Authoring and verification of clinical guidelines: A model driven approach

A model-driven transformation approach for the modelling of processes in clinical practice guidelines

Clinical Practice Guidelines (CPGs) include recommendations aimed at optimising patient care, informed by a review of the available clinical evidence. To achieve their potential benefits, CPG should be readily available at the point of care. This can be done by translating CPG recommendations into one of the languages for Computer-Interpretable Guidelines (CIGs). This is a difficult task for which the collaboration of clinical and technical staff is crucial. However, in general CIG languages are not accessible to non-technical staff. We propose to support the modelling of CPG processes (and hence the authoring of CIGs) based on a transformation, from a preliminary specification in a more accessible language into an implementation in a CIG language. In this paper, we approach this transformation following the Model-Driven Development (MDD) paradigm, in which models and transformations are key elements for software development. To demonstrate the approach, we implemented and tested an algorithm for the transformation from the BPMN language for business processes to the PROforma CIG language. This implementation uses transformations defined in the ATLAS Transformation Language. Additionally, we conducted a small experiment to assess the hypothesis that a language such as BPMN can facilitate the modelling of CPG processes by clinical and technical staff.

Toward a Symbolic AI Approach to the WHO/ACSM Physical Activity & Sedentary Behavior Guidelines

The World Health Organization and the American College of Sports Medicine have released guidelines on physical activity and sedentary behavior, as part of an effort to reduce inactivity worldwide. However, to date, there is no computational model that can facilitate the integration of these recommendations into health solutions (e.g., digital coaches). In this paper, we present an operational and machine-readable model that represents and is able to reason about these guidelines. To this end, we adopted a symbolic AI approach that combines two paradigms of research in knowledge representation and reasoning: ontology and rules. Thus, we first present HeLiFit, a domain ontology implemented in OWL, which models the main entities that characterize the definition of physical activity, as defined per guidance. Then, we describe HeLiFit-Rule, a set of rules implemented in the RDFox Rule language, which can be used to represent and reason with these recommendations in concrete real-world applications. Furthermore, to ensure a high level of syntactic/semantic interoperability across different systems, our framework is also compliant with the FHIR standard. Through motivating scenarios that highlight the need for such an implementation, we finally present an evaluation of our model that provides results that are both encouraging in terms of the value of our solution and also provide a basis for future work.

Acquiring guideline-enabled data driven clinical knowledge model using formally verified refined knowledge acquisition method

BACKGROUND AND OBJECTIVE

Validation and verification are the critical requirements for the knowledge acquisition method of the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method with the support of a rigorous validation process for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data for the treatment of oral cavity cancer. However, due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts.

METHODS

This paper presents the refined knowledge acquisition (ReKA) method, which uses the Z formal verification process. The ReKA method adopts the verification method and explores the mechanism of theorem proving using the Z notation. It enhances a hybrid knowledge acquisition method to thwart the inconsistencies using formal verification.

RESULTS

ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. These criteria ensure the validity of the final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the additional criteria by the ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57% compared to a similar approach with an accuracy of 69.7%. Furthermore, the ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs.

CONCLUSION

ReKA refined the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. As a formally proven method, it always yields a valid knowledge model having high quality, supporting local practices, and influenced by standard CPGs. Furthermore, the final knowledge model obtained from ReKA also preserves the performance such as the accuracy of the individual source knowledge models.

Reasoning about clinical guidelines based on algebraic data types and constraint logic programming

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.

Temporal data representation, normalization, extraction, and reasoning: A review from clinical domain

BACKGROUND AND OBJECTIVE

We live our lives by the calendar and the clock, but time is also an abstraction, even an illusion. The sense of time can be both domain-specific and complex, and is often left implicit, requiring significant domain knowledge to accurately recognize and harness. In the clinical domain, the momentum gained from recent advances in infrastructure and governance practices has enabled the collection of tremendous amount of data at each moment in time. Electronic health records (EHRs) have paved the way to making these data available for practitioners and researchers. However, temporal data representation, normalization, extraction and reasoning are very important in order to mine such massive data and therefore for constructing the clinical timeline. The objective of this work is to provide an overview of the problem of constructing a timeline at the clinical point of care and to summarize the state-of-the-art in processing temporal information of clinical narratives.

METHODS

This review surveys the methods used in three important area: modeling and representing of time, medical NLP methods for extracting time, and methods of time reasoning and processing. The review emphasis on the current existing gap between present methods and the semantic web technologies and catch up with the possible combinations.

RESULTS

The main findings of this review are revealing the importance of time processing not only in constructing timelines and clinical decision support systems but also as a vital component of EHR data models and operations.

