Analysis of Over 144,000 Patient Messages Reveals Key Challenges in Healthcare Communication

ATLANTA, Jan. 29, 2025 /PRNewswire/ -- A recent analysis of over 144,000 EHR messages across 76,000 patient-provider conversations has revealed critical insights into the performance and related challenges of modern healthcare communication. The study, conducted in partnership with Emory University, the Georgia Institute of Technology, and Switchboard, MD, highlights the vital role of nurses and support staff in managing the complex communication burden and the potential for adopting automation solutions.

Key findings include:

• 10.5 average touches per conversation and 5.5 average touches per message, reflecting the complexity of patient-provider communication before resolution.
• 350 average messages per health worker for nurses/CNA/MA, compared to 241 for non-physician practitioners, 166 for physicians, and 155 for support staff within two months.
• 55,384 total messages managed by nurses/CNA/MA, with 28.3% requiring direct action and the remainder serving as informational CCs.

"This analysis underscores the challenges of EHR-based communication in healthcare," said David Gutman, MD, PhD, Associate Professor in the Department of Pathology at Emory University School of Medicine. "While the network supports rapid information flow, the growing burden on essential roles highlights an urgent need for innovation to support healthcare workers and improve patient outcomes."

The study also revealed that the predominant use of carbon-copied (CC'ed) messages, particularly among nurses and support staff, contributes to significant cognitive load. It suggested that automating message triage and prioritization through AI could significantly reduce this burden, enabling health workers to dedicate more time to clinical care.

"Health organizations have a tremendous opportunity to alleviate these issues by adopting automated triage systems," said Andrew Hornback, PhD candidate in Computer Science at the Georgia Institute of Technology. "By using AI and machine learning to categorize and prioritize messages, they can ensure urgent tasks reach the right person faster while reducing the strain on overburdened staff—creating a more balanced workload with mitigated risks of burnout across teams."

To read the full analysis, including detailed metrics and network visualizations, please visit: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1422208/full

Switchboard, MD is a physician-led artificial intelligence & data science company with a mission to restore the human connection to medicine by improving engagement and outcomes while reducing inefficiency. Switchboard, MD's platform designs and deploys clinically relevant products that surface critical needs, accelerate patient care, and reduce administrative burdens—making it easier for providers and administrators to collaborate and deliver high-quality experiences for patients and staff.

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SOURCE Switchboard, MD