4 Strategies Behind High-Performing Lung Programs: What They Do Differently

Pulmonary nodule management extends far beyond the initial imaging finding.

As health systems expand lung cancer screening efforts and formalize incidental pulmonary nodule pathways, increasing attention is being placed on what happens after a nodule is identified. A pulmonary nodule may enter the system through a screening program, an emergency department encounter, a cardiac CT, or routine outpatient imaging. Patients may require serial imaging over months or years, multidisciplinary review, referral to pulmonary specialists, diagnostic workup, or ongoing surveillance based on evolving risk assessments. Throughout that process, continuity of care becomes just as important as the original finding itself.

For pulmonologists and lung program leaders, the challenge is rarely determining the appropriate next step. Established guidelines provide a framework for surveillance and follow-up. The greater challenge is maintaining continuity across a pathway that may span multiple departments, multiple care teams, and multiple surveillance intervals over time. The question now is: how do programs maintain visibility and continuity from the moment a pulmonary nodule is identified through specialist evaluation and intervention when necessary?

This is one of the reasons high-performing lung programs increasingly focus on continuity as a core operational objective. The goal is not simply to identify pulmonary nodules. The goal is to ensure that appropriate patients continue moving through surveillance, specialist evaluation, and intervention pathways in a timely and coordinated manner.

In this guide, we'll look at four operational strategies high-performing lung screening programs and incidental nodule clinics use to improve pulmonary nodule follow-up and create more reliable continuity across lung cancer care.

1. Establishing Clear Ownership Across the Pathway

One of the biggest reasons patients drift after pulmonary nodules are identified is unclear follow-up ownership. Maintaining continuity across pulmonary nodule surveillance often requires coordination between radiology, pulmonology, primary care, navigation teams, and scheduling staff. When ownership of the next step is not clearly defined, surveillance recommendations can become difficult to operationalize consistently across the pathway.

In many lung screening programs and incidental nodule clinics, surveillance responsibility becomes distributed across radiology, pulmonology, primary care, navigation teams, and schedulers. Recommendations may exist inside reports, referrals may technically be placed correctly, and surveillance intervals may already be documented, yet patients still fall through gaps because continuity depends heavily on manual coordination.

This becomes especially important for incidental pulmonary nodules discovered during imaging performed for unrelated reasons. Once the patient leaves the radiology workflow, maintaining visibility into the next step often becomes difficult.

Programs that scale successfully reduce this ambiguity early. They create structured workflows that help pulmonologists and program leads maintain visibility into unresolved findings, overdue surveillance, delayed referrals, and patients who still require action.

Several operational patterns consistently appear across stronger programs:

These programs spend less time trying to determine who owns the next step and more time keeping patients moving through surveillance consistently.

2. Creating Longitudinal Visibility Across the Patient Journey

Pulmonary nodule management rarely happens during a single encounter.

Pulmonologists often make decisions based on how findings evolve over time, including prior imaging, interval growth, surveillance adherence, referral history, and changing risk profiles. Lung program leads face a similar operational challenge. Understanding where patients are within the pathway becomes increasingly difficult as surveillance volumes rise.

Many lung programs still manage this information across PACS, EMRs, spreadsheets, navigation notes, referral systems, and manual reminders. By the time patients reach specialist evaluation, continuity may already be incomplete.

High-performing lung screening programs and incidental nodule clinics approach this differently. They create a clearer operational view across imaging, referrals, surveillance timelines, and follow-up history so teams can understand how the patient has moved through the pathway over time.

Programs using longitudinal patient management systems such as <a href="/product/qtrack">qTrack</a> help pulmonologists and lung program leads maintain continuity across surveillance workflows without increasing administrative burden. Instead of reconstructing fragmented histories manually, teams can quickly identify where patients are within the program and what action still needs to happen.

This becomes especially valuable for patients with delayed referrals, overdue surveillance imaging, interval growth across scans, or findings discovered outside formal screening programs where continuity can weaken quickly once the patient leaves the initial imaging workflow.

The strongest lung programs understand that pulmonary nodule care extends far beyond the initial diagnostic event. Patients often move through surveillance intervals, referrals, repeat imaging, and intervention planning over months or years. Maintaining continuity across that journey becomes increasingly important as screening programs and incidental detection volumes continue to scale.

3. Smoother Transition From Detection to Specialist Evaluation

Many lung cancer pathways lose momentum during the transition between detection and specialist evaluation.&nbsp;

A pulmonary nodule may be identified during routine imaging, a recommendation may appear clearly inside the report, and a referral may technically exist within the Electronic Medical Record (EMR), yet delays still emerge because the pathway between detection and action contains too many disconnected steps. Pulmonologists and lung program leads experience the downstream impact of this directly. Surveillance intervals stretch longer than intended, referrals slow down, and patients sometimes reach specialist evaluation later than expected.