CONCLUSIONS

Extracting temporal information in clinical narratives is a challenging task. The inclusion of ontologies and semantic web will lead to better assessment of the annotation task and, together with medical NLP techniques, will help resolving granularity and co-reference resolution problems.

Leveraging workflow control patterns in the domain of clinical practice guidelines

BACKGROUND

Clinical practice guidelines (CPGs) include recommendations describing appropriate care for the management of patients with a specific clinical condition. A number of representation languages have been developed to support executable CPGs, with associated authoring/editing tools. Even with tool assistance, authoring of CPG models is a labor-intensive task. We aim at facilitating the early stages of CPG modeling task. In this context, we propose to support the authoring of CPG models based on a set of suitable procedural patterns described in an implementation-independent notation that can be then semi-automatically transformed into one of the alternative executable CPG languages.

METHODS

We have started with the workflow control patterns which have been identified in the fields of workflow systems and business process management. We have analyzed the suitability of these patterns by means of a qualitative analysis of CPG texts. Following our analysis we have implemented a selection of workflow patterns in the Asbru and PROforma CPG languages. As implementation-independent notation for the description of patterns we have chosen BPMN 2.0. Finally, we have developed XSLT transformations to convert the BPMN 2.0 version of the patterns into the Asbru and PROforma languages.

RESULTS

We showed that although a significant number of workflow control patterns are suitable to describe CPG procedural knowledge, not all of them are applicable in the context of CPGs due to their focus on single-patient care. Moreover, CPGs may require additional patterns not included in the set of workflow control patterns. We also showed that nearly all the CPG-suitable patterns can be conveniently implemented in the Asbru and PROforma languages. Finally, we demonstrated that individual patterns can be semi-automatically transformed from a process specification in BPMN 2.0 to executable implementations in these languages.

CONCLUSIONS

We propose a pattern and transformation-based approach for the development of CPG models. Such an approach can form the basis of a valid framework for the authoring of CPG models. The identification of adequate patterns and the implementation of transformations to convert patterns from a process specification into different executable implementations are the first necessary steps for our approach.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax

OBJECTIVE

The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support.

METHODS AND MATERIALS

A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system.

RESULTS

We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy.

CONCLUSION

Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

Representation of Medical Guidelines with a Computer Interpretable Model

Nowadays medical software is tightly coupled with medical devices that perform patient state monitoring and lately even some basic treatment procedures. Medical guidelines (GLs) can be seen as specification of a medical system which requires their computer-interpretable representation of medical GLs. Until now most of the medical GLs are often represented in a textual format and therefore often suffer from such structural problems as incompleteness, inconsistencies, ambiguity and redundancy, which makes the translation process to the machine-interpretable language more complicated. Computer-based interpretation of GLs can improve the quality of protocols as well as the quality of medical service. Several GLs formal representation methods have been presented recently. Only some of them enable automatic formal verification by introducing an additional translation path to the existing model checking environments. However, if a verified property fails it is difficult to trace back the result needed to change the model. Moreover, these formalisms provide the notion of time mostly in terms of actions order. In this paper we preset the application of a well-know formal behaviour representation approach of embedded systems design domain to medical GLs interpretation. We use Timed Automata extended with Tasks (TAT) and TIMES toolbox to represent medical GLs as a system behaviour in a computer interpretable form. We discuss the verification issues with the help of the anticancer drug imatinib case study.

Computer-interpretable clinical guidelines: a methodological review

Clinical practice guidelines (CPGs) aim to improve the quality of care, reduce unjustified practice variations and reduce healthcare costs. In order for them to be effective, clinical guidelines need to be integrated with the care flow and provide patient-specific advice when and where needed. Hence, their formalization as computer-interpretable guidelines (CIGs) makes it possible to develop CIG-based decision-support systems (DSSs), which have a better chance of impacting clinician behavior than narrative guidelines. This paper reviews the literature on CIG-related methodologies since the inception of CIGs, while focusing and drawing themes for classifying CIG research from CIG-related publications in the Journal of Biomedical Informatics (JBI). The themes span the entire life-cycle of CIG development and include: knowledge acquisition and specification for improved CIG design, including (1) CIG modeling languages and (2) CIG acquisition and specification methodologies, (3) integration of CIGs with electronic health records (EHRs) and organizational workflow, (4) CIG validation and verification, (5) CIG execution engines and supportive tools, (6) exception handling in CIGs, (7) CIG maintenance, including analyzing clinician's compliance to CIG recommendations and CIG versioning and evolution, and finally (8) CIG sharing. I examine the temporal trends in CIG-related research and discuss additional themes that were not identified in JBI papers, including existing themes such as overcoming implementation barriers, modeling clinical goals, and temporal expressions, as well as futuristic themes, such as patient-centric CIGs and distributed CIGs.