From Infrastructure to Intelligence: Rethinking the Role of Medical Imaging
Introduction
For decades, healthcare organizations have invested in technologies designed to solve increasingly complex imaging challenges.
Film gave way to digital imaging. PACS transformed how images were stored, viewed, and interpreted. Vendor Neutral Archives (VNAs) helped organizations separate imaging information from individual applications. Enterprise Imaging expanded the conversation beyond radiology, creating strategies for managing and accessing imaging across departments, specialties, and the broader healthcare enterprise.
Each evolution addressed an important challenge. And each created the foundation for what came next.
Today, medical imaging is entering another period of transformation.
Imaging volumes continue to grow. Cloud adoption is accelerating. Artificial intelligence is introducing new possibilities. Clinical workflows increasingly cross departments, locations, applications, and organizations. Patients and physicians expect information to be available wherever and whenever it is needed.
Medical imaging has never been more connected. It has also never been more complex.
That creates an important question for healthcare leaders:
After spending decades building a connected imaging infrastructure, how do we begin using that foundation intelligently?
The Foundation Was the Beginning
The evolution toward Enterprise Imaging solved one of healthcare’s most important challenges: access to information.
Imaging information that once existed within departmental silos could increasingly become available across the enterprise connected through the patient’s clinical record. Organizations gained greater control over imaging data, improved interoperability, and created infrastructure capable of supporting multiple departments and clinical applications.
That work was critical and remains essential.
Enterprise Imaging is just as relevant today as it was a decade ago. It’s clearly something healthcare continues to need, and its ability to bridge formerly siloed information into a cohesive pool of data is something that needs to be expanded, not replaced. It is the foundation upon which the next generation of imaging capabilities can and will be built.
But connectivity alone doesn’t eliminate complexity. In many organizations, the opposite has occurred.
As more systems became and continue to become connected, more workflows must be coordinated. More applications exchange information. More departments depend upon shared infrastructure. More cloud services, AI applications, external partners, and clinical systems participate in the imaging ecosystem.
The infrastructure became connected. But the operating environment became increasingly complex, and that complexity is what needs to be solved next.
Connectivity Created New Opportunities
Consider what happens every time a modern imaging exam is performed.
A clinical decision is made to pursue an imaging study. An order whether written or electronic, will ultimately originate in one electronic system. Patients are scheduled for the exam while insurance coverage and co-pays are determined. Images are acquired on the day of the exam by the chosen modality. Information and image data may move through multiple applications before reaching an archive in its final state. AI applications may be invoked based on workflow or site preferences to pre-analyze the study before clinical interpretation by a radiologist. Based on system rules or departmental workflows, Radiologists choose studies from a worklist but oftentimes require prior examinations and relevant clinical information to be present before reading a study. Clinical results are recorded and final results are distributed to referring physicians. Finally, images may need to move across facilities, organizations, or cloud environments in their on-going lifecycle.
In other words, behind what appears to be a single clinical workflow is an increasingly sophisticated network of systems, people, information, rules, and decisions. And if other specialty departments need to be included (cardiology, oncology, etc.), the workflow, data movement, and clinical coordination become even more complex and creates even more challenges in coordinating patient care and delivering on better patient outcomes.
This is why the conversation around healthcare imaging is beginning to expand beyond interoperability. Healthcare organizations increasingly need to understand not only whether information can move, but how effectively the entire imaging environment is operating without dropping the ball or causing unnecessary delays anywhere in the coordinated effort of delivering optimal patient care.
Visibility Creates Understanding
Medical imaging generates an extraordinary amount of operational information along the care continuum that needs to be coordinated, tracked, and understood. Every study routed, image transferred, workflow completed, application accessed, and process delayed tells us something about how the imaging environment is performing. Yet much of that information remains difficult to see in context, let alone understand where the bottlenecks are without greater visibility.
Healthcare leaders may know when a system fails. But it may be much harder to identify subtle inefficiencies in a complex workflow ecosystem that spans across multiple systems, workflows, organizations, and caregivers.
Common questions healthcare leaders continue to ask are:
Where are the studies being delayed? Why and where are unnecessary manual processes occurring? Which workflows consistently create friction and what is the root cause of the friction? How effectively are our resources being utilized and where do we have waste or unnecessary inefficiencies? What operational patterns are beginning to emerge and how do we correct ones that are increasingly causing issues?
Answering these questions requires more than monitoring individual systems. It requires operational visibility across the imaging ecosystem, and understanding the inefficiencies that visibility provides, changes the conversation.
