Glossary of Enterprise Imaging Terms

Algorithm: a set of rules or instructions given to an artificial intelligence (AI) or other computer systems to help it independently learn.

Annotation: clinical notes within healthcare assets such as a diagram or medical imaging file.

Artificial intelligence (AI): intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. Artificial intelligence examples include: manufacturing robots, self-driving cars, smart assistants, proactive healthcare management, disease mapping, automated financial investing, virtual travel booking agent, social media monitoring, inter-team chat tool, conversational marketing bot, natural language processing (NLP) tools.

AI application: the application of Artificial intelligence, defined as intelligence exhibited by machines. More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading platforms, robot control, and remote sensing. AI has been used to develop and advance numerous fields and industries.

API: Application Programming Interface (API), is a software intermediary that allows two applications to talk to each other. Each time you use an app like Facebook, or Instagram to send a direct message, or check the weather on your phone, you’re using an API. In Enterprise imaging this is referred to as DICOM, HL7 standard
APIs or more recently FHIR, DICOMWEB Restful APIs.

Artificial neural network: computing systems that use an interconnected group of nodes within a vast network; an algorithm which attempts to mirror the way the human brain processes information: layers of connected ‘neurons’ sending each other information.

Availability: according to the Society of Imaging Informatics (SIIM) is the measure of time when a system is fully available for the business functions for which it was designed. For a PACS, for example, this would mean support of DICOM image management, archival, and visualization. All of the essential system functions must be operating for the system to qualify as available. (For example, image management and archive, but not visualization, would not qualify a PACS as “available” in the proper sense of the term.)

Big data: extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions. Big data in enterprise imaging is an extremely large amount of data from the radiology department. Big data is identified by four Vs; volume, velocity, variety, and veracity. Potential applications of big data in radiology include scheduling of scans, creating patient-specific personalized scanning protocols, radiologist decision support, emergency reporting, and virtual quality assurance for the radiologist.

Bias (limited data sample): bias from the research or from the researcher to suggest that a sample of data can result in an explanation of a phenomenon. Bias is the idea of a limited data sample since the model or experiment can not explain the data holistically, as there is not enough data to test the model or an experiment multiple times.

Blockchain: is a system in which a record of transactions made in any currency (mostly cryptocurrency) are maintained across several computers that are linked in a peer-to-peer network. Blockchain is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. A blockchain is essentially a digital ledger of transactions that is duplicated and distributed across the entire network of computer systems on the blockchain.

Classification: a supervised learning task that accepts labeled data (for example, a picture of a face and a picture of a car) so that it can learn to independently identify images. Classification is essential for enterprise imaging work.

The Cloud: is a vast network of remote servers around the globe which are hooked together and meant to operate as a single ecosystem. These servers are designed to either store and manage data, run applications, or deliver content or a service such as streaming videos, web mail, office productivity software, or social media. Instead of accessing files and data from a local or personal computer, you are accessing them online from any Internet-capable device—the information will be available anywhere you go and anytime you need it.

Healthcare enterprises use different methods to deploy cloud resources. There is a public cloud that shares resources and offers services to the public over the Internet, a private cloud that isn’t shared and offers services over a private internal network typically hosted on-premises, a hybrid cloud that shares services between public and private clouds depending on their purpose, and a community cloud that shares resources only between organizations, such as with government institutions.

Data bias: Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. There are different types of biases such as Cognitive biases, automated bias and ways to uncover and remove data bias in healthcare.

Data lake: is a system or repository of data stored in its natural/raw format, usually object blobs or files. A data lake is usually a single store of data including raw copies of source system data, sensor data, social data etc.,and transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. A data lake can include structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs) and binary data (images, audio, video). A data lake can be established “on premises” (within an organization’s data centers) or “in the cloud” (using cloud services from vendors such as Amazon, Microsoft, or Google).

Data swamp: a deteriorated and unmanaged data lake that is either inaccessible to its intended users or is providing little value.

