Understanding and Navigating Imaging Workflow Challenges in Healthcare

Enterprise imaging plays a central role in healthcare by supporting the diagnosis and treatment of a disease. Encompassing ultrasonography, x-rays, mammography, computed tomography (CT scans), and nuclear medicine, medical imaging is crucial in various medical settings and at all significant levels of health care. Diagnostic imaging services are essential in confirming, assessing, and documenting the course of many diseases and response to treatment.

However, as images have increased in importance and volume, hospitals often need help to effectively store, display, and distribute these images throughout the healthcare continuum. The key culprit for these challenges can frequently be correlated to inefficient workflows and incomplete solutions.

Enterprise Imaging Workflows

An imaging workflow is the sequence of steps necessary for the imaging procedure. Enterprise imaging workflows encompass the entire lifecycle of medical images, from acquisition and storage to interpretation and sharing. The workflows define how different departments interact with and utilize these images, ensuring seamless integration and data exchange while maintaining patient data privacy and security. Radiology workflow manages all aspects of radiology imaging, from the time of referral for an exam to when results occur.

The two types of imaging workflows are scheduled and encounter-based.

These two models cater to different clinical scenarios and patient needs, and an effective radiology department should have the flexibility to adapt to both types of workflows. In practice, many radiology workflows utilize a hybrid approach, combining encounter-based and scheduled workflows to balance the demands of urgent and routine cases.

Imaging Workflow Challenges

Up to 17 healthcare professionals from different specialties might see a patient during an average hospital stay. While radiology and cardiology services have historically created automated workflows for image acquisition and information systems for image distribution, other specialties still need to adopt these practices. As a result, most images are not readily available or visible to the team of doctors, nurses, therapists, technologists, and other clinicians caring for the patient.

Dicom System solves enterprise imaging workflow challenges for healthcare enterprises and government agencies. Dicom Systems has customers globally at large hospital systems, medical, radiology, teleradiology groups, imaging centers, CROs, academic medical centers, and children’s hospitals. Our flagship product, Unifier, addresses imaging workflow outliers, complex DICOM routing, load balancing and provides an enterprise-grade DICOM Modality Worklist across multiple sites.

This article outlines the most common imaging workflow challenges and proposes solutions. According to a HIMSS-SIIM Collaborative White Paper published in the Journal of Digital Imaging, workflow challenges traditionally fall into one or more categories below:

1. Workflow Adaptations + Incomplete Patient Studies
2. Patient Identification
3. Information Needed in an Image
4. Reporting
5. Metadata Data Normalization
6. Legal and Regulatory Concerns
7. Mobile Device Integration

In addition, we have identified the following challenges based on our recent experience observing, auditing, and optimizing enterprise imaging workflows across several healthcare enterprises.

8. Storage
9. Cloud vs. On-Prem Infrastructure
10. AI integration

Workflow Challenges In Enterprise Imaging: Incomplete Patient Studies

To perform an imaging study, radiology departments require an order. Historically, radiologists were not physicians working on the disease processes or doing intake. Instead, other clinicians evaluated the patient, created a differential diagnosis, and leveraged radiologic imaging to refine their differential diagnosis. The ordering physician asked the radiology department to perform a specific study in this scenario. Because the ordering clinician’s evaluation was separate in time and location from the radiology department, the two practices communicated the order.

However, in the modern radiology department, clinicians use orders for purposes beyond mere communication about which study they need to conduct. Today, the order helps to drive an automated workflow by creating a unique study identifier and a PACS/MIMPS worklist of patient studies requiring review.

Patient Identification in Imaging Workflows

Obtaining and documenting correct patient identification is imperative to the imaging workflow: accurate images relating to a patient must be matched and placed within the patient’s medical record every time. As such, all images must include patient identification. DICOM images automatically apply this identification with an order selected from the modality worklist supplying the necessary metadata.

