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We analyze five approaches to knowledge management in clinical decision support (CDS) systems: pattern recognition based on annotated imaging data, mining of stored structured medical data, text mining of published texts, computable knowledge design, and general or specific text corpora for large language models. Each method's strengths and limitations in automating clinical knowledge management while striving for a zero-error policy are evaluated, offering insights into their roles in enhancing healthcare through intelligent decision support. The study aims to inform decisions in the development of effective, transparent CDS systems in clinical and patient care settings.

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CONTEXT: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules but lacks a standard query data model and terminology. OBJECTIVE: Assess FHIR-enabled data access in a proposed version of Arden Syntax. METHOD: Queries in 340 MLMs were tabulated. RESULT: FHIR can serve as the Arden Syntax data model, but nonstandard terminologies still impede interoperability. CONCLUSION: The proposed Arden Syntax v3.0 FHIR-enabled data access is adequate as a query data model for MLMs.

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Arden Syntax, a medical knowledge representation and processing language for clinical decision support tasks supervised by Health Level Seven International (HL7), was extended with HL7's Fast Healthcare Interoperability Resources (FHIR) constructs to allow standardized data access. The new version, Arden Syntax version 3.0, was successfully balloted as part of the audited, consensus-based, iterative HL7 standards development process.

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CONTEXT: Arden Syntax encodes knowledge as Medical Logic Modules (MLMs). Current work involves improvement of its representation of business processes. OBJECTIVE: Assess how DMN can represent decision-making in MLMs. METHOD: A sample of 340 MLMs was tabulated for DMN representation. RESULT: While DMN can represent all MLMs at the decision requirements level, some are not supported at the decision logic level. CONCLUSION: DMN facilitates representation of business processes but would not address all decision logic.

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OBJECTIVE: To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety. TARGET AUDIENCE: This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards. SCOPE: This tutorial covers the following standards: Arden Syntax (using FHIR as the data model), Clinical Quality Language, FHIR Clinical Reasoning, SMART on FHIR, and CDS Hooks. Detailed descriptions and selected examples are provided.

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OBJECTIVES: This article presents a comparative study of two Health Level Seven International (HL7) standards for clinical knowledge representation, the Arden Syntax and the Clinical Quality Language (CQL), regarding their expressiveness and utility to represent knowledge for clinical decision support (CDS) systems. METHODS: We compiled a concatenated set of features from both languages and made descriptive comparisons of 27 categories covering areas of language characteristics, data, control statements, and operators. RESULTS: Both Arden and CQL have similar constructs that can be used for representing CDS knowledge but also have unique constructs that could support distinct use cases. They have constructs that fully or partially address several of the categories used in the comparison, except for data models and terminologies in Arden and event triggering and iteration statements in CQL. CONCLUSION: These standards can facilitate the sharing, management, and reuse of computable knowledge, and permit knowledge to be represented with their languages and converted to a machine-friendly executable code that can be shared and reused by other systems. Having support for standard data models and terminologies will continue to be a differential for adoption of a language. The HL7 working groups responsible for developing these standards can direct future development to enhance the functions of the standard and address the gaps identified in this study.

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CONTEXT: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules (MLMs) but lacks a standard query data model and terminology. OBJECTIVE: Assess to what extent FHIR can represent MLMs. METHOD: A convenience sample of 340 MLMs were examined and tabulated for representation using the PlanDefinition resource. RESULT: While alignment between structured subsections is uneven, the PlanDefinition resource adequately represents procedural medical knowledge encoded as MLMs. CONCLUSION: FHIR adequately represents Arden Syntax MLMs.

