This article explains what modern QA looks like in prenatal ultrasound, why Quality Control is the measurement system that makes QA stick, and how Sonio helps practices implement QA/QC at scale, without adding burden.
Why Prenatal Ultrasound Needs QA
Congenital malformations affect 1 in 33 births and are a leading cause of infant mortality in the United States, accounting for 1 in 5 infant deaths (2022). Prenatal ultrasound is a cornerstone of screening, and teams carry high expectations to identify abnormalities when they are present. Yet across routine screening, studies indicate that up to 50% of congenital malformations can be missed.
In day‑to‑day practice, the challenge isn’t only missing malformations, it’s that quality varies quietly over time, across sonographers, sites, shifts, patient factors, and exam complexity. Even strong teams can have blind spots because you don’t know what you don’t know without a consistent way to measure completeness and image quality and to surface patterns. And while practices may want to QA every exam and support every sonographer, doing that manually would require extensive shadowing and a major increase in staffing. That’s why scalable, objective approaches, including AI-enabled QA/QC, are increasingly important to help teams maintain high standards, identify where support is needed, and strengthen anomaly detection without adding unsustainable burden.
Why malformations go undetected
When congenital malformations go undetected, root causes often cluster into four categories:
- Incomplete evaluation: required views or structures are not captured, leaving gaps in assessment.
- Inadequate image quality: images are captured, but quality criteria are not met (e.g., suboptimal plane, key landmarks missing).
- Incorrect interpretation: images are adequate, but abnormal findings are not recognized.
- Inherent limitations: some findings may not be visible even on good-quality images, depending on timing and ultrasound constraints.
Among the 51% of fetal anomalies* missed during standard prenatal ultrasound screenings, 31% of these missed cases are resulting from misinterpretation of high-quality images**.
QA programs can directly influence the first three categories by standardizing what “complete” looks like, supporting acquisition quality, and creating feedback loops that improve consistency over time.
How to implement a QA / QC program
A sustainable QA program is less about “auditing people” and more about building a shared, supportive system for quality. A coaching-first approach helps teams participate without fear, and it makes improvements repeatable.
Here’s a practical way to implement QA within a practice:
Step 1: Align on standards and “definition of done”
- Choose the standards you’ll measure against (e.g., AIUM Practice Parameters, ISUOG Practice Guidelines)
- Translate guidelines into a clear checklist of required views and key quality criteria.
- Make it explicit what you consider “complete” vs “needs rework” so expectations are consistent.
Step 2: Build the workflow into daily practice
- Use protocol-driven scanning: the checklist/protocol should be visible during the exam.
- Reduce ambiguity: when possible, define quality criteria in a way that is observable (landmarks, plane requirements).
If QA is the “quality strategy,” Quality Control (QC) is the system that makes it measurable and sustainable. QC focuses on verifying outputs against standards through monitoring, audits, and trend tracking.
The most effective QC is trend-based rather than anecdote-based: it helps teams detect drift early, compare apples-to-apples across time, and prioritize coaching where it will have the most impact.
Step 3: Set up a QC review cadence that prioritizes improvements
- Define what you will measure each cycle (QC): completeness, quality criteria, and a small set of trend KPIs.
- Start simple (e.g. quarterly reviews) and increase depth as the process stabilizes.
- Use the QC review to answer: What are we improving? What support is needed? (not Who is at fault?)
- Share patterns at the team level first; then offer individual coaching privately when helpful.
The objective isn’t to “grade” people; it’s to reduce variability, improve consistency, and support better outcomes by ensuring standards are met across different clinicians, sites, patient factors, and ultrasound systems.
Step 4: Turn QC findings into a coaching plan
- Convert the measured gaps into specific, achievable actions like targeted training or protocol reminders.
- Assign owners and timelines so actions actually happen.
- Track the same QC measures over time to confirm improvement and keep momentum.
Why QC is hard to run at scale
Many practices want QC, but traditional approaches are hard to sustain:
- Sample-based review can be time-consuming and subjective → sample-based review review can be biased and it prevents the identification of trends over time (for both sonographers individually and across teams), slowing down feedback and coaching cycles.
- Data is fragmented across systems → when information lives in multiple tools, it’s hard to spot patterns across sites, teams, and individuals.
- Inconsistent framing creates friction → when it’s unclear how insights will be used, teams hesitate; slowing QA, feedback, and coaching.
In short, QA fails when it is infrequent, subjective, and operationally heavy.
How Sonio supports QA and QC, without adding burden
To democratize QA, evaluation needs to be: objective, automatic, live, actionable, seamless, accessible, durable, cutting-edge, safe & secure, and system-agnostic.
Sonio for Quality Assurance (QA): build the standard into the workflow
Sonio is a cloud‑native, AI‑powered reporting solution designed to support end‑to‑end QA, from scan to report completion.
Key capabilities include:
- Customizable protocols to support exam completeness and standardization.
- Automatic recognition of quality criteria to provide feedback on acquisition quality, especially for views with specific criteria.
- Annotation reading + view recognition to improve documentation reliability and highlight mismatches between annotations and recognized views.
- Automated report pre-filling to help reduce documentation workload.
Instead of relying on memory and manual spot checks, Sonio helps teams operationalize “what good looks like” during the exam, so quality is supported as care is delivered, not only reviewed after the fact.
Sonio for Quality Control (QC): Clinical Performance Analytics
In addition to enabling QA at the point of care, Sonio supports QC at scale through quarterly practice analytics informed by AI-powered data for actionable Quality Control, Clinical Performance, and Efficiency insights.
What this enables:
- Understand trends across sites and individual sonographers to support coaching and continuous improvement plans.
- Identify recurring gaps in exam completeness (e.g., commonly missed or incomplete views) and where they happen most.
- Track image quality patterns by view and by sonographer (e.g., planes/landmarks not consistently meeting criteria) to guide targeted training and support.
Examples of QC metrics you can track:
- Commonly missed views
- Commonly unverified quality criteria
- Time taken per exam type and per report
- Breakdowns by site and by sonographer
How it benefits the practice:
For lead sonographers & ultrasound managers
- Save time: move from exam-by-exam digging to high-level insights you can act on quickly.
- Coach with focus: pinpoint the biggest opportunities and track improvement over time.
- Standardize QC at scale: apply consistent quality programs across multiple sites and teams.
For sonographers
- Clear expectations: understand what “good” exam looks like, using consistent criteria.
- Faster, more helpful feedback: get unbiased coaching based on patterns over time, not one-off exams.
- Fairer support: compare like-for-like cases and spot where training will help most.
Key takeaways
- QA sets the standard; QC makes it measurable. You need both to reduce variability and sustain improvements.
- Make QA coaching-first: clear standards, transparent review, and supportive feedback loops drive adoption.
- Use technology to scale: real-time guidance and trend-based analytics help practices improve quality without adding manual workload.
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