The State of the Union: Structured Reporting in Radiology

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Structured reporting in radiology continues to gain traction, albeit more slowly than in other specialties.

Structured reporting uses IT, including artificial intelligence (AI), to automatically populate and arrange data into a template to create consistent, organized, and easily accessible radiology reports.1,2

Healthcare organizations are increasingly embracing structured reporting, and for good reason. It can improve the accuracy, consistency, clarity, and reproducibility of radiological operations. This can reduce diagnostic errors and inadvertent omissions, improve patient outcomes, minimize delays, support value-based models, and facilitate data mining.3,4,5,6,7

Standardization Creates Clarity

By minimizing subjectivity and ambiguity, structured reporting increases the accuracy and readability of reports. This opens the door to better communication within and across healthcare teams because they are all speaking a common language. Structured reporting can help ensure the imaging information needed to inform care decisions is available. This can be especially valuable when cross-disciplinary teams like tumor boards are involved. 8

Because structured reporting can enhance interoperability across health systems and their electronic health records (EHRs), picture archiving and communication systems (PACs), and more, it could potentially enable comprehensive longitudinal care across the continuum of care. 9

Increased report clarity also benefits the referring physician. Too often, physicians must go back to the radiologist for help deciphering the report, creating delays in diagnosis and treatment. Or they may order another round of imaging, which drives up costs, leads to longer delays, and adds to the patient and clinician burden.

Clinical and Business Efficiency

By improving the clarity of reports, standardized reporting supports clinical efficiency.

At RSNA 2022, Jason B. Wiesner, MD, MBA, executive director of imaging informatics at Sutter Health, talked about the practical impact of structured reporting. After implementing a structured reporting workflow, his team conducted a time and motion study. Among the findings:10

  • Technologists were twice as efficient and were able to spend more time with patients.
  • Radiologists became 20 percent faster and spent more time looking at images.
  • The number of addendums dropped.

Greater efficiency means the radiologist can focus on the types of interpretation and diagnostic decision-making only humans can do.

It can also increase reimbursements, with fewer billing opportunities lost due to incomplete documentation.7 Wiesner noted charge capture increased because the system was capturing work that had already been done. It can improve reimbursements in another important way. Data extracted from structured reports can help organizations maximize reimbursements under value-based models. 4

AI provides a powerful tool for extracting that data.

AI in Radiology Reporting

With the ability to identify patterns and detect abnormalities not visible to the human eye, AI can dramatically accelerate data mining, especially in clinical or research settings.3 Clinicians can mine the data for population health trends, healthcare organizations can find business insights, and clinical researchers can access data that will lead to more precise diagnostics and therapeutics.7

AI can analyze images to localize and classify abnormalities, quantify findings, and provide measurements and annotations that populate the radiology report. This leads to comprehensive results that can enhance decision-making and establish better patient care.11 Moreover, AI—specifically natural language processing—can support the better integration of speech into structured reporting.12

AI in Rad.jpg

Think of AI in radiology reporting as a symbiotic relationship: AI supports structured reporting and structured reporting supports AI. Data from structured reports can "teach" AI's deep learning algorithms, accelerating their development. This, in turn, can improve the accuracy and sensitivity of image analysis tools. 7,11

Early Adopters

Despite its clear advantages, the adoption of structured reporting varies across departments, specialties, and settings.

Early adopters include leading academic medical centers and large healthcare systems. Specialties involving clinical procedures, including neurology, oncology, maternal-fetal medicine, and cardiology also tend to have higher adoption.7 In contrast, many radiologists remain reluctant. Some consider structured reporting as a threat to their individuality.3 "They have their tried-and-true methods," explains Robert Dalquest, field connectivity leader, radiology & ultrasound IT at GE HealthCare.

Adoption Considerations for Structured Reporting

Winning the hearts and minds of radiologists is only one of the considerations. Several other factors contribute to the comparatively slow uptake of standardized reporting in radiology.

  • Resource investments. Like any change, moving to structured reporting requires an upfront investment in resources like time, technology, and training.
  • Workflow and automation. Structured reporting requires significant workflow changes, which can add to the short-term burden. Without automation, it could also add to the long-term burden, forcing radiologists to deal with multiple checklists and drop-down menus.7 But the use of AI and other tools to more completely pre-populate results and integrate voice commands resolves this issue.
  • Interoperability across systems. Structured reporting solutions can't work in isolation. They need to be centrally connected. "That's not just as simple as plugging in a cable and boom, you're connected," Dalquest warns. Integrating systems can take time. There's no plug-and-play. "It's something we get through," he says. It comes down to working with the right vendor, one that can help the IT team manage integration across the system.

Staying Ahead of the Technology Curve

Healthcare organizations planning to implement structured reporting need to work with a technology partner who can help seamlessly integrate structured reporting and bring the radiologists up to speed. The right partner with the right technology will be able to address concerns about interoperability, automation, and workflow burden.

Healthcare organizations will need to step up to this challenge. By some accounts, mainstream adoption of structured reporting in radiology will occur within the next five years.7 Now is the time for healthcare organizations, radiology teams, and health IT departments to prepare. "It's not a matter of if. It's a matter of when," Dalquest says.


1. Nobel JM, van Geel K, Robben SGF. Structured reporting in radiology: A systematic review to explore its potential. European Radiology. 2021 October;32:2837–2854. doi:

2. Nobel JM, Kok EM, Robben SGF. Redefining the structure of structured reporting in radiology. Insights Imaging. 2020;11(1):10. doi:10.1186/s13244-019-0831-6.

3. Granata V, De Muzio F, Cutolo C, et al. Structured reporting in radiological settings: Pitfalls and perspectives. Journal of Personalized Medicine. August 2022;12(8):1344. doi: 10.3390/jpm12081344.

4. Ganeshan D, Duong PAT, Probyn L, et al. Structured Reporting in Radiology. Academic Radiology. 2018;25(1):66-73. doi:

5. Mace S. How structured reporting can lead to better patient outcomes. Patient Safety & Quality Healthcare. Published March 30, 2022. Accessed April 17, 2023.

6. Ross J. Structured reporting: Resistance is futile. Radiology Business. Published April 12, 2019. Accessed April 17, 2023.

7. Thompson A. Structured reporting adoption in imaging IT—world—2021. Signify Research. Published October 2021. Accessed April 17, 2023.

8. Thompson A. Structured reporting — the battle of automation versus culture. HealthCare Business News. Published November 16, 2021. Accessed April 17, 2023.

9. Roth CJ, Clunie DA, Vining DJ, et al. Multispecialty enterprise imaging workgroup consensus on interactive multimedia reporting current state and road to the future: HIMSS-SIIM collaborative white paper. Journal of Digital Imaging. June 2021;34:495–522. doi:

10.  "GE Events Center." GE HealthCare Events Center, May 8, 2023.

11.Pinto Dos Santos D, Brodehl S, Baeßler B, et al. Structured report data can be used to develop deep learning algorithms: A proof of concept in ankle radiographs. Insights Imaging. 2019;10(1):93. doi:10.1186/s13244-019-0777-8.

12. Jorg, T., Kämpgen, B., Feiler, D. et al. Efficient structured reporting in radiology using an intelligent dialogue system based on speech recognition and natural language processing. Insights Imaging. 2023 March;14(47).

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