Our study provides the first multi-center analysis of EHR alert design elements in pediatric care settings, revealing substantial variation in content and design. These findings underline the need for future research to experimentally explore EHR alert design best practices to improve efficiency and effectiveness.
While there are many published pediatric AI models, the number of AI implementations is minimal with no standardized reporting of outcomes, care processes, or human performance measures. More comprehensive evaluations will help elucidate mechanisms of impact.
Further refinement of alert burden metrics is needed as current metrics do not adequately represent the impact on end
users when viewed through different dimensions. The best practices we describe here will allow institutions to establish monitoring and optimization programs to reduce alert burden.
Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.
We discuss benefits and challenges in developing a pediatric learning collaborative for clinical decision support benchmarking and sharing of best practices, describe the strengths and weaknesses of different approaches to alert burden measurement, demonstrate how alert prioritization can inform quality improvement initiatives to reduce alert burden, examine unique measurement challenges for non-EHR alerts such as phone calls and text messages, and discuss strategies for linking alert burden analytics to governance strategies that facilitate improvement.