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
- Scheduled Workflow (SWF) integrates the ordering, scheduling, imaging acquisition, storage, and viewing activities associated with radiology exams. This consistency is also the foundation for subsequent workflow steps, such as reporting.
- 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.
Imaging Workflow Challenges
During an average hospital stay, a patient may be seen by as many as 17 healthcare professionals from different specialties. While historically, the radiology and cardiology services have created automated workflows for image acquisition and information systems for image distribution, these practices have not been universally adopted by other specialties. As a result, the majority of images are not readily available or visible to the team of doctors, nurses, therapists, technologists, and other clinicians caring for the patient.
Dicom System is focused on solving enterprise imaging workflow challenges for healthcare enterprises and government agencies. Dicom Systems has customers globally at large hospital systems, medical, radiology and 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:
- Workflow Adaptations
- Patient Identification
- Information Needed in an Image
- Metadata Data Normalization
- Legal and Regulatory Concerns
- Mobile Device Integration
- Cloud vs. On-Prem Infrastructure
- AI integration
Workflow Challenges In Enterprise Imaging
In order 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 then leveraged radiologic imaging to refine their differential diagnosis. In this scenario, the ordering physician asked the radiology department to perform a specific study. Because the ordering clinician’s evaluation was separate in time and location from the radiology department, the order acted as a form of communication between the two practices.
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.
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.
For non-DICOM images (still frames as well as video clips), an automated solution must be implemented to ensure that patients are correctly identified. 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 means that 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.
Information Needed In An Image
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.
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 quickly distinguish normal from abnormal.
The effect of feet positioning to avoid foreshortening or elongation of the neck of femur during hip radiography.
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.
Two examples of radiology reports and the referenced “key images” (providing a visual reference to pathologies or other notable findings). Source: ResearchGate
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.
DICOM metadata from an image file. Source: ResearchGate
List of DICOM tags. Source: ResearchGate
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.
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.
When it comes to HIPAA-compliant workflows, every step must be analyzed to ensure patient data protection. Dicom Systems has implemented many safeguards to ensure its devices, services, websites and data systems are compliant with the regulations and conditions set forth in HIPAA. 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.
WinguMD app. Source: ZenSnap by WinguMD
We discussed the challenges as well as opportunities created by integrating mobile image capture into workflows in a webinar with the founder of WinguMD and our customer, Nicklaus Children’s Hospital. A recording and a transcript of Integrating Mobile Into Enterprise Imaging and Teleradiology Workflows are available.
Patient Record Storage
Medicare and most states require imaging to be retained for five years.
State laws concerning the retention of medical records and radiologic images vary considerably; for instance, in Massachusetts, hospital records must be retained for 30 years after the patient’s discharge or final treatment. In some states, the records of minors must be retained for as long as their 28th birthday. The scope of the “discovery rules” in other states mean that records should conceivably be held indefinitely. Evidence of “fraud” could extend the statute of limitations indefinitely.
Mammography sometimes has longer 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 would be discovered.
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 grow and is expected to 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, which is continually updating firewall security and other protection measures so that the only people reviewing the information are the people who are authorized to do so. Dicom Systems has leveraged the healthcare cloud to deploy enterprise imaging initiatives and advance interoperability for a number of clients globally. Our Cloud Partners include AWS, Google, Azure and Life Image.
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.
10 rules for successful clinical AI adoption:
- Be clinically relevant
- Know the Clinician’s perspective
- Respect the Clinician’s workflow preferences
- Be natively interoperable: use industry standards
- Neutralize bias in machine learning methodology
- Have a viable long-term business model
- Don’t introduce latency in clinical workflows
- Be more accurate than a human
- Generate usable results
- Be equally deployable on-perm and in the Cloud
- Cloud-based imaging workflows
- Intelligent, rules-based routing
- Optimizing teleradiology
- Connecting disparate systems
- Integrating and deploying AI
- Fixing broken imaging workflows