The ultimate goal of enterprise imaging is to integrate images and imaging data into a single pane, providing a 360° view of a patient's medical record. The goal is to make the information accessible to the extended network of physicians, care teams, and patients where and when it's needed and in a meaningful and concise format.
Dan Trott from Dell EMC.
The good news is that many of today's larger healthcare systems (400 or more beds) in the U.S. have already achieved this goal. The not-so-good news? This population of hospitals comprises only approximately 14% of the nearly 5,000 hospitals nationwide.1,2
So how do the remaining community hospitals and smaller facilities follow suit, especially when they are facing consolidation, limited IT resources, and dwindling budgets? How do they tackle the compilation and analysis of a digital imaging ecosystem that is becoming even more complex? How do they free the thousands, if not millions, of images and data that are stored in a proprietary format and trapped in silos of information that have been restricted to a select few, until now?
Smaller healthcare groups can begin to overcome these hurdles and accelerate the adoption of enterprise imaging by taking these three steps:
- Adopt software-defined systems managed in the cloud.
- Employ advanced analytics.
- Integrate data workflow software tools for streamlined collaboration.
Before we go further, I'd like to clarify my definition of enterprise imaging, as there are many such definitions circulating in the imaging community. I define it as the full spectrum of healthcare's digital imaging universe, including DICOM diagnostic images acquired in cardiology and radiology, camera photos taken by specialty physicians in wound care and the emergency room, digital pathology slides, and scanned documents that are generated by primary care offices. The term also goes beyond the visual image to include the embedded data and metadata. Enterprise imaging encompasses the act of reading, storing, viewing, and managing all of these images, along with all of the data across the entire healthcare system.
In short, enterprise imaging is about moving beyond systems of record that are static and held up in silos within departmental databases to systems that can connect with us, learn, and decide.
More and more hospitals are investigating options such as the cloud and dynamic data repositories to facilitate access to images. In fact, a 2016 market insight survey from the Healthcare Information and Management Systems Society (HIMSS) found that nearly 30% of respondents were considering enabling broader access to their PACS through the use of some type of image viewing software for smartphones, and 35.5% were contemplating that option for tablets.3 Still others are taking that a step further by adopting an intelligent, unified archiving model to help normalize image data and provide a centralized image repository with a secure role-based viewing tool for system-wide access to imaging data, regardless of image format.
Unified clinical archives for many smaller organizations, although appealing, are often cost-prohibitive. One approach is to adopt a software-defined system model coupled with a cloud or hybrid configuration. A software-defined system is one where all infrastructure is virtualized and delivered as a service; control of the data center is fully automated by software and the hardware configuration is maintained through "intelligent" software systems.
The combination results in policy-based provisioning and management of all images across the health network regardless of end-point device, combined with the business continuity, disaster recovery, and scalability required to support it. With this approach, providers can grow better organically with the right security, interoperability, and resources needed to support unified clinical archive functionality and also help reduce capital expenditure and automate operations as they scale.
Data analysis software tools offer a key ingredient to enterprise imaging, helping radiologists explore patient data and deliver diagnostic results that can then be integrated with a patient's electronic health record (EHR). Such tools can also be used to help disseminate important information to physicians and respective clinical teams at the point of care. For example, algorithms could provide pattern analysis to assist a radiologist in making decisions about pathology recognition. Remote monitoring applications could also be used to analyze the functionality of diagnostic devices ranging from MRI systems to patient monitoring devices.
Data workflow software tools help ensure that patient-specific data and images are accessible across the healthcare network, yielding a streamlined patient experience from one location to another. They can make it possible to incorporate images from nontraditional imaging devices into the EHR, applying metadata to these images -- much as the DICOM header does -- so that these can become subject to enterprise content management (ECM) policies and data mining.
In addition, data and images can be better incorporated into the reimbursement process. Today, many of these images remain siloed in their originating applications, and workflow tools to make these data available greatly help to create the complete longitudinal patient record.
They also provide radiologists with secured technology tools to capture, view, share, and manipulate patient data, regardless of their location. Efficient data workflows allow staff more time for collaboration, education, and improvement.
A modular approach
Small to midsized hospitals should facilitate a modular approach when seeking to accelerate the adoption of enterprise imaging in their systems. Entire infrastructure upgrades are far too expensive and disruptive in today's healthcare environments. Software-defined models and analytics tool upgrades can help hospital IT managers replace their infrastructures progressively, building on what is already installed.
Adopting this three-tiered approach that includes software-defined models in the cloud, data analytics, and data workflow will give small to midsized hospitals a path forward in being able to introduce enterprise imaging into a healthcare network and, with it, the ideals of collaboration and holistic patient diagnosis and treatment.
- Dunn L, Becker S. 50 things to know about the hospital industry. Becker's Hospital Review. http://www.beckershospitalreview.com/hospital-management-administration/50-things-to-know-about-the-hospital-industry.html. July 23, 2013. Accessed February 6, 2017.
- Fast facts on U.S. hospitals. American Hospital Association website. http://www.aha.org/research/rc/stat-studies/fast-facts.shtml. January 2017. Accessed February 6, 2017.
- HIMSS Analytics market insight study shows PACS adoption across healthcare at high level, still many opportunities for growth. HIMSS website. http://www.himss.org/news/himss-analytics-market-insight-study-shows-picture-archive-communications-systems-pacs-adoption. January 7, 2016. Accessed February 6, 2017.
Dan Trott is a healthcare strategist for Healthcare and Life Sciences at Dell EMC.
The comments and observations expressed herein are those of the author and do not necessarily reflect the opinions of AuntMinnie.com.
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