Instead of simply reacting when something goes wrong, organizations can now begin to understand how imaging operates across departments, across the enterprise, and only then can they begin to identify where opportunities exist for improvement.But even visibility alone isn’t enough because seeing a problem…doesn’t solve the problem. Solving problems requires identifying what the problem is, understanding what the root cause is, and improving processes while understanding the impact those improvements will have once they are implemented.
From Insight to Action
Once organizations understand what’s happening across their imaging environment, the next question becomes: What should happen next? This is where workflow orchestration becomes increasingly important in improving the outcome of coordinated care.
Orchestration provides a way to coordinate systems, information, rules, events, and people across increasingly complex imaging environments to ensure the best possible outcomes are achievable.
A study may need to be routed differently based on location, urgency, specialty, or system availability. Information may need to move automatically when a specific event occurs. An AI application may need to become part of an existing clinical workflow without causing disruption or slowing down the clinical process and creating another disconnected pathway. A workflow may need to dynamically adapt when conditions change. And some decisions will always require human judgment in the process, and for that, one must understand what the problem is and whether it can be solved by technology, or better workflow processes.
The goal isn’t to add automation for automation’s sake. The goal is to create an environment capable of responding more intelligently to what’s happening within it when given conditions present themselves.
Operational intelligence creates understanding. Workflow orchestration turns that understanding into action. Together, they begin changing imaging from a collection of connected technologies into a coordinated operating environment.
Imaging Becomes a Strategic Asset
This evolution also changes the role imaging plays within the healthcare enterprise. Historically, imaging infrastructure was often treated as a departmental technology investment.
You capture the image, store it, interpret it, then retrieve it when necessary.
Today, imaging influences far more. It affects clinical decision-making, physician productivity, operational performance, cloud strategy, cybersecurity, AI adoption, interoperability, patient engagement, and enterprise transformation initiatives.
Imaging has also become one of healthcare’s richest sources of clinical information and the potential insights that vast collections of clinical data can provide, makes imaging more than just infrastructure. It makes imaging a strategic enterprise asset that individually provides clinical insight into patient conditions, but collectively, can provide demographic and geographic insights that can change healthcare policy, improve machine learning, and drive solutions and clinical pathways that provide even better patient outcomes.And the strategic question begins changing from – How do we manage imaging? – to – How do we create more value from the information we have?
From Infrastructure to Intelligence
We’ve spent decades building robust infrastructure to acquire, distribute, store, protect, and connect healthcare data, and that investment created an extraordinary and necessary foundation for the future.
Now, the next evolution is about unlocking more value from the data we have. Connected infrastructure provides the access, operational visibility creates the understanding, workflow orchestration enables coordinated action across care teams, clinical information provides the context, and together all these capabilities begin to create something much larger:
Imaging Intelligence.
Imaging Intelligence isn’t another application or another replacement cycle of technology. It’s an operating model for thinking about how infrastructure, information, workflows, people, and emerging technologies can work together more intelligently across the healthcare enterprise.
The objective isn’t simply to make imaging technology smarter. It’s to help imaging better support the people who depend upon it, from IT and operational leaders to radiologists, physicians, technologists, and ultimately patients.
Building What’s Next
Medical imaging will continue to evolve as new technology, challenges, policies, and medicine continues to advance. New AI capabilities will emerge and early solutions will evolve. Clinical workflows will change to adapt to new care pathways and discoveries. Imaging volumes will grow, and clinicians continue to rely on imaging services to aid in patient diagnosis. New technologies will enter the ecosystem and expectations for access, efficiency, security, and improved patient experiences will continue to drive the continued evolution of imaging.
But organizations can’t rebuild their imaging infrastructure every time healthcare changes, so they will need a foundation capable of evolving with it.
At InsiteOne, we believe the next chapter of medical imaging will be defined not simply by how effectively organizations connect information, but by how intelligently they can use it.
That’s why we’re building toward a new model for imaging, one that brings together connected infrastructure, workflow orchestration, operational intelligence, clinical insight, and the ability to continuously adapt as healthcare evolves.
The infrastructure healthcare has spent decades building isn’t the end of the journey…it’s the foundation for what comes next.
As our industry, solutions, and company continues to evolve, one thing is certain: The future of imaging isn’t just connected.
It’s intelligent.
For more information on how InsiteOne can provide a tailored solution to meet your organization’s Imaging IT needs, including workflow orchestration and imaging intelligence solutions, contact us today at 866.467.4831 or visit us here.
