scholarly journals A Clinical Decision Support System Promotes Shared Decision-Making and Cardiovascular Risk Factor Management

2016 ◽  
Vol 3 (3) ◽  
pp. 218
Author(s):  
JoAnn M Sperl-Hillen ◽  
A.L. Crain ◽  
Heidi L Ekstrom ◽  
Karen L Margolis ◽  
Patrick J O'Connor
Keyword(s):  
Decision Making ◽  
Risk Factor ◽  
Decision Support ◽  
Cardiovascular Risk ◽  
Clinical Decision Support ◽  
Cardiovascular Risk Factor ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Shared Decision ◽  
Risk Factor Management

859-P: Impact of a Prediabetes Clinical Decision Support System on CV Risk Factor Control in Primary Care: Randomized Trial

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 859-P
Author(s):  
JAY R. DESAI ◽  
A. LAUREN CRAIN ◽  
DANIEL SAMAN ◽  
JOANN M. SPERL-HILLEN ◽  
CLAYTON ALLEN ◽  
...  
Keyword(s):  
Primary Care ◽  
Risk Factor ◽  
Decision Support ◽  
Decision Support System ◽  
Randomized Trial ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Risk Factor Control

PP145 VALIDATE Methodology For A Medication-Related Clinical Decision Support System: Innovating Or Going Back To Basics?

2021 ◽  
Vol 37 (S1) ◽  
pp. 21-22
Author(s):  
Carla Fernandez-Barceló ◽  
Elena Calvo-Cidoncha ◽  
Laura Sampietro-Colom
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Health Technology ◽  
Problem Definition ◽  
Cochrane Library

IntroductionIn the past decade, health technology assessment (HTA) has narrowed its scope to the analysis of mainly clinical and economic benefits. However, twenty-first century technology challenges require the need for more holistic assessments to obtain accurate recommendations for decision-making, as it was in HTA's foundations. VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology approaches complex technologies holistically to provide a deeper understanding of the problem through analysis of the heterogeneity of stakeholders’ views, allowing for more comprehensive HTAs. This study aimed to assess a pharmaceutical clinical decision support system (CDSS) using VALIDATE.MethodsA systematic review of the empirical evidence on CDSS was conducted according to PRISMA guidelines. PubMed, the Cochrane Library, and Web of Science databases were searched for literature published between 2000 and 2020. Additionally, a review of grey literature and semi-structured interviews with different hospital stakeholders (pharmacists, physicians, computer engineers, etc.) were conducted. Content analysis was used for data integration.ResultsPreliminary literature results indicated consensus regarding the effectiveness of CDSS. Nevertheless, when including multistakeholder views, CDSS appeared to not be fully accepted in clinical practice. The main reasons for this appeared to be alert fatigue and disruption of workflow. Preliminary results based on information from the literature were contrasted with stakeholder interview responses.ConclusionsIncorporation of facts and stakeholder values into the problem definition and scoping for a health technology is essential to properly conduct HTAs. The lack of an inclusive multistakeholder scoping can lead to inaccurate information, and in this particular case to suboptimal CDSS implementation concerning decision-making for the technology being evaluated.

scholarly journals A Clinical Decision Support System based on Ontology and Causal Reasoning Models

2019 ◽  
Vol 14 (2) ◽  
pp. 187-197
Author(s):  
Nur Raidah Rahim ◽  
Sharifalillah Nordin ◽  
Rosma Mohd Dom
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Causal Reasoning ◽  
Clinical Decision ◽  
Decision Making Process ◽  
Graphical Modeling

  Clinical decision support system (CDSS) is promising in assisting physicians for improving decision-making process and facilitates healthcare services. In medicine, causality has become the main concern throughout healthcare and decision-making. Causality is necessary for understanding all structures ofscientific reasoning and for providing a coherent and sufficient explanation for any event. However, thereare lack of existing CDSS that provide causal reasoning for the presented outcomes or decisions. Theseare necessary for showing reliability of the outcomes, and helping the physicians in making properdecisions. In this study, an ontology-based CDSS model is developed based on several key concepts andfeatures of causality and graphical modeling techniques. For the evaluation process, the Pellet reasoneris used to evaluate the consistency of the developed ontology model. In addition, an evaluation toolknown as Ontology Pitfall Scanner is used for validating the ontology model through pitfalls detection.The developed ontology-based CDSS model has potentials to be applied in clinical practice and helpingthe physicians in decision-making process. Keywords: clinical decision support system, ontology, causality, causal reasoning, graphical modeling

scholarly journals Implementing an Electronic Clinical Decision Support Tool Into Routine Care: A Qualitative Study of Stakeholders’ Perceptions of a Post-Mastectomy Breast Reconstruction Tool

2021 ◽  
Vol 6 (2) ◽  
pp. 238146832110420
Author(s):  
Jessica Boateng ◽  
Clara N. Lee ◽  
Randi E. Foraker ◽  
Terence M. Myckatyn ◽  
Kimi Spilo ◽  
...  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Breast Reconstruction ◽  
Shared Decision Making ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Routine Care ◽  
Evidence Based ◽  
Shared Decision ◽  
Consultation Time

Objective. To explore barriers and facilitators to implementing an evidence-based clinical decision support (CDS) tool (BREASTChoice) about post-mastectomy breast reconstruction into routine care. Materials and Methods. A stakeholder advisory group of cancer survivors, clinicians who discuss and/or perform breast reconstruction in women with cancer, and informatics professionals helped design and review the interview guide. Based on the Consolidated Framework for Implementation Research (CFIR), we conducted qualitative semistructured interviews with key stakeholders (patients, clinicians, informatics professionals) to explore intervention, setting characteristics, and process-level variables that can impact implementation. Interviews were transcribed, coded, and analyzed based on the CFIR framework using both inductive and deductive methods. Results. Fifty-seven potential participants were contacted; 49 (85.9%) were eligible, and 35 (71.4%) were enrolled, continuing until thematic saturation was reached. Participants consisted of 13 patients, 13 clinicians, and 9 informatics professionals. Stakeholders thought that BREASTChoice was useful and provided patients with an evidence-based source of information about post-mastectomy breast reconstruction, including their personalized risks. They felt that BREASTChoice could support shared decision making, improve workflow, and possibly save consultation time, but were uncertain about the best time to deliver BREASTChoice to patients. Some worried about cost, data availability, and security of integrating the tool into an electronic health record. Most acknowledged the importance of showing clinical utility to gain institutional buy-in and encourage routine adoption. Discussion and Conclusion. Stakeholders felt that BREASTChoice could support shared decision making, improve workflow, and reduce consultation time. Addressing key questions such as cost, data integration, and timing of delivering BREASTChoice could build institutional buy-in for CDS implementation. Results can guide future CDS implementation studies.

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