Radiology information systems with picture archiving and communication capabilities have together provided modern radiology practices with the unlimited data storage and sharing capabilities necessary to cope with the ever-increasing routine data-demands.  These systems offer patient-specific medical image management in a digitized format, according to the well-known standards elaborated in Digital Imaging and Communications in Medicine (DICOM) [1]. Digital image storage has potential to enable data access in a unified manner by departments even outside of the radiology department of a hospital provided server-side components and storage facilities are shared by all departments in the hospital.  At the viewer’s end, digital systems can further be optimized for access by viewer computer systems (i.e. client systems) according to custom requirements of each clinical department in terms of resolution of rendering and image-processing capabilities.

Image digitization has paved the way to effective structural visualization of diseased tissue or organs;  today imaging has begun to have implications that transcend merely diagnostic value and is entering the realm of surgical planning minimally- or non-invasive examination and treatment through the realistic depiction of three-dimensional ‘depths’ of medical imaging as it relates to specific anatomical shapes. Image post-processing capabilities embedded in digital image management systems today often amplify the value of visualization by facilitating extraction of two or three dimensional measurements which is useful for purposes of reporting, and may employ cutting-edge digital signal processing technologies that quantify (or semi-quantify) either static or time-series image datasets. This augments the end-user’s cognitive capabilities by serving as a physician’s second-reader to accurately diagnose disease or plan out surgical decisions. 

Quantification of images has in-turn led into the concept of computer aided diagnostics (CAD), wherein a physician receives a diagnosis or ‘result’ from a non-human entity.  CAD may be dubbed as ‘clinical intelligence’ to support daily radiology tasks and is often based on techniques employing machine-learning and data-based rule-learning technology which actually ‘arrive at’ a clinical decision rather than merely ‘guiding’ a physician towards one.  This concept itself germinated in the 1960s but today has matured into a major focus of biomedical and clinical research relating to imaging-based biomarker discovery. Developments in this field of CAD have been incorporated into the routine diagnostic radiology approach to the structural screening of breast cancer on mammograms, early detection of heart disease [3-5] and even estimation of rupture risk of vascular aneurysms, to name just a few applications being investigated in this mushrooming space. But wait… there’s more!  We can do even more with imaging today…

One of the research domains receiving the most attention from funding agencies including the National Institutes of Health in the recent past has been the enabling of existing capital equipment with capabilities of imaging neuronal, cardiovascular and cellular ‘function’ as an extension to the convention of visualizing the structure of tissue or an organ.  As opposed to structural imaging, functional imaging focuses on revealing physiological activities within a certain tissue or organ by employing medical image modalities that usually reflect a spatial distribution of injected tracers or probes within the body.  Functional imaging has probably seen some of its greatest application in cognitive neuroimaging i.e. understanding the link between neuronal activity and functional imaging signals.  A few functional imaging modalities which have made an impact in this space, to name a few, include positron emission tomography (PET), infrared imaging, Electroencephalography (EEG), Magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI) [2] to detect blood-oxygen-level-dependent contrast material as an indicator of brain neuronal activity, and diffusion weighted imaging conducted by the Human Connectome Project which aims to understand the details of neural connectivity and build for the first time an integrated roadmap of structural as well as functional neural connections within the brain [3]…


References:
[1] DICOM. http://medical.nema.org/Dicom/    
[2] Journal of Neuroscience Methods 74 (1997) 229–243. http://neurosci.info/courses/systems/FMRI/kim_fmri.pdf
[3] http://www.humanconnectomeproject.org/about/

Radiology practices maintaining electronic patient records through digital radiology information systems (RIS) and vast amounts of imaging data through picture archiving and communication systems (PACS) are continuously faced with the problem of managing “big data”.  The healthcare industry generates vast amounts of imaging data and therefore, PACS technology needs to keep up simply to cope with healthcare’s explosive production of data. An average hospital is expected to produce 665 Terabytes of medical data by 2015!  These gargantuan and ever-increasing data requirements to store medical images and patient records have demanded the expansion of PACS storage capacities in orders of magnitude previously unfathomable in the print-radiology era.

To gain some context in regard to the nature of this big data, let us try to visualize it. The infographic below [1], which was originally prepared by NetApp but which has since gained widespread popularity over the internet, provides an interesting quantitative perspective on the current scale as well as the projected scope of this “big data problem” that radiology practices in the healthcare industry faces today.

Given these projections of data requirement growth rates, how must radiology practices act upon this knowledge today..?  The right thoughts that should be running through the mind of a savvy healthcare IT professional are, “Should I be investing an inordinate amount of money in upfront capital investment on projected data storage capacities for the next decade, knowing only too well they will stay unused and idle during most of their lifetime..?”  Further, notwithstanding the preeminence of tried and tested software available for recovery in the event of a data corruption or device malfunction incident, the unpredictable total costs of ownership over the lifetime of data, which incidentally encompasses maintenance, expansion, backup and replacement or recovery, is far from an alluring angle of owning and maintaining one’s own data-warehouse for medical images.

What if it were possible to benefit from the digital age without having to belabor about shortcomings and unpredictable costs of ownership..?  The solution is the cloud-based PACS which facilitates both data archiving and communication for remote access and retrieval of imaging data, entirely hosted in an off-site cloud server, managed and maintained by a HIPAA compliant third-party data-warehousing service provider i.e. the PACS vendor. 

