Integrating User Preferences for Asthma Tools and Clinical Guidelines Into Primary Care Electronic Medical Records: Mixed Methods Study

Abstract

Background Asthma is a chronic respiratory disease that poses a substantial burden on individuals and the health care system. Despite published national guidelines for the diagnosis and management of asthma, considerable care gaps exist. Suboptimal adherence to asthma diagnosis and management guidelines contributes to poor patient outcomes. The integration of electronic tools (eTools) into electronic medical records (EMRs) represents a knowledge translation opportunity to support best practices. Objective The purpose of this study was to determine how best to integrate evidence-based asthma eTools into primary care EMRs across Ontario and Canada to improve adherence to guidelines as well as measure and monitor performance. Methods In total, 2 focus groups comprising physicians and allied health professionals who were considered experts in primary care, asthma, and EMRs were convened. One focus group also included a patient participant. Focus groups used a semistructured discussion-based format to consider the optimal methods for integrating asthma eTools into EMRs. Discussions were held on the web via Microsoft Teams (Microsoft Corp). The first focus group discussed integrating asthma indicators into EMRs using eTools, and participants completed a questionnaire evaluating the clarity, relevance, and feasibility of collecting asthma performance indicator data at the point of care. The second focus group addressed how to incorporate eTools for asthma into a primary care setting and included a questionnaire evaluating the perceived utility of various eTools. Focus group discussions were recorded and analyzed using thematic qualitative analysis. The responses to focus group questionnaires were assessed using descriptive quantitative analysis. Results Qualitative analysis of the 2 focus group discussions revealed 7 key themes: designing outcome-oriented tools, gaining stakeholder trust, facilitating open lines of communication, prioritizing the end user, striving for efficiency, ensuring adaptability, and developing within existing workflows. In addition, 24 asthma indicators were rated according to clarity, relevance, feasibility, and overall usefulness. In total, 5 asthma performance indicators were identified as the most relevant. These included smoking cessation support, monitoring using objective measures, the number of emergency department visits and hospitalizations, assessment of asthma control, and presence of an asthma action plan. The eTool questionnaire responses revealed that the Asthma Action Plan Wizard and Electronic Asthma Quality of Life Questionnaire were perceived to be the most useful in primary care. Conclusions Primary care physicians, allied health professionals, and patients consider that eTools for asthma care present a unique opportunity to improve adherence to best-practice guidelines in primary care and collect performance indicators. The strategies and themes identified in this study can be leveraged to overcome barriers associated with asthma eTool integration into primary care EMRs. The most beneficial indicators and eTools, along with the key themes identified, will guide future asthma eTool implementation.

Validation of adult asthma case definitions for primary care sentinel surveillance

“…In addition, in EMRs, there is no requirement for confirming asthma diagnosis through objective measures such as spirometry or a methacholine challenge test. EMRs should incorporate data elements such as those proposed by the Pan-Canadian Respiratory Standards Initiative for Electronic Health Records (PRESTINE) so that providers are able to document whether asthma is suspected or confirmed, and if confirmed by what method [7,20]. Data elements that capture if asthma has been confirmed would enable case definition search strategies to differentiate between suspected and confirmed asthma [19].…”

ERS International Congress 2022: highlights from the Respiratory Clinical Care and Physiology Assembly

“…A rule-based algorithm was developed to categorise confirmed and suspected asthma patients in an observational study, and support asthma educators to adhere to the best practice guidelines in recognising and managing uncontrolled severe asthma. The authors showed that a machine learning algorithm can be used to classify asthma patients in a future clinician decision support system, improving asthma underdiagnosis/misdiagnosis, and operating as a surveillance system [89][90][91][92].…”