The DICOM modality worklist uses various sources, including the electronic medical record (EMR) system, the RIS, the PACS/MIMPS, and the enterprise archive. The imaging professional can select the patient from the worklist in several ways, including focused query, direct selection, or barcode scanning.

In addition to demographic information, such as the patient’s name, medical record number, date of birth, gender, and procedure name, patient identification may include dozens of other attributes related to a patient record or parameters of the imaging study or DICOM tags. We share a sampling of the multitude of DICOM tags in a recent blog post covering tag morphing. We achieve tag morphing through various methods within the Dicom Systems Unifier framework. This approach to tag morphing significantly enhances speed and performance for our customers while guaranteeing the correct application of patient identification to the workflow.

Implementing an automated solution for non-DICOM images (still frames and video clips) becomes imperative to ensure the accurate identification of patients. The HIMSS-SIIM Collaborative White Paper mentions the following solutions: workflow reminders, adding patient-identifying information to every image, or creating a new modality worklist. Although these identification systems can be effective, they are inherently vulnerable to human error. Relying on staff to consistently execute the correct procedure for every patient introduces the risk of mistakes, which can potentially result in patient misidentification incidents.

Another low-tech solution to ensuring the completeness of patient data involves adding an identifier, such as a sticker or barcode placed on or near the patient and included in the photo. The article “Strategies for handling missing clinical data for automated surgical site infection detection from the EHR,” published by J Biomed Inform, outlines this method.

Information Needed In An Image

When images are used for diagnostic purposes or to help provide an objective baseline for long-term follow-up, all images must have standard measurements, color, and patient positioning to allow further study and comparison.

Standard Measurements: The measurement ability is a crucial feature of DICOM-based imaging. For example, in radiologic images, the diameter of a tumor as the exact size of each pixel is known and can be measured. DICOM images use an image ruler for calibration when size information is unavailable. The standard use of an image ruler helps mitigate against differences in the appearance of a lesion due to the zoom factor and the distance between the camera and the lesion.

Color Standardization: Most radiologic and cardiologic are imaged using shades of gray. However, in specialties that rely on medical photography, such as dermatology, pathology, and wound care, the issue of color standardization arises. Reproducible color in medical photography can often be challenging due to differences in lighting, shadowing, image filters, blemish correction, and camera settings, making many images unpredictable and unreproducible for healthcare providers. Visible Light Imaging: Clinical Aspects with an Emphasis on Medical Photography, a 2022 HIMSS-SIIM Enterprise Imaging Community Whitepaper, covers color standardization, among other workflow issues related to visible light imaging.

Observed variation in color between scanners and software. a, b The same slide imaged with the same scanner, viewed using two different software packages (screenshots). c The same slide imaged on two different scanners with IHC (top) and H&E (bottom) stains. The color rendition of the scans appears noticeably different from scanner A to B. Source: SpringerLink

Standard Patient Positioning: Standard positioning is crucial to many imaging services. Because radiologists and cardiologists identify abnormalities based on pattern recognition, they rely on a standard appearance of body parts. This standard positioning allows them to distinguish normal from abnormal quickly.

The effect of feet positioning to avoid foreshortening or elongation of the neck of femur during hip radiography.
Source: Healthcare-in-Europe.com

Reporting Challenges

Most radiology reports and health data use free-text narratives for organizations. This format can lead to challenges and communication difficulties due to the need for more consistency. This lack of consistency led to many reporting challenges related to free text reporting, as outlined in the HIMSS-SIIM Collaborative White Paper.

First, a report serves different purposes for different specialties. In radiology and cardiology, the report interprets the images. The report describes the entire visit in other specialties, such as dermatology. Because of these differences, reports reside in different locations in the EMR.

In addition, certain imaging studies may have multiple reports. For example, the report may contain the operative note and the detailed functional data in cardiac catheterization. In this instance, both reports give the image context and should be associated with the images. However, most EMR and enterprise viewing systems have no method for assigning multiple reports to the same imaging study.

