The HL7 Version 3 Development Framework (HDF) is a continuously evolving process that seeks to develop specifications that facilitate interoperability between healthcare systems. The HL7 RIM, vocabulary specifications, and model-driven process of analysis and design combine to make HL7 Version 3 one methodology for development of consensus-based standards for healthcare information system interoperability. The HDF is the most current edition of the HL7 V3 development methodology.
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The HDF not only documents messaging, but also the processes, tools, actors, rules, and artifacts relevant to development of all HL7 standard specifications. Eventually, the HDF will encompass all of the HL7 standard specifications, including any new standards resulting from analysis of electronic health record architectures and requirements.
HL7 specifications draw upon codes and vocabularies from a variety of sources. The V3 vocabulary work ensures that the systems implementing HL7 specifications have an unambiguous understanding of the code sources and code value domains they are using.
SAIF is a way of thinking about producing specifications that explicitly describe the governance, conformance, compliance, and behavioral semantics that are needed to achieve computable semantic working interoperability. The intended information transmission technology might use a messaging, document exchange, or services approach.
The overall system that we are considering is shown in Fig. 3 [8]. Here, we assume that a patient has possible intermittent diseases such as stroke or epilepsy but wants to live a normal life. To accomplish this, if there is a problem, devices or sensors notice this and inform an emergency center, hospital, or doctor. Therefore, the patient must wear sensor modules equipped with communication modems such as Zigbee or Bluetooth. These sensors measure biomedical data such as ECG, SpO2, or body temperature constantly and send them to a PDA that the patient is also wearing on wire or by several wireless communication methods such as Zigbee or Bluetooth. In this scenario, we assume that the sensors are implemented and meet the requirement of the IEEE 1451 standards. The wireless sensors are implemented to meet the specification of IEEE 1451.5 and the sensors meet IEEE 1451.4.
In Fig. 4, we extracted the components that are related with the transfer of medical data from Fig. 3. Since we assumed that all the sensors are implemented to meet the specification of the IEEE 1451 standard, the sensors carry TEDS. In the TEDS, all the information on the patient, smart sensors are stored and sent with the sensor data. The data which are collected and transmitted to the PDA, are transmitted to the NCAP (Network capable application processor) and eventually to the Hospital. In the NCAP and the hospital, they have transcoders or transformers between IEEE 1451 TEDS and HL7.
The overriding driving force behind the development of CDA R2 has been the desire to further encode the narrative clinical statements found in clinical reports, and to do so in such a way as to enable comparison of content from documents created on information systems of widely varying characteristics. Requirements used to guide the process have come from the implementations noted above, comparison with other models (such as CEN ENV 13606, openEHR, and DICOM11,12,13), national initiatives for exchange of referral and medical summary documents, trade show demonstrations, medical natural language processing models,14 comparison with other HL7 V3 specifications, and more. While CDA R2 does not fully enable plug and play semantic interoperability, it takes us yet another step closer.
The CDA schema is derived from the CDA R2 object model per the HL7 XML Implementation Technology Specification, which defines the XML conventions used by HL7 V3 specifications and algorithmically converts the object model into an XML schema.26,27 The CDA narrative block markup, which is the XML content model of Section.text, is manually crafted (i.e., not RIM derived) and represents an extended subset of XHTML20 that is backwardly compatible with CDA R1.
The HL7 Clinical Statement Model is a collaborative project between several committees, whose focus is on harmonizing clinical statement requirements into a single model that can be used in many V3 specifications, such as CDA. The model for CDA entries was used as the starting point for and is now derived from the shared HL7 Clinical Statement model. Sharing this model across various specifications ensures a common representation for medications, family history, signs and symptoms, etc., and fosters data reusability across HL7 V3 specifications.
By 1985, Simborg Systems (which developed hospital information systems) sought to have a non-proprietary protocol created because "standardization efforts at the time was either fragmented, in a different direction or with a different scope."[4] This led to a push to create a new standards organization, with initial meetings occurring at the end of March 1987. The meetings produced the term "HL7" and prompted a non-profit organization to be created, eventually known as Health Level Seven International. Version 1.0 of the HL7 specification was released in October 1987. The direction of HL7 was largely led by Simborg Systems; however, with greater practical use seen in furthering the protocol and non-profit, the first non-Simborg Systems chairperson, Ed Hammond, took the reigns in 1989.[4] By June 1990, Version 2.1 was published and included mechanisms for results reporting and billing. By the early- to mid-1990s news of HL7 was beginning to spread to international clinical sectors, particularly parts of Europe, including Netherlands, Germany, Canada, Japan, Australia, and the United Kingdom.[4][5]
Software engineers usually work by using UML to define the projects. UML is a standard model language proposed by the OMG (Object Management Group). This organization promotes the use of object-oriented technologies through the creation and preservation of guidelines, standards, and specifications. MDE (Model-Driven Engineering) is a paradigm that focuses on the creation and operation of domain models. A domain model is a conceptual model that describes entities, attributes, roles, relations and restrictions associated with the domain of the problem. It describes concepts dealing with the nature of the problem, instead of describing concepts related to software systems. It helps software engineers to decouple representation by focusing on the concepts. A metamodel is a model that describes the concepts used in a specific domain model [5, 6]. MDE is used in this study since this paradigm has been used successfully in many other research topics e.g. business process management [7], and in software testing area [8, 9] among others.
The Privacy Rule was designed to protect individually identifiable health information through permitting only certain uses and disclosures of PHI provided by the Rule, or as authorized by the individual subject of the information. However, in recognition of the potential utility of health information even when it is not individually identifiable, 164.502(d) of the Privacy Rule permits a covered entity or its business associate to create information that is not individually identifiable by following the de-identification standard and implementation specifications in 164.514(a)-(b). These provisions allow the entity to use and disclose information that neither identifies nor provides a reasonable basis to identify an individual.4 As discussed below, the Privacy Rule provides two de-identification methods: 1) a formal determination by a qualified expert; or 2) the removal of specified individual identifiers as well as absence of actual knowledge by the covered entity that the remaining information could be used alone or in combination with other information to identify the individual.
Sections 164.514(b) and(c) of the Privacy Rule contain the implementation specifications that a covered entity must follow to meet the de-identification standard. As summarized in Figure 1, the Privacy Rule provides two methods by which health information can be designated as de-identified.
(b) Implementation specifications: requirements for de-identification of protected health information. A covered entity may determine that health information is not individually identifiable health information only if:(1) A person with appropriate knowledge of and experience with generally accepted statistical and scientific principles and methods for rendering information not individually identifiable:(i) Applying such principles and methods, determines that the risk is very small that the information could be used, alone or in combination with other reasonably available information, by an anticipated recipient to identify an individual who is a subject of the information; and(ii) Documents the methods and results of the analysis that justify such determination; or
The implementation specifications further provide direction with respect to re-identification, specifically the assignment of a unique code to the set of de-identified health information to permit re-identification by the covered entity.
(c) Implementation specifications: re-identification. A covered entity may assign a code or other means of record identification to allow information de-identified under this section to be re-identified by the covered entity, provided that:(1) Derivation. The code or other means of record identification is not derived from or related to information about the individual and is not otherwise capable of being translated so as to identify the individual; and(2) Security. The covered entity does not use or disclose the code or other means of record identification for any other purpose, and does not disclose the mechanism for re-identification.
Beyond the removal of names related to the patient, the covered entity would need to consider whether additional personal names contained in the data should be suppressed to meet the actual knowledge specification. Additionally, other laws or confidentiality concerns may support the suppression of this information. 2ff7e9595c
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