Syed Sibte Raza Abidi

A knowledge creation info-structure to acquire and crystallize the tacit knowledge of health-care experts

Tacit knowledge of health-care experts is an important source of experiential know-how, yet due to various operational and technical reasons, such health-care knowledge is not entirely harnessed and put into professional practice. Emerging knowledge-management (KM) solutions suggest strategies to acquire the seemingly intractable and nonarticulated tacit knowledge of health-care experts. This paper presents a KM methodology, together with its computational implementation, to 1) acquire the tacit knowledge possessed by health-care experts; 2) represent the acquired tacit health-care knowledge in a computational formalism-i.e., clinical scenarios-that allows the reuse of stored knowledge to acquire tacit knowledge; and 3) crystallize the acquired tacit knowledge so that it is validated for health-care decision-support and medical education systems.

Healthcare knowledge management: the art of the possible

Healthcare knowledge management is an active, yet not a well characterized research topic. In this chapter, we attempt to characterize healthcare knowledge management and highlight the practical aspects of healthcare knowledge management vis-a-vis knowledge-centric services that aim to improve healthcare delivery and health outcomes. We investigate healthcare knowledge management from various perspectives--such as epistemological, organizational learning, knowledge-theoretic and functional. From an epistemological perspective we elicit the different types of healthcare knowledge and the heterogeneous modalities representing it. From a functional perspective we present a suite of healthcare knowledge management services that aim to assist healthcare stakeholders. From a knowledge-theoretic perspective, we present the frontiers of healthcare knowledge management, in particular for patient management through decision support and care planning via a Semantic Web based healthcare knowledge management framework. We conclude by highlighting the role and future outlook of healthcare knowledge management.

Bridging the Gap: Knowledge Seeking and Sharing in a Virtual Community of Emergency Practice

Disparities exist between rural and urban emergency departments with respect to knowledge resources such as online journals and clinical specialists. As knowledge is a critical element in the delivery of quality care, a web-based learning project was proposed to address the knowledge needs of emergency clinicians. One objective of this project was to evaluate the effectiveness of the online environment for knowledge exchange among rural and urban emergency clinicians. Descriptive and content analysis of the online discussion board revealed 202 postings with rural participants contributing the largest number of postings (75%; 152/202). Postings were used to establish a clinical presence (87/202), seek clinical information (52/202), and share clinical information (63/202). Postintervention survey results indicate that this modality introduced participants to new clinical experts and resources. The results provide direction for design of a virtual community of practice, which may reduce current knowledge resource disparities.

Leveraging XML-based electronic medical records to extract experiential clinical knowledge: An automated approach to generate cases for medical case-based reasoning systems

Case-based reasoning (CBR)-driven medical diagnostic systems demand a critical mass of up-to-date diagnostic-quality cases that depict the problem-solving methodology of medical experts. In practical terms, procurement of CBR-compliant cases is quite challenging, as this requires medical experts to map their experiential knowledge to an unfamiliar computational formalism. In this paper, we propose a novel medical knowledge acquisition approach that leverages routinely generated electronic medical records (EMRs) as an alternate source for CBR-compliant cases. We present a methodology to autonomously transform XML-based EMR to specialized CBR-compliant cases for CBR-driven medical diagnostic systems. Our multi-stage methodology features: (a) collection of heterogeneous EMR from Internet-accessible EMR repositories via intelligent agents, (b) automated transformation of both the structure and content of generic EMR to specialized CBR-compliant cases, and (c) inductive estimation of the weight of each case-defining attribute. The computational implementation of our methodology is presented as case acquisition and transcription info-structure (CATI).

Semantic Web Framework for Knowledge-Centric Clinical Decision Support Systems

Lately, there have been considerable efforts to computerize Clinical Practice Guidelines (CPG) so that they can be executed via Clinical Decision Support Systems (CDSS) at the point of care. We present a Semantic Web framework to both model and execute the knowledge within a CPG to develop knowledge-centric CDSS. Our approach entails knowledge modeling through a synergy between multiple ontologies---i.e. a domain ontology, CPG ontology and patient ontology. We develop decision-rules based on the ontologies, and execute them with a proof engine to derive CPG-based patient specific recommendations. We present a prototype of our CPG-based CDSS to execute the CPG for Follow-up after Treatment for Breast Cancer.

