Clinical Practice Guidelines (CPG), meant to express best practices in healthcare, are commonly presented as narrative documents communicating care processes, decision making, and clinical case knowledge. However, these narratives in and of themselves lack the specificity and conciseness in their use of language to unambiguously express quality clinical recommendations. This impacts the confidence of clinicians, uptake, and implementation of the guidance. As important as the quality of the clinical knowledge articulated, is the quality of the language(s) and methods used to express the recommendations. In this paper, we propose the BPM+ family of modeling languages as a potential solution to this challenge. We present a formalized process and framework for translating CPGs into a standardized BPM+ model. Further, we discuss the features and characteristics of modeling languages that underpin the quality in expressing clinical recommendations. Using an existing CPG, we defined a systematic series of steps to deconstruct the CPG into knowledge constituents, assign CPG knowledge constituents to BPM+ elements, and re-assemble the parts into a clear, precise, and executable model. Limitations of both the CPG and the current BPM+ languages are discussed.

BACKGROUND: Intracranial hypertension is a serious complication accompanying the intensive care of children with traumatic brain injury. Intensive care of these patients is based on internationally accepted guidelines and their local editions.
OBJECTIVE: The goal was to develop a software system for digital implementation of the clinical protocol for the treatment of intracranial hypertension in children, based on HL7 Arden Syntax clinical decision support.
METHODS: Arden Syntax, an HL7 medical knowledge representation and processing standard, was used to develop a digital version of the local guideline.
RESULTS: Comparison of 37 patients given conventional treatment with a second group of 84 patients treated with the digital clinical guideline yielded statistically significant differences.
CONCLUSIONS: The digital clinical guideline system undoubtedly improves the doctor\'s awareness of the patient\'s existing condition and potential complications of intracranial hypertension.

Data quality assessments (DQA) reveal quality problems in electronic medical records (EHR) data. Generally, DQA methods describe quality rules in programming languages through hard-coding, which limits the implementation of DQA between heterogeneous systems and the interoperability of quality rules. To cover this gap, we conducted a case study applying Guideline Definition Language (GDL) in DQA to assess the quality of patient admission data in an EHR system of a hospital in China.

Previously, the authors presented an overall framework aimed at improving the representation, quality and application of clinical guidelines in daily clinical practice. Regarding the quality improvement of guidelines, we developed a proposal to verify specific requirements in guidelines, using the SPIN model checker as verification tool. Additionally, we established a pattern-based approach for defining commonly occurring types of requirements in guidelines, in order to help non experts in their formal specification. In particular, among such patterns, we identified several which could not be verified by using such a proposal, thus leaving their verification as future work. In this paper, we provide a revised and extended version of that work by providing an overall proposal which mainly addresses previous shortcomings, while providing additional verification functionalities. More specifically, we have defined a complementary proposal to the previous one regarding the verification of guidelines. This proposal uses Formula, a model finding and design space exploration tool that is based on Algebraic Data Types (ADT) and Constraint Logic Programming (CLP). The main contributions of this paper are twofold: (1) providing a more complete set of patterns for defining commonly occurring types of requirements in guidelines, and (2) supporting the verification of a wider range of patterns by combining the use of our previous proposal, based on the SPIN model checker, with our Formula-based method. More specifically, our Formula-based proposal provides us with a solution to the verification of those patterns we were not able to verify previously. Additionally, our proposal has been implemented as an Eclipse plug-in developed based on Model Driven Development (MDD) techniques, which enables us to automatically generate the Formula specification of a guideline, making the process faster and less error-prone than a manual translation. This Formula specification, together with the requirements to be checked in the guideline, are finally taken as input of the Formula tool to check whether the guideline verifies the requirements. We show the feasibility of our overall approach by verifying properties in different clinical guidelines with encouraging results.

Clinical decision support systems (CDSSs) are being developed to assist physicians in processing extensive data and new knowledge based on recent scientific advances. Structured medical knowledge in the form of clinical alerts or reminder rules, decision trees or tables, clinical protocols or practice guidelines, score algorithms, and others, constitute the core of CDSSs. Several medical knowledge representation and guideline languages have been developed for the formal computerized definition of such knowledge. One of these languages is Arden Syntax for Medical Logic Systems, an International Health Level Seven (HL7) standard whose development started in 1989. Its latest version is 2.10, which was presented in 2014. In the present report we discuss Arden Syntax as a modern medical knowledge representation and processing language, and show that this language is not only well suited to define clinical alerts, reminders, and recommendations, but can also be used to implement and process computerized medical practice guidelines.
This section describes how contemporary software such as Java, server software, web-services, XML, is used to implement CDSSs based on Arden Syntax. Special emphasis is given to clinical decision support (CDS) that employs practice guidelines as its clinical knowledge base.
Two guideline-based applications using Arden Syntax for medical knowledge representation and processing were developed. The first is a software platform for implementing practice guidelines from dermatology. This application employs fuzzy set theory and logic to represent linguistic and propositional uncertainty in medical data, knowledge, and conclusions. The second application implements a reminder system based on clinically published standard operating procedures in obstetrics to prevent deviations from state-of-the-art care. A to-do list with necessary actions specifically tailored to the gestational week/labor/delivery is generated.
Today, with the latest versions of Arden Syntax and the application of contemporary software development methods, Arden Syntax has become a powerful and versatile medical knowledge representation and processing language, well suited to implement a large range of CDSSs, including clinical-practice-guideline-based CDSSs. Moreover, such CDS is provided and can be shared as a service by different medical institutions, redefining the sharing of medical knowledge. Arden Syntax is also highly flexible and provides developers the freedom to use up-to-date software design and programming patterns for external patient data access.

