# Improving Diagnosis in Gastrointestinal Cancer: Integrating Prediction Models into Routine Clinical Care

> **NIH NIH K08** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $276,527

## Abstract

PROJECT SUMMARY/ABSTRACT
Candidate: Andrew J. Read, MD, MS is a gastroenterologist and health services researcher at the University
of Michigan with a research focus on improving diagnosis of gastrointestinal (GI) tract cancers. Dr. Read has
prior training in biostatistics but not in mixed methods or implementation science. This NCI K08 award will train
him to become a leader in the translational science of medical prediction, providing him with the skills to
develop tailored implementation strategies for cancer prediction models and test these strategies in future
clinical trials.
Research Context: Iron deficiency anemia (IDA) is a common sign of many diseases, including GI tract
cancers. Despite this important association, IDA is often under-recognized or under-investigated, resulting in
delays in diagnosis. Fortunately, the electronic health record (EHR) contains potential diagnostic clues that can
be leveraged to improve diagnosis of GI tract cancers. Specifically, algorithms can be developed to detect
subtle changes in complete blood count (CBC) parameters to predict GI tract cancers. However, prediction
models have rarely been implemented in clinical practice. Identifying the barriers and facilitators to
implementing a model can allow for more customized implementation strategies to improve the chances of
successful implementation.
Research Aims: Dr. Read will (1) Refine a prediction model for detection of GI tract cancers using longitudinal
laboratory data from the Veterans Health Administration (VA), the largest integrated healthcare system in the
United States; (2) Identify barriers and facilitators to implementation of prediction models in clinical practice
using mixed methods with an explanatory sequential design, incorporating a clinician survey followed by semi-
structured clinician interviews; and (3) Develop and test components of a prediction model implementation
strategy in a clinical setting, using Implementation Mapping.
Training Aims: Dr. Read will develop expertise in: (1) Developing advanced longitudinal prediction models
using a national dataset; (2) Using and applying mixed methods and implementation science frameworks to
identify barriers and facilitators to successful implementation of a prediction model; (3) Applying
Implementation Mapping to develop and test an implementation strategy for a novel clinical prediction model.

## Key facts

- **NIH application ID:** 10877730
- **Project number:** 5K08CA279659-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Andrew J Read
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $276,527
- **Award type:** 5
- **Project period:** 2023-07-01 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10877730

## Citation

> US National Institutes of Health, RePORTER application 10877730, Improving Diagnosis in Gastrointestinal Cancer: Integrating Prediction Models into Routine Clinical Care (5K08CA279659-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10877730. Licensed CC0.

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