Clinical Decision Support Complements Evidence-Based Decision Making in Dental Practice

2007 ◽  
Vol 7 (1) ◽  
pp. 1-5 ◽  
Author(s):  
Michael G. Newman
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evidence Based ◽  
Dental Practice ◽  
Evidence Based Decision Making

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.

scholarly journals Factors that impact the adoption of clinical decision support systems (CDSS) in healthcare settings for evidence-based decision making

2020 ◽  
Author(s):  
MAH LAKA ◽  
ADRIANA MILAZZO ◽  
TRACY MERLIN
Keyword(s):  
Primary Care ◽  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Evidence Based ◽  
Perceived Benefits ◽  
Utaut Model ◽  
Healthcare Settings ◽  
Evidence Based Decision Making

Abstract Background: Clinical decision support systems (CDSS) are designed to promote evidence-based patient care and shared-decision making in healthcare settings. Despite these benefits, adoption and long-term use of the systems remain limited. There is a need to identify different factors that influence CDSS adoption in healthcare settings.Objective: The Unified Theory of Acceptance and Use of Technology (UTAUT) model was applied to determine different factors that influence the adoption of CDSS in healthcare settings.Methods. A cross-sectional online survey examining clinicians’ perceptions about CDSS implementation in hospital and primary care settings in Australia was undertaken from June - October 2019. Multivariate logistic regression was used to examine the association of UTAUT moderators (age, gender and experience) and care settings (hospital and primary care) with perceived benefits, barriers and facilitators to CDSS adoption.Results: Access to information required for evidence-based decision making, and improvements in quality and safety of patient care were the most common perceived benefits of CDSS. Mapping of different barriers and facilitators to the UTAUT model indicated that ease of use (effort expectancy), perceived benefit (performance expectancy) and a facilitating environment greatly influence the adoption of CDSS. Respondents indicated that systems providing a better fit between relevance, content and timeliness greatly facilitates uptake. Adoption of CDSS also depends on the ability of an organisation to create a facilitating environment that can help address the lack of users’ trust in these systems. The type of healthcare setting was found to be a significant predictor of lack of confidence in the content within CDSS, threat to professional autonomy, and time constraints as barriers to CDSS implementation. Therefore, setting dynamics as well as user-specific requirements need to be considered to improve the acceptability and use of CDSS. Conclusion: Our study has explored different factors that may help address implementation challenges for CDSS. By combining internal factors of users’ inclination and perceptions about the system’s perceived benefits, coupled with external factors of system design requirements, training and support, and stakeholders’ consultations, our findings highlight the need for a holistic implementation framework to enable effective CDSS adoption.

Clinical Decision Support Chairside Tools for Evidence-Based Dental Practice

2008 ◽  
Vol 8 (3) ◽  
pp. 119-132 ◽  
Author(s):  
George K. Merijohn ◽  
James D. Bader ◽  
Julie Frantsve-Hawley ◽  
Krishna Aravamudhan
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evidence Based ◽  
Dental Practice

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.

Clinical Decision Support System Adoption & Implementation in African Health Systems: A SWOT Analysis. (Preprint)

2021 ◽  
Author(s):  
Victoria Oluwafunmilola Kolawole
Keyword(s):  
Developing Countries ◽  
Decision Support ◽  
Health Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support System ◽  
Swot Analysis ◽  
Clinical Decision ◽  
Developed Countries ◽  
Evidence Based ◽  
African Health

BACKGROUND The clinical decision support system (CDSS) has been an important achievement of health technology in the 21st century. In developed countries, it has transformed the way health services are being delivered and has shown to be a tool that reduces medical errors and misdiagnoses in Healthcare. However, CDSS remains underutilized in developing countries in Africa. OBJECTIVE This study aims to review the literature to improve our understanding of the “strengths, weaknesses, opportunities and threats (SWOT)” associated with CDSS implementation in African health systems. METHODS This study included a literature review conducted in PubMed with a total of 19 articles between the year 2010 to date (past 10years) reviewed for key themes and categorized into one of 4 possible areas within the SWOT analysis. RESULTS Articles reviewed showed common strengths of efficiency at the workplace, Improved healthcare quality, benefits in developed countries, good examples of evidence-based decision making. unreliable electric power supply, inconsistent Internet connectivity, clinician's limited computer skills, and lack of enough published evidence of benefits in developing countries are listed as a weakness. The opportunities are high demand for evidence-based practice in healthcare, a strong demand for quality healthcare, growing interest to use modern technologies. The common threats identified are government policy, political instability, low funding and resistance of use by providers. CONCLUSIONS There’s the need to work on the technical, organizational and financial barriers to ensure high adoption and implementation of the CDSS in African Health systems. Also, the lag on the knowledge available on its impact in developing countries must be worked on by supporting more studies to add to the body of knowledge.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

Face-to-Face: Resident-led Radiology Medicine Rounds Facilitate Evidence-based Processes for Clinical Decision Support

2020 ◽  
Author(s):  
Adam E. Goldman-Yassen ◽  
Sara B. Strauss ◽  
Peter P. Vlismas ◽  
Anand D. Jagannath ◽  
Marshall Yuan ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evidence Based ◽  
Face To Face
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