Leveraging Artificial Intelligence for Expediting Implementation Efforts.

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice an...

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Publicado en:Creative Nursing Vol. 30; no. 2; pp. 111 - 118
Autores principales: Younas, Ahtisham, Reynolds, Staci S
Formato: Journal Article
Publicado: Sage Publications Inc. May2024
Acceso en línea:Ver este registro en EBSCOhost
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      dt: May2024
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Leveraging Artificial Intelligence for Expediting Implementation Efforts.
      aug:
        au:
          Younas, Ahtisham
          Reynolds, Staci S
        affil: Memorial University of Newfoundland, Canada
      sug:
        subj:
          Artificial Intelligence
          Implementation Science
          Health Care Delivery
          Program Implementation
          Strategic Planning
          Program Evaluation
          Nursing Practice, Evidence-Based
          Practice Guidelines
          Intensive Care Units Administration
          Data Management
      ab: Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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