Digital Contact Tracing Implementation Among Leaders and Health Care Workers in a Pediatric Hospital During the COVID-19 Pandemic: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Health systems had to rapidly implement infection control strategies to sustain their workforces during the COVID-19 pandemic. Various outbreak response tools, such as digital contact tracing (DCT), have been developed to monitor exposures and symptoms of health care workers (HCWs). Limited research evidence exists on the experiences with these technologies and the impacts of DCT innovations from the perspective of stakeholders in health care environments. OBJECTIVE: This study aims to identify the factors influencing the adoption of DCT, highlight variations in perspectives across 3 key stakeholder groups concerning the impact of DCT, and provide benchmarking evidence for future pandemic preparedness. METHODS: Guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, we conducted an exploratory qualitative study to investigate the implementation and impact of DCT at the Children's Hospital of Eastern Ontario between December 2022 and April 2023. We conducted 21 semistructured interviews with key stakeholders, including health care administrators (6/21, 29%), occupational health and safety specialists (8/21, 38%), and HCWs (7/21, 33%). Stakeholders were asked about the factors influencing engagement with the DCT tool, organizational-level uptake, the implementation process, long-term use and sustainability of DCT, and unintended consequences. Verbatim transcripts were subject to thematic analysis using NVivo (QSR International). RESULTS: The implementation of DCT was viable and well received. End users indicated that their engagement with the DCT tool was facilitated by its perceived ease of use and the ability to gain awareness of probable COVID-19 exposures; however, risk assessment consequences and access concerns were reported as barriers (reach). Participants commonly agreed that the DCT technology had a positive influence on the hospital's capacity to meet the demands of COVID-19 (effectiveness). Implementors and occupational specialists referred to negative staffing impacts and the loss of nuanced information as unintended consequences (effectiveness). Safety-focused communication strategies and having a DCT tool that was human-centered were crucial factors driving staff adoption of the technology. Conversely, adoption was challenged by the misaligned delivery of the DCT tool with HCWs' standard practices, alongside the evolving perceived threat of COVID-19. Stakeholders collectively agreed on the viability of DCT and its applicability to infectious disease practices (maintenance). CONCLUSIONS: Hospital stakeholders were highly satisfied with DCT technology and it was perceived as feasible, efficient, and having a positive impact on organizational safety. Challenges related to the alignment and delivery of DCT, alongside the evolving perspectives on COVID-19, posed obstacles to continued adoption by HCWs. Our findings contribute to evidence-based practices and present benchmarks that can inform preparedness for future pandemics and infectious disease outbreaks and help other organizations implement similar technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".