# Intelligent Clinical Decision Support for Perioperative Blood Management

> **NIH NIH K23** · WASHINGTON UNIVERSITY · 2024 · $173,609

## Abstract

Project summary/abstract
Dr. Lou is a cardiothoracic anesthesiologist with a long-term career goal to develop, implement, and
disseminate intelligent clinical decision support interventions that improve the care of surgical patients, with a
current focus on perioperative blood management. Her prior research training has allowed her to develop
expertise in data science and predictive model development using artificial intelligence (AI) techniques. This
proposal builds on Dr. Lou’s prior experience by providing the protected time, mentorship, and training
necessary to develop skills in the implementation of AI models as clinical decision support (CDS) tools, and in
the evaluation and dissemination of such tools. The proposed career development plan will involve mentorship,
didactic, and practical research training in the conduct of pragmatic clinical trials, the design and evaluation of
implementation strategies for CDS tools, governance for CDS interventions, and foundational training in clinical
research. She has brought together a multi-disciplinary group of mentors and advisors, including Thomas
Kannampallil, PhD, Michael Avidan, MBBCh, and Sachin Kheterpal, MD, MBA, each of whom have expertise
in clinical trials and the implementation of health technology innovations. Washington University provides the
ideal environment for Dr. Lou’s training given its infrastructure and strengths in informatics, clinical trials, and
implementation science. The proposed research plan provides the framework for Dr. Lou to acquire the skills
she needs to achieve her career goals. Presurgical testing and preparation for surgical transfusion is essential
for patient safety during surgery, yet excessive preparation is costly and contributes to blood product waste.
Given recent blood shortages, presurgical blood orders should be placed only for patients who need it; there is
an acute public health need for tools that accurately estimate the risk of transfusion to guide clinical decision-
making. In preliminary work, Dr. Lou has developed a personalized AI model, named S-PATH, to estimate
surgical transfusion risk, and demonstrated its validity across local and national datasets. The objective of this
proposal is to evaluate the feasibility and preliminary effectiveness of S-PATH as a CDS system embedded
within the Electronic Health Record (EHR) using a pragmatic cluster-randomized clinical trial. The expected
outcome is a generalizable, EHR-agnostic personalized CDS system to guide presurgical blood orders that is
feasible to deploy within preoperative workflow. This proposal is significant in its potential to change clinical
practice, with considerable public health impact for patient safety, blood conservation, and reduced healthcare
costs. The proposed research and training will provide Dr. Lou with the skills needed to launch an independent
research program to improve the delivery science for AI innovations in healthcare.

## Key facts

- **NIH application ID:** 10985862
- **Project number:** 1K23HL166880-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Sunny S. Lou
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $173,609
- **Award type:** 1
- **Project period:** 2024-07-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985862

## Citation

> US National Institutes of Health, RePORTER application 10985862, Intelligent Clinical Decision Support for Perioperative Blood Management (1K23HL166880-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10985862. Licensed CC0.

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