# Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes

> **NIH NIH R01** · CHILDREN'S RESEARCH INSTITUTE · 2023 · $634,802

## Abstract

PROJECT SUMMARY
Critically injured patients have a four-fold higher risk of death from medical errors than other hospitalized
patients, with nearly half of preventable deaths related to errors during the initial resuscitation phase. Although
protocols, simulation, and leadership training improve team performance in this setting, as many as 12 protocol
deviations per resuscitation have been observed, even with experienced teams. Given adverse outcomes that
can result from performance gaps, there is a critical need to establish novel approaches for applying real-time
decision support in critical-care settings. The long-term goal is to implement decision support for trauma
resuscitation and other fast-paced, high-risk critical care settings that improves performance, reduces errors,
and prevents adverse outcomes. The overall objective for this renewal is to vertically advance what was
achieved during the first funding period by designing, implementing and testing an intention-aware
recommender system that (1) recognizes and tracks current goals using sensor data, the output from patient
monitors, and data captured from digital devices, (2) derives recommendations that support adherence to goal-
based protocols, and (3) displays these recommendations in real time on wall displays. The central hypothesis
is that decision support aligning with intentions (“intended” or “current” goals) will enhance protocol
compliance, leading to improved outcomes related to trauma resuscitation. The rationale for this renewal is that
recommendations supporting protocol compliance that are aligned with team intentions are more likely to be
adopted by being less distracting and associated with lower cognitive load. Guided by preliminary data, the
central hypothesis will be tested by pursuing two specific aims: 1) design and implement an automated real-
time approach for predicting and monitoring the assessment and treatment goals of trauma resuscitation; and
2) generate and display a recommended plan of activities that supports current goal pursuit during trauma
resuscitation. For the first Aim, machine learning approaches will be applied for recognizing goals using data
obtained from sensors and other digital data sources. Under the second Aim, a machine learning strategy will
be implemented and tested that generates recommendations responsive to team intentions. The proposed
research is innovative because it focuses on development of real-time methods that integrate goals as an input
for making recommendations that meet the most current and relevant information needs. The proposed
research is significant because it is expected to improve the care of severely injured and other critically ill
patients by promoting timely and appropriate achievement of critical assessment and treatment goals in
settings that remain at high-risk for medical errors. The results of this research continuum are expected to have
an important positive impact on the outcome by addressing the mis...

## Key facts

- **NIH application ID:** 10629162
- **Project number:** 5R01LM011834-09
- **Recipient organization:** CHILDREN'S RESEARCH INSTITUTE
- **Principal Investigator:** RANDALL S. BURD
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $634,802
- **Award type:** 5
- **Project period:** 2014-08-01 → 2025-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10629162

## Citation

> US National Institutes of Health, RePORTER application 10629162, Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes (5R01LM011834-09). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10629162. Licensed CC0.

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