Deep Learning: a term which describes a type of machine learning which replicates the innate human ability to process data in abstract ways. The data must be processed through several ‘layers’ of meaning to arrive at a conclusion, as opposed to the relatively instinctive reasoning that a human can perform.

De-identification: using anonymization or pseudonymization, is the most common method to perform information removal within DICOM data. Common strategies include deleting or masking personal identifiers, such as personal name, and suppressing or generalizing quasi-identifiers, such as date of birth. The reverse process of using de-identified data to identify individuals is known as data re-identification.

DICOM: Digital Imaging and Communications in Medicine (DICOM) is the standard for the communication and management of medical imaging information and related data. DICOM is most commonly used for storing and transmitting medical images enabling the integration of medical imaging devices such as scanners, servers, workstations, printers, network hardware, and PACS (picture archiving and communication systems) from multiple manufacturers.

DICOM Modality Worklist (MWL): The DICOM modality worklist service provides a list of imaging procedures that have been scheduled for performance by an image acquisition device (sometimes referred to as a modality system). The items in the worklist include relevant details about the subject of the procedure (patient ID, name, sex, and age), the type of procedure (equipment type, procedure description, procedure code) and the procedure order (referring physician, accession number, reason for exam). An image acquisition device, such as a CT scanner, queries a service provider, such as a RIS or PACS, to get this information which is then presented to the system operator and is used by the imaging device to populate details in the image metadata.

DICOMweb: is a term applied to the family of RESTful DICOM services defined for sending, retrieving, and querying for medical images and related information.The intent is to provide a light-weight mobile device and web browser friendly mechanism for accessing images, which can be implemented by developers who have minimal familiarity with the DICOM standard and which uses consumer application friendly mechanisms like http, JSON and media types (like “image/jpeg”) to the maximum extent possible.

Disaster Recovery (DR) is a set of best practices designed to prevent or minimize data loss and business disruption resulting from catastrophic events— from localized power outages, equipment failures, cyberattacks, civil emergencies, criminal or military attacks, and natural disasters like hurricanes, tornados, or earthquakes, etc. VNA is essential to maintaining business continuity when such events occur. Whether on-premise or in the Cloud, a VNA safely stores files located in various facilities as well as individual data centers. VNAs also perform as backup DICOM image viewers. Additionally, a database affected by a disaster event may be rebuilt with a VNA leveraging indexed metadata via a DICOM header.

Electronic health record (EHR): An electronic health record (EHR) is the systematized collection of patient and population electronically stored health information in a digital format. These records can be shared across different health care settings. Records are shared through network-connected, enterprise-wide information systems or other information networks and exchanges. EHRs may include a range of data, including demographics, medical history, medication and allergies, immunization status, laboratory test results, radiology images, vital signs, personal statistics like age and weight, and billing information.

Electronic medical record (EMR): are the digital form of the paper charts that were used in the past and are the patient records. An electronic medical record contains the past medical history, medications, visit summaries, demographic and insurance information.

Enterprise imaging: is a set of strategies, initiatives, and workflows implemented across a healthcare enterprise to consistently and optimally capture, index, manage, store, distribute, view, exchange, and analyze all medical imaging and multimedia content to enhance the electronic health record. The concepts of enterprise imaging are elucidated in a series of papers by members of the HIMSS-SIIM Enterprise Imaging Workgroup.

Enterprise imaging workflows: the processes or customization involved when enabling interconnected, optimal and efficient routing, storing, viewing, distributing, storing, and exchanging of medical images and studies throughout healthcare organizations (enterprises).

Failback involves switching back to the original primary systems. Failback is the second stage of a two-part system for safeguarding information in a crisis mode during natural disasters or other events that can compromise an IT operation

Failover is the ability to switch automatically and seamlessly to a reliable backup system. When a component or primary healthcare infrastructure system fails, either a standby operational mode or redundancy should achieve failover.