High-performing programs reduce these delays by simplifying how patients move from detection to pulmonology evaluation and intervention planning. They create clearer referral pathways, reduce dependency on manual handoffs, and improve coordination between radiology, pulmonology, and navigation teams so patients move through the pathway with less friction.

Several programs are also expanding earlier detection opportunities during routine chest imaging. Chest X-ray AI tools such as <a href="/product/qxr">qXR</a>-LN help radiologists identify subtle pulmonary nodules during high-volume reads without disrupting existing workflows, helping more patients enter the pathway earlier.

The strongest programs understand that detection quality shapes the beginning of the pathway, while continuity afterward determines whether patients receive timely specialist evaluation.

4. Standardize Surveillance Across High-Volume Workflows

As pulmonary nodule volumes continue to rise, maintaining consistency across surveillance decisions becomes increasingly difficult.

Thoracic radiologists and pulmonologists work in environments defined by subtle findings, high imaging volumes, competing priorities, and multiple guideline considerations. Small 

differences in interpretation, measurement, or follow-up recommendations can create significant downstream variation across the pathway.

High-performing lung programs reduce this variability by creating more standardized approaches to pulmonary nodule surveillance while preserving clinical judgment within existing workflows.

Quantification tools such as qCT-LN Quant help support reproducible volumetric measurements and longitudinal growth tracking across chest CT workflows, helping pulmonologists and radiologists make more consistent follow-up decisions across different readers and time points.

The strongest surveillance programs typically create:

The goal is maintaining consistency across high-volume workflows where subtle findings can otherwise receive very different surveillance recommendations over time.

What High-Performing Lung Programs Ultimately Understand

The most effective lung screening programs and incidental nodule clinics reduce fragmentation across the entire continuum of care and create stronger continuity between detection, surveillance, referral management, and specialist evaluation.

Pulmonologists and lung program leads experience different parts of the same operational challenge every day. Follow-up timelines stretch, referrals slow down, visibility across the patient journey weakens, and continuity becomes harder to maintain as imaging volumes increase.

Pulmonary nodule management is inherently longitudinal. Detection, surveillance, specialist evaluation, and intervention planning occur across multiple encounters and often across multiple departments. Programs that maintain visibility across each stage of that pathway are generally better positioned to support timely evaluation and coordinated care.

As lung cancer programs continue to scale, the challenge is no longer simply identifying more pulmonary nodules. The larger challenge is ensuring fewer patients disappear between detection and decision-making.

Stop Losing Patients Between Detection and Follow-Up.

See how you can make the most out of your lung program.

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4 Strategies Behind High-Performing Lung Programs

Incidental
pulmonary nodules are now a routine part of clinical practice. With the
expansion of CT imaging, IPN programs and lung cancer screening, detection has
become more consistent than ever before.

What
remains difficult is everything that follows.

Managing
nodules requires more than identifying them. It involves understanding risk,
defining follow-up, tracking patients over time, and ensuring that recommended
care is completed. As volumes increase, this becomes difficult to sustain
through manual workflows alone.

Recent advances in technology are
beginning to address this problem, not by changing how clinicians make
decisions, but by improving how consistently those decisions are executed.

The technologies shaping
nodule management today

Current solutions used in pulmonary
nodule management and closed-loop follow-up can be broadly grouped into five
categories:

Each of these addresses a different gap
in the care pathway.

The first wave of innovation in this
space focused on improving detection.

AI-based
detection systems analyze imaging data, primarily CT scans, to identify
pulmonary nodules with high sensitivity. These systems are particularly helpful
for small or subtle nodules that may be difficult to detect consistently,
especially in high-volume settings.

However,
in real-world practice, detection remains an ongoing challenge, particularly
outside structured screening programs.

A
large proportion of patients first enter the system through <a href="https://www.qure.ai/blog/role-of-an-ai-for-reliable-screening-of-abnormality-in-x-rays">routine chest X-rays</a>, where nodules can be subtle,
overlapping with anatomical structures, or deprioritized in busy workflows.
This is where variability is highest, and where clinically meaningful findings
are most likely to be missed.

AI-based detection on chest X-rays is
beginning to address this gap.

For
example, solutions such as qXR act as a second reader on routine imaging,
highlighting suspected nodules in the 6–30 mm range and bringing attention to
findings that may otherwise go unnoticed. In real-world settings, this has
translated into the ability to identify up to 94% of nodules that were
initially missed, while operating at a sensitivity of 83%,
particularly for subtle findings in everyday clinical workflows1.

