Informed, data-driven decision-making is crucial for delivering quality healthcare and efficiency. However, data in a health facility is often scattered across multiple sites and may not be available as and when needed by clinicians. For example, many have different departmental systems, such as the Radiology Information System (RIS), Picture Archive and Communication System (PACS), Cardiovascular Information System (CVIS), Laboratory Information System (LIS) etc..Data in such systems is dispersed and often inaccessible, due to the presence of multiple IT silos. However, to permit the best use of healthcare resources and deliver the highest quality of care, all of them will need to interact mutually, and do so with the electronic medical record, too. This remains a major challenge. Healthcare data remains fragmented and heterogeneous. Given the sheer volume of imaging data in a hospital, enterprise imaging is being seen as the way to begin address such challenges.
Historical advantages of radiology
Experts generally consider radiology to be one of the best-placed clinical specialities to drive the integration of healthcare data at an enterprise level.
Radiology has some historical advantages for this mission. It has been the early adopter as far as advances in imaging technology and workflow are concerned.
Secondly, most radiology facilities have long since been digital and standards-compliant via protocols such as DICOM (digital imaging and communications in medicine). Given that radiology data is a critical component of a patient’s medical file, RIS and PACS can be appropriate launch pads for reconciling patient information, synchronizing order data and exchanging diagnostic results.
Many radiologists see themselves at the forefront of enterprise imaging by driving the agenda at the hospital management level and engaging with other image producers in their facility.
Need to cope with new demands
However, several issues have to also be taken into consideration.
The current generation of department PACS, in terms of their core architecture and workflow components, dates to the mid-2000s. They had been designed for use by a single physician in the imaging department. As a result, healthcare organizations now have multiple PACS systems. These aim to provide a common standard of care. As care benchmarks evolve, the PACS must cope with ever-new demands.
Given the presence of disparate systems across different departments, an enterprise imaging strategy seeks to harmonize medical imaging across an entire network or organization. Until recently, many healthcare facilities used a ‘forklift’ approach to implement an enterprise PACS design across all departments. However, its limits soon became apparent – especially in terms of lengthy implementation procedures, mission creep and change management, as well as price. Most experts now propose an architectural design which accounts for multi-vendor integration. This is not only cost-effective but also minimizes the impact of radical change management across multiple sites.
Vendor neutral archives
In recent years, vendor neutral archive (VNA) technology has emerged to address challenges posed by proprietary systems. VNA refers to an enterprise data storage and workflow solution. Its goal is to manage and share out large flows of information and address workflow challenges.
Data in a VNA is stored in non-proprietary formats, which permit open interchange. As a result, VNA permits sharing of DICOM and non-DICOM data.
VNA, coupled to the closely-related concept of Universal Viewer, allows healthcare facilities to store, distribute and view any electronically stored images without restrictions.
Making such developments even more pertinent is the availability of best practices- and standards-based frameworks from the Integrated Healthcare Enterprise (IHE) – which draws heavily on existing best-practices and processes – for example, the very specific needs of a cardiology or orthopedic department, or the time-critical processes at an emergency department (ED).
IHE frameworks merge customization with a standards-based approach, to allow for rapid integration of systems and sub-systems and accelerate the adoption of information sharing across what were previously silos.
By avoiding information duplication and workflow disruption, IHE also achieves its goals without extra overhead and cost. Indeed, one of the biggest barriers to system integration has consisted of disruption to an established care workflow.
On the other side, the integration of imaging workflow, from ordering through acquisition to reporting and billing, is considered to be a key factor to ensure that those viewing an image remotely are fully cognizant of both its context and presentation.
Next-generation PACS systems (or PACS 3.0) are likely to incorporate enterprise workflow/worklist applications, based on VNA, according to a noted US imaging technology expert, Michael Gray.
Plug-ins to the VNA, in Mr. Gray’s view, will feature diagnostic display applications used by different imaging departments, whether or not the images are in DICOM. Non-DICOM applications would deploy a front-end application to create the study from individual images and associate the proper patient and study metadata to the study.
In PACS 3.0, individual physician worklists present a list of specific studies to be consulted, while the underlying workflow launches the most appropriate display application, based on a physician’s pre-defined choices and the study selected from the list. In effect, the enterprise workflow/worklist application becomes the shared entry point for all interpreting physicians in every imaging department while the VNA is the data repository.
Enterprise imaging is nevertheless targeted well beyond the requirements of the radiology department alone. A large (and increasing) number of images are generated and interpreted in other departments – for example, from an orthopedic procedure.
