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author:("Shahar, duval")
1.  Supporting Shared Decision Making within the MobiGuide Project 
AMIA Annual Symposium Proceedings  2013;2013:1175-1184.
This paper describes our approach for fostering and facilitating communication among patients and caregivers in the context of shared decision making, i.e., when decisions must be taken not only on the basis of scientific evidence but also of the patient’s preferences and context. This happens because clinical practice guidelines cannot provide recommendations for every possible situation, and cannot foresee every change in a patient’s context, which might imply the deviation from a previously acknowledged recommendation. Within the EU-funded project MobiGuide (www.mobiguide-project.eu), supporting remote patient management, we propose decision theory as a methodological framework for a tool that, during face to face encounters, is used to tailor pre-defined, generic decision models to the individual patient, by involving the patient himself in the customization of the model parameters. Although this approach is not appropriate for all patients, it leads, in well-chosen cases, to a more informed choice, with potentially better treatment compliance.
PMCID: PMC3900138  PMID: 24551401
2.  Evaluation of an Architecture for Intelligent Query and Exploration of Time-Oriented Clinical Data 
Objective
Evaluate KNAVE-II, a knowledge-based framework for visualization, interpretation, and exploration of longitudinal clinical data, clinical concepts and patterns. KNAVE-II mediates queries to a distributed temporal-abstraction architecture (IDAN), which uses a knowledge-based problem-solving method specializing in on-the-fly computation of clinical queries.
Methods
A two-phase, balanced cross-over study to compare efficiency and satisfaction of a group of clinicians when answering queries of variable complexity about time-oriented clinical data, typical for oncology protocols, using KNAVE-II, versus standard methods: Both paper charts and a popular electronic spreadsheet (ESS) in Phase I; an ESS in Phase II. The measurements included the time required to answer and the correctness of answer for each query and each complexity category, and for all queries, assessed versus a predetermined gold standard set by a domain expert. User satisfaction was assessed by the Standard Usability Score (SUS) tool-specific questionnaire and by a “Usability of Tool Comparison” comparative questionnaire developed for this study.
Results
In both evaluations, subjects answered higher-complexity queries significantly faster using KNAVE-II than when using paper charts or an ESS up to a mean of 255 seconds difference per query versus the ESS for hard queries (p=0.0003) in the second evaluation. Average correctness scores when using KNAVE-II versus paper charts, in the first phase, and the ESS, in the second phase, were significantly higher over all queries. In the second evaluation, 91.6% (110/120) of all of the questions asked within queries of all levels produced correct answers using KNAVE-II, opposed to only 57.5% (69/120) using the ESS (p<0.0001). User satisfaction with KNAVE-II was significantly superior compared to using either a paper chart or the ESS (p=0.006). Clinicians ranked KNAVE-II superior to both paper and the ESS.
Conclusions
An evaluation of the functionality and usability of KNAVE-II and its supporting knowledge-based temporal-mediation architecture has produced highly encouraging results regarding saving of physician time, enhancement of accuracy of clinical assessment, and user satisfaction.
doi:10.1016/j.artmed.2008.03.006
PMCID: PMC2853917  PMID: 18442899
Medical Informatics; Clinical Decision-Support Systems; Human-Computer Interface; Information Visualization; Knowledge-Based Systems; Temporal Reasoning; Intelligent User Interfaces
3.  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.
doi:10.2174/1874431101004010255
PMCID: PMC3099486  PMID: 21611137
Medical informatics; clinical guidelines; decision support systems; knowledge representation; knowledge acquisition; knowledge bases; ontologies; information retrieval; human computer interaction; artificial intelligence; digital libraries; service oriented architecture.
4.  Medical Temporal-Knowledge Discovery via Temporal Abstraction 
Medical knowledge includes frequently occurring temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated temporal data mining. However, multivariate time-oriented data are often present at various levels of abstraction and at multiple temporal granularities, requiring a transformation into a more abstract, yet uniform dimension suitable for mining. Temporal abstraction (of both the time and value dimensions) can transform multiple types of point-based data into a meaningful, time-interval-based data representation, in which significant, interval-based temporal patterns can be discovered. We introduce a modular, fast time-interval mining method, KarmaLego, which exploits the transitivity inherent in temporal relations. We demonstrate the usefulness of KarmaLego in finding meaningful temporal patterns within a set of records of diabetic patients; several patterns seem to have a different frequency depending on gender. We also suggest additional uses of the discovered patterns for temporal clustering of the mined population and for classifying multivariate time series.
PMCID: PMC2815492  PMID: 20351898
5.  A Comparative Evaluation of Full-text, Concept-based, and Context-sensitive Search 
Objectives
Study comparatively (1) concept-based search, using documents pre-indexed by a conceptual hierarchy; (2) context-sensitive search, using structured, labeled documents; and (3) traditional full-text search. Hypotheses were: (1) more contexts lead to better retrieval accuracy; and (2) adding concept-based search to the other searches would improve upon their baseline performances.
