Abstract
Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.
Highlights
Background and IntroductionEvidence-based practice [1,2] has a long history, but evidence indicates that many opportunities to use research to inform health decision-making are currently being missed [3,4,5]
This tool utilizes the HL7 clinical document architecture (CDA) to identify relevant terms from different sections and generate a query. Both InfoButtons and CDAPubMed use electronic patient records to generate queries that are further refined by the users. We address this issue by utilizing the clinical decision support system (CDSS) knowledge base (KB) instead of the electronic patient record in order to minimize user involvement during query construction
The proposed method was tested by applying the knowledge base of Smart CDSS [26,33] to the oral cavity site within the head and neck cancer domain
Summary
Evidence-based practice [1,2] has a long history, but evidence indicates that many opportunities to use research to inform health decision-making are currently being missed [3,4,5]. For any evidence-based system to efficiently work in a domain, the context of that domain plays a critical role. Context provides the features for query generation in order to approach relevant information. A clinical decision support system (CDSS) can be considered one of the potential sources to be employed for automatic query construction to retrieve research evidence from online resources. CDSSs are widely used around the world [6,7], and “Meaningful Use” regulations for electronic health records (EHR) [8] considered CDSS an essential feature of EHR. Every CDSS has at least two core components: the knowledge base (KB)
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