There also are certain kinds of images which are not formally considered imaging studies, for example during a dermatology consultation or during the course of wound care treatment. This kind of data is, however, becoming of clinical significance in areas such as personalized medicine, where it is an important part of the medical record of a patient.
Such images need to be shared, sometimes immediately. For example, pre-surgical imaging of a complicated ankle fracture in the emergency department could require transmission to not only an orthopedic surgeon but also of a vascular surgeon – with regard to blood flow in the poorly-vascularized talus. In such a case, instant access to previous images of ankle fractures would clearly enable an emergency department to best interpret new images.
Such circumstances are also apparent in the case of patients who present at different providers, since a second provider is at a disadvantage without access to earlier images.
Enterprise imaging and patient care
Acquiring data from a range of systems in different departments demands a buy from the top echelons of management and a commitment by all concerned members of the healthcare facility.
One argument for such alignment is the role of physicians – to provide the best-available patient care. Good enterprise imaging ensures that this is made possible by providing physicians with the most efficient tools and resources.
Indeed, it is not rare for the patient experience to get lost in the context of technology paradigm shifts or major process overhauls such as enterprise image/data integration. To avoid this and ensure maximum effectiveness, healthcare organization need to closely focus on both the individual patient as well as the complete continuum of care.
From EMRs to image lifecycle management
Drivers of enterprise imaging also come from the side of the electronic medical record. Hospitals have been seeking to stretch the frontiers of the latter by enhancing communication of both data as well as images. Enterprise platforms, once looked at as no more than a storage medium, are now being geared up to give a comprehensive view of a patient’s medical history.
One challenge here is the rapid growth in the volume of imaging data. This is compounded by fragmentation and an ad-hoc approach to image management. As storage requirements have grown, data has also become more distributed in terms of multi-site PACS as well as storage tiers, based on clinical urgency or relevance as well as legal and regulatory requirements.
Benefits from enterprise imaging solutions aim at better control of the lifecycle of a medical image – not least by providing better control over storage capacities and aligning storage costs with operational priorities.
Hospital managers who are renewing or upgrading to a newer PACS system usually seek some degree of future proofing, in the form of scalable solutions and methods to manage a growing corpus of images, many of which are dated. Identifying older images which can be compressed or deleted saves on storage space.
The Enterprise Imaging Program at Cleveland Clinic
The prestigious Cleveland Clinic in Ohio provides a good definition of enterprise imaging strategy as means to address the overarching need “for standardization of clinical image acquisition, management, storage and access.”
The Cleveland Clinic enterprise imaging program incorporates all producers into its clinical image library, which is connected to electronic medical records. In total, this includes images from 11 different healthcare service lines, in addition to radiology images. By the end of 2016, according to one report, 440 different image-generating devices residing outside the radiology service had been integrated.
Today’s marketplace already offers a range of enterprise imaging solutions for healthcare enterprises.
Typical examples include diagnostic-quality images provided to clinicians on demand, as well as interfaces with third-party applications to enhance programmes. Some focus on providing a comprehensive view of the healthcare workflow. Others improve image routing and support telemedicine services.
Emergence of artificial intelligence
One of the latest additions in the enterprise imaging arsenal is artificial intelligence (AI).
In recent years, as radiologists have been forced to cope with the explosion in medical imaging procedures and storage capacity, AI seems to be showing early promise. AI is also being used to directly help the care delivery process.
Some medical technology vendors have showcased AI applications integrated with their enterprise imaging platforms. These typically consist of imaging analytics software that assists radiologists diagnose diseases before symptoms occur, and more accurately interpret findings. For example, machine vision AI algorithms pinpoint anomalies within images in real time, alerting radiologists to incidental findings. Physicians could then screen patients further for what may still be asymptomatic conditions – but could develop into a major disease.
No one doubts that radiologists will work increasingly in the future with AI, both to improve the technology itself and to reduce routine, repetitive tasks such as confirming line placements and looking at scans to find nodules. On its part, AI is also likely to become increasingly smarter, to improve efficiency, for example by prioritizing cases, putting thresholds on data acquisition, improving workflow by escalating cases with critical findings to the worklist of a radiologist and providing automatic alerts to both radiologists and other concerned clinicians. Such steps would not only free up resources for additional testing but also improve patient care, thereby making radiologists even more integral in the care management process. These perspectives are of course central to a robust enterprise imaging strategy.