Much has been documented about the role of enterprise imaging as part of the larger effort to integrate a single longitudinal patient record. Despite the many ways an imaging effort reflects an organization’s electronic medical record (EMR) effort, there remain some distinct differences. For one, an enterprise imaging effort requires more careful understanding of peripheral and disparate systems. As an initiative, this primarily will serve as a support technology to the EMR and will be successful based on how it optimizes existing systems. Second, departmental workflow must remain uncompromised as part of an efficacious care delivery ecosystem. The challenges associated with deploying an EMR and the well-documented dissatisfaction on clinical workflow cannot, and should not, characterize enterprise imaging.
Many organizations are now on the path toward an imaging strategy that manages image objects of all types in a single or integrated platform. Some are at the trailhead looking up long, winding and challenging paths. Some are balancing precariously on this path, studying maps and re-evaluating their next steps. A handful can claim they are in sight of, or near, the summit. Many, however, are somewhere near the trailhead gathering knowledge, building a team and selecting technology. There is a lot of work being done to frame strategy within organizations in addressing enterprise imaging and rightly so. There are any number of reasons why this must be approached carefully yet purposefully. Here are four reasons that come to mind.
It’s Important
Everyone understands the criticality of imaging in patient care. Beyond that, imaging is growing in prominence in how we provide and document patient care. There are issues surrounding image management and dataflow that hold implications both to data integrity as well as the cost of infrastructure. New hybrid imaging technologies, as well as low-cost image acquisition devices including smartphones, are advancing imaging in both specialization and ubiquity. Beyond that, imaging is playing a greater role in documenting encounters and procedures, including areas like telemedicine and surgery. Because it is important, organizations want, and need, to get it right.
It’s Complicated
Despite our standards, imaging is quite complicated, and that is just within the arena of DICOM. Vendor strategies of bending the standards combined with the innovative cycle of better technology makes establishing a target difficult. Outside of DICOM it becomes even more challenging due to the expansive nature of formats, use cases and constraints related to legacy systems. Determining what to do with the wide range of imaging generated across the enterprise requires a careful and considered approach, both in how to qualify the data as well as manage the data.
It’s Expensive
There is no way around the fact that imaging is some of the most expensive technology both in capital and operations outside of the EMR. From acquiring modalities to licensing systems to hardware and storage to support staff, organizations are heavily invested in imaging and there is no margin for error.
It’s a Victim of its Own Success
Radiology adopted digital information technology earlier than nearly everyone else in healthcare. That success has led to several decades of doing business within a largely homogenous paradigm. One proven model for adopting new ideas is the use case. Use cases help us contextualize the technology we are assessing and how it will either help or hinder our objectives and outcomes. They also allow organizations to build a narrative around solutions rather than extending existing models.
One recent example of this is the rise of what is often referred to as cross-enterprise imaging. This technology model is re-imagining the paradigm of centralizing data. Forgoing the historical standard of data migration and system replacement, this technology deployment serves as the aggregator and federator of data from disparate systems. This is only one of many ways innovation can break down outdated paradigms.
Image Generation and Acquisition Use Cases
Who, what, when, where, why and how — these are all critical questions to ask when framing use cases within enterprise imaging. Specialties like radiology and cardiology that have historically been imaging-dependent in patient care are the known quantities. Conducting a full analysis of where imaging is being used (which may or may not be within the organization’s realm of policy or governance) will inform a comprehensive imaging acquisition use case framework. There is significant growth in areas like primary care and surgery, but not all devices are designed to play inside an enterprise platform. Discovery and analysis will inform your long-term strategy in these areas.
Image acquisition growth can be attributed to many different internal and external factors as well. With the rapid growth and commoditization of HIPAA-compliant capture solutions, there is an explosion in imaging simply related to additional points of documentation. In many cases this is to better record patient encounters for future reference. In some cases, it is simply archiving data that may prove critical for documenting care delivery. A good strategy will incorporate a model for addressing each of these scenarios.
Image/Data Context and Relevance Use Cases
Deploying successfully requires better knowledge of how users interact with imaging in context with other patient information. Integration to the medical record for imaging has become a requirement for most organizations. But beyond pure imaging, there are needs for aggregating data elements along with images for reference and reporting. Surgery, cardiology and oncology among others want to see images in context with other patient information for the purpose of planning, treatment, monitoring or reporting
Some imaging is best presented as embedded within a report. In some cases, imaging needs to be displayed alongside other historically relevant objects. In many cases the image needs to simply be archived for an appropriate period and will likely never be viewed in the context of patient care. Understanding all of the various use cases for data context and relevance will assist with a technology design process that accommodates each use case based on image metadata that informs how to manage it for visualization and access.
Image Object Relevance Use Cases
Images serve various purposes across the care continuum. The role of imaging in patient care and data management plays an influential role in how the image is stored and accessed. Diagnostic imaging, which has served the longest role in imaging within healthcare, has specific standards (DICOM) to ensure meta and pixel data meets medical quality requirements. There continues to be growth and innovation within this area related to image acquisition, distribution, display, collaboration, archiving and analytics.
There remain many other use cases for imaging related to non-diagnostic workflow scenarios. These include procedural monitoring (surgery), treatment (oncology) or documentation (telemedicine). There remains ambiguity and discrepancies in approach across the industry in managing the growing numbers and types of these imaging objects. A successful deployment will approach these as part of an enterprise imaging program, and account for images that are generated and archived primarily for reasons other than diagnosis. There can be options for how these images are stored including retention rules related to compression, file type conversion, storage platforms and even deletion.
Data Modeling Use Cases
What is the future value of an imaging object? Most can articulate the immediate value of imaging. It is the future state value that informs how we manage that image object. There have been innumerable discussions around DICOM and non-DICOM images and whether all imaging should conform to the DICOM standard. The answer should be mapped directly to what and how you value that image in the future. Whether using images for reference, secondary use (population health, research) or documentation of record, there should be a careful consideration of the data model.
We continue down the path of analytics at a rapid acceleration. They will play a critical role in imaging strategies. How an organization determines its approach to analytics and how the various solution statements are defined and designed will inform the data modeling use cases. What data must remain attached to an image to leverage analytics to its full capacity? How will this image object bring value in the future? These are two important questions in building an imaging strategy.
Retention Use Cases
Our industry decided that every image should be retained in perpetuity. What began as a safeguard against inadvertent image deletion in PACS systems grew into a policy where all medical imaging is archived forever. We are now closing in on the point where an immovable object meets an unstoppable force. The sheer amount of data archived for many organizations is making it incredibly disruptive to look at systems or vendor options. Petabytes of archive data stand in the way of adopting newer or better solutions. Both the cost and disruptive nature of this effort requires rethinking the importance of keeping image data forever.
Image lifecycle management rules continue to advance and many organizations are reviewing their image retention policies. Deletion remains elusive for most as an option, but here remain alternatives including compression, cloud archiving and data retirement strategies. A close look at archiving and retention, as well as designing the rules and policies to effect careful management of this data, closes the loop on an effective enterprise imaging strategy.
Jef Williams works as managing partner for Paragon Consulting Partners LLC, a consulting firm focused on medical imaging strategy, data management,
technology deployment and operations management.
April 11, 2024 
