By Stacy Pur, MBA, BSN, RN, Sr. VP of Product, eFax®, by Consensus Cloud Solutions
LinkedIn: Stacy Pur
LinkedIn: eFax, A Consensus Cloud Solution
Despite widespread technology advances, the majority of healthcare providers still rely on manual processes and outdated platforms to exchange medical information, and imaging centers are no exception. At least 75% of patient records and referrals are still sent via fax between health systems or shared in non-digital forms, requiring documents like imaging orders to be manually rekeyed into the EHR through labor-intensive, error-prone workflows. Manually rekeying data isn’t just slow; it introduces clinical risk. A typo in a patient’s ID or a miskeyed ICD-10 code can lead to insurance denials at best, and at worst, delays in critical diagnoses or imaging performed on the wrong body part.
The time and expense involved in these manual workflows can be detrimental to business operations, patient wait times and outcomes, while overwhelming understaffed imaging centers. About half of all healthcare workers experience burnout, but because of the heavy administrative burden surrounding imaging orders, radiologists are at an even higher risk, with 71% reporting stress. This labor strain contributes to more unfilled vacancies for radiology technologists than any other group of health professionals.
Minimizing the impact of ongoing labor shortages and staff burnout in radiology requires uncomplicated solutions for streamlining manual processes without sacrificing throughput. By automating the workflows involved in processing radiology orders, imaging facilities can empower their teams to serve patients more efficiently while recapturing revenue leaks. The right automation solution can alleviate administrative burden, freeing radiology staff to focus on patient care rather than document management.
Accelerating appointment scheduling
An imaging center’s ability to capture and retain patients, and the revenue associated with each case, depends on its speed of referral response. But when imaging orders are received via fax, email, or printed requests hand-delivered to an office, those orders must be manually keyed into the EHR or other system before they can be scheduled. When team members have to call patients to schedule appointments, this slows down the response time even more, while also adding to the administrative strain. The longer patients must wait for an exam, the more likely they may choose another imaging provider or never schedule their exam in the meantime.
This lag in response time creates a critical friction point where patients often drop out of the care funnel entirely. We call this ‘revenue leakage,’ but from a clinical perspective, it is care abandonment. A patient waiting days for a call back is a patient living with anxiety and a delayed diagnosis. While the financial impact is significant, averaging $1,885 per exam, the operational cost is even higher. Manual workflows force our staff to act as gatekeepers rather than caregivers, delaying access to vital imaging services.
It’s an area where AI holds strong potential to make a big difference for radiology workflows. By using AI to extract unstructured data from imaging orders, whether faxed, scanned, emailed, or even handwritten, and map these details to structured fields in the radiology information system (RIS), imaging centers can drastically speed referral-to-appointment response while alleviating the administrative burden.
Empowering radiology staff
One imaging group, processing an average of 30,000 orders per month, decided to invest in an AI-based approach to streamline its document management workflows. With a goal of increasing its referral response time (which frequently exceeded two weeks with a two to three-day backlog), while decreasing the associated administrative workload (which totaled several hours per day), the group aimed to reduce revenue leakage through the power of AI.
Using a healthcare-specific solution that seamlessly integrates into its existing technology platform, the organization began processing imaging orders with the help of AI to translate unstructured information into structured data. This tool automatically classifies the type of document being received (whether it’s an urgent imaging referral or just routine correspondence) and extracts key data, such as the patient name, the type of imaging modality being ordered, and other important medical details.
Then, through a built-in integration engine, the AI tool automatically translates data into an HL7 file format to create an imaging order that can be ingested by the group’s RIS. This digital document management happens almost instantaneously with minimal human intervention, freeing staff to focus on more value-added tasks, such as interactions with patients, instead of tedious data entry.
Automation decreases the risk of burnout and the potential for turnover, critical metrics in an industry where hiring has become increasingly difficult. By minimizing the amount of manual processing required, the imaging group saw an increase in throughput with faster appointment scheduling and reduced revenue leakage.
Adding value via imaging
Faced with increasing patient volumes and referral backlogs, imaging centers are seeking ways to manage complex workflows more efficiently without adding extra strain on their staff. AI-powered intelligent data extraction can alleviate some of the administrative burden weighing down imaging centers by automating the transformation of structured data from unstructured documents to streamline imaging orders.
Leveraging technology to lighten this workload empowers imaging staff to shift their focus away from manual data entry toward more engaged patient care. As a result, workers can operate at the top of their license, referrals get scheduled sooner, improving patient experiences and outcomes, and organizations increase throughput, reducing revenue leakage and enabling them to treat more patients, more efficiently.