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BACKGROUND: The initial version of the Arden Syntax for Medical Logic Systems was created to facilitate explicit representation of medical logic in a form that could be easily composed and interpreted by clinical experts in order to facilitate clinical decision support (CDS). Because of demand from knowledge engineers and programmers to improve functionality related to complex use cases, the Arden Syntax evolved to include features typical of general programming languages but that were specialized to meet the needs of the clinical decision support environment, including integration into a clinical information system architecture. METHOD: Review of the design history and evolution of the Arden Syntax by workers who participated in this evolution from the perspective of the standards development organization (SDO). RESULTS: In order to meet user needs, a variety of features were successively incorporated in Arden Syntax. These can be grouped in several classes of change, including control flow, data structures, operators and external links. These changes included expansion of operators to manipulate lists and strings; a formalism for structured output; iteration constructs; user-defined objects and operators to manipulate them; features to support international use and output in different natural languages; additional control features; fuzzy logic formalisms; and mapping of the entire syntax to XML. The history and rationale of this evolution are summarized. CONCLUSION: In response to user demand and to reflect its growing role in clinical decision support, the Arden Syntax has evolved to include a number of powerful features. These depart somewhat from the original vision of the syntax as simple and easily understandable but from the SDO perspective increase the utility of this standard for implementation of CDS. Backwards compatibility has been maintained, allowing continued support of the earlier, simpler decision support models.

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CONTEXT: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules (MLMs) but lacks a standard query data model and terminology. OBJECTIVE: Assess to what extent SNOMED CT can represent MLM query data elements. METHOD: 340 MLMs containing 3268 queries were examined. RESULT: SNOMED can be used to represent all data elements except for clinical decision support system terms such as "alert." CONCLUSION: SNOMED CT adequately represents data queried using the Arden Syntax.

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The Los Angeles Data Resource (LADR) is a joint project of major Los Angeles health care provider organizations. The LADR helps clinical investigators to explore the size of potential research study cohorts using operational clinical data across all participating institutions. The Charles R. Drew University of Medicine and Science (CDU) LADR team sought to develop an innovative data architecture that would aggregate de-identified clinical data from safety-net providers in the community into CDU LADR node. This in turn would be federated with the other nodes of LADR for a shared view in a way that was never available before. This led to a self-service system to assess patients matching study criteria at each medical center and to search patients by demographics, ICD-9 codes, lab results and medications.

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BACKGROUND: Prior work has established that fuzzy logic is prevalent in clinical practice guidelines and that Arden Syntax is suitable for representing clinical quality measures (CQMs). Approved since then, Arden Syntax v2.9 (2012) has formal constructs for fuzzy logic even as new formalisms are proposed to represent quality logic. OBJECTIVES: Determine the prevalence of fuzzy logic in CQMs and assess the utility of a contemporary version of Arden Syntax for representing them. METHODS: Linguistic variables were tabulated in the 329 Assessing Care of the Vulnerable Elderly (ACOVE-3) CQMs, and these logic statements were encoded in Arden Syntax. RESULTS: In a total of 392 CQMs, linguistic variables occurred in 30.6%, and Arden Syntax could be used to represent these formally. CONCLUSIONS: Fuzzy logic occurs commonly in CQMs, and Arden Syntax offers particular utility for the representations of these constructs.

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BACKGROUND: Structured output has been suggested for the Arden Syntax to facilitate interoperability. OBJECTIVE: Tabulate the components of WRITE statements in a corpus of medical logic modules (MLMs)in order to validate requiring structured output. METHODS: WRITE statements were tabulated in 258 MLMs from 2 organizations. RESULTS: In a total of 351 WRITE statements, email destinations (226) predominated, and 39 orders and 40 coded output elements also were tabulated. Free-text strings predominated as the message data. CONCLUSIONS: Arden WRITE statements contain considerable potentially structured data now included as free text. A future, normative structured WRITE statement must address a variety of data types and destinations.

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The retrieval and manipulation of data from large public databases like the U.S. National Health and Nutrition Examination Survey (NHANES) may require sophisticated statistical software and significant expertise that may be unavailable in the university setting. In response, we have developed the Data Retrieval And Manipulation System (DReAMS), an automated information system to handle all processes of data extraction and cleaning and then joining different subsets to produce analysis-ready output. The system is a browser-based data warehouse application in which the input data from flat files or operational systems are aggregated in a structured way so that the desired data can be read, recoded, queried and extracted efficiently. The current pilot implementation of the system provides access to a limited amount of NHANES database. We plan to increase the amount of data available through the system in the near future and to extend the techniques to other large databases from CDU archive with a current holding of about 53 databases.

