How AI Can Fix Radiology’s Backend Connectivity Crisis
Radiology doesn’t just have one connectivity problem, but many: lack of integration between electronic health record (EHR) and radiology information system (RIS) solutions, roadblocks to patient communication, and overly manual processes collectively slow referrals and diagnoses, and frustrate patients and staff. While the demand for imaging services continues to rise, radiology has been built around proprietary technologies that can silo imaging centers while legacy processes hinder patient access.
Even with an estimated 7,000 diagnostic imaging facilities in the U.S., competition is fierce to accommodate increasing patient needs. This rising patient demand doesn’t just mean more clinical work, but it adds to staff workload as well. More referrals mean more manual outreach and prior authorization checks, which are key drivers of burnout, while often also leading to delays in patient care. Like many healthcare specialties, radiology’s legacy systems and workflows are setting the profession behind at a time when expectations around interoperability are heightened.
The good news is that AI can offer multiple solutions to address existing connectivity challenges causing operational strain, patient leakage, and diagnostic delays, while laying the groundwork for scale and success in healthcare’s tech-heavy future. There are three key areas where AI can drive the highest operational impact for imaging centers today:
- Increasing interoperability between technology solutions
- Automating and speeding referral workflows
- Preparing patient files for clinician review
#1: Creating Connectivity Across Radiology Tools
Virtually every entity in healthcare is focused on solving our industry’s widespread interoperability issues. For radiologists, this can be especially tricky.
Centers operating as part of a hospital or health system may have some access to the organization’s EHR, but the need for an RIS remains. Standard EHR solutions are designed for broad applications and lack the capacity to accommodate the high volume of specialized tasks radiologists take on daily. This means even a hospital-owned imaging center can struggle to receive orders and referrals from its parent organization if there is limited or no EHR-RIS integration.
For independent radiology practices, this issue can be exacerbated by larger volumes and sources (faxes, portal uploads, etc.) of referrals from other independent healthcare organizations, using a wider range of technology solutions and channels.
The latest AI tools address these interoperability challenges in two ways: first, by ingesting healthcare data and documents automatically, and second, by then formatting them to work within both the EHR and RIS, ensuring all systems have complete and accurate documentation and information. The AI functions as both a referral and data-sharing translator to ensure the right data — either structured or unstructured — makes it into the appropriate format for the technology.
For instance, fax, still the most commonly used referral method in healthcare, can benefit from a much-needed dose of AI functionality. Digital fax makes layering AI tools such as intelligent document processing (IDP) easier, all while maintaining the security and usability of fax processes. Organizations can extract unstructured information from virtually any source and automatically structure it to fit into the RIS or other platform as needed. Historically, the process of adding information from a document or system into the RIS or EHR was cumbersome, with a high margin of error when data was being manually entered.
By automating this process with AI, information flows accurately and seamlessly between systems, while also automating work usually performed by office staff. Whether from an EHR or handwritten note, these AI solutions can identify the right data and insert it in the right places to close this connectivity gap, advancing interoperability across multiple organizations and platforms.
#2: Centers Must Account for Patient Access Preferences
Patient demand for imaging services is at an all-time high, with a predicted growth of up to 26% in imaging utilization in the coming years. This spike in demand, alongside the increasing number of options where patients can receive imaging services, means that though patients are generally steered to radiology centers based on insurance coverage, other things such as ease-of-access and wait times for an appointment also factor into their decisions. Ultimately, even an in-network center may lose patient business if it doesn’t provide a smooth enough referral and intake process, or if a patient is kept waiting too long.
The surging volume of patients often leads to a days-long backlog of referrals. These are usually handled by staff manually, who must constantly whittle down an ever-growing number of appointment requests, in addition to their other duties. A side effect of these multiday bottlenecks could be that patients get tired of waiting to be contacted by an imaging center and will head to Google or schedule an appointment with a clinic that can see them faster. It’s yet another area where consumer behaviors are impacting healthcare processes, and when only 55% of referral revenue attributed to employed primary care physicians stays in-network, centers must do everything they can to make the referral and intake process easy.
AI can likewise help streamline the referral process to create an easier, and speedier, avenue for patient access. It takes a staff member roughly 10 minutes to manually review and process information from a referral. AI can accomplish it in around 30 seconds, reducing the backlog significantly faster. Eliminating referral bottlenecks means that staff can reach out to patients days sooner than if they were manually working through a pile of referrals, eliminating frustrating wait times that might drive patients to different, out-of-network centers.
Even better, leading AI tools go beyond just reading and summarizing a referral. They can prioritize them based on urgency and patient need, and automatically route other requests to the right department, further reducing manual staff labor and ensuring the most critical patient referrals are prioritized to minimize gaps in care.
#3: Clinician Burnout Remains a Critical Challenge
Burnout affects nearly every healthcare professional — front-end, clinical, and back-end staff alike. In radiology, staff attrition is rising across every subspecialty, leaving existing staff burdened with a higher workload as organizations attempt to keep up with patient volume.
Unlike other specialties, radiology has used early rules-based programs and AI models for decades to read images and diagnose things like bone tumors. While the diagnostic capabilities are helpful, these tools don’t solve radiologists’ interoperability issues. Let’s consider again the disconnect between the EHR and RIS from the clinical perspective.
If referral data, including clinical indications, prior imaging results, and relevant patient history, doesn’t flow from the EHR into the RIS, radiologists are missing most of the patient picture. Providers must manually retrieve these documents and data before they can open a current scan, adding time and unnecessary cognitive burden to their day. It’s a process that can add minutes to a case review, which isn’t feasible, especially when some radiologists read around 50 cases a day, leaving them only minutes per patient.
But the rise of agentic AI can change this. New systems are built not just to review an image, but to automatically complete a workflow to compile the complete patient record and relevant information. By retrieving this information before a radiologist reviews a patient case, the AI can create a complete summary to surface the right information to best impact patient care. Pre-compiled clinical history can shave 1.5 minutes off patient review.
For burdened and burned-out clinicians reviewing 50 cases per day, AI can feasibly provide up to an hour of extra time back daily, as well as reduce cognitive burden associated with chart and imaging retrieval — both known contributors to burnout.
Radiology has long been a leading adopter of advanced technology, using AI solutions to complement and advance human imaging review for many years. But as operations become more complicated, and connection becomes more critical across staff, patients, and technology, AI usage must expand. By applying readily available AI tools, like intelligent document processing, referral automation and triage, or agentic record retrieval, to areas of weak communication or connectivity, radiology practices can become more competitive with patients while supporting stronger clinical outcomes and alleviating staff burden.