Design
Use our Vaidurya architecture, for search and retrieval evaluation, of structured documents classified by a conceptual hierarchy, on a clinical guidelines test collection.
Measurements
Precision computed at different levels of recall to assess the contribution of the retrieval methods. Comparisons of precisions done with recall set at 0.5, using t-tests.
Results
Performance increased monotonically with the number of query context elements. Adding context-sensitive elements, mean improvement was 11.1% at recall 0.5. With three contexts, mean query precision was 42% ± 17% (95% confidence interval [CI], 31% to 53%); with two contexts, 32% ± 13% (95% CI, 27% to 38%); and one context, 20% ± 9% (95% CI, 15% to 24%). Adding context-based queries to full-text queries monotonically improved precision beyond the 0.4 level of recall. Mean improvement was 4.5% at recall 0.5. Adding concept-based search to full-text search improved precision to 19.4% at recall 0.5.
Conclusions
The study demonstrated usefulness of concept-based and context-sensitive queries for enhancing the precision of retrieval from a digital library of semi-structured clinical guideline documents. Concept-based searches outperformed free-text queries, especially when baseline precision was low. In general, the more ontological elements used in the query, the greater the resulting precision.
doi:10.1197/jamia.M1953
PMCID: PMC2213470  PMID: 17213502
6.  A Graphical Framework for Specification of Clinical Guidelines at Multiple Representation Levels 
Formalization of a clinical guideline for purposes of automated application and quality assessment mainly involves conversion of its free-text representation into a machine comprehensible representation, i.e., a formal language, thus enabling automated support. The main issues involved in this process are related to the collaboration between the expert physician and the knowledge engineer. We introduce GESHER - a graphical framework for specification of clinical guidelines at multiple representation levels. The GESHER architecture facilitates incremental specification through a set of views adapted to each representation level, enabling this process to proceed smoothly and in a transparent fashion, fostering extensive collaboration among the various types of users. The GESHER framework supports specification of guidelines at multiple representation levels, in more than one specification language, and uses the DeGeL digital guideline library architecture as its knowledge base. The GESHER architecture also uses a temporal abstraction knowledge base to store its declarative knowledge, and a standard medical-vocabularies server for generic specification of key terms, thus enabling reuse of the specification at multiple sites.
PMCID: PMC1560835  PMID: 16779126
7.  Applying Hybrid-Asbru Clinical Guidelines Using the Spock System 
Clinical Guidelines are a major tool for improving the quality of medical care. Currently, a major research direction is automating the application of guidelines at the point of care. To support that automation, several requirements must be fulfilled, such as specification in a machine-interpretable format, and connection to an electronic patent record. We propose an innovative approach to guideline application, which capitalizes on our Digital electronic Guidelines Library (DeGeL). The DeGeL framework includes a new hybrid model for incremental specification of free-text guidelines, using several intermediate representations. The new approach was implemented, in the case of the Asbru guideline ontology, as the Spock system. Spock’s hybrid application engine supports application of guideline represented at an intermediate format. Spock was evaluated in a preliminary fashion by applying several guidelines to sample patient data.
PMCID: PMC1560650  PMID: 16779161
8.  A Framework for Intelligent Visualization of Multiple Time-Oriented Medical Records 
Management of patients, especially chronic patients, requires presentation and processing of very large amounts of time-oriented clinical data. Using regular means such as text or tables is often ineffective, thus we propose to use the visual presentation of the information in decision support, especially in the medical domain. Displaying only raw data is not sufficient, because it still requires the user to derive meaningful conclusions from large amount of data. In order to support the computation process, we provide automated mechanisms for temporal abstraction. These mechanisms perform derivation of context-specific, interval-based abstract concepts from raw time-stamped clinical data, by using a domain-specific knowledge base. Then, these abstractions can be visualized and explored. In addition, in many cases (e.g. when comparing the effect of new drugs on various groups of patients) a view of multiple records is more effective than a view of each individual record separately. We have designed and implemented a system called VISITORS (VisualizatIon of Time-Oriented RecordS) which includes several tools for intelligent visualization and exploration of raw data and abstracted concepts for multiple patient records.
