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Related Concept Videos

Proofreading01:43

Proofreading

Overview
Proofreading01:43

Proofreading

Overview
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Methods of Documentation III: PIE01:21

Methods of Documentation III: PIE

Problem-intervention-evaluation (PIE) is a systematic approach to documentation used in healthcare settings for clinical decision-making and patient care planning. It is a structured approach to organizing patient data based on problems, interventions, and evaluations. Here's a breakdown of its key features and considerations:
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic illness...
Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare settings,...

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Updated: May 11, 2026

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis
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Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.

Scott W Perkins, Justin C Muste, Taseen Alam

    Perspectives in Health Information Management /
    |March 26, 2025
    PubMed
    Summary
    This summary is machine-generated.

    Artificial Intelligence (AI) tools can enhance clinical documentation efficiency and quality by structuring data and identifying trends. While a fully automated AI assistant is not yet available, current AI applications offer targeted improvements for clinicians.

    Keywords:
    Artificial intelligenceautomationclinical guidelinesdocumentationelectronic health recordsinformatics

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    Area of Science:

    • Medical Informatics
    • Artificial Intelligence
    • Clinical Documentation

    Background:

    • Clinicians spend substantial time on clinical documentation, leading to significant opportunity costs.
    • Artificial Intelligence (AI) presents a potential solution for improving both the quality and efficiency of clinical documentation.

    Purpose of the Study:

    • To systematically review peer-reviewed Artificial Intelligence (AI) tools.
    • To understand how AI can potentially reduce the opportunity cost associated with clinical documentation.

    Main Methods:

    • Systematic review of studies published in PubMed, Embase, Scopus, and Web of Science databases.
    • Inclusion of original, English-language research studies reporting AI tool development, application, or validation for clinical documentation, up to July 2024.
    • Extraction and analysis of 129 studies from an initial pool of 673 candidate studies.

    Main Results:

    • AI tools enhance documentation through data structuring, note annotation, quality evaluation, trend identification, and error detection.
    • Real-time AI assistance during office visits shows promise but is limited by moderate accuracy.
    • Existing AI techniques, particularly data structuring, provide specific enhancements to clinical documentation workflows.

    Conclusions:

    • Current AI tools offer targeted improvements for clinical documentation, addressing aspects like data organization and quality assessment.
    • While a comprehensive, end-to-end AI documentation solution is not yet realized in published research, AI shows clear potential to optimize clinician workflows.
    • Further development is needed to overcome accuracy limitations for broader real-time AI implementation in clinical settings.