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Lack of adequate consumer health information about clinical research contributes to health disparities among low health literate minority multicultural populations and requires appropriate methods for making information accessible. Enhancing understanding of health research can enable such minority multicultural consumers to make informed, active decisions about their own health and research participation. This qualitative study examines the effectiveness and acceptability of an animated video to enhance what we call health research literacy among minority multicultural populations. A team analyzed the transcripts of 58 focus groups of African Americans, Latinos, Native Hawaiians, and Filipinos in Los Angeles/Hawaii. Participants were accepting of animation and the video's cultural appropriateness. Communicating information about health research via animation improved participants' ability to identify personal information-gaps, engage in meaningful community-level dialogue, and ask questions about health research.

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A standards-based, service-oriented architecture for clinical decision support (CDS) has the potential to significantly enhance CDS scalability and robustness. To enable such a CDS architecture, the Health Level 7 CDS Work Group reviewed the literature, hosted multi-stakeholder discussions, and consulted domain experts to identify and prioritize the services and capabilities required from clinical information systems (CISs) to enable service-oriented CDS. In addition, relevant available standards were identified. Through this process, ten CIS services and eight CIS capabilities were identified as being important for enabling scalable, service-oriented CDS. In particular, through a survey of 46 domain experts, five services and capabilities were identified as being especially critical: 1) the use of standard information models and terminologies; 2) the ability to leverage a Decision Support Service (DSS); 3) support for a clinical data query service; 4) support for an event subscription and notification service; and 5) support for a user communication service.

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Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome.

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Despite their potential to significantly improve health care, advanced clinical decision support (CDS) capabilities are not widely available in the clinical setting. An important reason for this limited availability of CDS capabilities is the application-specific and institution-specific nature of most current CDS implementations. Thus, a critical need for enabling CDS capabilities on a much larger scale is the development and adoption of standards that enable current and emerging CDS resources to be more effectively leveraged across multiple applications and care settings. Standards required for such effective scaling of CDS include (i) standard terminologies and information models to represent and communicate about health care data; (ii) standard approaches to representing clinical knowledge in both human-readable and machine-executable formats; and (iii) standard approaches for leveraging these knowledge resources to provide CDS capabilities across various applications and care settings. A number of standards do exist or are under development to meet these needs. However, many gaps and challenges remain, including the excessive complexity of many standards; the limited availability of easily accessible knowledge resources implemented using standard approaches; and the lack of tooling and other practical resources to enable the efficient adoption of existing standards. Thus, the future development and widespread adoption of current CDS standards will depend critically on the availability of tooling, knowledge bases, and other resources that make the adoption of CDS standards not only the right approach to take, but the cost-effective path to follow given the alternative of using a traditional, ad hoc approach to implementing CDS.

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BACKGROUND: Evidence demonstrates that clinical decision support (CDS) is a powerful tool for improving healthcare quality and ensuring patient safety. However, implementing and maintaining effective decision support interventions presents multiple technical and organizational challenges. PURPOSE: To identify best practices for CDS, using the domain of preventive care reminders as an example. METHODS: We assembled a panel of experts in CDS and held a series of facilitated online and in-person discussions. We analyzed the results of these discussions using a grounded theory method to elicit themes and best practices. RESULTS: Eight best practice themes were identified as important: deliver CDS in the most appropriate ways, develop effective governance structures, consider use of incentives, be aware of workflow, keep content current, monitor and evaluate impact, maintain high quality data, and consider sharing content. Keys themes within each of these areas were also described. CONCLUSION: Successful implementation of CDS requires consideration of both technical and socio-technical factors. The themes identified in this study provide guidance on crucial factors that need consideration when CDS is implemented across healthcare settings. These best practice themes may be useful for developers, implementers, and users of decision support.

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BACKGROUND: The lack of computable clinical quality indicators in standard format makes use of the measures in electronic health records (EHR) difficult. OBJECTIVE: Assess Arden Syntax as a formalism for quality indicators. METHOD: Thirty-nine measures were encoded as medical logic modules and shortfalls identified. RESULTS: All logic components of the measures were expressible. 38% of QIs were limited by lack of data in an EHR. CONCLUSION: Arden Syntax is suitable for representation of quality indicators.

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