This
is particularly relevant because chest X-rays are often the first imaging
touchpoint, not just in screening but across emergency, outpatient, and
inpatient settings. Improving detection at this stage effectively expands the
front end of the pathway, allowing more patients to enter appropriate follow-up
earlier.

At
scale, this approach has already been implemented widely, with imaging AI
systems deployed across 5,200+ sites and contributing to impacting over 43
million lives, reflecting both clinical adoption and real-world utility.

Detection
technologies, therefore, are not just about improving sensitivity in controlled
environments. They are about reducing variability in routine care, where
the majority of patients are first seen.

Volumetric measurement and
longitudinal analysis tools

Once a nodule is detected, the focus
shifts to understanding its behavior over time.

Traditional
measurement approaches rely on diameter, which can vary depending on technique
and interpretation. Small differences in measurement can lead to uncertainty,
particularly when assessing growth.

Volumetric analysis offers a more precise
alternative.

By
evaluating nodules in three dimensions, these tools allow clinicians to track
changes in volume across serial scans. This is clinically important because
growth is one of the strongest indicators of malignancy.

In
practice, volumetric tools reduce inter-reader variability and provide a more
reliable basis for comparing nodules over time. They also make it easier to
detect subtle changes that may not be apparent through diameter measurements
alone.

This shift from single timepoint
assessment to longitudinal analysis is central to modern nodule management.

In
real-world settings, this is increasingly supported by systems that combine quantitative
measurement with longitudinal tracking. <a href="https://www.qure.ai/us">AI healthcare solutions</a> such as qCT enable semi-automated
nodule segmentation and provide standardized measurements across timepoints,
including diameter, volume, and density. This allows clinicians to move beyond
visual comparison and rely on reproducible metrics when assessing change.

An important addition is the ability to
calculate volume doubling time (VDT) across serial scans, which provides
a more objective indicator of growth dynamics. When combined with integrated
clinical frameworks such as Lung-RADS classification, Brock risk scoring, and
guideline-based follow-up recommendations, this creates a more structured
approach to decision-making.

What this enables in practice is a shift
from:

This
level of precision becomes particularly important in cases where growth is
subtle, and where small differences in interpretation can significantly
influence management decisions.

Structured reporting and
guideline integration systems

Another important area of advancement is how findings are documented and communicated.

<a href="https://www.qure.ai/blog/teaching-machines-to-read-radiology-reports">Radiology
reports</a> have traditionally been narrative, which can lead to
variability in how nodules are described and how follow-up recommendations are
conveyed.

Structured reporting systems aim to
address this by integrating guideline-based frameworks, such as Lung-RADS or
Fleischner, directly into reporting workflows.

Instead of relying solely on descriptive
text, these systems provide:

This improves communication between
radiologists and referring clinicians and reduces ambiguity in management
decisions.

In
practice, structured reporting helps ensure that recommendations are not only
made, but are also actionable.

Report interpretation: from
NLP to LLM-based systems

Even
with improvements in reporting, a large amount of clinically relevant
information remains embedded in unstructured text.

Natural
language processing (NLP) has been used to extract information from radiology
reports by identifying keywords and predefined patterns. These systems can
detect mentions of nodules and capture basic attributes such as size or
follow-up recommendations.

However, NLP approaches often struggle
with variability in language and lack the ability to fully interpret clinical
context. They may miss nuanced findings or incorrectly flag non-actionable
mentions.

More recent approaches using large
language models (LLMs) are beginning to address these limitations.

Unlike traditional NLP, LLM-based systems
are designed to interpret meaning rather than simply extract terms. This allows
them to:

In
practical terms, this reduces the need for manual chart review and improves the
accuracy of identifying patients who require follow-up.

The
shift from NLP to LLM-based interpretation represents a move from pattern
recognition to contextual understanding, which is particularly important in
complex clinical narratives.

Longitudinal tracking and
closed-loop workflow platforms

The most critical gap in nodule management has historically been follow-up over time.

Electronic medical records are effective
at documenting care, but they are not designed to track patients longitudinally
in a structured way. This creates a disconnect between recommendations and
execution.

<a href="https://www.qure.ai/blog/how-lung-nodule-tracking-software-supports-nurse-navigators">Longitudinal
tracking systems</a> are designed specifically to address this.

These
platforms maintain a continuous view of patients with nodules, tracking:

More
importantly, they introduce a time-based workflow, where patients are surfaced
when action is required and missed follow-ups are flagged.

This transforms follow-up from a passive
process into an actively managed one.

