This study was conducted following the COREQ (Consolidated Criteria for Reporting Qualitative Research (Checklist 1) [26].

Individual online interviews were conducted with health care providers and researchers involved in pandemic response using the Zoom video platform (Zoom Video Communications). Eligible participants included family physicians, ED physicians, and infectious disease specialists whose work primarily involves managing ARIs. Researchers with expertise in pandemic response were also included. Inclusive eligibility criteria ensured representation across a range of clinical roles and practice settings, including providers from both urban and rural contexts. This approach enabled the capture of diverse perspectives from professionals directly impacted by EWS and supported a comprehensive understanding of potential challenges and needs in ARI surveillance. Participants were recruited through professional clinical and research networks focused on primary care, emergency medicine, infectious diseases, and pandemic preparedness, including investigator networks associated with adaptive platform trials, respiratory infection surveillance initiatives, and health services research collaborations in Canada. Recruitment was conducted via email using a standardized recruitment script approved by the Unity Health Toronto Communications Department. Recruitment took place between August and October 2024. The response rate was approximately 52%, with eleven of twenty-one invited individuals participating in the study.

Interviews were conducted by 2 researchers (KM and MF) between August and October 2024. At the beginning of each session, participants completed a brief demographic questionnaire consisting of 9 questions. The demographic questionnaire collected information on participants’ professional role, years of relevant experience, practice setting (eg, urban or rural), and involvement in ARI management or pandemic response. Responses were manually recorded to ensure confidentiality, and this portion was not audio-recorded. A semistructured interview guide was informed by relevant literature on EWS and ARI surveillance, as well as the study objectives and the research team’s research experience (see Multimedia Appendix 1). The guide was designed using Bearman’s step-wise approach [27] to interview schedule development and followed DeJonckheere and Vaughn’s framework [28] for semistructured interviewing in primary care research. The interview guide was not formally pilot tested; however, it was reviewed and refined by the research team prior to data collection and iteratively adapted during the study to reflect participants’ experiences and explore perspectives on the design, implementation, and evaluation of EWS. The full semistructured interview guide, including all questions, is provided in Multimedia Appendix 1.

All interviews were audio-recorded, transcribed, and deidentified by a professional transcription service. Participants were offered the opportunity to review their transcripts for accuracy; however, no participants requested transcript review or provided feedback.

Data were analyzed using a codebook-informed reflexive thematic analysis, following Braun and Clarke’s 6-phase framework [29-31]. An inductive approach was used to allow themes to emerge directly from the data. All transcripts were coded and analyzed by a single researcher (JQ), who has training and prior experience in qualitative methodology. Iterative feedback from the principal investigator (BH) was used to refine the coding framework and interpretation. Following familiarization with the data, an initial set of codes was generated and iteratively refined as analysis progressed [32,33]. A finalized codebook was developed and validated by applying it to a new transcript before proceeding with full coding. A hierarchical coding structure outlining themes, subthemes, and example codes is provided in Multimedia Appendix 2.

A codebook was developed to support analytic organization and was refined through iterative application across transcripts. Subthemes were developed when related codes clustered under a broader theme. Reflexivity was maintained throughout the analysis, with process through memoing and regular discussions within the research team, which were used to document reflections, potential biases, interpretive decisions, and support analytic rigor [34].

Interviews were coded using NVivo software (version 14 plus; Lumivero). Data analysis began after all interviews were completed. Data saturation was assessed through ongoing discussions, where it was determined that additional data collection was unlikely to yield new insights [35]. Regular meetings were held with the study team to ensure alignment in interpretation and contextual accuracy.

This study was approved by the Unity Health Toronto Research Ethics Board (REB 24‐101) prior to the initiation of data collection. The informed consent process, including the consent form, was reviewed and approved by the REB. Written informed consent was obtained via email from all participants before participation. Participants were informed that their participation was voluntary and that they could withdraw from the study at any time without consequence.

To protect participant privacy and confidentiality, interviews were transcribed and deidentified by a professional transcription service before being shared with the research team. All study materials and data were securely stored on a restricted-access drive on St. Michael’s Hospital’s server and were accessible only to research staff approved under the REB application. Interviews were scheduled for one hour, and participants received a CAD $40 (approximately US $ 30, based on an exchange rate of 1 CAD ≈ US $ 0.74 in 2024) honorarium as compensation for their time.

