Erez Shalom

A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools

Clinical guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not in a formal, executable format, and are not easily accessible to clinicians at the point of care. We introduce a Web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in chosen target guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application, and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification and domain knowledge; it also includes four content-representation formats: free text, semi-structured text, semi-formal representation, and a formal representation. These formats support increasingly sophisticated computational tasks. The DeGeL tools for support of guideline-based care operate, at some level, on all guideline ontologies. We have demonstrated the feasibility of the architecture and the tools for several guideline ontologies, including Asbru and GEM.

DEGEL: A Hybrid, Multiple-Ontology Framework for Specification and Retrieval of Clinical Guidelines

Clinical Guidelines are a major tool in improving the quality of medical care. However, most guidelines are in free text, not machine comprehensible, and are not easily accessible to clinicians at the point of care. We introduce a Web-based, modular, distributed architecture, the Digital Electronic Guideline Library (DeGeL), which facilitates gradual conversion of clinical guidelines from text to a formal representation in a chosen guideline ontology. The architecture supports guideline classification, semantic markup, context-sensitive search, browsing, run-time application, and retrospective quality assessment. The DeGeL hybrid meta-ontology includes elements common to all guideline ontologies, such as semantic classification, and domain knowledge. The hybrid meta-ontology also includes three guideline-content representation formats: free text, semi-structured text; and a formal representation. These formats support increasingly sophisticated computational tasks. All tools are designed to operate on all representations. We demonstrated the feasibility of the architecture and the tools for the Asbru and GEM guideline ontologies.

A quantitative assessment of a methodology for collaborative specification and evaluation of clinical guidelines

We introduce a three-phase, nine-step methodology for specification of clinical guidelines (GLs) by expert physicians, clinical editors, and knowledge engineers and for quantitative evaluation of the specifications quality. We applied this methodology to a particular framework for incremental GL structuring (mark-up) and to GLs in three clinical domains. A gold-standard mark-up was created, including 196 plans and subplans, and 326 instances of ontological knowledge roles (KRs). A completeness measure of the acquired knowledge revealed that 97% of the plans and 91% of the KR instances of the GLs were recreated by the clinical editors. A correctness measure often revealed high variability within clinical editor pairs structuring each GL, but for all GLs and clinical editors the specification quality was significantly higher than random (p<0.01). Procedural KRs were more difficult to mark-up than declarative KRs. We conclude that given an ontology-specific consensus, clinical editors with mark-up training can structure GL knowledge with high completeness, whereas the main demand for correct structuring is training in the ontologys semantics.

A Scalable Architecture for Incremental Specification and Maintenance of Procedural and Declarative Clinical Decision-Support Knowledge

Clinical guidelines have been shown to improve the quality of medical care and to reduce its costs. However, most guidelines exist in a free-text representation and, without automation, are not sufficiently accessible to clinicians at the point of care. A prerequisite for automated guideline application is a machine-comprehensible representation of the guidelines. In this study, we designed and implemented a scalable architecture to support medical experts and knowledge engineers in specifying and maintaining the procedural and declarative aspects of clinical guideline knowledge, resulting in a machine comprehensible representation. The new framework significantly extends our previous work on the Digital electronic Guidelines Library (DeGeL) The current study designed and implemented a graphical framework for specification of declarative and procedural clinical knowledge, Gesher. We performed three different experiments to evaluate the functionality and usability of the major aspects of the new framework: Specification of procedural clinical knowledge, specification of declarative clinical knowledge, and exploration of a given clinical guideline. The subjects included clinicians and knowledge engineers (overall, 27 participants). The evaluations indicated high levels of completeness and correctness of the guideline specification process by both the clinicians and the knowledge engineers, although the best results, in the case of declarative-knowledge specification, were achieved by teams including a clinician and a knowledge engineer. The usability scores were high as well, although the clinicians’ assessment was significantly lower than the assessment of the knowledge engineers.

Patient-oriented Computerized Clinical Guidelines for Mobile Decision Support in Gestational Diabetes

Background: The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. Methods: The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). Results: We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. Conclusions: The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system.

An architecture for a continuous, user-driven, and data-driven application of clinical guidelines and its evaluation