BACKGROUND: As a result of the disease\'s high prevalence, chronic kidney disease (CKD) has become a global public health problem. A clinical decision support system that integrates with computer-interpretable guidelines (CIGs) should improve clinical outcomes and help to ensure patient safety.
OBJECTIVE: The openEHR guideline definition language (GDL) is a formal language used to represent CIGs. This study explores the feasibility of using a GDL approach for CKD; it also attempts to identify any potential gaps between the ideal concept and reality.
METHODS: Using the Kidney Disease Improving Global Outcomes (KDIGO) anemia guideline as material, we designed a development workflow in order to establish a series of GDL guidelines. Focus group discussions were conducted in order to identify important issues related to GDL implementation.
RESULTS: Ten GDL guidelines and 37 archetypes were established using the KDIGO guideline document. For the focus group discussions, 16 clinicians and 22 IT experts were recruited and their perceptions, opinions and attitudes towards the GDL approach were explored. Both groups provided positive feedback regarding the GDL approach, but raised various concerns about GDL implementation.
CONCLUSIONS: Based on the findings of this study, we identified some potential gaps that might exist during implementation between the GDL concept and reality. Three directions remain to be investigated in the future. Two of them are related to the openEHR GDL approach. Firstly, there is a need for the editing tool to be made more sophisticated. Secondly, there needs to be integration of the present approach into non openEHR-based hospital information systems. The last direction focuses on the applicability of guidelines and involves developing a method to resolve any conflicts that occur with insurance payment regulations.

This study examined ways to improve the accuracy of translating clinical practice guidelines (CPGs) into a computer-interpretable guideline (CIG) for pressure-ulcer management using the Shareable Active Guideline Environment (SAGE) guideline model, and aimed to verify the accuracy of the obtained CIG. The study was conducted using the following procedures: selecting CPGs, extracting rules from the selected CPGs, developing a CIG using the SAGE guideline model, and verifying the obtained CIG with test cases using an execution engine. The CIG for pressure-ulcer management was developed based on 38 rules and three algorithms at the semiformal representation level using MS Excel and MS Visio. The CIG was encoded by two Activity Graphs consisting of 115 instances representing algorithms and rules as knowledge elements in the SAGE guideline model. Two errors were found and corrected. Results of the study demonstrated that a CIG representing knowledge on pressure-ulcer management can be effectively developed using commonly available programs and the SAGE guideline model, and that the obtained CIG can be verified with a locally developed execution engine. The CIG developed in the study could contribute to health information management once it is implemented successfully in a clinical decision support system.

Clinical practice guidelines are valuable sources of clinical knowledge for healthcare professionals. However, the passive dissemination of clinical practice guidelines like publishing in medical journals is ineffective in changing clinical practice behaviour. In this work, we proposed a framework to help adopting an active clinical practice guideline dissemination approach by automatically extracting clinical knowledge from clinical practice guidelines into a clinical decision support system-friendly format. The proposed framework is intended to help human modellers by automating some of the manual formalization activities in order to minimize their manual effort. We evaluated our framework using all recommendations from two clinical practice guidelines produced by the Scottish Intercollegiate Guidelines Network: the \'Management of lung cancer\' clinical practice guideline and the \'Management of chronic pain\' clinical practice guideline. We conclude that the proposed framework can be effectively used to formalize drug and procedure recommendation in clinical contexts.

OBJECTIVE: Doc\'CISMeF (DC) is a semantic search engine used to find resources in CISMeF-BP, a quality controlled health gateway, which gathers guidelines available on the internet in French. Visualization of Concepts in Medicine (VCM) is an iconic language that may ease information retrieval tasks. This study aimed to describe the creation and evaluation of an interface integrating VCM in DC in order to make this search engine much easier to use.
METHODS: Focus groups were organized to suggest ways to enhance information retrieval tasks using VCM in DC. A VCM interface was created and improved using the ergonomic evaluation approach. 20 physicians were recruited to compare the VCM interface with the non-VCM one. Each evaluator answered two different clinical scenarios in each interface. The ability and time taken to select a relevant resource were recorded and compared. A usability analysis was performed using the System Usability Scale (SUS).
RESULTS: The VCM interface contains a filter based on icons, and icons describing each resource according to focus group recommendations. Some ergonomic issues were resolved before evaluation. Use of VCM significantly increased the success of information retrieval tasks (OR=11; 95% CI 1.4 to 507). Nonetheless, it took significantly more time to find a relevant resource with VCM interface (101 vs 65 s; p=0.02). SUS revealed \'good\' usability with an average score of 74/100.
CONCLUSIONS: VCM was successfully implemented in DC as an option. It increased the success rate of information retrieval tasks, despite requiring slightly more time, and was well accepted by end-users.

Clinical medicine and health-care developments in recent years testified a tremendous increase in the number of available guidelines, i.e., \"best practices\" encoding and standardizing care procedures for a given disease. Clinical guidelines are subject to continuous development and revision by committees of expert physicians and health authorities and, thus, multiple versions coexist as a consequence of the clinical and healthcare activities. Moreover, several alternatives are usually included in order to make the guidelines as general as possible, making them difficult to handle both in manual and automated fashions. In this work, we will introduce techniques to model and to provide efficient personalized access to very large collections of multi-version clinical guidelines, which can be stored both in textual and in executable format in an XML repository. In this way, multiple temporal perspectives, patient profile and context information can be used by an automated personalization service to efficiently build on demand a guideline version tailored to a specific use case.