Eleven participants from across Canada (Ontario, Newfoundland and Labrador, Quebec, British Columbia, and Manitoba) were interviewed between August and October 2024, with most interviews lasting 30-45 minutes. Demographic characteristics are summarized in Table 1. Participants were evenly distributed by sex (6/11, 55% male) and age group (6/11, 55% aged 30‐44 years). The most common occupation was family physician (5/11, 45%), followed by ED physicians (2/11, 18%), infectious disease physicians (2/11, 18%), a critical care physician (1/11, 9%), and a primary care scientist (1/11, 9%). Most (10/11, 90%) had over 10 years of experience, and 9 out of 11 (82%) worked in urban settings. Regarding EWS knowledge, most participants reported moderate familiarity, while 27% (3/11) had minimal knowledge, and 9% (1/11) were highly knowledgeable.

Three main themes were identified from the analysis: (1) general knowledge of EWS for ARI, (2) desired attributes of EWS for ARI, and (3) anticipated challenges in EWS implementation. Within each theme, 3-4 subthemes were identified.

Participants reported varying levels of experience with EWS but demonstrated a broad understanding of their role in ARI outbreak monitoring and detection. Most discussions focused on lessons learned from the COVID-19 pandemic, with specific emphasis on the utility of EWS in ARI detection and management.

Participants identified 4 key attributes essential for an effective EWS for ARI, including accuracy, timeliness, integration, and equity. These qualities were viewed as critical for ensuring that the system reliably detects outbreaks, provides timely alerts, seamlessly integrates into clinical workflows, and addresses disparities in health care access.

Participants identified challenges at 4 key stages of EWS development, including initiation, implementation, use, and evaluation. While multiple concerns were raised for each stage, only the most predominant issues are reported below.

This qualitative study explored the perspectives of frontline clinicians and researchers involved in pandemic response on the role of EWS in monitoring and managing ARI in Canada. Three key themes emerged, including general knowledge of EWS, desired attributes, and anticipated challenges. While participants recognized the potential benefits of EWS, the absence of a widely recognized and established EWS for ARI in Canada limited their ability to engage in in-depth discussions about specific platforms. Participants identified accuracy, timeliness, integration, and equity as essential attributes of an effective system and highlighted challenges at each stage of development, including initiation, implementation, use, and evaluation. These findings underscore the complexities of designing and implementing a robust, sustainable, and equitable EWS for ARI in Canada. Including participants from diverse specialties and practice settings strengthened the relevance of our findings across varied clinical and health system contexts.

Our findings align with existing literature emphasizing that EWS primarily serves to detect outbreaks, guide clinical decision-making, and inform public health responses [36,37]. EWS has been shown to support real-time tracking of disease trends, early identification of emerging risks, and allocation of health care resources [38]. Similar to our participants’ perspectives, previous reviews highlight that effective EWS must be timely, accurate, and seamlessly integrated into existing health care workflows to maximize their utility [39]. Participants’ references to a 24‐72 hour alert window reflect the need for timely situational awareness to support public health and clinical response, rather than a specific evidence-based mortality threshold. Previous studies further suggest that “accuracy” in EWS reflects trade-offs between sensitivity and specificity. For example, increasing sensitivity can reduce missed events but often results in more false-positive alerts and greater alert burden, whereas prioritizing specificity may lower alert frequency but increase the risk of delayed detection of deterioration [39]. Participants’ concerns about alert fatigue and missed signals are consistent with these documented trade-offs.

In Canada, multiple surveillance systems monitor respiratory infections, including FluWatchers [17], TARRANET [18], and SPSN [19]. However, these systems primarily function as population-level surveillance tools and are not designed to provide real-time clinical decision support at the point of care. In contrast, several countries, including Australia, China, and parts of Europe, operate more integrated national respiratory infection surveillance systems that combine data from multiple sources, such as laboratory, syndromic, and sentinel surveillance [40,41]. In addition, some international EWS frameworks have begun integrating machine learning and artificial intelligence approaches to enhance early signal detection, identify high-risk populations, and support timely public health alerts [42,43]. Potential reasons for the disparity in EWS for respiratory infection between Canada and other countries may reflect a combination of system-level factors. First, Canada’s health system is highly decentralized, with responsibility for health care delivery and public health surveillance largely residing at the provincial and territorial levels. This governance structure can present challenges for the development of a unified, nationally integrated EWS. Second, ongoing variation in data infrastructure, as well as differences in privacy legislation and data-sharing agreements across jurisdictions, may limit the timely integration of surveillance data at a national level. Finally, persistent gaps in access to primary care in Canada, including a substantial proportion of individuals who are unattached to a regular primary care provider, may further constrain surveillance capacity. Individuals experiencing socioeconomic marginalization are disproportionately represented among this population, which may contribute to gaps in population-level monitoring. These findings point to the need for implementation-focused research to design and evaluate integrated EWS that link surveillance, clinical care, and public health decision-making in real time while minimizing alert fatigue and inequitable coverage.

