Impacts and Risks of Adopting Clinical Decision Support Systems

Bonney, Wilfred

doi:10.5772/16265

Cited by 12 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… 32 Identify the areas where the CDSS can have the greatest impact and determine the desired outcomes. 33 …”

Section: Current State Of Cdssmentioning

confidence: 99%

Harnessing the power of clinical decision support systems: challenges and opportunities

Chen,

Liang,

Zhang

et al. 2023

Open Heart

View full text Add to dashboard Cite

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 the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.

show abstract

“… 32 Identify the areas where the CDSS can have the greatest impact and determine the desired outcomes. 33 …”

Section: Current State Of Cdssmentioning

confidence: 99%

Harnessing the power of clinical decision support systems: challenges and opportunities

Chen,

Liang,

Zhang

et al. 2023

Open Heart

View full text Add to dashboard Cite

show abstract

“…The second type of CDSS is based on the non-knowledge based systems, which depends on machine learning techniques for the analysis of clinical based data (Alther and Reddy 2015). The architectural parts in the conventional structures of CDSS consist of; user, knowledge base, inference engine and user interface (Bonney 2011).…”

Section: Typesmentioning

confidence: 99%

Clinical Decision Support Systems: A Visual Survey

Farooq

Khan

Niazi

et al. 2018

IJCAI

View full text Add to dashboard Cite

Clinical Decision Support Systems (CDSS) form an important area of research. In spite of its importance, it is difficult for researchers to evaluate the domain primarily because of a considerable spread of relevant literature in interdisciplinary domains. Previous surveys of CDSS have examined the domain from the perspective of individual disciplines. However, to the best of our knowledge, no visual scientometric survey of CDSS has previously been conducted which provides a broader spectrum of the domain with a horizon covering multiple disciplines. While traditional systematic literature surveys focus on analyzing literature using arbitrary results, visual surveys allow for the analysis of domains by using complex network-based analytical models. In this paper, we present a detailed visual survey of CDSS literature using important papers selected from highly cited sources in the Thomson Reuters web of science. We analyze the entire set of relevant literature indexed in the Web of Science database. Our key results include the discovery of the articles which have served as key turning points in literature. Additionally, we have identified highly cited authors and the key country of origin of top publications. We also present the Universities with the strongest citation bursts. Finally, our network analysis has also identified the key journals and subject categories both in terms of centrality and frequency. It is our belief that this paper will thus serve as an important role for researchers as well as clinical practitioners interested in identifying key literature and resources in the domain of clinical decision support.

show abstract

“…The massive availability of clinical datasets in the repository of EHRs, therefore, presents a great opportunity for medical researchers and data analysts to discover hidden knowledge from medical records. The discovered knowledge has the potential to support early disease detection, improve population health outcomes, and facilitate the development of clinical decision support systems (Bonney, 2011;Razavi, Gill, Åhlfeldt, & Shahsavar, 2005).…”

Section: Introductionmentioning

confidence: 99%

Standardizing Biochemistry Dataset for Medical Research

Bonney

Doney

Jefferson

2014

Proceedings of the International Conference on Health Informatics

View full text Add to dashboard Cite

Harnessing clinical datasets from the repository of electronic health records for research and medical intelligence has become the norm of the 21st century. Clinical datasets present a great opportunity for medical researchers and data analysts to perform cohort selections and data linkages to support better informed clinical decision-making and evidence-based medicine. This paper utilized Logical Observation Identifiers Names and Codes (LOINC®) encoding methodology to encode the biochemistry tests in the anonymized biochemistry dataset obtained from the Health Informatics Centre (HIC) at the University of Dundee. Preliminary results indicated that the encoded dataset was flexible in supporting statistical analysis and data mining techniques. Moreover, the results indicated that the LOINC codes cover most of the biochemistry tests used in National Health Service (NHS) Tayside, Scotland.

show abstract

Impacts and Risks of Adopting Clinical Decision Support Systems

Cited by 12 publications

References 28 publications

Harnessing the power of clinical decision support systems: challenges and opportunities

Harnessing the power of clinical decision support systems: challenges and opportunities

Clinical Decision Support Systems: A Visual Survey

Standardizing Biochemistry Dataset for Medical Research

Contact Info

Product

Resources

About