FHIR RESTful API: fast Healthcare Interoperability Resources (FHIR, pronounced “fire”) is a standard describing data formats and elements (known as “resources”) and an application programming interface (API) for exchanging electronic health records (EHR). FHIR builds on previous data format standards from HL7, and is easier to implement because it uses a modern web-based suite of API technology, including a HTTP-based RESTful protocol.

Hanging Protocols provides a unique set of viewing instructions determining the layout and display of medical images, allowing the user to view an image or study based on the modality type, number of images, and comparison images as well as other user-specified criteria. Predefined Hanging Protocols allow the viewer to immediately begin the interpretation process using optimal viewing settings for the specific image or exam type. Effective use of adequately configured hanging protocols alleviates the need to arrange and adjust images upon initial display.

Healthcare informatics: or biomedical informatics is the branch of science and engineering that apply informatics fields to medicine. The health domain provides an extremely wide variety of problems that can be tackled using computational techniques

HIPAA Safe Harbor De-Identification: HIPAA safe harbor de-identification is the process of the removal of specified identifiers of the patient, and of the patient’s relatives, household members, and employers. The requirements of the HIPAA safe harbor de-identification process become fully satisfied if, and only if, after the removal of the specific identifiers, the covered entity has no actual knowledge that the remaining information could be used to identify the patient.

Once protected health information has been de-identified, it is no longer considered to be PHI; as such, there are no longer restrictions on its use or disclosure. By definition, de-identified health information neither identifies nor provides a reasonable basis to identify a patient.

Healthcare information system (HIS): is the core informational system for patient management across the health-care system and radiology information system.

HL7: a set of international standards for transfer of clinical and administrative data between software applications used by various healthcare providers. HL7 standards support clinical practice and the management, delivery, and evaluation of health services.

Interoperability: the ability of different information systems, devices and applications (systems) to access, exchange, integrate and cooperatively use data in a coordinated manner, within and across organizational, regional and national boundaries, to provide timely and seamless portability of information and optimize the health of individuals and populations globally.

Health data exchange architectures, application interfaces and standards enable data to be accessed and shared appropriately and securely across the complete spectrum of care, within all applicable settings and with relevant stakeholders, including the individual.

Image Lifecycle Management is a process of storing an organization’s medical images and data and the tools needed to migrate content from one repository to another as new modalities are deployed and older ones are decommissioned. This data retention tool is used to manage the lifespan of medical images and data. Storing data at a centralized point of integration like a VNA makes it easier to manage the data and helps reduce the effort associated with multiple storages.

Image Object Change Management: ensures data integrity between systems and allows one system to communicate changes to the other ones that manage copies of the same imaging objects in their local environment. This typically includes deletions of studies and images and mergers of studies. In a VNA, an Image Object Change Management assures data integrity between systems by defining how one system can transmit local modifications to other systems that process copies of the same imaging objects in their own local systems. This usually entails study merges and deletions, and medical image expungements.

Image segmentation: a task performed by AI dividing up a digital image into regions corresponding to the image contents, such as visually identifying the different parts of a car.

Information technology (IT): is the use of computers to store or retrieve data and information, typically used within the context of business operations as opposed to personal or entertainment technologies IT and is considered to be a subset of information and communications technology (ICT). An information technology system (IT system) is generally an information system, a communications system, or, more specifically speaking, a computer system – including all hardware, software, and peripheral equipment – operated by a limited group of IT users.

Integrating the Healthcare Enterprise (IHE): is an initiative by health care professionals and industry to improve the way computer systems in health care share information. It brings together users and developers of healthcare information technology (HIT) in an annually recurring four-step process.

Intelligent Routing: is complex image routing that is not supported by a PACS/MIMPS system. This includes routing of special procedures, image-level rerouting for study segmentation, and unique exception workflows. Route triggers can include any standard or private DICOM tag, date or time, HL7 field or group of fields. Data can be cached for future delivery and the platform performs incomplete study delivery checks to ensure full study transfer.