While an end-user (client-side) installation of a traditional fully-equipped PACS system would involve a significant lead-time to set up and then an expensive end-user license agreement involving several annual (or multi-annual) scheduled maintenance updates, the cloud PACS services are usually plug-and-play services which are paid for on either an annual (and renewable) basis or a pay-per-patient (or per-access) service model. An important and highly valuable corollary of the fact that cloud-based systems require little to no time commitment from the customer for software or hardware maintenance activities, is that a cloud-based PACS always remains up-to-date as it is maintained by the PACS vendor! Another plus of a PACS-vendor on the cloud is the benefit of secure global access to your hospital data over an internet connection, behind the safeguards of robust protocols for authentication, authorization, and secrecy. 

To summarize, a new a cloud-based PACS can potentially offer your radiology practice better control and reliability in regard to imaging requirements outside of offering up huge saving in terms of unnecessary costs of owning and managing unwieldy data management equipment which eventually depreciate annually in terms of their asset value. So, if you’re struggling to keep up with your increasing patient data volumes, consider opting for the smart solution of a PACS-on-the-cloud which will seamlessly and effortlessly scale with your business and keep you at the helm of digital-imaging competitiveness, for absolutely no additional capital expense!
 
References: 
[1] Big-Data infographic by NetApp: https://communities.netapp.com/docs/DOC-23102 . Also published in the blog of the Institute for health technology transformation,“The body as a source of big data.” Infographic Friday, March 15, 2013.  Web: http://ihealthtran.com/wordpress/2013/03/infographic-friday-the-body-as-a-source-of-big-data/ .

Print radiology systems are inherently inefficient and often of poor quality, therefore making them unsustainable. Owing to both the slowness of image preparation and diminished quality of images, a print-based radiology practice invites a higher rate of professional second opinions and unnecessary re-scanning. This is not only a waste of time that could be spent on managing more patients but such inefficiencies also present a financial drain on the healthcare system. The solution to these shortcomings is the use of digital radiology systems.  However, in order to truly appreciate the impact of digital systems in the radiology practice, one must first understand how the individual component systems of a digital radiology practice operate. A digital practice primarily involves two key components - radiology information systems (RIS) and picture arhiving and communication systems (PACS). This article provides a high-level overview of these component systems as well as how they have together revolutionized the radiology practice.

Prior to the implementation of the digital systems in radiology practices, studies have indicated that physicians spent an average of one to three hours ‘searching’ for hard-copy films during the day [1].  Shown below is a flow-diagram illustrating a typical hospital workflow for reviewing radiology images (adapted from [1]) in the pre-digital (or pre-PACS) era as opposed to reviewing images on a digital system (namely, PACS).  This illustration helps one truly appreciate the inefficiencies of a pre-digital radiology practice, specifically in terms of time spent developing, retrieving and interpreting radiology images. Digital data storage and retrieval eliminates several steps in an otherwise convoluted print pipeline, paving the way for real-time reporting, on-the-fly quantitative analyses and minimal paper-pushing.  The digital age and its manifestation in radiology practice has helped lower costs of radiology practices the world over, while increasing efficiency and quality of reporting.

The RIS is a computer system designed to support operational and business workflows within a radiology department. It is a repository of patient data and report which is often populated into the electronic patient record. However, an RIS by itself is debilitated in the capability to store and access the radiology images themselves!  This is the role that PACS fills in; while the RIS manages patient data and department scheduling task, PACS specifically focuses on images. PACS is in principle a three-component assembly integrated together by digital networks which is constituted of an image data acquisition gateway (i.e. connections to the imaging systems themselves!), a server with substantial data-archival facilities, and of-course several display workstations for retrieving, reviewing and reporting on the acquired and stored images. An RIS integrated with a PACS makes patient data and image access lightning-fast for busy physicians whom shouldn’t be spending inordinate amounts of time on data or image access.

While an RIS integrated with a PACS, like any digital system, could certainly fall prey to the known risks for technological disaster such as unforeseen power-failure or data-corruptions in storage media, their advantages greatly outweigh such low-likelihood adverse events. In any event, modern PACS are equipped with the ability to recover from such disasters through programs that regulate routine data-backups and disaster recovery, reasserting the fact that digital data storage and retrieval is quite a reliable system.  Further, digital image storage through direct connectivity with source imaging acquisition systems (eg: X-Ray, MRI, CT etc.) drastically diminishes the chance that images are “lost in transit”, presenting another major advantage over print-based radiology.

To conclude, RIS and PACS – the cornerstones of today’s digital radiology era – have together made a tremendous impact on the overall sustainability and quality-of-care offered by modern radiology practices.  PACS-empowered digital radiology practices have great potential to further augment quality of care through integration with quantitative image post-processing software packages, therefore making them an infinitely extendable platform technology equipped to catapult your radiology practice into the future of quantitative imaging based healthcare.

References:

[1] Srinivasan, M. (2012). Saving time, improving satisfaction: the impact of a digital radiology system on physician workflow and system efficiency. Digital _Medicine, 1(1).