PMCID: PMC1560450  PMID: 16779071
9.  Medical Quality Assessment by Scoring Adherence to Guideline Intentions 
Quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe an approach for evaluating and consistently scoring clinician adherence to medical guidelines using the intentions of guideline authors. We present the Quality Indicator Language (QUIL) that may be used to formally specify quality constraints on physician behavior and patient outcomes derived from medical guidelines. We present a modeling and scoring methodology for consistently evaluating multi-step and multi-choice guideline plans based on guideline intentions and their revisions.
doi:10.1197/jamia.M1236
PMCID: PMC419428
10.  A Distributed, Collaborative, Structuring Model for a Clinical-Guideline Digital-Library 
The Digital Electronic Guideline Library (DeGeL) is a Web-based framework and a set of distributed tools that facilitate gradual conversion of clinical guidelines from free text, through semi-structured text, to a fully structured, executable representation. Thus, guidelines exist in a hybrid, multiple-format representation The three formats support increasingly sophisticated computational tasks. The tools perform semantic markup, classification, search, and browsing, and support computational modules that we are developing, for run-time application and retrospective quality assessment. We describe the DeGeL architecture and its collaborative-authoring authorization model, which is based on (1) multiple medical-specialty authoring groups, each including a group manager who controls group authorizations, and (2) a hierarchical authorization model based on the different functions involved in the hybrid guideline-specification process. We have implemented the core modules of the DeGeL architecture and demonstrated distributed markup and retrieval using the knowledge roles of two guidelines ontologies (Asbru and GEM). We are currently evaluating several of the DeGeL tools.
PMCID: PMC1480281  PMID: 14728241
11.  Developing Quality Indicators and Auditing Protocols from Formal Guideline Models: Knowledge Representation and Transformations 
Automated quality assessment of clinician actions and patient outcomes is a central problem in guideline- or standards-based medical care. In this paper we describe a model representation and algorithm for deriving structured quality indicators and auditing protocols from formalized specifications of guidelines used in decision support systems. We apply the model and algorithm to the assessment of physician concordance with a guideline knowledge model for hypertension used in a decision-support system. The properties of our solution include the ability to derive automatically (1) context-specific and (2) case-mix-adjusted quality indicators that (3) can model global or local levels of detail about the guideline (4) parameterized by defining the reliability of each indicator or element of the guideline.
PMCID: PMC1480136  PMID: 14728124
13.  Interactive Visualization and Exploration of Time-oriented Clinical Data Using a Distributed Temporal-Abstraction Architecture 
KNAVE-II is a system for visualization and exploration of large amounts of time-oriented clinical data and of multiple levels of clinically meaningful abstractions derivable from these data. KNAVE-II uses a distributed temporal-abstraction architecture that integrates a set of knowledge services, each interacting with a domain-specific knowledge source, a set of data-access services, each interacting with a clinical data source, and a computational service for deriving knowledge-based abstractions of the data.
PMCID: PMC1480003  PMID: 14728507
15.  Semi-automated Entry of Clinical Temporal-abstraction Knowledge 
Objectives: The authors discuss the usability of an automated tool that supports entry, by clinical experts, of the knowledge necessary for forming high-level concepts and patterns from raw time-oriented clinical data.
Design: Based on their previous work on the RESUMÉ system for forming high-level concepts from raw time-oriented clinical data, the authors designed a graphical knowledge acquisition (KA) tool that acquires the knowledge required by RÉSUMÉ. This tool was designed using Protégé, a general framework and set of tools for the construction of knowledge-based systems. The usability of the KA tool was evaluated by three expert physicians and three knowledge engineers in three domains—the monitoring of children's growth, the care of patients with diabetes, and protocol-based care in oncology and in experimental therapy for AIDS. The study evaluated the usability of the KA tool for the entry of previously elicited knowledge.
Measurements: The authors recorded the time required to understand the methodology and the KA tool and to enter the knowledge; they examined the subjects' qualitative comments; and they compared the output abstractions with benchmark abstractions computed from the same data and a version of the same knowledge entered manually by RÉSUMÉ experts.
Results: Understanding RÉSUMÉ required 6 to 20 hours (median, 15 to 20 hours); learning to use the KA tool required 2 to 6 hours (median, 3 to 4 hours). Entry times for physicians varied by domain—2 to 20 hours for growth monitoring (median, 3 hours), 6 and 12 hours for diabetes care, and 5 to 60 hours for protocol-based care (median, 10 hours). An increase in speed of up to 25 times (median, 3 times) was demonstrated for all participants when the KA process was repeated. On their first attempt at using the tool to enter the knowledge, the knowledge engineers recorded entry times similar to those of the expert physicians' second attempt at entering the same knowledge. In all cases RÉSUMÉ, using knowledge entered by means of the KA tool, generated abstractions that were almost identical to those generated using the same knowledge entered manually.
Conclusion: The authors demonstrate that the KA tool is usable and effective for expert physicians and knowledge engineers to enter clinical temporal-abstraction knowledge and that the resulting knowledge bases are as valid as those produced by manual entry.
PMCID: PMC61392  PMID: 10579607
18.  Plan Recognition and Revision in Protocol-Based Care 
Automated support for protocol-based care can be viewed as a collaborative effort of two planning agents: the physician and an automated planner. Achieving this collaboration with sufficient flexibility involves a recognition of the physician's intentions and plans, and a consideration of potential revisions to the protocol's or to the guideline's therapy plan.
PMCID: PMC2579826

Results 1-18 (18)