OBJECTIVES Design, implement, and evaluate a new architecture for realistic continuous guideline (GL)-based decision support, based on a series of requirements that we have identified, such as support for continuous care, for multiple task types, and for data-driven and user-driven modes. METHODS We designed and implemented a new continuous GL-based support architecture, PICARD, which accesses a temporal reasoning engine, and provides several different types of application interfaces. We present the new architecture in detail in the current paper. To evaluate the architecture, we first performed a technical evaluation of the PICARD architecture, using 19 simulated scenarios in the preeclampsia/toxemia domain. We then performed a functional evaluation with the help of two domain experts, by generating patient records that simulate 60 decision points from six clinical guideline-based scenarios, lasting from two days to four weeks. Finally, 36 clinicians made manual decisions in half of the scenarios, and had access to the automated GL-based support in the other half. The measures used in all three experiments were correctness and completeness of the decisions relative to the GL. RESULTS Mean correctness and completeness in the technical evaluation were 1±0.0 and 0.96±0.03 respectively. The functional evaluation produced only several minor comments from the two experts, mostly regarding the outputs style; otherwise the systems recommendations were validated. In the clinically oriented evaluation, the 36 clinicians applied manually approximately 41% of the GLs recommended actions. Completeness increased to approximately 93% when using PICARD. Manual correctness was approximately 94.5%, and remained similar when using PICARD; but while 68% of the manual decisions included correct but redundant actions, only 3% of the actions included in decisions made when using PICARD were redundant. CONCLUSIONS The PICARD architecture is technically feasible and is functionally valid, and addresses the realistic continuous GL-based application requirements that we have defined; in particular, the requirement for care over significant time frames. The use of the PICARD architecture in the domain we examined resulted in enhanced completeness and in reduction of redundancies, and is potentially beneficial for general GL-based management of chronic patients.

Ability of expert physicians to structure clinical guidelines: reality versus perception

RATIONALE, AIMS AND OBJECTIVES Structuring Textual Clinical Guidelines (GLs) into a formal representation is a necessary prerequisite for supporting their automated application. We had developed a collaborative guideline-structuring methodology that involves expert physicians, clinical editors and knowledge engineers, to produce a machine-comprehensible representation for automated support of evidence-based, guideline-based care. Our goals in the current study were: (1) to investigate the perceptions of the expert physicians and clinical editors as to the relative importance, for the structuring process, of different aspects of the methodology; (2) to assess, for the clinical editors, the inter-correlations among (i) the reported level of understanding of the guideline structuring ontologys (knowledge schemes) features, (ii) the reported ease of structuring each feature and (iii) the actual objective quality of structuring. METHODS A clinical consensus regarding the contents of three guidelines was prepared by an expert in the domain of each guideline. For each guideline, two clinical editors independently structured the guideline into a semi-formal representation, using the Asbru guideline ontologys features. The quality of the resulting structuring was assessed quantitatively. Each expert physician was asked which aspects were most useful for formation of the consensus. Each clinical editor filled questionnaires relating to: (1) the level of understanding of the ontologys features (before the structuring process); (2) the usefulness of various aspects in the structuring process (after the structuring process); (3) the ease of structuring each ontological feature (after the structuring process). Subjective reports were compared with objective quantitative measures of structuring correctness. RESULTS Expert physicians considered having medical expertise and understanding the ontological features as the aspects most useful for creation of a consensus. Clinical editors considered understanding the ontological features and the use of the structuring tools as the aspects most useful for structuring guidelines. There was a positive correlation (R = 0.87, P < 0.001) between the reported ease of understanding ontological features and the reported ease of structuring those features. However, there was no significant correlation between the reported level of understanding the features - or the reported ease of structuring by using those features - and the objective quality of the structuring of these features in actual guidelines. CONCLUSIONS Aspects considered important for formation of a clinical consensus differ from those for structuring of guidelines. Understanding the features of a structuring ontology is positively correlated with the reported ease of using these features, but neither of these subjective reports correlated with the actual objective quality of the structuring using these features.

Towards a realistic clinical-guidelines application framework: desiderata, applications, and lessons learned

Clinicians can benefit from automated support to guideline (GL) application at the point of care. However, several conceptual dimensions should be considered for a realistic application: 1) The representation of the knowledge might be through structured text (semi-formal), or specified in a machine-comprehensible language (formal); 2) The availability of electronic patient data might be partial or full; 3) GL-based recommendations might be triggered by a human-initiated (synchronous) session, or data---driven (asynchronous). In addition, several requirements must be fulfilled, such as an evaluation of the GL application engine by a GL simulation engine. Finally, to apply multiple GLs, by multiple users, in multiple settings, the GL-application engine should be designed as an enterprise architecture that can plug into any Electronic Medical Record (EMR). We present an architecture fulfilling these desiderata, describe application examples with different conceptual dimensions and requirements, using our new GL-application engine, PICARD, discuss lessons learned, and briefly describe a clinical evaluation of the current framework in the domain of pre-eclampsia/toxemia of pregnancy.

MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients.

Use of the virtual medical record data model for communication among components of a distributed decision-support system

MobiGuide is a distributed decision-support system (DSS) that provides decision support for patients and physicians. Patients receive support using a light-weight Smartphone DSS linked to data arriving from wearable monitoring devices and physicians receive support via a web interface connected to a backend DSS that has access to an integrated personal health record (PHR) that stores hospital EMR data, monitoring data, and recommendations provided for the patient by the DSSs. The patient data model used by the PHR and by all the system components that interact in a service-oriented architecture is based on HL7s virtual medical record (vMR) model. We describe how we used and extended the vMR model to support communication between the system components for the complex workflow needed to support guidance of patients any time everywhere.