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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Generative Language Models and Open Notes: Exploring the Promise and Limitations.

Charlotte Blease1,2, John Torous2, Brian McMillan3

  • 1Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden.

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|January 4, 2024
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Summary
This summary is machine-generated.

Patient online record access (ORA) presents benefits but challenges for clinical documentation. Generative artificial intelligence (AI) offers a potential solution to improve note clarity, accuracy, and reduce clinician workload.

Keywords:
ChatGPTOpen Notesclinical documentationcommunication tooldocumentationempathygenerative language modelslanguage modellarge language modelsmedical educationonline record accesspatient-centered care

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Area of Science:

  • Health Informatics
  • Medical Documentation
  • Artificial Intelligence in Healthcare

Background:

  • Patient online record access (ORA) is expanding globally, granting patients access to comprehensive health information, including clinical notes.
  • This increased access necessitates changes in clinical documentation practices, raising concerns about accuracy, completeness, and clinician workload.
  • Existing challenges include adapting documentation for patient readability and managing potential increases in clinician time spent on notes and queries.

Purpose of the Study:

  • To explore current approaches for addressing challenges in clinical documentation due to patient online record access.
  • To propose generative artificial intelligence (AI) as a novel strategy to support clinicians in creating patient-friendly narrative summaries.
  • To examine the potential benefits and risks of integrating generative AI into clinical note documentation.

Main Methods:

  • This viewpoint paper reviews existing literature on patient online record access and its impact on clinical documentation.
  • It discusses proposed clinician and patient education strategies.
  • It introduces and evaluates the potential application of generative AI for enhancing clinical note summarization.

Main Results:

  • Generative AI may enhance the clarity, accuracy, and patient-centeredness of clinical notes.
  • AI could potentially mitigate documentation work burdens for clinicians.
  • Current generative AI technologies have associated risks that require careful consideration.

Conclusions:

  • Generative AI presents a promising long-term strategy for optimizing clinical documentation in the era of patient online record access.
  • Successful integration requires a focus on co-creation of clinical notes and adequate support for clinicians using AI tools.
  • Addressing the risks and ensuring clinician training are crucial for harnessing AI's potential in improving patient communication and care.