Artificial intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be only the province of human experts. In this Review Article, we outline recent breakthroughs in AI technologies and their biomedical applications, identify the challenges for further progress in medical AI systems, and summarize the economic, legal and social implications of AI in healthcare.

Artificial Intelligence in Healthcare

Objectives: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts. Cli

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Extracting information from textual documents in the electronic health record: a review of recent research.

https://xml.coverpages.org/CDA-20040830v3.pdf

HL7 Clinical Document Architecture, Release 2.

Automated encoding of clinical documents based on natural language processing.

Secure and scalable data sharing is essential for collaborative clinical decision making. Conventional clinical data efforts are often siloed, however, which creates barriers to efficient information exchange and impedes effective treatment decision made for patients. This paper provides four contributions to the study of applying blockchain technology to clinical data sharing in the context of technical requirements defined in the "Shared Nationwide Interoperability Roadmap" from the Office of the National Coordinator for Health Information Technology (ONC). First, we analyze the ONC requirements and their implications for blockchain-based systems. Second, we present FHIRChain, which is a blockchain-based architecture designed to meet ONC requirements by encapsulating the HL7 Fast Healthcare Interoperability Resources (FHIR) standard for shared clinical data. Third, we demonstrate a FHIRChain-based decentralized app using digital health identities to authenticate participants in a case study of collaborative decision making for remote cancer care. Fourth, we highlight key lessons learned from our case study.

FHIRChain: Applying Blockchain to Securely and Scalably Share Clinical Data.

The HL7 Clinical Document Architecture.

The HL7 SGML/XML Special Interest Group is developing the HL7 Document Patient Record Architecture. This draft proposal strives to create a common data architecture for the interoperability of healthcare documents. Key components are that it is under the umbrella of HL7 standards, it is specified in Extensible Markup Language, the semantics are drawn from the HL7 Reference Information Model, and the document specifications form an architecture that, in aggregate, define the semantics and structural constraints necessary for the exchange of clinical documents. The proposal is a work in progress and has not yet been submitted to HL7's formal balloting process.

HL7 document patient record architecture: an XML document architecture based on a shared information model.

OBJECTIVE: To report on the use of SGML and XML (a proper subset of SGML) as transfer syntaxes for HL7 Version 2.3 and Version 3.0 messages. METHODS: The methodology has focused largely on two questions: Can it be done? How best to do it? The first question is addressed by attempting to build an SGML/XML representation of HL7 messages. The second question requires a consideration of several metrics: message length, speed of message creation and parsing, interversion compatibility, local customization, conformance determination, and the availability of software tools and skill on the format. RESULTS: Detailed specifications for expressing HL7 in SGML and XML have been developed. Some HL7 requirements are not readily expressed, while some ambiguous areas of the HL7 standard are made explicit in the SGML/XML representation. With the current design, an SGML/XML parser can extract any component of any data type from a message. CONCLUSIONS: SGML and XML can both serve as implementable message specifications for HL7 Version 2.3 and Version 3.0 messages. The ability to explicitly represent an HL7 requirement in SGML/XML confers the ability to validate that requirement with an SGML parser. The optimal message representation will be a balance of functional, technical, and practical requirements.

SGML and XML as interchange formats for HL7 messages.

OBJECTIVES: To validate the ease by which a Clinical Practice Guideline (CPG) can be web-enabled using an XML-based semi-automated process. DESIGN AND IMPLEMENTATION: An XML DTD for Clinical Practice Guidelines and an MS Word authoring template were created in an earlier project. We took an existing guideline, Bedside Smoking Cessation Intervention, placed it into the MS Word template, converted it into XML, and then to HTML for deployment over the Kaiser Permanent intranet. CONCLUSIONS: We were able to use the MS Word authoring template and automatically generate both an XML representation of our guideline, and an HTML representation, which we have deployed on our intranet. The Bedside Smoking Cessation Intervention guideline was automatically merged into the online guidelines collection. Placing it on our intranet allowed for rapid and easy access by physicians and other health care providers throughout the Kaiser Permanente Medical Care Program.

Paul V. Biron

Papers

HL7 Clinical Document Architecture, Release 2.

The HL7 Clinical Document Architecture.

HL7 document patient record architecture: an XML document architecture based on a shared information model.

SGML and XML as interchange formats for HL7 messages.

Validation of an XML-based process to automatically web-enable clinical practice guidelines: experience with the smoking cessation guideline.