Radiology workflow challenges occur at key points in the reading process: case selection, prior retrieval, sequence protocoling, reporting, and communication. These breakdowns are typically caused by disconnected systems, manual steps, and lack of automation, and can be addressed with workflow orchestration, integrated systems, and native AI capabilities.
Wasting time searching for the next case to read
Radiologists lose time when worklists require manual navigation instead of automatically surfacing the next most appropriate case. At the start of each read, the radiologist has to decide what to open next, but the system doesn’t always make that decision easy.
- Worklists are often static or loosely sorted (e.g., by time, modality, or location)
- Subspecialty expertise isn’t always factored into case distribution
- Radiologists may need to scroll, filter, or manually search before selecting a study
Research from the Journal of the American College of Radiology highlights that variability in worklist management contributes directly to turnaround time variability, especially in high-volume environments.
How to Solve it
A system that maximizes productivity is one that can remove the “what to read next” decision entirely. A few features are needed to streamline this process:
- Automated case distribution: Assigns studies based on subspecialty, workload, and availability
- Dynamic prioritization: Reorders cases in real time based on urgency and clinical signals
- Auto-next functionality: Loads the next appropriate study immediately
This is where workflow orchestration tools such as InteleOrchestrator come into play, helping guide case distribution and reduce the need for manual worklist navigation.
Accessing prior imaging
Radiologists lose time when prior imaging is not automatically available at the time of interpretation. When priors are missing or require manual retrieval, it interrupts reading flow and can limit clinical context. Prior studies may be stored in a different PACS, need to be searched for, or must be imported before they can be viewed.
This creates a stop-and-start workflow where radiologists pause interpretation to track down historical imaging. Access to prior imaging is critical for accurateinterpretation, particularly in longitudinal disease monitoring, where comparison directly impacts diagnosis.
How to Solve it
To solve this, prior imaging must be automatically available at the time of reading. To accomplish that, an imaging platform would need:
- Automated pre-fetching: Retrieves relevant prior studies before the case is opened
- Enterprise-wide access: Connects imaging across systems, facilities, and archives
- Patient matching and normalization: Ensures priors are correctly linked to the current study
This automatic availability allows radiologists to review, with context instantly, something unified imaging platforms are designed to support.
Context switching during reads
Radiologists must often move between multiple systems: PACS, RIS, EHR, and reporting tools, to complete a single case. This forces radiologists to constantly switch tabs, log into different tools, or reorient themselves across interfaces just to gather the full clinical picture.
This fragmented workflow delays care, interrupts focus, adds time to each read,and increases the risk of missed context.
How to Solve it
The only way to reduce constant context switching is to bring imaging, patient data, and reporting into one continuous workflow. Radiologists should be able to move seamlessly from clinical context to image analysis to reporting without leaving their workspace.
This can be supported through integration layers or unified platforms like IntelePACS.
Urgent cases end up delayed
Urgent findings can be delayed when worklists rely on static prioritization or manual review instead of dynamically surfacing the most critical studies. When urgency isn’t continuously reassessed, high-priority cases can sit behind routine exams and critical cases can end up buried in the queue until they are manually identified or escalated.
This increases time-to-diagnosis for conditions, which has the potential to affect patient outcomes.
How to Solve it
Urgency must be continuously evaluated and reflected in the reading queue to ensure correct prioritization. A few worklist features need to be in place for optimal prioritization:
- AI-assisted triage: Flags studies with suspected critical findings
- Dynamic prioritization: Reorders worklists in real time based on clinical signals
- Automated escalation: Surfaces high-risk cases without requiring manual intervention
- Context-aware distribution: Accounts for which radiologists are available, their subspecialties, and current workload when determining who should read each case
Inconsistent data and workflows
Radiology workflows often break down when incoming patient data, imaging metadata, and workflows are not standardized across systems. If incoming studies are structured differently, the data will not flow into the receiving system.
That results in radiologists and technologists having to pause and reconcile details or manually correct studies before moving forward. Because these issues occur across many studies, they create a persistent drag on overall workflow efficiency.