The third reporting challenge stems from reports created at different image acquisition stages. These images always precede the overall report because the radiology report is an interpretation of the images. However, in dermatology, the images serve to document the findings. In this way, one can obtain the images after dictating the report.

Two examples of radiology reports and the referenced “key images” (providing a visual reference to pathologies or other notable findings). Source: ResearchGate

Finally, images must be associated with reports bi-directionally, meaning there must be a way to view images within the report in the EMR, a method to view the report within the enterprise viewer, and a link to launch the encounter in the EMR. Associating reports and images in both systems allows medical providers to review patient information according to their preferred workflow. A pediatrician may read an operative report and click a link to view the photographs from a surgery. While viewing the operative images, they may also see pathology images. In this scenario, the pediatrician should not have to return to the EHR system to read the pathology report. Instead, they would click a report button within the viewer to read the pathology report. It is challenging to create an association between images and a report, mainly if there is no order for the images. As encounter-based imaging becomes a standard workflow, the EHR systems must enable this functionality.

Today, radiology departments and health systems are slowly adopting structured reporting. Structured reporting, sometimes called synoptic reporting, is a method of clinical documentation that captures and displays specific data elements within a particular format. In radiology, structured reporting templates provide consistency and clarity, prompt entry of all necessary data elements, and are amenable to scalable data capture, interoperability, and exchange.

Each device, such as a CT scanner or MRI machine, produces a set of data in a structured manner, which helps eliminate or reduce the need for the technician to input each field manually or by dictation. These modalities typically format their outputs according to DICOM standards, representing a significant move toward structured reporting. However, there are still differences between reports due to the nature of the scan. For instance, a CT scan contains information about the dosage of radiation. This cumulative radiation score becomes part of the patient’s medical records.

Metadata Challenges

DICOM metadata from an image file. Source: ResearchGate

List of DICOM tags. Source: ResearchGate

In DICOM-based imaging, metadata occurs at the patient, study, series, and image levels. This metadata can include patient health information (PHI) such as patient name, medical record number, and date of birth, as well as image acquisition parameters such as image dimensions, voxel size, repetition time (TR), and voxel data type. The specific workflow challenges related to metadata fall into the following categories:

Body part: structure based on DICOM standard body parts. In some cases, the naming structure is too specific; in others, it needs to be more detailed. The humerus is an example of a term cited in the HIMSS-SIIM Collaborative White Paper. While this term makes sense for an X-ray of the upper arm, it does not make sense for a picture of the skin of the exact location.

Procedure Description: Study-level metadata is applied directly from the order, and one field may contain information relating to up to four variables. For example, the procedure description “RAD Hand 2-3V Right” tells the provider that the imaging study is (1) a radiograph (RAD) containing (2) 2–3 views of the (3) right (4) hand. While the procedure description has worked in radiology, enterprise imaging has several limitations, as there is no standard way to create a procedure description.

Department: The DICOM field is of limited value in many radiology and cardiology PACS/MIMPS. As all of the images in the hospital come together, this field becomes crucial. A dermatologist must quickly find and distinguish all the dermatology images from radiologic images of the same body part. Image viewers must be able to allow users to search and sort based on this field.

Imaging Source: This includes traditional imaging modalities and patients taking photographs of themselves to share with a health care provider. As patients begin to upload their images to an EMR or enterprise imaging archive, there will likely be a need to be able to tag studies as either patient-obtained or provider-obtained. The difference may be necessary for quality assurance purposes, liability concerns, and reporting related to meaningful use.

There can also be metadata or data normalization challenges with transfer syntax, proprietary formats, or duplicate studies.

Legal and Regulatory Concerns: HIPAA-Compliant Imaging Workflows

One of the top challenges of imaging workflows is ensuring they are HIPAA-compliant. Sharing medical images in a HIPAA-non-compliant fashion violates patient privacy and could lead to fines and potential legal action. HIPAA compliance refers to the elements of the Health Insurance Portability and Accountability Act. Companies that deal with protected health information (PHI) must have physical, network, and process security measures and follow them strictly.

