# EnhanCed HandOffs (ECHO)

> **NIH HS R01** · WASHINGTON UNIVERSITY · 2026 · $399,999

## Abstract

ABSTRACT: ECHO PROJECT
Patients undergoing complex surgeries are most vulnerable during the immediate postoperative period; thus,
handoffs from the OR (operating room) to ICU (intensive care unit) require seamless communication and
coordination between surgical, anesthesia, and critical care teams. Postoperative handoffs are a threat to
patient safety, causing ~35% of medical errors in the US. To mitigate these errors, the National Patient Safety
Goal (2E) necessitated the “standardization” of handoff process and content, which resulted in adoption of
information transfer checklists, handoff process-based protocols, or both. Although such strategies have
improved handoff quality, our meta-analysis found that such improvements were temporary and had limited
sustainability, due to the structured formats imposing “rigid” standardization with limited flexibility and support
for interactive and personalized communication. Our central hypothesis is that a flexible standardization
approach will lead to not only improvements in information sharing, but also improvements in shared
understanding of patient risks, handoff interactivity, and handoff duration. Towards this end, we propose to
develop the INTERACT (Intelligent interactive care continuity) handoff bundle, a flexible, standardized, EHR-
integrated, and resilient sociotechnical intervention comprised of a: (1) telemedicine-augmented handoff
process (i.e., the social component) supported by a (2) machine learning (ML)-augmented handoff report (i.e.,
the technical component). INTERACT underscores the importance of using a perioperative telemedicine suite
as a safety net to support resilience to errors in OR-ICU handoff process and content. The ML-augmented
handoff report supports personalized communication of core (i.e., standardized) and tailored (flexible) content
based on predicted patient risks for postoperative complications. Aim 1 will focus on updating our current ML
models for predicting risks associated with p

## Key facts

- **NIH application ID:** 11308336
- **Project number:** 5R01HS029324-04
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Joanna  Abraham
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** HS
- **Fiscal year:** 2026
- **Award amount:** $399,999
- **Award type:** 5
- **Project period:** 2023-04-01T00:00:00 → 2028-03-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11308336

## Citation

> US National Institutes of Health, RePORTER application 11308336, EnhanCed HandOffs (ECHO) (5R01HS029324-04). Retrieved via AI Analytics 2026-07-28 from https://api.ai-analytics.org/grant/nih/11308336. Licensed CC0.

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