# ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization

> **NIH AHRQ R21** · UNIVERSITY OF ROCHESTER · 2024 · $172,785

## Abstract

More than 20% of laboratory tests in the intensive care unit are medically unnecessary. Laboratory
overutilization contributes to reduced care quality and adverse complications including iatrogenic anemia.
Multiple medical societies have highlighted the urgent need to address this problem, but the vast majority of
interventions in the literature are quality-improvement initiatives with poor generalizability. The literature is even
sparser in the highly vulnerable pediatric population, who are more susceptible to iatrogenic anemia given their
smaller blood volumes than adult patients. With the rapid expansion of electronic health records (EHRs) and
the development of massive clinical databases, machine learning (ML) has become a promising tool that can
address the problem of laboratory overutilization. While few models have been developed to predict laboratory
results in adults, among pediatric patients model development is extremely limited. Furthermore, very few
predictive ML models in any clinical domain are translated into usable clinical decision support (CDS). Despite
strong recommendations from nearly all best practice informatics resources, most CDS implementations rely
on “out-of-box” deployment rather than employing user-centered design principles. This can leave
burdensome, sometimes harmful systems in place without demonstrated effectiveness.
 This staged-award proposal will leverage the PICU Data Collaborative (PDC), a multicenter
collaboration of pediatric intensive care units (PICUs) across the United States, to develop ML-based CDS to
predict future laboratory values for the purpose of reducing laboratory overutilization. In the R21 phase, ML
models will be trained on 188,000+ unique PICU patient encounters in the PDC database to forecast future
laboratory values (Aim 1). Concurrently, guided by the Practical Robust Implementation and Sustainability
Model (PRISM) framework, contextual factors will be identified to inform an ML-based CDS implementation
within the PICU sociotechnical environment. In the R33 phase, new a priori identified features will be
incorporated to enhance the ML models developed in Aim 1. These retrained models will be silently evaluated
on prospective data from selected PDC sites. In Aim 4, an EHR-embedded CDS tool will be designed for a
selected PDC site, incorporating user-centered design principles informed by the results of Aim 2. The final ML
model from Aim 3 will then be deployed in a pilot study at the single site, where we will measure the
implementation outcomes of reach and adoption.
 The result of this work will establish a useful, usable CDS tool to reduce laboratory overutilization based
on multicenter data and framed in the PICU contextual environment. Our pilot deployment will establish the
groundwork for a future effectiveness-implementation hybrid multicenter trial. These processes will be
generalizable and serve as a blueprint for developing data-driven translational decision support tools th...

## Key facts

- **NIH application ID:** 10948765
- **Project number:** 1R21HS030123-01
- **Recipient organization:** UNIVERSITY OF ROCHESTER
- **Principal Investigator:** Adam C. Dziorny
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2024
- **Award amount:** $172,785
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10948765

## Citation

> US National Institutes of Health, RePORTER application 10948765, ML-ROVER: Machine Learning to Reduce Laboratory Test Overutilization (1R21HS030123-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10948765. Licensed CC0.

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