If an organization still uses tangible media such as CDs, DVDs, or thumb drives to share a medical image, the best way to avoid HIPAA violations is by improving data management processes. Everyone who encounters medical imaging data must be trained and vigilant.

Concerning HIPAA-compliant workflows, every step must be analyzed to safeguard patient data. Dicom Systems has implemented many safeguards to ensure its devices, services, websites, and data systems comply with HIPAA regulations and conditions. As a Business Associate per the definition in the HIPAA Act and by assignment of the HIPAA-covered entity, Dicom Systems is subject to Administrative, Physical, and Technical safeguards.

Another way we ensure that our customers’ workflows are HIPAA-compliant is through patient data de-identification. We offer a proven and scalable de-identification of medical images solution that unlocks valuable imaging studies for research, policy assessment, and comparative effectiveness studies. Dicom Systems Unifier platform can de-identify DICOM, XML, TIFF, JPEG, PDF, and other image formats complying with HIPAA safe harbor de-identification of Protected Health Information (PHI) requirements. Images and data are received and translated into a standardized format that is transferred or accessed by referring physicians, radiologists, PACS/MIMPS, RIS, or any radiology workstation, regardless of physical location.

Mobile Devices and Imaging Workflows

The adoption of mobile devices within healthcare organizations has created another workflow challenge: a physician may use a personal device to take or send a photo during their day. A personal mobile device is not traditionally integrated into the enterprise and has no access or minimal access to EHR or the PACS/MIMPS functionality. Yet, there are many benefits to incorporating mobile devices into enterprise imaging workflows. Using mobile devices for image capture reduces lead times and costs.

Connecting the appropriate image capture software to the enterprise imaging system allows specialists to take photos directly using a smartphone, camera, or tablet. Images are immediately uploaded and stored centrally in the enterprise imaging system, and via integration with the (EMR), they are accessible across the enterprise.

Patient Record Storage

One of the top workflow challenges is migrating petabytes of data to the cloud or into a new on-prem solution. Just like moving into a new home, an opportunity arises to purge and declutter. In medical imaging, this means deciding how long data needs to be stored. In our personal lives, we typically know that we should keep tax records for seven years. However, in medical imaging, the need for clear universal guidelines poses a significant challenge for purging data.

Do you maintain duplicate studies? Are studies from pediatric patients who are now adults kept? How long should the records of deceased patients be kept?

Medicare and the majority of states mandate retaining imaging records for five years.

State laws display significant variation in maintaining medical records and radiologic images. For instance, in Massachusetts, hospitals must keep patient records for 30 years following discharge or the conclusion of treatment. In some states, records related to minors persist until they reach their 28th birthday.

Mammography sometimes has more extended requirements, toxic exposures can trigger longer storage, and the American College of Radiology (ACR) recommends holding images through the statute of limitations for a potential medical malpractice case—two or three years after a malpractice claim.

Some healthcare organizations make a business decision to store medical data indefinitely, while others identify and manage their records in a timeline specific to their needs. The lack of clear, universal guidelines for medical imaging data retention complicates migrating and purging large volumes of data during system transitions, posing significant workflow challenges for healthcare organizations.

Cloud vs. On-Prem Storage

Healthcare organizations are increasingly embracing cloud-based solutions to enhance patient care, streamline operations, and drive cost efficiencies. Cloud computing enables greater integration and collaboration between hospitals, medical organizations, and healthcare providers, addressing what was previously considered a primarily fragmented and siloed industry.

