For community health centers

Your EHR has the answers. LungIQ finds them.

USPSTF guidelines require a 20+ pack-year smoking history for LDCT eligibility. That number demands both intensity and duration, and it is almost never in a structured field. Patients underreport. Charts are incomplete.

LungIQ uses AI to recover pack-year history from clinical notes, medications, and diagnoses, then delivers a ranked worklist your team can act on immediately. No EHR integration required. Start from a CSV.

94%
Precision in top-ranked patients contacted
0.96
External AUROC validation
3,600
Confirmed candidates from 35K patients
BATCH COMPLETE
847USPSTF Eligible
234Incomplete Hx
1,106Needs Review
USPSTF Eligible
Incomplete Smoking Hx
Needs Review
Not Eligible
PatientPack-YearsWhy flagged
M. Thompson32"2 PPD x 16 yrs noted in HPI 3/2024"
R. Garcia25"Chantix Rx + COPD dx; 25 PY in pulm note"
J. Williams28"Current smoker, 1 PPD, smoking noted since age 30"
S. Chen--"Varenicline Rx + CAD; no pack-yr documented"
D. Okafor20"Former smoker per intake; quit 2019"
The identification problem

Pack-year history is the rate-limiting step

LDCT eligibility requires a 20+ pack-year smoking history. But that data is almost never in a structured, reportable field. It is the specific reason NCQA has not been able to advance a HEDIS lung cancer screening measure.

Data quality
Patients underreport. Charts are incomplete.

Patients minimize or deny smoking history. Intake forms go unfilled. Even when smoking is documented, pack-year quantities are rarely entered into structured EHR fields. The data is there, scattered across years of clinical notes, or it is not there at all.

Scale
Manual chart review does not work at 10,000+ patients

Reviewing a single chart for smoking history takes 15+ minutes. Multiply that by your patient panel. No staff has the bandwidth to check every chart, so eligible patients go unidentified year after year.

Downstream
You find them. Then what?

FQHCs do not have CT scanners. Eligible patients need referral to imaging, insurance verification, pre-authorization, and follow-up. Without a downstream pathway, identification alone does not move the needle.

How LungIQ works
Step 1: Export
Send us a flat file. CSV is fine.

No EHR integration, no FHIR interface, no IT project. Export demographics, clinical notes, diagnoses, and medications as delimited text files under a signed BAA. We map your columns at configuration time. Any EHR, any schema.

Step 2: AI extraction
LLM + ML recovers what the chart buries

A large language model reads clinical notes to extract smoking status, pack-years, and 9 tobacco-associated risk factors. A machine learning classifier combines this with ICD, CPT, and medication signals to score every patient's probability of eligibility.

Step 3: Triaged worklist
Five queues, ranked, with plain-language explanations

Every patient is sorted into an action queue: USPSTF Eligible, Incomplete Smoking History, Needs Review, Not Currently Eligible, or Non-Candidate. Each patient carries a written explanation of why they were flagged, so the caller knows before dialing.

Built for the way FQHCs work

No EHR integration project. No new hardware. No vendor lock-in. LungIQ works with any EHR and starts from a simple flat-file export. We help connect FQHCs with consortium partners, including health systems and health plans, who have a shared interest in closing the screening gap.

Revenue from every screening visit

Every shared decision-making visit is billable under your PPS rate. G0296 coding is built into the workflow. Your FQHC captures visit revenue while improving UDS Table 6B screening metrics and positioning for the expected HEDIS lung cancer screening measure.

Close the screening gap

Your patients are eligible. We help them get screened.

Millions of FQHC patients qualify for lung cancer screening but never receive it. Oatmeal Health identifies those patients in your EHR and connects them to imaging partners. No CT scanner required.

Your starting point
LungIQ
Find the patients hiding in your EHR

AI analyzes your patient records to surface everyone who meets USPSTF lung cancer screening criteria. LungIQ extracts smoking history and clinical risk factors from unstructured notes, then generates a ranked worklist your care team can act on immediately.

  • Works from a CSV or flat file export. No EHR integration needed.
  • Ranked by eligibility probability so outreach teams start with the strongest candidates
  • 94% of top-ranked patients confirmed eligible on chart review
  • Supports UDS reporting and quality measure tracking
0.96
AUROC for predicting USPSTF eligibility, validated on 35K-patient FQHC
At the imaging partner
LungAI
AI diagnostic support at the point of imaging

When your patient reaches the imaging partner for their LDCT, LungAI provides the radiologist with a 0 to 100 malignancy probability for each nodule found on the scan. Your patients get the benefit of advanced AI analysis at no cost to your center.

  • Continuous malignancy score augments categorical Lung-RADS
  • Runs at the imaging partner site, not at your FQHC
  • Results flow back to the referring provider
  • Helps close the loop on patient follow-up
0.96
Nodule-level AUROC, retrospective held-out NLST dataset
How it works for your health center
LungIQ at your center
Identify eligible patients
Export patient data. LungIQ returns a ranked worklist of screening-eligible patients your team would otherwise miss.
Your care team
Shared decision making and referral
Provider conducts G0296 SDM visit (billable). Patient is referred to a nearby imaging partner in the network.
Imaging partner
LDCT scan with AI analysis
Patient receives LDCT at the partner site. LungAI score appears alongside the radiology read. Results go back to your center.

You have the patients. Imaging partners have the scanners. Oatmeal Health connects both sides so your patients can access screening without leaving the network. 91% of patients who receive proper education agree to proceed with their CT scan.

No CT scanner? No problem.

LungIQ runs on patient data you already have. You do not need imaging equipment, new hardware, or an EHR integration to start. Export a flat file, and LungIQ returns your screening worklist. Consortium partners and grant funding can help offset costs for qualifying health centers.

~6%
of eligible Americans currently screened for lung cancer
ACS/JAMA, Nov 2025
34M+
patient lives accessible through the Oatmeal Health FQHC network
91%
of educated patients agree to proceed with lung cancer screening
Oatmeal Health SDM pilot data

LungIQ performance validated on a 35,000-patient FQHC deployment. LungAI performance from internal retrospective validation on NLST held-out data. LungAI is not FDA-cleared and is not available for clinical use. Prospective clinical performance has not been established. Consortium structure and partner availability vary by region.

Go live in one week

1
Discovery call to scope your data. We sign a BAA and configure the column mapping.
2
Export your patient data as CSV. LungIQ runs a batch and returns a triaged worklist.
3
Your team works the ranked list. Each patient has a plain-language explanation of why they were flagged.
Request a demo

Works with any EHR. No vendor lock-in. No IT project.