# Scalable decision support and shared decision making for lung cancer screening

> **NIH AHRQ R18** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2021 · $393,596

## Abstract

PROJECT SUMMARY
Lung cancer is the second most commonly diagnosed cancer in the United States, and it is the leading cause
of cancer-related deaths among both men and women. A screening test known as low-dose computed
tomography (LDCT) can detect lung cancer early and reduce lung cancer deaths among individuals with a
history of heavy smoking, but this test requires patients and their physicians to consider potential benefits and
harms that differ according to each patient’s individual risk profile, and less than 5% of eligible patients receive
the screening every year. This project will address this urgent need by analyzing patients’ electronic health
records, prompting eligible patients and their physicians to consider lung cancer screening, and providing
individually-tailored information on the potential benefits and harms of lung cancer screening, so that patients
and their physicians can make informed, patient-centered decisions regarding this potentially life-saving test.
This project will build off of a stand-alone tool for LDCT shared decision making that has been progressively
enhanced by the project team through real-world clinical use, and it will be fully integrated with the electronic
health record (EHR) so that it can pull in relevant patient risk data and be seamlessly integrated with routine
clinical workflows. This tool, which we call Decision Precision+, will present individually-tailored information on
the potential benefits and harms of screening, enabling patients and their physicians to make informed and
patient-centered decisions on whether to screen for lung cancer through LDCT testing. Additional tools will also
be developed for prompting use of Decision Precision+ for eligible patients and for collecting required smoking
history when it is missing. Following its design and development, Decision Precision+ will be implemented and
evaluated at the primary care clinics of University of Utah Health. Decision Precision+ will also be made
available to other healthcare organizations as a free tool that can be downloaded and used with their own EHR
systems. The project team will support the use of Decision Precision+ by other healthcare organizations, so
that as many patients as possible can benefit from appropriate LDCT testing and associated reductions in
deaths due to lung cancer.

## Key facts

- **NIH application ID:** 10141232
- **Project number:** 5R18HS026198-03
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Kensaku Kawamoto
- **Activity code:** R18 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $393,596
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10141232

## Citation

> US National Institutes of Health, RePORTER application 10141232, Scalable decision support and shared decision making for lung cancer screening (5R18HS026198-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10141232. Licensed CC0.

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