Participants identified challenges across all 4 stages of EWS development (initiation, implementation, use, and evaluation), with challenges at the initiation stage emerging as the most pressing concern. In particular, participants emphasized the lack of sustained investment and policy commitment to pandemic preparedness outside of crisis periods. While funding and interest peak during outbreaks, maintaining long-term support for EWS was viewed as difficult, a concern that aligns with prior research describing intermittent funding in surveillance infrastructure [44]. Participants noted that challenges during the initiation phase, such as limited sustained investment and unclear long-term priorities, often shaped downstream issues related to implementation, system fit, and long-term use.

Challenges related to implementation and use were also frequently discussed. Participants highlighted the fragmentation of electronic health records across provinces and the absence of standardized data-sharing protocols as major barriers to integrating EWS into clinical workflows. These issues are consistent with broader challenges observed in decentralized health care systems, where data silos and variable policies limit timely information exchange and generate real-time decision support [45]. In addition, workforce shortages, increased administrative burden, and limited system capacity were seen as factors that could further impede the adoption and effective use of EWS, reflecting system pressures that have been widely documented in the Canadian health context [46]. Prior studies of digital surveillance and decision-support implementation have shown that workforce shortages and competing clinical demands can limit engagement with alerting systems and reduce their effective use [39].

Public trust and engagement were identified as critical to the effectiveness of EWS, particularly in relation to data governance and equity. Participants emphasized concern that poorly designed or inadequately governed systems could exacerbate existing health disparities or undermine public confidence. Similar concerns have been noted in the digital health literature, where issues related to data validity, transparency, and legal framework have been shown to influence the utility and acceptability of surveillance technologies [47]. Prior Canadian and international studies have identified equity-related challenges in EWS implementation, including limited applicability to Indigenous, rural, and remote contexts and concerns around data governance and trust [48]. Participants raised similar issues, emphasizing Indigenous leadership, community-specific needs, and contextual relevance. To address these concerns, participants advocated for the importance of co-designing EWS with communities, particularly Indigenous and marginalized populations. Participants described co-design as involving early engagement with community leadership, shared decision-making around data governance and system priorities, and ongoing feedback during pilot testing and implementation. Evidence from disaster management and health equity suggests that co-design approaches can enhance cultural responsiveness and system relevance, supporting more equitable and effective implementation of digital surveillance tools [49,50].

Despite these challenges, there is growing global momentum toward strengthening respiratory surveillance. Many countries have adopted disease-specific and syndromic surveillance models that rely on real-time data collection and clinical case reporting [51]. For example, the RESP-NET in the United States and Canada’s FluWatchers program demonstrates the feasibility of large-scale respiratory surveillance [52,53]. However, our findings suggest that existing surveillance efforts in Canada are not yet configured as a unified system that can support both public health monitoring and real-time frontline clinical decision-making. Moving forward, efforts should focus on designing an EWS that bridges these domains. Key considerations include improving interoperability between electronic health records, laboratory networks, and syndromic surveillance tools; establishing clear and consistent frameworks for data governance, privacy, and equitable access; and involving key stakeholders in system design to support cultural safety and equity in implementation.

In the Canadian context, advancing transparent governance and equitable data use will require clearer coordination across federal, provincial, and territorial jurisdictions. Participants’ emphasis on trust also points to the importance of embedding equity considerations early in system design, including meaningful engagement with communities that experience greater social and structural barriers. Together, these considerations may help enable Canada’s future EWS to be perceived as legitimate, trustworthy, and responsible to the needs of its diverse population.

This study has several limitations. First, we did not include policymakers, public health officials, and data scientists involved in the design and governance of EWS, whose perspectives may have provided additional insights for policy development, data integration strategies, and long-term sustainability. Second, interviews were conducted between August and October 2024, outside peak winter respiratory virus season. Participants’ perspectives may therefore reflect experiences during relatively stable surveillance periods rather than peak seasonal demand; however, discussions frequently drew on pandemic-era and outbreak experiences. Finally, patient perspectives were not included, limiting our understanding of public engagement and trust in EWS adoption.

This study explored health care providers’ and researchers’ perspectives on EWS for ARI in Canada. Participants highlighted the potential of EWS to support outbreak detection, clinical decision-making, and pandemic preparedness, while also identifying persistent system-level challenges related to data integration, governance, and trust. These findings underscore the need for accurate, timely, integrated, and equitable systems that meaningfully bridge public health surveillance and clinical decision-making. Future research should incorporate policymaker and patient perspectives and examine how emerging digital and AI-enabled approaches can be responsibly integrated into EWS. With sustained investment and inclusive system design, Canada has the opportunity to strengthen its EWS capacity and improve preparedness for future respiratory outbreaks.