Present technologies in radiation oncology and medical imaging are highly complicated in a computer-driven world. Further, it is becoming more difficult for most specialists within the medical profession to understand comprehensively how or what is happening while employing these systems. While such challenges are affecting the medical profession, medical physicists and technologists continue to look for ways to alleviate critical predicaments. The world of health informatics by definition refers to the convergence of patient data, expertise from health professionals, and computer science applications. Such convergence has accorded great advances in the provision of quality patient care through the availability and management of information. Increasingly, the majority of clinics, hospitals, health-related organizations, and ambulatory facilities worldwide employ the use of informatics as an important role ("What is Imaging Informatics | ABII," 2018). More and more, informatics represents the intersection of computer science, information science, and healthcare through the management of information acquisition, retrieval, and storage. This intersection allows the use of information in healthcare to transpire seamlessly, thus improving access and overall quality
The Past and Future
Developments in radiology information systems (RIS) and picture archiving and communication systems (PACS) are recent. As early as 1983, during standards development, the first American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) met and came up with the ACR-NEMA published in 1985 for the first time (Bidgood & Horii, 1992). Onwards, in 1993, an increasing rise of digital modalities and the exponential development of parallel network technologies, which are robust in nature, fostered the continual development of digital imaging and communication in medicine (DICOM). Prior to the development of imaging informatics, medical professionals relied on cumbersome processes that involved the cross-sectional examination of hundreds of pages. Using dark rooms, films, light boxes, film libraries, and multi-changers, clinicians were able to analyze patient images. Through using these techniques, results were often posted slowly and had numerous errors. In addition, the propensity for significant data loss increased exponentially. Current technologies continue to disrupt this old setting, resulting in the loss of certain jobs such as file room clerks. Consequently, the number of required radiologists has diminished significantly as PACs results in increased proficiency and overall productivity in understanding and interpreting medical data.
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Presently, imaging informatics is in the middle of a strong paradigm change. Urging this change on is the rapid improvement and emergence of networking technologies that are taking the information world by storm. Moreover, the future brings about the emergence of cloud computing, which encompasses novel technological advancements and services augmentation. In essence, cloud computing involves a wide array of pertinent services available within a network, most preferably, the internet. In the dispensation of quality health care, confidentiality and security are of particular significance. As such, the influence of cloud computing over the world of radiology has augmented tremendously. Moreover, in the medical field, among doctors, radiologists, and medical technicians, the use of wireless technologies particularly in smartphones and tablets is becoming a norm. While medical practitioners do not deal extensively with devices that are portable in nature, their application to the medical world is gradually being assimilated. Also, an increase of the processing power in numerous devices is taking place and at a cheaper cost. This fact has enabled the emergence of several technologies in our devices and personal computers.
Services such as voice recognition and dictation systems and post-processing solutions among others required heavy processing 15 years ago. However, present processing power and server technologies continue to advance and alter rapidly. Numerous applications nowadays are being delivered as solutions on the side of the server. In such scenarios, the workstation or the client computer becomes a terminal that does not utilize its processing power. Instead, the performance of processing takes place on a more centralized and powerful server. Using this methodology, distribution of the end-product, which is critical information, transpires among local workstations. Moreover, the development of “virtual machines,” which works in a way that makes one server host several other stand-alone servers, has optimized the processing power of autonomous devices within a network. The current state of imaging informatics allows the scheduling, interpretation, reporting, archiving, billing, and sharing of generated data. Moreover, through the performance of quality analytics and reasonable research, medical information transforms into an advancement of knowledge and an augmentation of performance.
Fundamental Building Blocks
Standards
Increases in developments relating to imaging technologies have often been enabled by the use and advancement of pertinent standards. Within the medical community, not only must there be an existence of standards, but also the community at large has to agree to these standards as the norm. Here, a good example is the PACS and DICOM 3 standards. Fundamentally, PACS would not have been possible without the overall acceptance of DICOM 3. It is clear that digital modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) were in place prior to the establishment of DICOM 3 (Mendelson & Rubin, 2013). While this was the case, the transportation and archival of such images took place manually, resulting in a chaotic environment until vendors and providers unanimously subscribed to a common standard. Similarly, while considering radiology information systems (RIS), standards are a core feature. Due to the implementation of standard protocols, present vendor systems are able to interface with each other seamlessly, subsequently, providing a strong medium for medical information exchange.
