2023
DOI: 10.1136/openhrt-2023-002432
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Harnessing the power of clinical decision support systems: challenges and opportunities

Zhao Chen,
Ning Liang,
Haili Zhang
et al.

Abstract: Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore th… Show more

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Cited by 23 publications
(8 citation statements)
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“…Moreover, in interventional cardiology procedures like TAVR or device placements for conditions like ASD or VSD, AI assists in optimizing procedural planning, reducing complications, and enhancing patient safety. The integration of AI-driven decision support systems aids healthcare providers in delivering individualized cardiovascular care, improving treatment efficacy, and ultimately elevating patient outcomes in the management of chronic cardiac conditions [39].…”
Section: Procedures Time and Complication Rate Reductionmentioning
confidence: 99%
“…Moreover, in interventional cardiology procedures like TAVR or device placements for conditions like ASD or VSD, AI assists in optimizing procedural planning, reducing complications, and enhancing patient safety. The integration of AI-driven decision support systems aids healthcare providers in delivering individualized cardiovascular care, improving treatment efficacy, and ultimately elevating patient outcomes in the management of chronic cardiac conditions [39].…”
Section: Procedures Time and Complication Rate Reductionmentioning
confidence: 99%
“…AI technologies, including machine learning, natural language processing (NLP), and deep learning, have revolutionized the capabilities of CDSS, enabling it to process and interpret vast amounts of healthcare data with unprecedented speed and accuracy. Machine learning algorithms, a branch of AI that teaches computers to learn from data and improve their performance over time without 1,2 being explicitly programmed, such as neural networks and decision trees, empower CDSS to discern patterns, recognize correlations, and derive insights from complex datasets [3]. These algorithms continuously learn from new data inputs, refining their predictive capabilities and adapting to evolving clinical scenarios.…”
Section: Empowering Cdss: the Growing Role Of Aimentioning
confidence: 99%
“…According to the Agency for Healthcare Research and Quality (AHRQ), CDSS interventions have been shown to enhance healthcare quality by facilitating adherence to clinical guidelines, reducing medication errors, and minimizing adverse drug events [ 1 ]. Moreover, Chen et al emphasized the versatility and widespread applicability of CDSS, demonstrating its utility across various healthcare settings, ranging from primary care clinics to intensive care units [ 2 ].…”
Section: Introductionmentioning
confidence: 99%
“…One key aspect of personalized medicine is the use of AI-driven clinical decision support systems (CDSS) [19]. These systems analyze patient data, including clinical records, genetic information, and symptom profiles, to provide evidence-based treatment recommendations.…”
Section: B Ai-driven Clinical Decision Support Systemsmentioning
confidence: 99%