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author:("Shalom, ere")
1.  Developing Nursing Computer Interpretable Guidelines: a Feasibility Study of Heart Failure Guidelines in Homecare. 
AMIA Annual Symposium Proceedings  2013;2013:1353-1361.
Homecare is the fastest growing healthcare sector and evidence based information systems are critically needed. Nurses provide most of the care in homecare setting, yet there is a lack of knowledge on the feasibility of applying existing methodologies to generate computer interpretable nursing guidelines for home care. This study examined the feasibility of encoding homecare nursing heart failure guideline into a computer interpretable format. First, we achieved experts’ consensus on the relevant guideline. Then, after training on the graphical tool for gradual knowledge specification (Gesher), we generated a comprehensive, hierarchical and time-oriented computer interpretable guideline using one of the guideline modeling languages (Asbru). The final guideline included 167 recommendations and experts’ evaluation confirmed the adequacy of guideline knowledge representation. Future work should expand the applicability of our methodology and tools to nursing specialties other than heart failure and develop methods for comprehensive quality evaluation of the resulting guidelines.
PMCID: PMC3900150  PMID: 24551412
2.  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.
3.  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
4.  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

Results 1-4 (4)