of KT has been organized through the development of the Knowledge to Action (KTA) Framework put forward by Graham et al. [
13]. KTA is broken down into two distinct phases: Knowledge Creation and Knowledge Action. The Knowledge Creation phase involves analysis of the available research on the topic of interest from primary studies to systematic reviews. The Knowledge Action phase occurs simultaneously or after Knowledge Creation. Knowledge Action involves synthesizing available research, identifying potential barriers, and implementing interventions [
13].
KT has become a priority for many stakeholders with an interest in improving asthma diagnosis and care as its approach to integrating research from various fields provides an opportunity to create new tools to assist practitioners, particularly in primary care [
14,
15]. KT initiatives are required for quality improvement in asthma care as research findings must be translated into usable interventions that create actionable behaviour change in physicians. By following the KTA framework, researchers can ensure novel research findings are implemented effectively to reach more health care providers and improve decision making [
13].
Despite this understanding, practical KT tools to support primary care practitioners are not widely available. Electronic medical records (EMRs) offer an opportunity to improve practitioner performance and support quality improvement efforts by accurately identifying patients with asthma. This review aims to describe the current state of KT initiatives for asthma, to assess the limitations of KT tools for asthma and identify opportunities for how to improve asthma diagnosis and surveillance through digital innovations.
Methods
Systematic literature searches were conducted on Ovid MEDLINE and Ovid MEDLINE Daily Epub Ahead of Print, In-Process & Other Non-Indexed Citations. The first search criteria included the key words: “asthma”, “validation study”, and “electronic health records”. Supplemental searches included the terms: “translational medical research”, “asthma surveillance system” “asthma definition”, “primary health care”, “quality improvement”, and “professional practice gaps”. The search criteria included original articles; had a date restriction limiting results from 2012 to 2022; and was limited to articles in English. Studies referenced in research articles from the original literature search were also identified. Articles were reviewed if they fit within one of four themes: (1) electronic KT tools for asthma; (2) electronic asthma surveillance; (3) quality improvement of asthma diagnosis in primary care; and (4) gaps in asthma diagnosis and surveillance in primary care.
In total, the search returned 971 articles, of which each title and abstract were reviewed. Following review of title and abstract, a total of 163 articles were considered sufficiently relevant to review and were subsequently read in full. Of the 163 articles read in full, 43 were met one of the themes identified. An additional 28 articles were included from references of the articles read in full. After exclusion of articles that had outdated information and were either not relevant to the topic or contained redundant information, 75 articles were included.
Conclusion
This review identifies opportunities to improve the accuracy of asthma diagnosis and surveillance through the use of KT eTools to improve the quality of asthma care. The key barriers to effective KT for asthma using EMR data are lack of documentation of confirmation of an asthma diagnosis, challenges related to creating a valid EMR case definition for asthma in the absence of this documentation, and the limitations of data sources that can inform KT eTools. Limited access to and use of pulmonary function tests and specialist consultation contribute to misdiagnosis and suboptimal management. Existing KT tools for asthma have been limited in scope and many fail to address barriers and challenges in primary care, where the majority of asthma diagnoses are made. As a result, future research should focus on KT initiatives that integrate surveillance systems that can be used with multiple EMR vendors with system-level quality improvement strategies to improve health care provider adherence with guideline-recommended care on a national and international scale. By promoting and documenting accurate asthma diagnoses, KT tools and surveillance systems based on reliable EMR case definitions can be used for performance evaluation and optimization of asthma care.
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