Within health care, the HL7 and DICOM 3 are perhaps the best-known standards that continue to support imaging informatics. While this is the case, there are several other standards employed by apposite systems to provide functionalities such as interoperability. In other cases, health institutions use multiple standards to accomplish certain specific tasks. Among engineers, every relevant standard is familiar. Moreover, present health care settings require the customization of interfaces so that systems can communicate effectively. Organizations such as Integrating the Healthcare Enterprise usually work on the goal of attaining interoperability that is transparent in nature ("IHE.net," 2018). This organization has numerous domains, which continually examine normal workflows in health care and the various standards available. Overall, voluntary collaboration on the vendors’ part and the end users create the application of IHE profiles as a means of implementing a group of standards to the overall workflow within a healthcare institution. Such standards and the implementation of transparent interoperability have enabled numerous advantages in healthcare including a significant reduction of costs and time required to receive treatment.
Terminology
To implement imaging informatics systems efficiently, standardized vocabularies or lexicons are highly necessary. Terminology is part of the overall management and use of informatics systems particularly those used in healthcare settings. In order to facilitate the development of smart systems capable of various functions such as transactions, report interpretations, and the performance of data mining, lexicons are central in the process. To understand their role, consider the ACR in the establishment of its Dose Index Registry. While its task was the collection and comparison of dose data regarding CT scans of the head, the system discovered more than a thousand names with an association to “head” and “brain” and implemented this to what is fundamentally the same examination of the “brain” from other facilities. In such a case, well-defined terminologies would have helped in the identification of preferred terms. The current state of affairs in health care is yet to define appropriate and standardized terminologies; however, there are several emerging as notable contenders, including the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT). This system presents comprehensive clinical terminologies initially created by the College of American Pathologists (CAP) (Mendelson & Rubin, 2013).
It is presently becoming clear that organized radiology, pertaining to imaging informatics, requires standardized terminologies. While medical nomenclatures are numerous, those pertaining to radiological functions are quite a few and under-defined. Within radiology, certain relationships and terms are unique and well defined to the discipline. Perhaps the most notable radiology nomenclature is included in the Breast Imaging Reporting and Data System (BI-RADS) a solution attuned to quality assurance. This nomenclature system proscribes methodologies that define the descriptions and terminologies of mammograms. Presently, the Radiological Society of North America (RSNA) has already contributed towards the development of the RadLex project, a terminology structure attuned specifically to radiology (Rubin, 2007). In imaging informatics, the standardization of a vocabulary enables seamless interoperability and is an added advantage to the overall system.
The Metadata of the Image
The goal of informatics is to present information in a format that is easily computable. Once this information is in its required format, there are present systems capable of performing mundane tasks that are simply out of the reach of human capabilities. After processing, some of this information becomes available in what is referred to as image metadata, which comprises of both quantitative and semantic information contained in a particular image. Within image data, quantitative information entails calculations within pertinent areas of interest, measurements of abnormalities, and numerical features extracted from images by computers. The semantic information comprises of the image type, the imaging features, and the imaging plane. Such data give radiologists and doctors the chance to analyze various anatomical structures and their locations.
A good example here is an application that embeds dose information from an imaging study automatically into the official radiology report. Such an application would need to access the identifier of the study, the study type, and its name, which all represent semantic data. In addition, the application will access information pertaining to the dosage, which represents quantitative data. Under imaging informatics, particularly radiology, evolving IT technologies have given birth to the DICOM GSPS (gray-scale soft-copy presentation state), the annotation and image markup (AIM), and the DICOM SR (structured report). These three solutions are a reflection of a transition from a predominant display of graphics and measurements to the simple exposure of elements in a way that facilitates data mining, analytics, and application development.