The worldwide public cloud computing market continues to expand, reaching an estimated 723.4 billion U.S. dollars in 2025. According to Gartner, Cloud use cases continue to expand with increasing focus on distributed, hybrid, cloud-native, and multicloud environments supported by a cross-cloud framework, making the public cloud services market achieve a 21.5% growth in 2025. As healthcare organizations adopt mobile applications, storing clinical data in the cloud provides users more complete access. In a cloud-based format, confidential patient-based information is protected by a third party, continually updating firewall security and other protection measures so that only people reviewing it are authorized. Cloud-based disaster recovery solutions also provide healthcare organizations with comprehensive backup plans that aid disaster recovery, rapid scalability, and geographic separation of data storage.

While cloud adoption in healthcare imaging is growing, many organizations still use on-premises storage, creating another workflow challenge.

Dicom Systems has leveraged the healthcare cloud to deploy enterprise imaging initiatives and advance interoperability for many clients globally. Our Cloud Partners include AWS, Google, Azure, and private cloud providers.

AI Integration

AI applications are entering radiology at rapid and increasing rates. ​With AI, radiologists foresee a future in which machines enhance patient outcomes and reduce misdiagnosis. More than other medical disciplines, radiology has a long history of storing studies digitally, with plenty of images to train AI algorithms. Algorithms draw from millions of digital photos and can aid diagnostics. AI distinguishes patterns and irregularities in extensive data collection, making radiology an ideal application. However, AI integration is still facing many challenges. As a Health IT company trusted by top healthcare facilities to simplify enterprise imaging management, Dicom Systems has a unique perspective on the challenges of deploying AI in clinical workflows.

One challenge observed is that AI vendors only sometimes have a solid understanding of clinical imaging workflows. As a result, some AI algorithms don’t account for the nuances of enterprise imaging workflows, rendering them less usable. This challenge often appears when a need arises to integrate with other vendors to deploy an algorithm successfully into a production environment. Without the experience of deploying integration with live clinical workflows, the algorithm may not live up to its full potential, no matter how effective it may be in fulfilling its diagnostic function. To mitigate this challenge, AI vendors need to become sufficiently educated on imaging industry standards before implementing AI.

10 rules for successful clinical AI adoption:

1. Be clinically relevant
2. Know the Clinician’s perspective
3. Respect the Clinician’s workflow preferences
4. Be natively interoperable: use industry standards
5. Neutralize bias in machine learning methodology
6. Have a viable long-term business model
7. Don’t introduce latency in clinical workflows
8. Be more accurate than a human
9. Generate usable results
10. Be equally deployable on-perm and in the Cloud

Enterprise Imaging Solutions from Dicom Systems

Dicom Systems offers versatile enterprise imaging solutions to streamline workflows, enhance interoperability, and support seamless integration across healthcare environments. Whether your organization operates within a hub-and-spoke model or requires advanced routing capabilities for complex imaging workflows tailored to your unique needs.

Key Features of Dicom Systems Solutions:

• Comprehensive DICOM Routing Models: Choose from our Rapid DICOM Router, Rapid+ DICOM Router, or the Unifier platform to optimize imaging data flow and ensure efficient delivery of patient studies.
• Advanced Workflow Optimization: Simplify imaging workflows across multiple sites with robust solutions for DICOM routing, load balancing, and enterprise-grade modality worklists.
• Enhanced Interoperability: Enable seamless communication between imaging systems, electronic health records (EHR), and enterprise archives to improve collaboration and patient care.
• Scalable AI Integration: Our platform supports the integration of AI tools, helping healthcare providers leverage the power of artificial intelligence for diagnostics and decision-making.
• Cloud and On-Prem Options: Flexibly deploy imaging solutions on-premises or in the cloud, depending on your organization’s operational and compliance needs.

Ready to Transform Your Enterprise Imaging Workflows?

Dicom Systems is a trusted partner in simplifying enterprise imaging management with a proven track record of supporting hospitals, radiology groups, imaging centers, and academic medical institutions globally. Our solutions empower healthcare providers to overcome imaging workflow challenges, ensure regulatory compliance, and focus on delivering exceptional patient care.

Discover how Dicom Systems can help your organization overcome enterprise imaging challenges and achieve seamless workflows.