scholarly journals Guideline Formalization and Knowledge Representation for Clinical Decision Support

2013 ◽  
Vol 1 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Tiago Oliveira ◽  
José Neves ◽  
Paulo Novais
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Clinical Decision Support ◽  
Decision Model ◽  
Clinical Decision ◽  
Medical Community ◽  
Clinical Process ◽  
Generic Tasks ◽  
Medical Recommendations ◽  
Representation Formalism

The prevalence of situations of medical error and defensive medicine in healthcare institutions is a great concern of the medical community. Clinical Practice Guidelines are regarded by most researchers as a way to mitigate theseoccurrences; however, there is a need to make them interactive, easier to update and to deploy. This paper provides a model for Computer-Interpretable Guidelines based on the generic tasks of the clinical process, devised to be included in the framework of a Clinical Decision Support System. Aiming to represent medical recommendations in a simple and intuitive way. Hence, this work proposes a knowledge representation formalism that uses an Extension to Logic Programming to handle incomplete information. This model is used to represent different cases of missing, conflicting and inexact information with the aid of a method to quantify its quality. The integration of the guideline model with the knowledge representation formalism yields a clinical decision model that relies on the development of multiple information scenarios and the exploration of different clinical hypotheses.

scholarly journals Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Matthias Samwald ◽  
Jose Antonio Miñarro Giménez ◽  
Richard D Boyce ◽  
Robert R Freimuth ◽  
Klaus-Peter Adlassnig ◽  
...  
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Clinical Decision Support ◽  
Clinical Decision

scholarly journals A Comparison of Arden Syntax and Clinical Quality Language as Knowledge Representation Formalisms for Clinical Decision Support

2021 ◽  
Vol 12 (03) ◽  
pp. 495-506
Author(s):  
Andrey Soares ◽  
Robert A. Jenders ◽  
Robert Harrison ◽  
Lisa M. Schilling
Keyword(s):  
Decision Support ◽  
Knowledge Representation ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Data Models ◽  
Clinical Quality ◽  
Standard Data ◽  
Data Control ◽  
Language Characteristics ◽  
Arden Syntax

Abstract Objectives This article presents a comparative study of two Health Level Seven International (HL7) standards for clinical knowledge representation, the Arden Syntax and the Clinical Quality Language (CQL), regarding their expressiveness and utility to represent knowledge for clinical decision support (CDS) systems. Methods We compiled a concatenated set of features from both languages and made descriptive comparisons of 27 categories covering areas of language characteristics, data, control statements, and operators. Results Both Arden and CQL have similar constructs that can be used for representing CDS knowledge but also have unique constructs that could support distinct use cases. They have constructs that fully or partially address several of the categories used in the comparison, except for data models and terminologies in Arden and event triggering and iteration statements in CQL. Conclusion These standards can facilitate the sharing, management, and reuse of computable knowledge, and permit knowledge to be represented with their languages and converted to a machine-friendly executable code that can be shared and reused by other systems. Having support for standard data models and terminologies will continue to be a differential for adoption of a language. The HL7 working groups responsible for developing these standards can direct future development to enhance the functions of the standard and address the gaps identified in this study.

scholarly journals Development and use of a clinical decision support tool for behavioral health screening in primary care clinics

2017 ◽  
Vol 08 (02) ◽  
pp. 412-429 ◽  
Author(s):  
Timothy Burdick ◽  
Rodger Kessler
Keyword(s):  
Primary Care ◽  
Decision Support ◽  
Behavioral Health ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Process ◽  
Primary Care Clinics ◽  
Clinical Decision Support Tool

SummaryObjective: Screening, brief intervention, and referral for treatment (SBIRT) for behavioral health (BH) is a key clinical process. SBIRT tools in electronic health records (EHR) are infrequent and rarely studied. Our goals were 1) to design and implement SBIRT using clinical decision support (CDS) in a commercial EHR; and 2) to conduct a pragmatic evaluation of the impact of the tools on clinical outcomes.Methods: A multidisciplinary team designed SBIRT workflows and CDS tools. We analyzed the outcomes using a retrospective descriptive convenience cohort with age-matched comparison group. Data extracted from the EHR were evaluated using descriptive statistics.Results: There were 2 outcomes studied: 1) development and use of new BH screening tools and workflows; and 2) the results of use of those tools by a convenience sample of 866 encounters. The EHR tools developed included a flowsheet for documenting screens for 3 domains (depression, alcohol use, and prescription misuse); and 5 alerts with clinical recommendations based on screening; and reminders for annual screening. Positive screen rate was 21% (≥1 domain) with 60% of those positive for depression. Screening was rarely positive in 2 domains (11%), and never positive in 3 domains. Positive and negative screens led to higher rates of documentation of brief intervention (BI) compared with a matched sample who did not receive screening, including changes in psychotropic medications, updated BH terms on the problem list, or referral for BH intervention. Clinical process outcomes changed even when screening was negative.Conclusions: Modified workflows for BH screening and CDS tools with clinical recommendations can be deployed in the EHR. Using SBIRT tools changed clinical process metrics even when screening was negative, perhaps due to conversations about BH not captured in the screening flowsheet. Although there are limitations to the study, results support ongoing investigation.Citation: Burdick TE, Kessler RS. Development and use of a clinical decision support tool for behavioral health screening in primary care clinics. Appl Clin Inform 2017; 8: 412–429 https://doi.org/10.4338/ACI-2016-04-RA-0068

Clinical decision support

2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision
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