Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.
This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screenin...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 11 |
|---|---|
| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
8/12/2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179605614&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179605614 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 8/12/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179605614 179605614 179605614 10.1007/s10916-024-02098-4 179605614 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery. aug: au: Ouanes, Khaled Farhah, Nesren affil: https://ror.org/05ndh7v49 Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia sug: subj: Artificial Intelligence Decision Support Systems, Clinical Health Care Delivery Medical Practice Health Care Costs Quality Improvement Outcomes (Health Care) Thematic Analysis Content Analysis Early Diagnosis Decision Making, Clinical Medication Errors Prevention and Control Attitude of Health Personnel Medical Practice, Evidence-Based Sensitivity and Specificity Machine Learning Deep Learning Neural Networks (Computer) Chronic Disease Diagnosis Prediction Models Sepsis Kidney Failure, Acute Risk Factors Risk Assessment Diagnosis, Computer Assisted Human Systematic Review PubMed Medline ab: This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|