Studies
have shown that a substantial proportion of patients with pulmonary nodules do
not receive appropriate follow-up. Closed-loop systems are designed to reduce
this gap by ensuring that patients remain within the pathway until follow-up is
completed.

In
practice, this is where dedicated tracking layers become essential. Platforms
such as qTrack are designed to manage patients longitudinally across both
screening and incidental findings, creating a single, unified view of
follow-up. By bringing together imaging data, recommendations, real-time
customizable dashboards and follow-up status into one workflow, these systems
help ensure that patients do not fall out of the pathway between scans.

What
this enables is not just tracking, but active coordination, where
patients who require action are surfaced, prioritized, and managed in a
structured way. For clinical teams, this reduces reliance on manual tracking
and fragmented workflows, allowing follow-up to be handled more consistently at
scale.

Individually, each of these technologies
addresses a specific part of the problem.

The
real value emerges when these layers are integrated.

Closed-loop systems bring these
components together into a continuous workflow, where findings move from
detection to follow-up without being lost in transition.

As
imaging volumes continue to grow, maintaining this level of coordination
manually becomes increasingly difficult.

qTrack
is designed to support this infrastructure layer by providing longitudinal
tracking across both screening and incidental findings. By maintaining
visibility across imaging and time, and organizing patients within a structured
workflow, these systems help ensure that recommended follow-up is completed.

This allows clinical teams to focus on
interpretation and decision-making, while reducing gaps in execution.

The management of incidental pulmonary
nodules is evolving.

The next phase of progress is not about
detecting more nodules.
&nbsp;It is about managing them more reliably.

Advances
in technology are enabling this shift by improving consistency, reducing
variability, and ensuring that patients remain visible within the care pathway.

Ultimately,
the goal is straightforward: every detected nodule should lead to appropriate,
timely follow-up.

Schedule a consultation
with an expert today.&nbsp;Reach out to us on partner@qure.ai. 

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What Are the Latest Technologies Used in the Management and Closed-Loop Follow-Up of Incidental Pulmonary Nodules?

In routine clinical practice, incidental
lung nodules are not rare findings. They are encountered across a wide range of
imaging studies - CT scans performed for trauma, cardiac evaluation, or
non-respiratory complaints often reveal unexpected abnormalities in the lung.

Pulmonary nodules are detected in up
to 25-50% of <a href="https://www.qure.ai/blog/qct-lung-catching-lung-cancer-early">chest CT scans</a>,
depending on the patient population and imaging protocols1. For most
clinicians, the detection itself is not the challenge. The real issue begins
after the finding is documented.

In many cases, a nodule is noted in the
radiology report, a follow-up is suggested, and then the patient disappears
from the pathway. Months later, that same patient may reappear with disease
progression that could have been addressed earlier.

This is not a failure of individual
clinicians. It reflects how our systems are currently designed.

Missed follow-up is rarely due to a
single breakdown. It is the result of multiple small gaps across the care
pathway.

1.
Findings Are Documented but Not Acted Upon

Radiology reports often include follow-up
recommendations, but these are embedded within narrative text and not always
translated into a clear next step.

In practice, what is missing is not
information, but operational clarity:

Without these elements, even
well-documented findings may not lead to action.

2.EMR Systems Do Not Support Longitudinal Tracking

Electronic medical records are designed
to store information, but they are not built to manage longitudinal <a href="https://www.qure.ai/blog/on-deep-learning-healthcare-and-qure-ai">clinical
workflows</a>.

This creates a gap between what is
recommended and what actually happens.

3.The Handoff Between Specialties Is Variable

The transition from radiology to
pulmonology or primary care is a critical step, and often an inconsistent one.

A recommendation such as “consider
follow-up” leaves room for interpretation. Without a defined interval, clear
ownership, or a structured pathway, the next step may be delayed or missed
entirely.

In many cases, responsibility for
follow-up is not clearly assigned.

Depending on the setting, this may fall
to:

High-functioning
programs address this by assigning follow-up to a defined pathway,
rather than an individual clinician. This is often operationalized through a
lung nodule or IPN program with centralized oversight.

4.Follow-Up Relies on Memory Instead of Systems

In the absence of structured tracking,
follow-up often depends on:

This approach does not scale,
particularly in systems managing large imaging volumes. Studies suggest that up
to 50% of patients with incidental pulmonary nodules do not receive appropriate
follow-up2.

A reliable system
does not depend on remembering, it depends on visibility and tracking.

What Actually Works:
Building a Reliable Follow-Up System

Improving follow-up requires a shift from
reactive workflows to structured, longitudinal management. It requires systems
designed for reliability.

Effective follow-up begins with clarity.
Each finding should be associated with:

The difference between “consider
follow-up” and “repeat CT in 6 months” is operationally significant.