IT Infrastructure: Operational Management
Ordering, Scheduling, and Billing
Under imaging informatics, the processes mentioned above are not entirely novel. However, they continue to augment and evolve in the face of technological advancements, which make them more efficient and simpler. As discussed above, IHE profiles provide one approach to better orchestrates the use of IT tools. In addition, through parallel efforts from the Society for Imaging Informatics in Medicine (SIIM) and its implementation of the TRIP (Transforming the Radiological Interpretation Process) initiative, the above processes are easily managed ("TRIP INITIATIVE," 2018). Within the medical world, there is a growing consensus of the need to standardize imaging processes. A good example here is a liver CT scan done to exclude neoplasia. Such a scan has several sequences and the automation of its ordering, scheduling, and billing makes it simpler. Most departments focused on imaging usually commence through an order from within an electronic medical record (EMR), which would employ the use of a radiology-ordering module with an inclusion of a standardized exam dictionary. The RadLex playbook presents an important directive aimed at developing exam dictionaries with a nomenclature attuned to the explanation of procedures. What results is the harmonization of chargemasters and dictionaries across enterprises, further facilitating interoperability and ultimately efficacy.
Radiology and Clinical Decision Support
Bates et al. (2003) note that tools used to aid in the ordering of apposite medical tests have the propensity to alter the medical practice entirely. It is common sense that when the clinician gets the diagnosis right, the patient benefits. Therefore, a huge amount of information is available for most clinicians to integrate into their practice of medicine. Presently, the radiology order entry clinical decision support (CDS) is morphing to become a prime IT solution that will aid in availing needed information to clinicians. Further, this technology offers quality, safety, and a much-needed reduction in overall costs. In America, extensive analyses indicate the inappropriate use of imaging services, which results in numerous economic burdens and most importantly disrupt patient and population health immensely. Therefore, in the order entry, there is a need for precise information dissemination and the reduction of inappropriate utilization. Several programs that have implemented a decision support system attest to its efficiency in providing quality and timely solutions to pertinent problems. In Minnesota, a novel pilot study illustrated that through the use of decision support, there was a limitation to imaging growth and an improvement in the rate of noted examinations.
Conclusion
At best, imaging informatics is comprehended as a set of modules, which facilitate a continual cycle in the enhancement of medical examinations. Most importantly, these modules ensure quality control, research, and reporting. As the field grows in the healthcare industry, several tools may seem inappropriate for a particular healthcare facility while in another, they may fit perfectly. In addition, some of these tools may seem mundane while others are highly pioneering. However, when combined in the right sequence, they ooze a particular synergy that makes the radiology profession, as well as other medical areas requiring imaging, interesting and fresh. In the last decade, the focus of imaging informatics has highly altered from an infrastructure that is basic, to one that employs the use of big data and image navigational systems (Chang et al., 2016). New requirements in relation to image manipulations and analysis within clinical practices continue to augment the challenges of information storage. However, advances in information technology and the inclusion of cloud computing in the processing of image information are leveling the field amply.
References
Bates, D., Kuperman, G., Wang, S., Gandhi, T., Kittler, A., & Volk, L. et al. (2003). Ten Commandments for Effective Clinical Decision Support: Making the Practice of Evidence-based Medicine a Reality. Journal Of The American Medical Informatics Association , 10 (6), 523-530. http://dx.doi.org/10.1197/jamia.m1370
Bidgood, W., & Horii, S. (1992). Introduction to the ACR-NEMA DICOM standard. Radiographics , 12 (2), 345-355. http://dx.doi.org/10.1148/radiographics.12.2.1561424
Chang, Y., Foley, P., Azimi, V., Borkar, R., & Lefman, J. (2016). Primer for Image Informatics in Personalized Medicine. Procedia Engineering , 159 , 58-65. http://dx.doi.org/10.1016/j.proeng.2016.08.064
IHE.net . (2018). Ihe.net . Retrieved 10 April 2018, from http://www.ihe.net/
Mendelson, D., & Rubin, D. (2013). Imaging Informatics. Academic Radiology , 20 (10), 1195-1212. http://dx.doi.org/10.1016/j.acra.2013.07.006
Rubin, D. (2007). Creating and Curating a Terminology for Radiology: Ontology Modeling and Analysis. Journal Of Digital Imaging , 21 (4), 355-362. http://dx.doi.org/10.1007/s10278-007-9073-0
TRIP INITIATIVE . (2018). TRIP INITIATIVE . Retrieved 10 April 2018, from http://www.tripinitiative.com/
What is Imaging Informatics | ABII . (2018). Abii.org . Retrieved 10 April 2018, from https://www.abii.org/About-Imaging-Informatics.aspx