How to Solve it
All ingested imaging data must be normalized before it enters the workflow. Automated ingestion features will typically standardize patient identifiers and metadata and ensure external studies are formatted and structured consistently.
Then once the data flows into the system, unified workflows become more important. Solutions should apply consistent logic across sites and modalities, and have an integrated data pipeline that reduces the need for manual reconciliation between systems.
Slow, repetitive, and inconsistent report creation
Report creation can slow radiology workflows when radiologists rely on manual dictation, repetitive phrasing, and disconnected reporting tools. Even after image interpretation is complete, generating the final report requires additional steps that interrupt workflow and add variability.
This creates a second phase of work that is disconnected from image interpretation. Time is spent dictating standard findings, re-entering information, and switching between tools, yet the final output can still vary in structure and clarity across clinicians.
How to Solve it
Reporting should be a natural extension of interpretation, not a separate, manual step. For the most efficient reporting experience, leveraging AI and structured templates is essential.
- Structured reporting templates: Standardize common exam types and findings
- Auto-populated data fields: Pull patient and study information directly into the report
- Integrated reporting workflows: Enable report creation within the same environment as image review
- AI-assisted draft generation: Generate structured report drafts based on imaging context and clinical data with natural language dictation
- Context-aware workflows: Adapt reporting logic based on exam type, subspecialty, and clinical indication
Through Intelerad’s partnership with RADPAIR, clinicians can generate structured, context-aware report drafts, and surface relevant clinical information during dictation within the reporting workflow.
Inefficient communication with other physicians
Communication can slow radiology workflows when radiologists have to step outside their reading environment or manually reach out to share findings. Whether it’s clarifying orders or escalating critical results, disconnected communication channels introduce delays and increase the risk of missed or delayed follow-up.
Since communication steps are not always documented within the imaging workflow, this can also introduce compliance risk.
How to Solve it
The goal is to make communication a seamless extension of the reporting workflow, and the best way to accomplish this is with embedded communication tools within the reading environment. Closed-loop tracking and automated critical result alerts can also make it easier not to miss or delay follow-upactivities.
To simplify the reporting process, communication should be linked to documentation, automatically captured within the patient record and reporting workflow.
Slow remote reading
Remote reading can introduce delays when radiologists rely on VPNs, fragmented systems, or inconsistent access to imaging and patient data. This creates an inconsistent experience where workflow efficiency depends on technical conditions rather than clinical priorities.
Remote inefficiencies compound quickly because they affect every step of the reading process. This is especially important as remote reading has become the norm, according to a 2023 study, 91% of radiologists reported interpreting studies remotely at least part of the time.
How to Solve it
Remote reading is here to stay, and there are plenty of ways to make it feel almost as consistent as on-site workflows, regardless of location or network conditions. Intelerad’s solutions are built for teleradiology, with features like:
- Pre-caching and auto-loading: Cache upcoming studies from the worklist so the next case is ready as soon as the current one is completed
- Optimized bandwidth requirements: Support efficient reading workflows without requiring high-end connectivity, for consistent performance at moderate speeds
- Distributed streaming architecture: Use strategically placed nodes to support long-distance reading without latency impacting performance
- Progressive image streaming: Deliver images in prioritized segments, starting with the region of interest, so radiologists can begin reading before the full dataset loads
- Resilient data transfer: Maintain continuity even in less stable network conditions by correcting packet loss during transmission
- Local pre-fetching of large datasets: Ensure complex studies are available on the workstation before interpretation begins
- Embedded communication workflows: Allow issues or clarifications to be assigned and returned directly to the worklist, reducing manual outreach
How can you start improving your radiology workflow today?
Radiology workflow challenges rarely exist in isolation, they’re often connected across data, systems, and daily reading tasks. Addressing them starts with identifying where friction occurs most often, whether that’s worklist management, data access, reporting, or remote performance.
Modern imaging environments that unify workflows, standardize data, and embed automation can help reduce these disruptions and create a more consistent reading experience.
If you’re evaluating your current setup, our team can help you identify where manual steps, system gaps, or delays may be slowing your workflow, and explore how a more connected, end-to-end approach could fit your environment. Schedule a demo or contact us to learn more.