2.Treat Follow-Up
as a Longitudinal Workflow

Follow-up is not a single event. It is a
process that extends across time and encounters.

This requires moving
from episodic care to programmatic tracking.

3.Centralize
Visibility Across the Program

In high-performing programs, follow-up is
not managed through individual inboxes.

This allows teams to focus on patients
who need action, rather than searching through reports.

Many systems rely on nurse navigators or
coordinators to manage follow-up.

When these roles are supported by
structured systems, they can manage significantly larger patient volumes with
greater consistency.

A closed-loop system is not defined by
documentation. It is defined by completion.

Anything short of this leaves the pathway
open.

The Role of Technology
in Supporting Reliability

Technology is most effective when it
reduces dependence on manual processes.

When <a href="https://www.qure.ai/blog/what-we-learned-deploying-deep-learning-at-scale-for-radiology-images">integrated into
existing workflows</a>, this shifts follow-up from a reactive process to
a managed system.

Why This
Matters for Patient Outcomes

The goal of managing lung nodules is not
simply documentation. It is timely intervention when needed.

Incidental lung nodules are a routine
part of clinical practice. The challenge is not in detection, but in ensuring
that findings lead to appropriate action.

Missed follow-up reflects a gap between clinical
intent and system capability.

In many health systems, this is enabled
through infrastructure that supports tracking across both <a href="https://www.qure.ai/blog/scaling-up-tb-screening-with-ai-deploying-automated-x-ray-screening-in-remote-regions">screening
populations</a> and incidental findings.

Platforms such as qTrack function as a
longitudinal layer within the care pathway, helping maintain visibility of
patients over time and supporting teams in ensuring that recommended follow-up
is completed.

By organizing patients within a
structured workflow and reducing reliance on manual tracking, this approach
allows clinical teams to focus on decision-making and patient care, rather than
coordination overhead.

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How to Prevent Missed Follow-Up on Incidentally Detected Lung Nodules in Hospital Systems

A patient comes in for <a href="https://www.qure.ai/blog/qct-lung-catching-lung-cancer-early">lung cancer
screening</a>. The scan is reviewed, and somewhere in the report, there
it is - a pulmonary nodule.

Sometimes it’s expected. Sometimes it’s
not.

The question is rarely "what is this?" The real questions is "what do i do with this now?"

Most nodules are benign. But a small
number represent early malignancy, and those are the ones that matter. The
challenge is not just identifying nodules, it’s deciding which ones require
action and which ones can be safely followed.

That’s where variability starts to creep
in.

On paper, nodule management seems
straightforward. Guidelines exist. Thresholds are defined. Follow-up intervals
are published.

A 5 mm nodule in one patient may be low risk. The same nodule in another patient may not be.

Growth may be subtle. Measurements may
vary. Prior imaging may not be easily accessible. And sometimes, what is
written in the report doesn’t fully translate into a clear plan.

That’s why managing incidental nodules
during screening is less about memorizing thresholds and more about applying consistent
clinical reasoning.

Start with a framework,
but don’t stop there

Most clinicians rely on Lung-RADS
within screening programs and Fleischner guidelines for incidental
findings outside of them.

These frameworks are essential. They
provide structure and reduce unnecessary variation.

But they are starting points, not
endpoints.

Nodule size is often the first thing we
look at.

Small nodules, particularly those under 6
mm, are usually low risk. Larger nodules require more structured follow-up. But
size alone is not enough.

Two nodules of the same size can behave
very differently.
That’s why management decisions need to be contextual, not formulaic.

If there is one principle that
consistently guides nodule management, it is this:

A single scan provides a snapshot. Serial imaging
provides a pattern.

Stability over time generally suggests benign disease. Growth, even subtle, changes the equation.

This is why follow-up intervals matter so
much. Not just whether follow-up happens, but when it happens.

Too early, and you may not see meaningful change. Too late, and you risk delaying diagnosis.

Subsolid nodules often require a
different approach than solid nodules.

This means follow-up may extend over
years, not months.

Recognizing these differences is
essential. Applying a single approach to all nodules leads to either
over-management or missed risk.

Most nodules will remain under
surveillance.

But certain changes should prompt
escalation:

At that point, the question shifts from “should
we follow this?” to “should we intervene?”

This is where multidisciplinary input becomes important. Decisions around PET imaging, biopsy, or
surgical evaluation are rarely made in isolation.

Even with guidelines, variability remains
a reality.

These are not knowledge gaps. They are execution gaps.

Management decisions are only as
effective as their follow-through.

Even when the right recommendation is
made, it still depends on:

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