Machine learning: a process whereby a machine will learn and change without human prompting, based on the data it is ‘fed’. Over time, it will recognize patterns and adapt to predict outcomes based on the data it is fed.

Masking: a process of graphics software such as Photoshop to hide some portions of an image and to reveal some portions. It is a non-destructive process of image editing, and is a post-processing task. For example, in radiography, the technique of masking enables the comparative study of small areas of both breasts in mammograms.

MIMPS (Medical Image Management and Processing Systems): formerly known as PACS picture archiving and communication system.

Natural language processing (NLP): is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The goal is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves.

Non-DICOM Content: includes file formats PDF, XML, or images generated by specialized equipment and modalities. VNAs have the ability to store non-DICOM content in addition to DICOM images and data. For example, a scanned document may be stored as a PDF which is converted by the PACS/MIMPS system into a DICOM PDF object with the metadata needed for easy identification and management. A key feature driving the adoption of VNAs is the ability to manage all non-DICOM data in addition to DICOM data and images and particularly non-DICOM data.

Optical character recognition (OCR): is a technology that enables the conversion of different types of documents, such as scanned paper documents, PDF files or images captured by a digital camera into editable and searchable data. OCR is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text, whether from a scanned document, a photo of a document, a scene-photo (for example the text on signs and billboards in a landscape photo) or from subtitle text superimposed on an image (for example: from a television broadcast).

On-premises or on prem: is software that is installed and runs on computers on the premises of the person or organization using the software, rather than at a remote facility such as a server farm or cloud. On-premises software is sometimes referred to as “shrinkwrap” software, and off-premises software is commonly called “software as a service” (“SaaS”) or “cloud computing.”

Overfitting: a term describing a problem sometimes encountered in supervised learning where a machine intelligence specializes in recognizing patterns in the curated data it has trained on, becoming unable to easily identify patterns in new data.

PACS/MIMPS: A picture archiving and communication system (PACS) is a medical imaging technology which provides economical storage and convenient access to images from multiple modalities (source machine types). Electronic images and reports are transmitted digitally via PACS; this eliminates the need to manually file, retrieve, or transport film jackets, the folders used to store and protect X-ray film. The universal format for PACS image storage and transfer is DICOM. Non-image data, such as scanned documents, may be incorporated using consumer industry standard formats like PDF (Portable Document Format), once encapsulated in DICOM.

Protected Health Information (PHI): The HIPAA Privacy Rule provides federal protections for personal health information held by covered entities and gives patients an array of rights with respect to that information. The PHI Privacy Rule is balanced so that it permits the disclosure of personal health information needed for patient care and other important purposes.

QIDO-RS: is a DICOMweb term applied to the family of RESTful DICOM services defined for sending, retrieving, and querying for medical images and related information. QIDO-RS is a query based on ID for DICOM Objects (QIDO) enabling the search for studies, series, and instances by patient ID, and receiving of their unique identifiers for further usage (i.e., to retrieve their rendered representations).

Radiology: is a branch of medicine that uses imaging technology to diagnose and treat disease. Radiology may be divided into two different areas, diagnostic radiology and interventional radiology. Doctors who specialize in radiology are called radiologists.

Radiology Information System (RIS): is a networked software system for managing medical imagery and associated data. A RIS is especially useful for tracking radiology imaging orders and billing information, and is often used in conjunction with PACS and vendor neutral archives (VNAs) to manage image archives, record-keeping and billing.

RESTful API: Representational State Transfer (REST, RESTful) is a type of API designed to take advantage of existing protocols. REST can be used over nearly any protocol and when used for web APIs it typically takes advantage of HTTP. This means that developers do not need to install additional software or libraries when creating a REST API. One of the key advantages of REST APIs is that they provide a great deal of flexibility. Data is not tied to resources or methods, so REST can handle multiple types of calls, return different data formats and even change structurally with the correct implementation of hypermedia.