Applying Social Network Analysis to Understand the Knowledge Sharing Behaviour of Practitioners in a Clinical Online Discussion Forum

Background Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment—an online discussion forum—for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. Objective The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Methods Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. Results The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional communication, but a dearth of non-nurse participants has been identified as a shortcoming. Conclusions The results of the analysis suggest that the discussion forum is active and healthy, and that, though few, the interprofessional and interinstitutional ties are strong.

Ontology Engineering to Model Clinical Pathways: Towards the Computerization and Execution of Clinical Pathways

Clinical pathways translate evidence-based recommendations into locally practicable, process-specific algorithms that reduce practice variations and optimize quality of care. Our objective was to abstract practice-oriented knowledge from a cohort of real clinical pathways and represent this knowledge as a clinical pathway ontology. We employed a four step methodology: (1) knowledge source identification and classification of clinical pathways according to variations in setting, stage of care, patient type, outcome and specialty; (2) iterative knowledge abstraction using grounded theory; (3) ontology engineering as adapted from the Model-based Incremental Knowledge Engineering approach; and, (4) ontology evaluation through encoding a sample of real clinical pathways. We present our clinical pathway ontology that offers a detailed ontological model describing the structure and function of clinical pathways. Our ontology can potentially integrate with a healthcare semantic web, and ontologies for clinical practice guidelines, patients and institutions to form the foundational knowledge for generating patient-specific CarePlans.

Analyzing Data Clusters: A Rough Sets Approach to Extract Cluster-Defining Symbolic Rules

We present a strategy, together with its computational implementation, to intelligently analyze the internal structure of inductively-derived data clusters in terms of symbolic cluster-defining rules. We present a symbolic rule extraction workbench that leverages rough sets theory to inductively extract CNF form symbolic rules from unannotated continuous-valued data-vectors. Our workbench purports a hybrid rule extraction methodology, incorporating a sequence of methods to achieve data clustering, data discretization and eventually symbolic rule discovery via rough sets approximation. The featured symbolic rule extraction workbench will be tested and analyzed using biomedical datasets.

Healthcare Knowledge Sharing: Purpose, Practices, and Prospects

In knowledge management parlance, knowledge sharing can be regarded as a systematically planned and managed activity involving a group of like-minded individuals engaged in sharing their knowledge resources, insights, and experiences for a defined objective. The objective of knowledge sharing may span from organizational learning, to collaborative problem solving, to peer support to capacity building. These objectives entail the explication of knowledge and facilitating its flow throughout a community of practice, i.e. a group of individuals who share a common interest, need, or enterprise towards the knowledge being shared. In a knowledge sharing set-up, the overall available knowledge is perceived as the collection of individual knowledge resources such that the entire knowledge resource is viewed as a community-owned commodity [1]. The dynamics of knowledge sharing are complex, involving an active interplay between an assortment of determinants, such as culture, community, incentives, medium, context, needs, motivation, facilitation, outreach, ubiquity, and, most importantly, trust [2].

An Ontology-Based Electronic Medical Record for Chronic Disease Management

Effective chronic disease management ensures better treatment and reduces medical costs. Representing knowledge through building an ontology for Electronic Medical Records (EMRs) is important to achieve semantic interoperability among healthcare information systems and to better execute decision support systems. In this paper, an ontology-based EMR focusing on Chronic Disease Management is proposed. The W3C Computer-based Patient Record ontology [7] is customized and augmented with concepts and attributes from the Western Health Infostructure Canada chronic disease management model [2] and the American Society for Testing and Materials International EHR. The result is an EMR ontology capable of representing knowledge about chronic disease. All of the clinical actions of the proposed ontology were found to map to HL7 RIM classes. Such an EMR ontology for chronic disease management can support reasoning for clinical decision support systems as well as act as a switching language from one EMR standard to another for chronic disease knowledge.