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Enterprise Imaging Workflow Challenges

Enterprise imaging plays a central role in modern medicine 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 a variety of medical settings and at all major levels of health care. The use of diagnostic imaging services is 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 struggle to effectively store, display, and distribute these images throughout the healthcare continuum of care. In many cases, the key culprit for these inefficiencies is directly related 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.

  1. Scheduled Workflow (SWF) integrates the ordering, scheduling, imaging acquisition, storage, and viewing activities associated with radiology exams. This consistency is the foundation for subsequent workflow steps, such as reporting. Radiology services are pre-planned and scheduled in advance in a scheduled workflow, often for routine procedures such as breast cancer screening or non-emergent cases. This model is prevalent in outpatient settings and for follow-up studies.
  2. Encounter-Based Imaging Workflow (EBIW) captures images acquired in the context of an encounter between a patient and a healthcare provider, links them with critical metadata, and notifies the EMR. This approach is often associated with emergency departments, urgent care centers, and inpatient settings where patients present with acute medical issues.

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.

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, and 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 for each of them.
According to a HIMSS-SIIM Collaborative White Paper published in the Journal of Digital Imaging, workflow challenges are usually caused by one or more of the 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 with observing, auditing, and optimizing enterprise imaging workflows across a number of healthcare enterprises.
  1. Storage
  2. Cloud vs. On-Prem Infrastructure
  3. AI integration

Workflow Challenges In Enterprise Imaging

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, orders are used for more than just communication between two clinicians about which study needs to be performed. Today, the order helps to drive an automated workflow by creating a unique study identifier and a PACS/MIMPS worklist of patient studies needing review.

Patient Identification

Obtaining and documenting correct patient identification is imperative to the imaging workflow: correct 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. With DICOM images, this identification is automatically applied with an order selected from the modality worklist supplying the necessary metadata.

The DICOM modality worklist can be created by 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 a number of 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 the procedure name, patient identification may also 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. Within the Dicom Systems Unifier framework, tag morphing can be achieved in a number of ways. This approach to tag morphing substantially improves speed and performance for our customers and ensures that patient identification is correctly applied to the workflow.

To ensure the accurate identification of patients, implementing an automated solution for non-DICOM images (still frames and video clips) becomes imperative. HIMSS-SIIM Collaborative White Paper mentions the following solutions: workflow reminders, adding patient identifying information to every image, or creating a new modality worklist. While these systems can be effective, they rely on humans to remember to perform the correct procedure every time, which, unfortunately, errors can occur and lead to patient misidentification.

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. This method is outlined in an article titled “Strategies for handling missing clinical data for automated surgical site infection detection from the EHR” published by J Biomed Inform 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 for further study and comparison.

Standard Measurements: The ability to perform a measurement is a key 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. In cases where the size information is not available, DICOM images can be calibrated using an image ruler. The standard use of an image ruler helps mitigate against differences in the appearance of a lesion due to zoom factor and the distance between the camera and the lesion.

Images used to determine tumor diameter. The maximum tumor diameters were determined along three orthogonal.
Source: ResearchGate

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 are organized as free-text narratives. This reporting format can lead to certain reporting challenges and communication difficulties due to the lack of 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 is used to provide an interpretation of the images. In other specialties, such as dermatology, the report is used to describe the entire visit. Because of these differences, reports reside in different locations in the EMR.
In addition, certain imaging studies may have multiple reports. For example, in cardiac catheterization, the report may contain both the operative note and the detailed functional data. In this instance, both reports give the image context and should be associated with the images. However, in most EMR systems and enterprise viewing systems, there is 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 in a bi-directional manner. This means that there must be a way to view images within the report in the EMR, as well as 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 see that pathology images are also present. In this scenario, the pediatrician should not have to click back to the EHR system to read the pathology report. Instead, they would click a report button within the viewer to read the pathology report. Currently, it is difficult to create the association between images and a report, particularly 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 starting to adopt structured reporting. Structured reporting, sometimes called synoptic reporting, is a method of clinical documentation that captures and displays specific data elements in a specific 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 generate outputs that are generally formatted according to DICOM standards. This is a large step 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 radiation score is cumulative and becomes part of the patients’ medical records.

Metadata Challenges

DICOM metadata from an image file. Source: ResearchGate

List of DICOM tags. Source: ResearchGate

In DICOM-based imaging, metadata is applied at the patient, study, series, and image level. 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 while in others, it is not specific enough. An example of a term that is cited in the HIMSS-SIIM Collaborative White Paper is the humerus. 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 same 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, there are several limitations in the setting of enterprise imaging, as there is no standard way to create a procedure description.

Department: In many radiology and cardiology PACS/MIMPS, the department DICOM field is of limited value. As all of the images in the hospital come together, this field becomes crucial. It is important that a dermatologist can find all of the dermatology images quickly and distinguish them 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 own 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 important for quality assurance purposes, liability concerns, and even 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 Workflows

One of the top challenges of imaging workflows is ensuring they are HIPAA-compliant.
Sharing medical images in a HIPAA-non compliant fashion is a violation of 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. This means that 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 needs to have proper training and remain vigilant.

Concerning HIPAA-compliant workflows, it is necessary to analyze every step 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. You will find the details in our HIPAA statement.

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 areas such as 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 can then be transferred to or accessed by referring physicians, radiologists, PACS/MIMPS, RIS, or any radiology workstation, regardless of its physical location.

Mobile Devices and Enterprise Imaging

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 very limited 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 retaining medical records and radiologic images. For instance, in Massachusetts, hospitals must maintain 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. In particular jurisdictions, the broad scope of “discovery rules” imposes an indefinite retention obligation for records. It’s worth noting that the presence of “fraud” as evidence may result in an unlimited extension of the statute of limitations.

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. There is a wide range of variability from state to state and organization to organization.

Cloud vs. On-Prem Storage

Adopting cloud computing solutions can make healthcare operations more efficient and cost-effective. Cloud computing is enabling greater integration and collaboration between hospitals, medical organizations, and healthcare providers, addressing what was previously considered a largely fragmented and siloed industry. However, with many healthcare organizations still using on-prem storage, and without universal adoption of the cloud, another workflow challenge arises. Moving into the cloud represents a shift for many practices, particularly small- and medium-sized groups.

The worldwide public cloud computing market continues to expand, and experts predict it will reach an estimated 482 billion U.S. dollars in 2022. Cloud data storage options have become more widely adopted in healthcare over the past several years, as potential concerns about storing data “off-premise” have been addressed. As organizations adopt mobile applications, storing clinical data in the cloud provides users with 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 to do so. 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. ​Thanks to 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 images and can aid diagnostics. AI distinguishes patterns and irregularities in large collections of data, which makes 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 do not always 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 in order to successfully deploy an algorithm 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, prior to 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 is becoming a mainstream expectation of patients, hospitals, and healthcare providers. As the industry begins to address this need, many imaging workflow and solution challenges remain. Dicom Systems provides a versatile suite of enterprise imaging solutions that simplify workflows.
To learn more, schedule time with an expert to discuss:
  • Cloud-based imaging workflows
  • Intelligent, rules-based routing
  • Optimizing teleradiology
  • Connecting disparate systems
  • Integrating and deploying AI
  • Fixing broken imaging workflows
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