Recovery point objective (RPO) refers to the amount of data you can afford to lose in a disaster. RPO is defined as the maximum tolerable length of time that a computer, system, network or application can be down after a failure or disaster occurs. This includes copying data to a remote data center continuously so that an outage will not result in any data loss. Some organizations determine that losing five minutes or one hour of data is acceptable.

Relevant Priors: refers to prior imaging studies that provide vital information and context to the radiologist when interpreting a current study, even resulting in a possible change of diagnosis. A “prefetch” step is common in imaging workflows. Prefetching locates prior studies that may be relevant as context for reading the current study, and makes them immediately accessible to the radiologist. Prefetching is typically triggered by another preceding event, such as receiving an HL7 order or ADT message (ORM, ADT) or by querying an existing DICOM Modality Work List to determine upcoming exams that will be performed shortly. In the absence of a HL7 or DMWL a “postfetch” for relevant priors can be triggered upon receipt of a DICOM object, such as the first image in a new exam, or a DICOM Structured Report (SR) from a modality. Prefetching is always preferable to postfetching, as it typically ensures that all relevant priors have been retrieved before the radiologist begins to read the new exam. If a radiologist is made to wait for prior exams, it can slow them down and cause subsequent workflow delays, as well as frustration for the radiologist. This works fairly well for priors that were performed locally, even if they have to be pulled back from longer term storage to be ready for the radiologist.

Radiology Information System (RIS): is a networked software system for managing medical imagery and associated data. A RIS is especially useful for tracking radiology imaging orders and billing information, and is often used in conjunction with PACS and vendor neutral archives (VNAs) to manage image archives, record-keeping and billing.

Strong AI: a machine that thinks and communicates on the level of a human, or higher. Currently theoretical, restricted to science fiction.

STOW-RS: is a DICOMweb term applied to the family of RESTful DICOM services defined for sending, retrieving, and querying for medical images and related information. STOW-RS is for storing (sending) of DICOM PS3.10 files or separated metadata and bulk data.

Supervised learning: a form of machine learning where a machine intelligence learns from annotated data samples to correctly generate the desired output. Its algorithm mathematically generalizes data patterns and through close analysis becomes (in theory) better at predicting patterns than humans.

Structured Reporting: sometimes called synoptic reporting, is a method of clinical documentation that captures and displays specific data elements in a specific format. In radiology, the majority of reports are free text narratives, which are variably formatted and prone to the omission of important data. In contrast, structured reporting templates provide consistency and clarity, prompt entry of all necessary data elements, and are amenable to scalable data capture, interoperability, and exchange.

Most published examples of structured reporting templates are based on the technique and body part examined. A variant known as contextual reporting provides fields relevant to the disease or examination indication.

Teleradiology: the transmission of radiological patient images, such as CT, MRI, or X-Rays from one location to another for the purposes of sharing studies with other radiologists and physicians.

Teleradiology workflows: the processes or customization involved when enabling interconnected, optimal and efficient routing, storing, viewing, distributing, and exchanging of medical images and studies throughout locations where the radiologist may be at a location different from the one where the images are generated.

Turing Test: originally called the imitation game by Alan Turing in 1950, is a test of a machine’s ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses.

Unsupervised Learning: a form of machine learning where a machine intelligence processes unmoderated data samples and simply learns from whatever patterns and regularities it encounters.

Vendor Neutral Archive (VNA): a medical imaging technology in which images and documents (and potentially any file of clinical relevance) are stored (archived) in a standard format with a standard interface, such that they can be accessed in a vendor-neutral manner by other systems.

WADO-RS: is a DICOMweb term applied to the family of RESTful DICOM services defined for sending, retrieving, and querying for medical images and related information. WADO-RS is for retrieval of DICOM PS3.10 files, meta data in XML or JSON forms, bulk data separated from the meta data and rendered consumer format images

Weak artificial intelligence (AI): a machine specialized to mirror human intelligence in a single field (such as deep analysis of data sets).