# Engaging patients to enable interoperable lung cancer decision support at scale

> **NIH AHRQ R18** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2022 · $994,482

## Abstract

PROJECT SUMMARY
Lung cancer is the leading cause of cancer-related deaths in the US, with over 135,000 deaths in 2020. 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
doctors to consider potential benefits and harms that differ according to each patient’s individual risk profile.
Due in part to the complexity of calculating and considering such patient-specific risk profiles in busy clinical
settings, less than 5% of eligible patients receive the screening every year. To address this urgent need, we
have developed a tool known as Decision Precision+ that analyzes patients’ electronic health records (EHRs),
prompts doctors to consider lung cancer screening for eligible patients, and provides doctors with individually-
tailored information on the potential benefits and harms of lung cancer screening. In doing so, Decision
Precision+ helps patients and their doctors make informed, patient-centered decisions regarding this
potentially life-saving test. Decision Precision+ has been in clinical use at University of Utah Health since 2020,
and the rate of lung cancer screening among eligible patients has increased dramatically. So as to help other
health systems increase their rates of appropriate lung cancer screening, Decision Precision+ is being made
available to other organizations as a free tool that can be downloaded and used with their own EHR systems.
This project will build upon this promising work in three ways. First, we will build a version of the tool that
integrates with the patient portal and can be used directly by patients. We will call this patient-centered tool
MyLungHealth. Doctors and health systems will be able to “prescribe” MyLungHealth to eligible patients, so
that they can make sure their smoking profile in the EHR is accurate and can learn about whether lung cancer
is right for them from the comfort of their own homes. Second, we will introduce MyLungHealth to primary care
practices at the University of Utah and New York University and rigorously study how much added benefit
MyLungHealth provides in clinical settings that already have access to Decision Precision+. Finally, we will
study how best to help other health systems implement these tools, and we will develop self-service resources
to help with implementation. In these ways, we will seek to help make sure that as many patients as possible
can benefit from appropriate lung cancer screening and the associated reduction in lung cancer deaths.

## Key facts

- **NIH application ID:** 10410792
- **Project number:** 1R18HS028791-01
- **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:** 2022
- **Award amount:** $994,482
- **Award type:** 1
- **Project period:** 2022-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10410792

## Citation

> US National Institutes of Health, RePORTER application 10410792, Engaging patients to enable interoperable lung cancer decision support at scale (1R18HS028791-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10410792. Licensed CC0.

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